Operation and maintenance service real-time matching system, method and equipment based on multi-dimensional intelligent matching

Abstract

The invention relates to an operation and maintenance service real-time matching system, method and device based on multi-dimensional intelligent matching. The system comprises a demand side client, an engineer client and a cloud server platform. The cloud server platform comprises an order management module used for creating, publishing, circulating and filing operation and maintenance service orders; the user and authentication management module is used for managing a user account, performing skill authentication on an engineer user based on a pre-constructed skill label library, and generating an engineer portrait containing an authenticated skill label; and the intelligent matching and order sending module is used for responding to a newly created operation and maintenance service order, screening out a matched engineer user from an authentication engineer pool through a multi-dimensional intelligent matching algorithm based on the engineer portrait, the real-time position data, the engineer state and the dynamic reputation score, and carrying out order pushing according to a preset strategy. The problem of information asymmetry in operation and maintenance service is solved, the response speed is increased, and resource configuration is optimized.

Description

Technical Field

[0001] This application relates to the field of operation and maintenance service technology, and in particular to a real-time matching system, method and device for operation and maintenance services based on multi-dimensional intelligent matching. Background Technology

[0002] In modern enterprise operations, the stable operation of various equipment is a core element for ensuring business continuity. This includes IT infrastructure such as servers and network equipment, everyday office equipment such as printers and air conditioning systems, and machinery and monitoring devices in industrial production. Once equipment fails, enterprises generally face multiple maintenance challenges.

[0003] Information asymmetry makes it difficult for demanders to efficiently identify repair engineers or service providers with matching skills, reliable service, and reasonable prices. Traditional methods relying on telephone communication, personal recommendations, or online searches are not only lengthy but also lack service quality assurance, easily leading to a crisis of trust. Slow response times are particularly prominent in sudden emergency failure scenarios. Cumbersome repair procedures and long waiting periods often cause business interruptions, resulting in significant economic losses. High service costs put significant pressure on SMEs. Professional maintenance service providers' pricing standards often exceed the reasonable range for non-core equipment repairs, while the lack of transparency in engineer pricing further weakens the bargaining power of enterprises. Uneven resource allocation is reflected on the supply side. Many independent engineers or small teams with professional skills face long-term issues of idle workload and unstable income due to limited access to information. Although industries such as food delivery and ride-hailing have effectively improved the efficiency of supply and demand matching through platform models, the maintenance service field still lacks a comprehensive solution that can connect equipment failure demanders with professional maintenance engineers in real time and intelligently. There is an urgent need for a matching mechanism that can dynamically integrate skill tags, geographical location, status availability, and reputation scores. Summary of the Invention

[0004] The purpose of this application is to propose a real-time matching system, method and device for operation and maintenance services based on multi-dimensional intelligent matching, so as to solve the problem of information asymmetry in operation and maintenance services, improve response speed, optimize resource allocation and reduce service costs.

[0005] To address the aforementioned technical issues, this application provides a real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching, comprising: a demand-side client, an engineer client, and a cloud server platform; The cloud server platform includes: The order management module is used to create, publish, transfer, and archive operation and maintenance service orders. The operation and maintenance service orders include structured fault description information, geographical location information, and service budget information. The user and authentication management module is used to manage user accounts and perform skill authentication for engineer users based on a pre-built skill tag library, generating engineer profiles containing certified skill tags; The intelligent matching and order dispatch module is used to respond to newly created operation and maintenance service orders. Based on the engineer profile, real-time location data, engineer status and dynamic reputation score, it uses a multi-dimensional intelligent matching algorithm to select matching engineer users from the certified engineer pool and push the order according to a preset strategy.

[0006] To address the aforementioned technical problems, embodiments of this application provide a real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching, including: Receive and parse maintenance service requests submitted by demand-side clients, and generate maintenance service orders containing skill tags; In response to the maintenance service order, perform multi-dimensional intelligent matching calculations to filter out a set of engineer users who meet the criteria; Order notifications are pushed to the set of engineer users according to the selected order dispatch strategy; In response to the order acceptance operation of the engineer's client, the order is locked and a communication link is established between the requester and the engineer accepting the order; Track and update the status of the service execution process, and trigger the payment settlement process after the requesting party confirms that the service has been completed; After payment is completed, the evaluation data between the requester and the engineer is collected and stored, and the dynamic reputation score is updated based on the evaluation data.

[0007] To solve the above-mentioned technical problems, one technical solution adopted by the present invention is to provide a computer device, including one or more processors; and a memory for storing one or more programs, so that the one or more processors implement the real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching as described above.

[0008] This invention provides a real-time matching system, method, and device for operation and maintenance services based on multi-dimensional intelligent matching. The system includes a demand-side client, an engineer client, and a cloud server platform. The cloud server platform includes: an order management module for creating, publishing, transferring, and archiving operation and maintenance service orders, wherein each order includes structured fault description information, geographical location information, and service budget information; a user and authentication management module for managing user accounts and authenticating engineer users' skills based on a pre-built skill tag library, generating engineer profiles containing certified skill tags; and an intelligent matching and dispatch module for responding to newly created operation and maintenance service orders, and using a multi-dimensional intelligent matching algorithm to select matching engineer users from a certified engineer pool based on the engineer profiles, real-time location data, engineer status, and dynamic reputation scores, and pushing orders according to a preset strategy. This embodiment achieves real-time intelligent matching through a cloud server platform using a multi-dimensional intelligent matching algorithm, efficiently solving the information asymmetry problem in operation and maintenance services, improving response speed, optimizing resource allocation, and reducing service costs. Attached Figure Description

[0009] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This application provides a real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching. Figure 2 This is a schematic diagram of the intelligent matching and dispatching module provided in the embodiments of this application; Figure 3 This is a flowchart of an implementation of the real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching provided in this application embodiment; Figure 4 This is a flowchart illustrating the implementation of a sub-process in the real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching provided in this application embodiment; Figure 5 This is a schematic diagram of the computer device provided in the embodiments of this application. Detailed Implementation

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0012] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0013] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0014] In modern enterprise operations, the stable operation of various equipment is crucial. However, traditional operation and maintenance services face problems such as information asymmetry, slow response speed, high service costs, and uneven allocation of engineer resources. These problems collectively restrict the efficiency and quality of operation and maintenance services.

[0015] Please refer to Figure 1 This application provides an embodiment of a real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching, which can be specifically applied to various electronic devices.

[0016] like Figure 1 As shown, the real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching in this embodiment includes: a demand-side client 10, an engineer client 20, and a cloud server platform 30. The cloud server platform 30 includes: The order management module 301 is used to create, publish, transfer and archive operation and maintenance service orders, wherein the operation and maintenance service orders include structured fault description information, geographical location information and service budget information; The user and authentication management module 302 is used to manage user accounts and perform skill authentication for engineer users based on a pre-built skill tag library, generating engineer profiles containing certified skill tags. The intelligent matching and order dispatch module 303 is used to respond to newly created operation and maintenance service orders, and to select matching engineer users from the certified engineer pool based on the engineer profile, real-time location data, engineer status and dynamic reputation score through a multi-dimensional intelligent matching algorithm, and to push the order according to a preset strategy.

[0017] For ease of understanding, the following explains some key terms in this embodiment: Demand-side client 10: An application or interface used by demand-side users to submit maintenance service requests, view order status, communicate with engineers, and provide service evaluations. In one specific embodiment, demand-side client 10 is geared towards enterprises or individuals with equipment repair needs. Users can use this client to register, post fault reports, view order status, communicate with engineers, make online payments, and provide service evaluations.

[0018] Engineer Client 20: An application or interface used by engineers to receive order notifications, accept or reject orders, update service progress, communicate with clients, and submit solution reports. In one specific embodiment, Engineer Client 20 is intended for engineers or technical teams providing repair services. Engineers can use this client to register, obtain skill qualification certifications, set service scopes, receive order notifications, accept / bid orders, report service progress, submit completion reports, and manage revenue.

[0019] Cloud server platform 30: A backend service cluster that carries the system's core business logic, data storage and processing functions. It is responsible for coordinating the interaction between the demand side and the engineers, and executing various services such as intelligent matching, order management, and user authentication.

[0020] Maintenance service orders: Service request records submitted by the requester, containing structured information such as fault description, geographical location, and service budget, are the basis for the system to match and dispatch orders.

[0021] Skill Tag Library: A predefined set of standardized skill categories and tags used to quantify and classify engineers' professional abilities, serving as the foundational data for building engineer profiles.

[0022] Engineer Profile: A comprehensive data model built on information such as engineer's certified skill tags, historical service data, and reputation evaluation, used to describe the engineer's professional competence, service preferences, and reliability.

[0023] Multi-dimensional intelligent matching algorithm: A calculation method that comprehensively considers multiple factors (such as skill matching degree, geographical proximity, status availability, service reputation, etc.) to evaluate the degree of matching between engineers and orders.

[0024] Dynamic Reputation Score: A comprehensive evaluation score updated in real time based on the engineer's historical service performance, client feedback, service completion rate, etc., used to measure the engineer's service quality and reliability.

[0025] In this embodiment, the system includes a demand-side client 10, an engineer client 20, and a cloud server platform 30. The demand-side client 10 can be implemented as a mobile application or a web interface, allowing demand-side users to submit service requests and query order status. The engineer client 20 can be implemented as a mobile application or a web interface, allowing engineer users to receive orders and update service progress. The cloud server platform 30, as the core of the system, is responsible for handling all business logic and data interaction.

[0026] The order management module 301 is configured to handle the entire lifecycle of maintenance service orders. Specifically, when a user submits a service request through the user client 10, this module is responsible for creating a new maintenance service order. After the order is created, its status can be set to "Pending Matching" and published to the system. During service execution, the order status can change, for example, from "Pending Matching" to "In Service," and then to "Pending Confirmation." After the service is completed, the order can be archived for historical querying and data analysis. Maintenance service orders are designed to include structured fault description information, such as fault type and equipment model; geographical location information, such as the latitude and longitude of the service address; and service budget information, such as the range of fees the user is willing to pay. This information can be manually entered by the user or selected through preset templates.

[0027] The User and Authentication Management module 302 is configured to manage all user accounts in the system, including client users and engineer users. For engineer users, this module is responsible for skills certification. For example, engineer users can submit their professional certificates or qualification certificates, which are then manually reviewed by the platform or verified through a third-party interface. Based on a pre-built skill tag library, such as "network maintenance," "server troubleshooting," and "printer repair," skill tags are assigned to certified engineer users. This generates an engineer profile containing certified skill tags, which simply records the list of skills possessed by the engineer.

[0028] The intelligent matching and dispatch module 303 is configured to respond to newly created maintenance service orders. This module is triggered when a new order is generated and published. It uses a multi-dimensional intelligent matching algorithm to filter matching engineer users from the certified engineer pool based on generated engineer profiles, real-time location data reported by engineer clients 20, the engineer's current status (e.g., "idle," "on service"), and the engineer's dynamic reputation score. For example, the matching algorithm may first filter engineers with matching skills, then perform preliminary sorting based on the distance between their geographical location and the order location, and further filter based on the engineer's idle status and basic reputation. The filtered engineer users are then pushed orders according to a preset strategy. As one implementation, the preset strategy can simply push orders to all engineers who meet the preliminary screening criteria, allowing them to decide whether to accept the order.

[0029] This system achieves efficient connection between the supply and demand sides of operation and maintenance services by constructing a demand-side client 10, an engineer client 20, and a cloud server platform 30. The order management module 301 standardizes service requests, reducing information asymmetry. The user and authentication management module 302 ensures the professional competence of engineers and improves service reliability. The intelligent matching and order dispatch module 303 utilizes multi-dimensional algorithms, comprehensively considering skills, location, status, and reputation, to achieve accurate and rapid engineer screening and order push, significantly shortening response time, optimizing resource allocation, and reducing service costs.

[0030] In the process of pushing orders based on preset strategies, the preset strategies may lack flexibility and cannot adapt to the needs of different operation and maintenance scenarios. For example, emergency faults require rapid response or high-precision matching requires optimal selection, resulting in low matching efficiency, response delay or uneven resource allocation.

[0031] Please see Figure 2 , Figure 2 A schematic diagram of the intelligent matching and dispatching module 303 is shown. The module includes a dispatching strategy unit configured with a bidding mode and an intelligent dispatching mode. The bidding mode broadcasts the maintenance service order to a group of engineer users whose matching score is higher than a threshold. The intelligent dispatching mode directs the maintenance service order to a designated engineer user with the highest comprehensive score calculated by the multi-dimensional intelligent matching algorithm.

[0032] The order dispatch strategy unit is a core component responsible for intelligently selecting and executing the most suitable order push strategy based on the specific characteristics of the maintenance service order, the requirements of the requester, and the current operating status of the system. It can be a software module that encapsulates various order dispatch logics, dynamically invoking different dispatch algorithms based on input parameters (e.g., order urgency, service type, requester's preference for response speed or service quality). Furthermore, this unit can also be designed as a configurable component, allowing platform operators to flexibly switch or combine different order dispatch rules in the background to adapt to constantly changing business needs and market environments.

[0033] The order-grabbing mode is used to broadcast and push maintenance service orders to a group of engineer users whose matching degree is higher than a threshold. The order-grabbing mode is a push mechanism designed to quickly facilitate the acceptance of maintenance service orders through widespread notification and competition. Specifically, when the system selects the order-grabbing mode, it will simultaneously broadcast and push detailed information about the maintenance service order (including structured fault descriptions, geographical location information, and service budget information) to all engineer users who meet the preset matching degree threshold through various channels, such as in-app notifications, SMS, email, or the platform's "order pool" / "task square." These engineer users can actively click the "grab order" button to express their intention to accept the order after receiving the notification. The system can determine the final order recipient based on a first-come, first-served principle, or by combining other auxiliary factors (such as the engineer's real-time location data, historical response speed, current workload, etc.). This mode is particularly suitable for emergency fault scenarios with high response speed requirements, effectively stimulating the enthusiasm of engineers and shortening order response time.

[0034] Simultaneously, the intelligent order dispatch mode is used to direct the operation and maintenance service orders to the designated engineer user with the highest comprehensive score calculated by the multi-dimensional intelligent matching algorithm. The intelligent order dispatch mode is a mechanism designed to ensure that the optimally matched engineer user accepts the operation and maintenance service order through precise calculation and targeted push. In this mode, the system first uses a multi-dimensional intelligent matching algorithm to comprehensively score all engineer users in the certified engineer pool based on multiple dimensions such as engineer profile, real-time location data, engineer status, and dynamic reputation score, and identifies the designated engineer user with the highest comprehensive score. Subsequently, the system sends an exclusive order dispatch notification to the engineer user with the highest comprehensive score. This notification typically includes a countdown timer for accepting the order to ensure a rapid response. If the engineer user confirms acceptance within the specified time, the order is locked and assigned to that engineer; if the engineer refuses to accept the order or fails to respond within the timeout period, the system can proceed with subsequent processing according to a preset strategy (e.g., dispatching the order to the engineer user with the second highest comprehensive score, or switching to a bidding mode after multiple failed attempts). This model is suitable for scenarios with high requirements for service quality and matching accuracy. It can maximize the optimization of resource allocation and ensure that the service is provided by the most suitable engineer.

[0035] This application effectively solves the problems of a single preset strategy lacking flexibility in adapting to different operation and maintenance scenarios, leading to low matching efficiency, response delays, or uneven resource allocation. Specifically, by introducing a dispatch strategy unit and configuring a bidding mode and an intelligent dispatch mode, the system can flexibly select the most suitable dispatch strategy based on the actual needs and characteristics of operation and maintenance service orders. When operation and maintenance service orders require rapid response, such as sudden emergency failures, the system can activate the bidding mode, broadcasting the order to a group of engineer users with a matching degree higher than a threshold. Utilizing the group competition mechanism, the system significantly shortens the order response time and improves order acceptance efficiency. When operation and maintenance service orders have high requirements for service quality and matching accuracy, the system can switch to the intelligent dispatch mode. Through a multi-dimensional intelligent matching algorithm, the system accurately calculates the engineer user with the highest comprehensive score and performs targeted dispatch, thereby ensuring that the order is undertaken by the most professional and suitable engineer user, maximizing service quality and user satisfaction. This dual-mode dispatch strategy enables the system to achieve efficient and accurate order matching in different scenarios, optimizes the allocation of engineer resources, and improves the overall response speed and quality of operation and maintenance services.

[0036] The lack of transparent collection of engineer quotes and selection mechanisms for demanders during the order delivery process can lead to potentially high and opaque service costs.

[0037] In response, this application further proposes that the order dispatch strategy submodule be configured in bidding mode; the bidding mode is used to push orders to engineer users with matching skills and collect quotation information for demand-side users to choose from.

[0038] Specifically, the order dispatch strategy submodule is a component of the order dispatch strategy unit in the aforementioned intelligent matching and order dispatch module 303, responsible for managing and executing specific order dispatch strategies. Configuring it as a bidding mode means that the system will enable a mechanism that allows engineers to compete for service orders. This bidding mode is a market-based order allocation mechanism; the system no longer simply pushes or directly dispatches orders, but instead publishes order information to qualified engineers and allows these engineers to submit service quotes based on their own circumstances (such as time, cost, professional capabilities, etc.). The implementation of this mode can include: after receiving an operation and maintenance service order submitted by the client 10, the system uses a multi-dimensional intelligent matching algorithm to filter out engineer users who meet the skill requirements and pushes the order details (excluding budget information) to these engineers in the form of bidding invitations. After receiving the invitation through their engineer client 20, the engineer user can submit their service quote within a limited time. Alternatively, the system can set a bidding period during which all qualified engineers can view orders and submit quotes. The quote can be a fixed price or a plan that includes detailed information such as the service scope and estimated working hours. The system will display the current lowest price or a list of all prices in real time to encourage competition among engineers.

[0039] The bidding mode is used to push orders to engineer users with matching skills and collect quotation information. "Pushing orders to engineer users with matching skills" means that before initiating the bidding process, the cloud server platform 30 uses the aforementioned multi-dimensional intelligent matching algorithm to compare the structured fault description information and geographical location information contained in the maintenance service order with the certified skill tags and real-time location data in the engineer profile, filtering out engineer users with corresponding skill and geographical advantages. Only engineers recognized by the system as having "matching skills" are eligible to participate in the bidding for that order. Push methods may include: sending detailed information about the bidding order (such as fault type, geographical location, service requirements, etc.) to eligible engineer users via instant message notifications, in-app pop-ups, or SMS / email from the engineer client 20. Alternatively, these orders can be displayed in the "Orders to be Bid" list on the engineer client 20, allowing engineers to actively browse and select to participate in the bidding. "Collecting quotation information" means that the system provides an interface or function that allows participating engineer users to submit their service quotations for the maintenance service order. Quotation information is not limited to price but may also include estimated completion time, service commitments, and materials used. Collection methods may include: engineers inputting the quotation amount and other additional information through a client interface and submitting it to a cloud server platform 30. The platform stores the quotation in a structured manner. Alternatively, the system can provide a quotation template to guide engineers in filling out a detailed quotation plan, including basic service fees, travel expenses, spare parts fees, and other details, ensuring the completeness and comparability of the quotation information.

[0040] The bidding model collects quote information for users with specific needs to choose from. This means that after the bidding period ends or a preset number of quotes are reached, the system will summarize all collected valid quotes and present them to users with specific needs in a clear and comparative manner. Users with specific needs can comprehensively consider multiple dimensions such as quote amount, engineer reputation, service plan, and estimated completion time to independently select the engineer that best meets their needs. Selection methods may include: the user's client 10 displays a quote list, listing all participating engineers and their quotes, dynamic reputation scores, and summaries of past reviews; the user can click to view details and choose to accept a quote. Alternatively, the system can provide filtering and sorting functions to help users with specific needs quickly locate suitable quotes based on price, reputation level, and other criteria, and finally confirm their selection. Once the user makes a selection, the system will lock the order and notify the selected engineer.

[0041] This application introduces a bidding model based on the existing order push mechanism, effectively solving the problems of opaque service costs and limited choices for service requesters in operation and maintenance service orders. Specifically, by configuring the order dispatch strategy submodule to bidding mode, the system can push orders to a group of engineers with matching skills selected through intelligent matching, and transparently collect quotation information from different engineers. This quotation information is then aggregated and presented to the requesting user, enabling them to independently compare and select based on multiple dimensions such as price, service plan, and engineer reputation. This mechanism not only promotes healthy competition among engineers, helping to reduce service costs, but also enhances the decision-making power and bargaining power of the requesting user in the service selection process, thereby improving user experience and overall service efficiency. In addition, the bidding model, combined with the aforementioned multi-dimensional intelligent matching algorithm, ensures that only engineers with the corresponding skills can participate in bidding, avoiding interference from invalid quotations and guaranteeing a balance between service quality and cost-effectiveness.

[0042] In the process of selecting matching engineer users from the pool of certified engineers, the algorithm may not fully consider all key dimensions, resulting in suboptimal matching results that affect service response speed and efficiency. For example, insufficient skill matching increases the risk of service failure, ignoring geographical location prolongs response time, lack of status availability causes resource idleness, and lack of service reputation reduces service quality.

[0043] In this regard, this application further proposes that the multi-dimensional intelligent matching algorithm at least comprehensively calculates a weighted score of skill matching degree, geographical proximity, status availability, service reputation and price preference information.

[0044] Among the key performance indicators (KPIs), the primary matching criterion is: Skill Matching: Accurately matching the skill tags required for the order with those in the engineer's profile. Geographic Proximity: Calculating the real-time distance between the engineer and the faulty equipment based on GPS or cell tower location, prioritizing nearby engineers to ensure rapid response. Availability Status: Real-time monitoring of engineer status ("idle," "in transit," "on service"), pushing orders only to engineers in the "idle" status. Service Reputation: Building a dynamic reputation score model for engineers based on historical service reviews, completion rates, and response times, prioritizing high-scoring engineers. Price Preference Information: Initial price matching based on the order budget and the engineer's historical pricing habits.

[0045] Among them, the multi-dimensional intelligent matching algorithm refers to a computational method that can comprehensively consider multiple independent but interrelated factors to evaluate and rank potential matching objects. It aims to generate a unified evaluation index by quantifying data from different dimensions, thereby achieving more accurate and comprehensive matching decisions. This algorithm can employ a rule-based expert system, which pre-sets a series of matching rules and priorities, and performs logical judgments and scoring based on input data; alternatively, it can use machine learning models, such as support vector machines, decision trees, or neural networks, to learn and predict the best matching results by training on historical matching data.

[0046] The skill matching degree measures the degree of match between the skills possessed by the engineer user and the skills required for the operation and maintenance service order. This can be achieved by comparing skill tags extracted from the structured fault description information of the order with certified skill tags in the engineer profile, calculating the overlap or similarity score between the two; or by using natural language processing technology to perform semantic analysis on the fault description and engineer skill description, identifying potential skill correlations, and matching can be performed even if the tags are not completely identical.

[0047] The geographical proximity represents the geographical distance or time cost between the engineer's current location and the location where the maintenance service order was placed. This can be achieved by using real-time location data and a geographic information system to calculate the straight-line distance or shortest path distance between the engineer and the service location; or by considering dynamic factors such as real-time traffic conditions and congestion, estimating the estimated time required for the engineer to reach the service location by calling a map service API, and then converting this estimate into a proximity score.

[0048] The availability status reflects whether an engineer user is able to accept and execute new maintenance service orders within a specific time period. This can be achieved by the engineer user actively setting their online, busy, or idle status via the client, allowing the system to determine availability; or by the system automatically inferring availability based on information such as the number of orders currently being processed, estimated completion time, historical workload, and preset work schedules.

[0049] The service reputation assessment is a comprehensive indicator of the engineer user's past service performance, reflecting their service quality, reliability, and user satisfaction. It can be implemented by calculating the engineer user's service reputation based on historical order evaluation data from client users (such as star ratings and written reviews) using a weighted average or cumulative summation method; or by comprehensively considering multiple objective indicators such as the engineer user's order completion rate, complaint rate, response speed, and service duration, combined with evaluation data, using a multi-factor model.

[0050] The weighted score is calculated by assigning different weights to the matching metrics of the aforementioned multiple dimensions according to their importance in the matching decision, and then performing linear or nonlinear combination calculations to obtain a comprehensive matching score. This can be achieved by pre-setting fixed weight coefficients; for example, based on experience or business needs, different percentage weights can be assigned to skill matching degree, geographical proximity, status availability, and service reputation, and then the scores of each dimension are multiplied by their corresponding weights and summed. Alternatively, a dynamic weight adjustment mechanism can be used to adjust the weights of each dimension in real time based on the urgency, complexity, and service type of the maintenance service order to adapt to matching needs in different scenarios.

[0051] In this embodiment, the multi-dimensional intelligent matching algorithm can more accurately evaluate the overall matching degree of engineer users. Specifically, skill matching degree ensures that engineers have the professional ability to solve specific faults, reducing the risk of service failure; geographical proximity shortens the engineer's response time, which is especially crucial for emergency faults; status availability avoids assigning orders to busy or unavailable engineers, improving resource utilization efficiency; and service reputation guarantees service quality and user experience. By weighting these dimensions, the system can balance the importance of different factors and generate a more comprehensive and objective matching score, enabling the intelligent matching and order dispatch module 303 to filter out engineer users who not only have matching skills and rapid response, but are also currently available and provide reliable service. This significantly improves the matching efficiency and success rate of operation and maintenance service orders, optimizes the service experience of users with demand, and effectively solves the problems of slow response and uneven service quality in traditional matching.

[0052] The lack of a secure payment mechanism to guarantee fund settlement after transaction completion during order management and matching processes may lead to payment disputes, fund security risks, or reduced user trust.

[0053] In this regard, this application further proposes that the cloud server platform 30 also includes a payment and settlement module; the payment and settlement module is used to connect to a third-party payment gateway, and after the service is completed, to settle the fees prepaid by the requesting user to the platform to the engineer user who accepted the order.

[0054] The payment and settlement module is a functional unit within the cloud server platform 30 specifically responsible for handling operations related to fund transfers. Its core function is to ensure the security, efficiency, and compliance of funds during transactions. Specifically, this module manages the escrow of user prepayments, fund allocation after service completion, and handling of potential refunds or disputes. In one implementation, the payment and settlement module can be designed as an independent microservice, communicating asynchronously or synchronously with other core services such as the order management module 301 and the user and authentication management module 302 through clearly defined API interfaces, thereby achieving decoupling and high cohesion between modules. In another implementation, the payment and settlement module can be integrated as a subsystem of the cloud server platform 30 within the main application, collaborating with other business logic modules through internal function calls or shared databases.

[0055] The aforementioned access to third-party payment gateways aims to leverage the mature technologies and security systems of professional payment service providers to offer the platform a secure, convenient, and compliant channel for fund collection and payment. This avoids the platform having to handle complex payment clearing, risk control, and compliance issues independently, reducing operating costs and technical risks. Specifically, the platform can integrate the SDKs (Software Development Kits) or APIs (Application Programming Interfaces) provided by mainstream third-party payment service providers (such as Alipay, WeChat Pay, and UnionPay QuickPass) to enable online prepayment, escrow, and final settlement of user funds. Furthermore, by partnering with professional payment aggregation service providers, the platform can access multiple third-party payment channels in a unified manner, providing a one-stop payment solution and simplifying the platform's integration process.

[0056] Upon completion of the service, the prepaid fees from the requesting user to the platform are settled with the engineer who accepted the order. This mechanism ensures the fairness and timeliness of fund transfers, protects the engineer's service compensation, and provides financial security for the requesting user, avoiding the risk of payment before service completion. In one implementation, when the order management module 301 receives a service completion confirmation signal from the requesting client 10, it triggers the payment and settlement module to execute the settlement process. The payment and settlement module deducts the corresponding service fee from the platform's escrow account and transfers it to the pre-bound bank account or third-party payment account of the engineer who accepted the order according to preset settlement rules (e.g., after deducting the platform service fee). In another implementation, the platform can set an automatic settlement cycle after service completion (e.g., automatically triggering settlement if there is no objection from the requesting user within 24 hours after service completion), or, under specific circumstances, the platform operations personnel can manually trigger the settlement operation after manually reviewing the service completion status.

[0057] In this embodiment, the cloud server platform 30 incorporates a payment and settlement module and connects to a third-party payment gateway. This enables the settlement of fees prepaid by the requesting user to the receiving engineer user after the service is completed. This design effectively addresses the issues of payment disputes, fund security risks, and insufficient user trust inherent in traditional operations and maintenance services. Specifically, the prepaid fees are held in escrow by the platform, ensuring fund security and avoiding the risks associated with direct transactions. Simultaneously, the connection to a third-party payment gateway leverages its professional security technology and compliance system to further enhance the reliability of the payment process. Funds are only settled to the engineer user after the service is completed. This not only guarantees the engineer user's service compensation but also incentivizes them to provide high-quality services, thereby improving the transaction fairness and user satisfaction of the entire operations and maintenance service matching system and building a safer, more efficient, and trustworthy operations and maintenance service ecosystem.

[0058] In the process of order management and intelligent matching, the lack of a mechanism for collecting evaluation data and solution reports after order completion results in the inability to dynamically update engineer reputation and accumulate solution knowledge, which affects the long-term optimization and matching efficiency of the system.

[0059] In this regard, this application further proposes that the cloud server platform 30 also includes a reputation evaluation and knowledge base module. The reputation evaluation and knowledge base module is used to collect evaluation data upon order completion and to receive and review solution reports submitted by engineer users.

[0060] Specifically, the reputation evaluation and knowledge base module is a functional unit integrated on the cloud server platform 30, specifically responsible for handling tasks related to service quality feedback and technical knowledge accumulation. This module can function as an independent microservice module, interacting with the order management module 301, user and authentication management module 302, etc., through an application programming interface (API) to achieve seamless data flow and functional decoupling; alternatively, it can also be a logical component within the cloud server platform 30, implementing its functions through a specific database table structure and business logic code to ensure data consistency and processing efficiency.

[0061] The function for collecting evaluation data upon order completion aims to obtain user feedback on service quality, providing a data foundation for dynamic reputation scoring. Specifically, after the order status changes to "completed," the system can automatically push evaluation requests to the client 10 (customer) and the engineer's client 20, providing input interfaces such as star ratings and text comments so that users can easily express their service experience; or, through preset evaluation templates, it can guide users to quantitatively evaluate multiple dimensions such as service attitude, professional skills, and response speed, and allow the uploading of pictures or videos as evaluation evidence, thereby obtaining more comprehensive and objective evaluation information.

[0062] The function of receiving and reviewing solution reports submitted by engineer users aims to accumulate fault solutions and form a searchable knowledge base, thereby improving the efficiency of future fault handling. For example, after completing a service, an engineer can submit a structured report containing information such as fault phenomena, diagnostic process, solutions, and tools used through the engineer client 20. The system backend can have reviewers or artificial intelligence (AI) review mechanisms to review the content quality and accuracy to ensure the professionalism and accuracy of the report; alternatively, after the report is submitted, the system will automatically extract and classify keywords, and human experts will conduct a final review to ensure the professionalism and practicality of the report before it is added to the database.

[0063] This application effectively addresses the lack of feedback mechanisms and knowledge accumulation after maintenance services are completed. Specifically, by collecting evaluation data after an order is completed through a reputation evaluation and knowledge base module, the system can obtain real-time feedback from both requesting users and engineer users regarding the service process. This evaluation data can be used to dynamically update the reputation scores of engineer users, thereby providing the intelligent matching and dispatch module 303 with more accurate and real-time dynamic reputation score data for engineers, significantly improving the accuracy of subsequent order matching and the reliability of service quality. Simultaneously, by receiving and reviewing solution reports submitted by engineer users, this application can systematically accumulate and store verified fault solutions, forming a searchable knowledge base. This knowledge base not only provides reference for engineer users, accelerating the fault diagnosis and resolution process, but also serves as a training resource, continuously improving the professional capabilities and response efficiency of the entire service network, thereby further optimizing the long-term operational efficiency and user experience of the entire maintenance service matching system.

[0064] During the process of collecting evaluation data and reviewing solution reports upon order completion, the evaluation data was not automatically used to update the dynamic reputation score, and the solution reports were not structured and stored to form a searchable knowledge base.

[0065] To address this, this application further proposes a reputation evaluation and knowledge base module comprising an evaluation collection unit and a solution report storage unit. The evaluation collection unit is a core component of the reputation evaluation and knowledge base module, its main function being to acquire and manage user feedback on service quality. Specifically, the evaluation collection unit can be a software module that guides both client users and engineer users to submit evaluations of each other after service completion through a pre-defined user interface (such as star ratings, text comment boxes, predefined tag selection, etc.). This unit is responsible for the initial processing and storage of this evaluation data, providing raw data for subsequent reputation score updates. The solution report storage unit is another core component of the reputation evaluation and knowledge base module, focusing on managing solution reports submitted by engineer users. The solution report storage unit can be a database management system or document management system, used to receive reports uploaded by engineer users containing detailed information such as fault diagnosis, repair process, and solutions. This unit not only stores the reports but may also include preliminary report verification functions to ensure the reports' integrity and format compliance.

[0066] Furthermore, the evaluation collection unit is used to collect and store evaluation data between the requester and the engineer after the order is completed, and to update the dynamic reputation score based on the evaluation data. Specifically, the evaluation collection unit triggers the evaluation process after the maintenance service order is completed. For example, the system can send evaluation request notifications to the requester client 10 and the engineer client 20. The requester can rate and comment on the engineer's service attitude, professional skills, response speed, etc.; the engineer can also evaluate the requester's cooperation and the accuracy of the fault description, etc. This evaluation data, such as star ratings, text comments, tag selections, etc., will be collected and stored in the database of the cloud server platform 30 and associated with the corresponding order and user. After collecting new evaluation data, the evaluation collection unit will call the reputation score update algorithm in the intelligent matching and dispatching module 303 to update the engineer's dynamic reputation score in real time or periodically. For example, a weighted average algorithm can be used to assign higher weight to the most recent evaluations; or a time decay-based algorithm can be used to gradually reduce the influence of older evaluations; or a machine learning model can be used to calculate a more accurate dynamic reputation score by integrating multi-dimensional evaluation data (such as service completion rate, user satisfaction, complaint rate, etc.). This dynamic update mechanism ensures the real-time nature and accuracy of the reputation score, reflecting the latest service performance of engineer users.

[0067] Meanwhile, the solution report storage unit receives and reviews solution reports submitted by engineer users, and stores approved solutions in a structured manner to form a searchable fault solution knowledge base. Specifically, after completing maintenance services, engineer users submit detailed solution reports to the solution report storage unit through the engineer client 20. Upon receiving the report, the unit initiates a review process. Review can be conducted manually to ensure the accuracy, completeness, and professionalism of the report content; alternatively, automated tools can be used to perform preliminary checks on the report's format, keywords, and missing key information. Only reports that pass review are included in the knowledge base. For approved solution reports, the solution report storage unit performs structured processing. This means that key information in the report, such as fault type, equipment model, fault symptoms, diagnostic methods, solution steps, required tools, consumables, and resolution time, is extracted and stored in predefined database fields, rather than simply as unstructured text files. This structured storage method facilitates subsequent data querying, analysis, and utilization. Through structured storage, all solution reports collectively constitute a searchable fault solution knowledge base. This knowledge base supports multiple query methods, such as keyword search, fault type filtering, and equipment model filtering. When a new maintenance service order encounters a similar fault, the intelligent matching and dispatch module 303 or engineer users can quickly find solutions to similar faults through this knowledge base, thereby improving the efficiency of fault diagnosis and resolution.

[0068] This application effectively solves the problems of unautomated utilization of evaluation data and unstructured storage of solution reports. Specifically, by setting up an evaluation collection unit, the system can automatically and in real-time collect evaluation data between the requester and the engineer after an order is completed, and dynamically update the engineer's reputation score based on this data. This allows the engineer's reputation evaluation to reflect their latest service quality in a timely manner, improving the accuracy and timeliness of the reputation score. Consequently, the intelligent matching and dispatch module 303 can screen engineers and dispatch orders based on more reliable reputation data, improving the accuracy of matching. Simultaneously, by setting up a solution report storage unit, the system can receive and review solution reports submitted by engineer users, and store approved reports in a structured manner. This structured storage method transforms unstructured report content into queryable and analyzable data, thereby constructing a searchable fault solution knowledge base. This knowledge base not only provides valuable experience references for engineer users, shortening fault diagnosis and resolution time, but also accumulates operational and maintenance knowledge assets for the platform, improving overall service efficiency and quality.

[0069] In the process of achieving intelligent matching and dispatching of operation and maintenance services, there are problems such as low matching calculation efficiency, untimely dispatching response, and lack of closed-loop management of service processes, which leads to increased information asymmetry, slow response speed, and high service costs.

[0070] Please see Figure 3 , Figure 3 This paper illustrates a specific implementation of a real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching. This method is applied to the aforementioned operation and maintenance service matching system.

[0071] It should be noted that if substantially the same result is obtained, the method of this invention is not based on... Figure 3 Limited to the order of the processes shown, this method includes the following steps: S1: Receive and parse the maintenance service request submitted by the client, and generate a maintenance service order containing skill tags; S2: In response to the maintenance service order, perform multi-dimensional intelligent matching calculation to filter out a set of qualified engineer users; S3: Push order notifications to the set of engineer users according to the selected order dispatch strategy; S4: In response to the order acceptance operation of the engineer client, lock the order and establish a communication link between the client and the accepting engineer; S5: Track and update the status of the service execution process, and trigger the payment settlement process after the client confirms the completion of the service; S6: After payment is completed, collect and store the evaluation data between the client user and the engineer user, and update the dynamic reputation score according to the evaluation data.

[0072] Specifically, the system first receives and parses maintenance service requests submitted by client-side requests, generating maintenance service orders containing skill tags. In practice, the client-side can provide a user-friendly form interface, allowing users to fill in details such as fault type, equipment model, fault description, geographical location, expected service time, and service budget. Upon receiving these requests, the system uses Natural Language Processing (NLP) technology to intelligently parse the fault description information and, combined with a pre-built skill tag library, automatically or semi-automatically assigns accurate skill tags to the maintenance service order. Alternatively, preset fault templates or options can be provided, allowing users to quickly select and submit requests. The system then automatically associates the selected template with corresponding skill tags and obtains geographical location information via GPS positioning or manual user input. This method transforms unstructured service requests into structured maintenance service orders, laying the foundation for subsequent accurate matching.

[0073] Secondly, in response to the maintenance service order, a multi-dimensional intelligent matching calculation is performed to filter out a set of qualified engineer users. Once a new maintenance service order is generated, the system immediately triggers the intelligent matching algorithm. This algorithm comprehensively considers multiple dimensions, such as the engineer's skill tags, certification level, real-time geographical location (proximity to the order's geographical location), current status (e.g., idle, busy, offline), and dynamic reputation score. By comprehensively evaluating these dimensions, the system can calculate the degree of match between each candidate engineer and the current order, and accordingly filter out the set of engineer users that best meets the criteria.

[0074] Next, order notifications are pushed to the set of engineer users according to the selected order dispatch strategy. The order dispatch strategy can be flexibly configured according to actual needs. For example, it can be selected by the requester when posting an order, or the platform can intelligently recommend based on factors such as the urgency of the order and the type of service. For instance, when the order dispatch strategy is configured in a bidding mode, the system will broadcast maintenance service orders to all engineer users whose matching score is higher than a preset threshold, allowing them to bid for the orders. When the order dispatch strategy is configured in an intelligent order dispatch mode, the system will target maintenance service orders to the designated engineer user with the highest comprehensive score calculated by a multi-dimensional intelligent matching algorithm. Furthermore, the order dispatch strategy can also be configured in a bidding mode, which pushes orders to engineer users with matching skills and collects their quotes for requesters to choose from. Order notifications can be sent in various ways, such as mobile application push notifications, SMS, or in-app messages, ensuring that information is delivered to engineers in a timely and effective manner.

[0075] Then, in response to the engineer's order acceptance action on the client side, the system locks the order and establishes a communication link between the requester and the accepting engineer. Once the engineer confirms acceptance on the client side, the system immediately updates the status of the maintenance service order to "Accepted" and takes measures to ensure that the order cannot be accepted by other engineers, thus achieving unique order locking. Simultaneously, the system provides convenient instant messaging functions for both the requester and the accepting engineer, such as built-in chat and voice calls, and shares necessary contact information between the two parties, subject to privacy policy compliance, to facilitate direct communication and collaboration and accelerate service startup.

[0076] Subsequently, the system tracks and updates the status of the service execution process, and triggers the payment and settlement process upon confirmation of service completion by the requesting party. During service execution, the engineer's client can provide a series of status update options, such as "Departed," "Arrived," "Service Started," and "Service Completed," allowing engineers to operate according to the actual progress. The system receives and synchronizes these status updates to the requesting party's client in real time, enabling the requesting party to clearly understand the service progress. When the requesting party confirms on the client that the service has been completed, the system will automatically send an instruction to the payment and settlement module, triggering the preset payment and settlement process to ensure the smooth execution of the transaction and the secure transfer of funds.

[0077] Finally, after payment is completed, the system collects and stores the mutual evaluation data between the requester and the engineer, and updates the dynamic reputation score based on this data. After payment settlement, the system automatically pushes evaluation requests to both the requester and engineer clients, encouraging them to evaluate the service provided. Evaluations can include star ratings, text comments, and preset tag selections. The evaluation collection unit receives and stores this evaluation data and updates the engineer's dynamic reputation score in real-time or periodically based on preset algorithms (such as weighted average, time decay models, etc.). This evaluation data not only helps establish and maintain the engineer's reputation system but also helps identify potential service problems or malicious behavior. The updated dynamic reputation score will serve as an important basis for subsequent order matching, thus forming a closed-loop management mechanism for continuous service quality optimization.

[0078] This application effectively addresses the issues of information asymmetry, slow response times, and lack of closed-loop management in operation and maintenance services. Structured operation and maintenance service orders and multi-dimensional intelligent matching algorithms ensure precise matching of supply and demand, significantly reducing information ambiguity and invalid matching, thereby improving matching efficiency. Flexible order dispatch strategies (such as bidding, intelligent dispatch, or auction modes) ensure that orders quickly and efficiently reach the most suitable engineers, greatly shortening response time. From order locking and communication link establishment to service status tracking, and then to requester confirmation and payment settlement, the entire service process achieves transparency and closed-loop management, ensuring transaction security and user experience. Crucially, by collecting and storing mutual evaluation data after payment and updating engineers' dynamic reputation scores in real time, this application establishes a continuous feedback and optimization mechanism, incentivizing engineers to provide high-quality services, continuously improving the overall service level and reliability of the platform, ultimately reducing service costs and increasing user satisfaction.

[0079] In the process of implementing multi-dimensional intelligent matching calculations to efficiently screen engineer user groups, a more structured and specific implementation method is needed to ensure that the calculation process is objective, fair, and comprehensively considers multiple key dimensions to avoid subjective judgment bias and to solve the problems of information asymmetry and slow response speed.

[0080] Please see Figure 4 , Figure 4 The specific implementation method of step S2 is shown, including: S21: In response to the maintenance service order, calculate a weighted total score based on the skill matching degree, geographical proximity, status availability and service reputation of each candidate engineer user; S22: Sort the candidates according to the weighted total score to select the set of engineer users who meet the conditions.

[0081] Specifically, when the cloud server platform receives an operation and maintenance service order submitted by the client, and the order management module creates and publishes it, the intelligent matching and dispatching module immediately initiates a multi-dimensional intelligent matching calculation process. This response mechanism ensures the timeliness of the matching process, avoids manual intervention or delays, and thus improves the overall response speed.

[0082] Skill matching degree refers to the degree of consistency between the certified skills of candidate engineer users and the skills required by the structured fault description information in the operation and maintenance service order. Its calculation methods may include, but are not limited to: based on a pre-built skill tag library, parsing the order fault description information into the required skill tags, and then performing exact or fuzzy matching with the certified skill tags in the engineer profile, assigning corresponding scores based on the number, level, or importance of matches. For example, if the order requires "network fault diagnosis" and "server hardware repair," and the engineer possesses "network fault troubleshooting" and "server maintenance" skills, the matching degree is high. Alternatively, Natural Language Processing (NLP) technology can be used to perform semantic analysis on the fault description text and the engineer's skill description text, calculating the semantic similarity between the two to gain a deeper understanding of skill demand and supply, thereby deriving a skill matching degree score.

[0083] Geographic proximity refers to the distance or accessibility between a candidate engineer's current real-time location and the geographic location information in the maintenance service order. Its calculation methods may include, but are not limited to: calculating the straight-line distance (Euclidean distance) between the engineer and the order location based on geographic coordinates (latitude and longitude), with a higher proximity score for closer distances. Alternatively, it can combine real-time traffic data and road network information to calculate the shortest time or actual travel distance required for the engineer to reach the order location and convert it into a proximity score, for example, by calling a map API to obtain the estimated travel time.

[0084] Status availability refers to the current work status and schedule of a candidate engineer user when receiving an order. Its calculation methods may include, but are not limited to: determining whether the engineer has the ability to respond immediately or within a specified time based on their reported online status (online / offline), busy status (busy / idle), and schedule (e.g., other orders already received, scheduled time slots), and assigning a corresponding availability score. For example, engineers who are online and idle have the highest availability score. Alternatively, considering the engineer's set service time or rest time, if the order time falls within the engineer's available service time, the availability score is higher; otherwise, it is lower or zero.

[0085] Service reputation refers to the comprehensive evaluation of a candidate engineer's past service performance. Its calculation methods may include, but are not limited to: calculating indicators such as the engineer's average rating, positive review rate, negative review rate, complaint rate, and order completion rate based on the evaluation data collected and stored by the reputation evaluation and knowledge base module between the requester and the engineer, and then combining these indicators to generate a reputation score. Alternatively, it may combine data such as the engineer's historical service duration, response speed, problem-solving efficiency, and the quality of solution reports, and use machine learning models for comprehensive evaluation to obtain a more refined service reputation score.

[0086] Weighted score calculation involves multiplying each of the above matching factors (skill matching, geographical proximity, status availability, and service reputation) by a preset weighting coefficient, and then summing all the weighted scores to generate a comprehensive weighted total score. Implementation methods can include, but are not limited to: Presetting fixed weights: Based on the general characteristics of the maintenance service, a fixed weight is assigned to each matching factor; for example, skill matching weight 0.4, geographical proximity weight 0.3, status availability weight 0.2, and service reputation weight 0.1. Alternatively, dynamically adjusting weights: The weights of each matching factor are dynamically adjusted based on information such as the urgency of the maintenance service order, the type of fault, and the service budget. For example, for urgent fault orders, the weight of geographical proximity can be increased; for complex technical problems, the weights of skill matching and service reputation can be increased.

[0087] After calculating the weighted total score of all candidate engineer users, the system will sort them from highest to lowest weighted total score. The methods for selecting the set of engineer users who meet the criteria can include, but are not limited to: setting a score threshold and including all engineer users with a weighted total score higher than that threshold in the set; or setting a maximum number of users, for example, selecting the top N engineer users by weighted total score as the set of eligible users; or combining the above two methods, for example, selecting the top N engineer users with a weighted total score higher than the threshold.

[0088] This application provides an objective, fair, and comprehensive matching mechanism that considers multiple key dimensions by calculating a weighted score for each candidate engineer user based on their skill matching degree, geographical proximity, availability, and service reputation. This multi-dimensional weighted calculation avoids subjective judgment biases caused by single factors, ensuring the comprehensiveness and accuracy of the matching results. Subsequently, sorting based on the weighted total score efficiently filters out the set of engineers who meet the criteria, greatly solving the problems of information asymmetry and slow response speed in the operation and maintenance service field. This solution enables cloud server platforms to quickly and accurately identify the engineers most suitable for handling specific operation and maintenance service orders, thereby significantly improving the efficiency and success rate of operation and maintenance service matching, and ensuring service quality and user experience.

[0089] In the process of pushing order notifications to a group of engineer users according to the selected order dispatch strategy, the ambiguity of the push strategy may lead to low order push efficiency, response delay, or uneven allocation of engineer resources, thereby affecting the real-time matching effect of operation and maintenance services.

[0090] In response, this application further proposes to push order notifications to a set of engineer users based on a selected order dispatch strategy, including: if the order dispatch strategy is a bidding mode, then push notifications to all engineer users whose matching score is higher than a threshold; if the order dispatch strategy is an intelligent order dispatch mode, then exclusively dispatch orders to the engineer user ranked first in score.

[0091] Specifically, when the order dispatch strategy is a bidding mode, it aims to encourage competition among multiple qualified engineer users by simultaneously releasing maintenance service orders, thereby achieving rapid response and order coverage. In this mode, after calculating the matching score for each engineer user using a multi-dimensional intelligent matching algorithm, the system compares this score with a preset bidding mode threshold. All engineer users whose scores exceed this threshold are included in the push list. The system can simultaneously send the maintenance service order information to these engineer users with matching scores above the preset threshold via instant messaging, in-app notifications, or SMS, and set a bidding countdown, allowing first-come, first-served. Alternatively, the system can publish the maintenance service order information to a public order pool and notify all engineer users with matching scores above the preset threshold that the order has been released, allowing them to actively enter the order pool to bid. This ensures that maintenance service orders are pushed to engineers who are capable and willing to accept them, while avoiding resource waste.

[0092] On the other hand, when the dispatch strategy is in intelligent dispatch mode, it aims to directly allocate the maintenance service order to the single engineer user who best meets the criteria through precise calculation and evaluation, thereby achieving optimal matching and efficient service. In this mode, after the system calculates the comprehensive score of all candidate engineer users using a multi-dimensional intelligent matching algorithm, it automatically identifies the engineer user with the highest score as the sole candidate to accept the order. The system sends the maintenance service order notification and order acceptance permission only to the single engineer user with the highest comprehensive score as evaluated by the intelligent matching algorithm, giving them an exclusive opportunity to accept the order. For example, after the system generates the maintenance service order notification, it only sends the notification to the engineer user with the highest comprehensive score and reserves a dedicated order acceptance time window for them, during which other engineer users cannot see or compete for the order; or, when sending the notification to the engineer user with the highest score, the system can attach more detailed maintenance service order information and incentive measures to increase their willingness to accept the order and clearly inform them that it is an exclusive dispatch.

[0093] This application's embodiments effectively address the issues of low order push efficiency, response delays, and uneven allocation of engineer resources by clearly defining the order push mechanisms in both the order-grabbing and intelligent order-dispatch modes. In the order-grabbing mode, maintenance service orders are broadcast to engineer users with matching scores above a threshold. This competition among engineers ensures rapid response and broad coverage of maintenance service orders, while the matching score threshold effectively filters out engineers with low matching scores, optimizing resource allocation. In the intelligent order-dispatch mode, maintenance service orders are exclusively assigned to the engineer user with the highest score, directly locking in the optimal engineer resources. This significantly reduces the decision-making chain and waiting time, thereby greatly improving service efficiency and quality. The dynamic selection and specific execution methods of these two modes enable the maintenance service matching system to flexibly and accurately allocate engineer resources according to the actual needs of the maintenance service orders, significantly improving the real-time matching effect of the overall maintenance service, increasing user satisfaction, and optimizing the utilization rate of engineer resources.

[0094] The following example will provide a more detailed explanation of the above technical solution: Enterprise A, located at location A, experienced a sudden core server failure, causing business interruption. Enterprise A urgently needed a skilled IT operations engineer for on-site repair. Faced with the problems of inefficiency, unreliable service quality, slow response times, and opaque costs associated with traditional methods of finding engineers, Enterprise A decided to seek help through this system.

[0095] User A submits a maintenance service request to the cloud server platform through their client application. The order management module processes this request in a structured manner, generating a maintenance service order. This order details the fault description (e.g., "Linux server cannot start, displaying a hard drive error"), geographical location information (the specific coordinates of location A), and user A's preset service budget. The order management module is responsible for creating this order and placing it into a pending matching state.

[0096] Upon receiving this newly created maintenance service order, the cloud server platform's intelligent matching and dispatch module responds immediately. At this point, the user and authentication management module has already pre-certified the skills of a large number of registered engineer users based on a pre-built skill tag library, and generated an engineer profile for each engineer containing their certified skill tags. For example, Engineer B's profile shows that they possess skill tags such as "Linux server maintenance" and "hard drive fault diagnosis and repair," and that their dynamic reputation score is high.

[0097] The intelligent matching and dispatch module activates a multi-dimensional intelligent matching algorithm to filter all engineer users in the certified engineer pool. This algorithm comprehensively calculates multiple weighted scores, including: 1. Skill matching: The algorithm identifies engineers such as Engineer B and Engineer C who have "Linux server maintenance" skills, which are highly matched with the fault description of the order.

[0098] 2. Geographical proximity: The algorithm determines that engineer B is closest to location A based on engineer B's real-time location data.

[0099] 3. Status Availability: The algorithm queries engineer B's current work status to confirm whether he is idle or available to accept orders.

[0100] 4. Service Reputation: The algorithm references Engineer B's past service evaluations, and his dynamic reputation score is relatively high.

[0101] By weighting these dimensions, Engineer B received the highest overall score.

[0102] Based on the preset order dispatch strategy, the intelligent matching and order dispatch module can push orders in different modes.

[0103] If the system is configured in intelligent dispatch mode, the maintenance service order will be directly dispatched to the designated engineer user B with the highest comprehensive score.

[0104] If the system is configured for order-grabbing mode, the order will be broadcast to a group of engineer users (e.g., engineer B, engineer C, engineer D) whose matching degree is higher than a preset threshold, and they will then grab the order.

[0105] If the system is configured in auction mode, the system will push order notifications to engineer users with matching skills and collect their submitted quotes for user A to choose from.

[0106] In this example, assume the system uses an intelligent order dispatch mode, where an order is exclusively pushed to engineer B. Engineer B receives the order notification through their engineer client and views the order details. After confirming everything is correct, engineer B accepts the order on their client. The system then locks the order and automatically establishes a communication link between user A and engineer B, facilitating further communication between them.

[0107] Engineer B travels to location A to perform server fault repair service. During the service execution, the system continuously tracks and updates the service status, such as from "order accepted" to "under service." Once Engineer B completes the repair work, User A confirms the service completion through the client application. At this point, the payment and settlement module of the cloud server platform is triggered. This module connects to a third-party payment gateway, deducts the platform service fee from the prepaid amount by User A, and then settles the payment with Engineer B.

[0108] After payment is completed, the reputation evaluation and knowledge base module begins operation. The evaluation collection unit prompts user A to evaluate engineer B's service, and simultaneously prompts engineer B to evaluate user A. This mutual evaluation data is collected and stored, and engineer B's dynamic reputation score is updated in real time based on this data for future order matching reference. In addition, engineer B is required to submit a solution report regarding the server failure. The solution report storage unit receives and reviews the report. Once approved, the solution is structured and stored, forming a searchable failure solution knowledge base to provide reference for similar failures in the future.

[0109] Through this system, User A quickly found Engineer B, who possessed matching skills, responded swiftly, and had a good reputation, resolving the server malfunction and preventing prolonged business interruption. This effectively overcame the problems of information asymmetry, slow response times, and opaque service costs inherent in traditional operations and maintenance services. Simultaneously, Engineer B also gained a stable source of orders through the platform, fully utilizing their professional skills and resolving the issue of uneven resource allocation. The entire process was efficient and transparent, significantly improving the matching efficiency and quality of operations and maintenance services.

[0110] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 5 , Figure 5 This is a basic structural block diagram of the computer device in this embodiment.

[0111] Computer device 7 includes a memory 71, a processor 72, and a network interface 73 that are interconnected via a system bus. It should be noted that... Figure 5 Only a computer device 7 with three components—memory 71, processor 72, and network interface 73—is shown. It should be understood that implementing all shown components is not required; more or fewer components may be implemented alternatively. Those skilled in the art will understand that this computer device is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), and embedded devices.

[0112] Computer devices can include desktop computers, laptops, handheld computers, and cloud servers. These devices allow for human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0113] The memory 71 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 71 may be an internal storage unit of the computer device 7, such as the hard disk or memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 7. Of course, the memory 71 may also include both internal storage units and external storage devices of the computer device 7. In this embodiment, the memory 71 is typically used to store the operating system and various application software installed on the computer device 7, such as the program code of a real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching. In addition, the memory 71 can also be used to temporarily store various types of data that have been output or will be output.

[0114] In some embodiments, processor 72 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. This processor 72 is typically used to control the overall operation of computer device 7. In this embodiment, processor 72 is used to run program code stored in memory 71 or process data, for example, to run the program code of the above-described real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching, to implement various embodiments of the real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching.

[0115] The network interface 73 may include a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the computer device 7 and other electronic devices.

[0116] This application also provides another implementation method, namely, providing a computer-readable storage medium storing a computer program that can be executed by at least one processor to cause the at least one processor to perform the steps of the above-described real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching.

[0117] Obviously, the embodiments described above are merely some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the scope of this application. This application can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of protection of this application.

Claims

1. A real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching, characterized in that, include: Demand-side clients, engineer clients, and cloud server platforms; The cloud server platform includes: The order management module is used to create, publish, transfer, and archive operation and maintenance service orders. The operation and maintenance service orders include structured fault description information, geographical location information, and service budget information. The user and authentication management module is used to manage user accounts and perform skill authentication for engineer users based on a pre-built skill tag library, generating engineer profiles containing certified skill tags; The intelligent matching and order dispatch module is used to respond to newly created operation and maintenance service orders. Based on the engineer profile, real-time location data, engineer status and dynamic reputation score, it uses a multi-dimensional intelligent matching algorithm to select matching engineer users from the certified engineer pool and push the order according to a preset strategy.

2. The real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching according to claim 1, characterized in that, The intelligent matching and order dispatch module includes an order dispatch strategy unit, which is configured to accept orders in a bidding mode and an intelligent order dispatch mode. The order-grabbing mode is used to broadcast and push the operation and maintenance service order to a group of engineer users whose matching degree is higher than a threshold. The intelligent dispatch mode is used to direct the operation and maintenance service order to the designated engineer user with the highest comprehensive score calculated by the multi-dimensional intelligent matching algorithm.

3. The real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching according to claim 2, characterized in that, The order dispatch strategy submodule is configured as an auction mode; the auction mode is used to push orders to engineer users with matching skills and collect quotation information for demanders to choose from.

4. The real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching according to claim 1, characterized in that, The multi-dimensional intelligent matching algorithm comprehensively calculates a weighted score based on at least the skill matching degree, geographical proximity, status availability, service reputation, and price preference information.

5. The real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching according to claim 1, characterized in that, The cloud server platform also includes a payment and settlement module and a credit evaluation and knowledge base module; The payment and settlement module is used to connect to a third-party payment gateway and, after the service is completed, settle the fees prepaid by the requesting user to the platform with the engineer user who accepted the order. The reputation evaluation and knowledge base module is used to collect evaluation data upon order completion and to receive and review solution reports submitted by the engineer user.

6. The real-time matching system for operation and maintenance services based on multi-dimensional intelligent matching according to claim 5, characterized in that, The reputation evaluation and knowledge base module includes an evaluation collection unit and a scheme report storage unit; The evaluation collection unit is used to collect and store the evaluation data between the requester and the engineer after the order is completed, and update the dynamic reputation score based on the evaluation data. The solution report storage unit is used to receive and review the solution reports submitted by the engineer user, and to store the approved solutions in a structured manner to form a searchable fault solution knowledge base.

7. A real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching, characterized in that, The method, applied to the operation and maintenance service matching system as described in any one of claims 1 to 6, comprises: Receive and parse maintenance service requests submitted by demand-side clients, and generate maintenance service orders containing skill tags; In response to the maintenance service order, perform multi-dimensional intelligent matching calculations to filter out a set of engineer users who meet the criteria; Order notifications are pushed to the set of engineer users according to the selected order dispatch strategy; In response to the order acceptance operation of the engineer's client, the order is locked and a communication link is established between the requester and the engineer accepting the order; Track and update the status of the service execution process, and trigger the payment settlement process after the requesting party confirms that the service has been completed; After payment is completed, the evaluation data between the requester and the engineer is collected and stored, and the dynamic reputation score is updated based on the evaluation data.

8. The real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching according to claim 7, characterized in that, In response to the maintenance service order, a multi-dimensional intelligent matching calculation is performed to filter out a set of qualified engineer users, including: In response to the maintenance service order, a weighted total score is generated by calculating a weighted score based on the skill matching degree, geographical proximity, status availability, and service reputation of each candidate engineer user. The engineer users are sorted according to their weighted total scores to filter out the set of users who meet the criteria.

9. The real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching according to claim 7, characterized in that, The step of pushing order notifications to the engineer user group according to the selected order dispatch strategy includes: If the order dispatch strategy is a bidding mode, a notification will be pushed to all engineer users whose matching score is higher than the threshold. If the order dispatch strategy is the intelligent order dispatch mode, then the engineer user ranked first in the score will be exclusively assigned an order.

10. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the real-time matching method for operation and maintenance services based on multi-dimensional intelligent matching as described in any one of claims 7 to 9.

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