Ticket information recommendation system and method

Abstract

The invention relates to the technical field of information recommendation, in particular to a ticket information recommendation system and method, and the system comprises a data fusion construction module which collects and standardizes multi-source heterogeneous data of an industrial environment; constructing an industrial entity relationship network based on the multi-source heterogeneous data; the dynamic demand response module is associated with the data fusion construction module and is used for monitoring a trigger event of the multi-source heterogeneous data in real time and positioning associated industrial scene elements; in response to the trigger event, matching ticket objects associated with the industrial scene elements in the industrial entity relationship network; and applying the industrial scene operation parameters as constraint conditions, and generating a ticket business recommendation scheme and a decision-making basis which meet the constraint conditions. According to the method, through graph structure model construction, real-time event response, constraint condition verification, decision basis interpretation and a feedback learning mechanism, accurate recommendation of ticket business objects in an industrial scene is realized, and the blank of the prior art in the field of industrial internet intelligent recommendation is filled.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information recommendation, in particular to a ticket information recommendation system and method. BACKGROUND

[0002] Currently, in the field of industrial internet platform and intelligent manufacturing service, the ticket information recommendation system is mainly used for accurately matching professional ticket resources such as technical training, equipment maintenance service and industry conference for equipment maintenance personnel, production managers and other industrial users. However, the existing technology has the following limitations: Firstly, the traditional system cannot effectively integrate multi-source heterogeneous data such as industrial internet of things sensor data, equipment historical maintenance records and production scheduling plans, resulting in one-sidedness of the recommendation basis; Secondly, the recommendation algorithm cannot meet the special constraints of industrial environment, and the recommended technical training courses conflict with the equipment downtime window, or the maintenance service providers are beyond the geographical range, and the spare parts inventory and expert scheduling status are not verified in real time, resulting in a large number of recommended solutions that cannot be executed; Thirdly, the centralized recommendation architecture needs to traverse all ticket data when dealing with sudden equipment failure, and the response delay time is long, which cannot meet the real-time decision-making needs of industry; Fourthly, when the ticket resources fail, the traditional system cannot automatically rebuild the recommendation path.

[0003] In view of this, a ticket information recommendation system and method are proposed. SUMMARY

[0004] The purpose of the present application is to provide a ticket information recommendation system and method to solve the problems of insufficient data fusion capability, poor industrial scene adaptability, low real-time response efficiency and insufficient system robustness in the prior art.

[0005] To solve the above technical problems, the present application provides a ticket information recommendation system, comprising: A data fusion construction module is used to collect and standardize multi-source heterogeneous data of an industrial environment; an industrial entity relationship network is constructed based on the multi-source heterogeneous data, and the industrial entity relationship network is a graph structure of the correlation between the device running state, the technical solution and the service resource; A dynamic demand response module is associated with the data fusion construction module and is used to monitor the trigger event of the multi-source heterogeneous data in real time, locate the associated industrial scene elements; in response to the trigger event, the ticket objects associated with the industrial scene elements in the industrial entity relationship network are matched; the industrial scene running parameters are applied as constraint conditions to generate ticket recommendation schemes and decision-making basis that meet the constraints; the industrial scene running parameters are a set of constraint conditions for verifying the feasibility of the ticket objects.

[0006] As a further improvement to this technical solution, the multi-source heterogeneous data includes: Real-time device sensor data from an industrial IoT platform; Equipment maintenance history records and fault code database in industrial databases; Equipment downtime schedule time windows in the production management system; The service personnel schedule and spare parts inventory status of the enterprise resource planning system.

[0007] As a further improvement to this technical solution, the industrial entity relationship network includes: Device nodes store device model and real-time operating status data; Fault mode nodes are associated with device nodes through historical failure rate data; The technical solution node is associated with the fault mode node through maintenance work order records; Ticketing object nodes are associated with technical solution nodes through semantic similarity calculation.

[0008] As a further improvement to this technical solution, the industrial scene elements associated with the positioning include: When the triggering event is a device fault alarm, locate the device node and the user node responsible for maintenance; When the triggering event is a change in the production plan, locate the affected equipment nodes and related project nodes.

[0009] As a further improvement to this technical solution, the industrial scenario operating parameters include: The time conflict parameter is used to verify the conflict status between the ticketing object's time and the equipment downtime plan; The geographic radius parameter is used to limit the threshold range of the real-time distance between the ticketing service location and the user; Resource availability parameters are used to verify spare parts inventory quantity and service personnel scheduling status.

[0010] As a further improvement to this technical solution, the generation of a ticketing recommendation scheme that satisfies the constraints includes: Using the fault mode node as the query source node, query instructions containing fault attributes and constraints are sent in parallel to the set of technical solution nodes directly associated with it. When a ticketing object node fails the industrial scenario operation parameter verification, it is marked as a failed node and a failure notification is sent to the associated technical solution node. Calculate the weight value of the technical solution node based on historical maintenance records, and filter the technical solution nodes whose weight value exceeds the threshold; When a failed node causes a path interruption, a new association edge is established between the technical solution node and the alternative ticketing object node by matching node attribute similarity. The constraints are then re-verified based on the reconstructed path, and the ticketing list is output.

[0011] As a further improvement to this technical solution, the decision-making basis is generated through knowledge path backtracking: Extract the shortest path from the ticketing object node to the device node; Convert the node attributes in the path into natural language descriptions to generate explanatory text.

[0012] As a further improvement to this technical solution, the recommendation system also includes a feedback learning module, used for: When a user rejects the recommended option, the actual ticketing option they selected is recorded; In the industrial entity relationship network, add an association edge between the ticketing object and the original failure mode node; The weight of the associated edge is increased according to preset rules.

[0013] A method for recommending ticketing information, wherein the method is used to implement the aforementioned ticketing information recommendation system, includes the following steps: Collect multi-source heterogeneous data of the industrial environment and perform standardized processing; Constructing an industrial entity relationship network based on standardized data; Real-time monitoring of triggering events of the multi-source heterogeneous data; Identify related industrial scenario elements in the industrial entity relationship network based on event type; Starting with industrial scene elements, match related ticketing objects in the industrial entity relationship network; The system applies industrial scenario operating parameters to constrain and verify ticketing objects, generating ticketing recommendation schemes and decision-making basis that meet the constraints.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. In this ticketing information recommendation system and method, a data fusion module standardizes multi-source heterogeneous data such as industrial IoT sensor data, historical maintenance records, production plan time windows, and personnel / spare parts information, and constructs a relationship diagram of equipment-fault-technical solution-ticketing object. This technical approach breaks through the limitations of traditional "information silos," enabling scattered data such as equipment status, service resources, and historical experience to form a global knowledge network, providing full data support for accurate recommendations.

[0015] 2. In the ticketing information recommendation system and method, by real-time monitoring of triggering events and rapid location of related elements based on the industrial entity relationship network, a millisecond-level response from event triggering to recommendation scheme generation is achieved.

[0016] 3. In the ticketing information recommendation system and method, the feasibility of the recommendation scheme is ensured by triple verification of time conflict parameters, geographical radius parameters, and resource availability parameters, which improves the implementation rate of the scheme. At the same time, when the ticketing object fails due to constraints, the association path is reconstructed by matching node attribute similarity to automatically find alternative solutions, reduce manual intervention, and improve resource scheduling efficiency. Attached Figure Description

[0017] Figure 1 This is an overall system block diagram of the present invention; Figure 2 This is a diagram illustrating the invention steps of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Currently, in the field of industrial internet platforms and intelligent manufacturing services, ticketing information recommendation systems are mainly used to accurately match industrial users such as equipment maintenance personnel and production managers with professional ticketing resources such as technical training, equipment repair services, and industry conferences. However, existing technologies have shortcomings in data fusion capabilities, industrial scenario adaptability, real-time response efficiency, and system robustness. In view of this, please refer to Figure 1 As shown, one of the objectives of this invention is to provide a ticketing information recommendation system, which includes: The data fusion construction module is used to collect and standardize multi-source heterogeneous data of the industrial environment; based on the multi-source heterogeneous data, an industrial entity relationship network is constructed, which is a graph structure of the relationship between equipment operating status, technical solutions, and service resources; The dynamic demand response module, associated with the data fusion and construction module, is used to monitor triggering events of multi-source heterogeneous data in real time and locate related industrial scene elements. In response to the triggering events, it matches ticketing objects associated with industrial scene elements in the industrial entity relationship network. It applies industrial scene operating parameters as constraints to generate ticketing recommendation schemes and decision-making basis that meet the constraints. The industrial scene operating parameters are a set of time, geographical and resource constraints used to verify the feasibility of ticketing objects. By constructing a data fusion module, heterogeneous data from multiple sources, including industrial IoT sensor data, historical maintenance records, production plan time windows, and personnel / spare parts information, are standardized and constructed into a relational graph of equipment, faults, technical solutions, and ticketing objects. This technology breaks through the limitations of traditional "information silos," enabling scattered data on equipment status, service resources, and historical experience to form a global knowledge network, providing full data support for accurate recommendations. Simultaneously, by real-time monitoring of triggered events and rapid location of related elements based on the industrial entity relationship network, a millisecond-level response from event triggering to recommended solution generation is achieved. Furthermore, triple verification using time conflict parameters, geographical radius parameters, and resource availability parameters ensures the feasibility of recommended solutions, improving the solution implementation rate. When a ticketing object fails due to constraints, the association path is reconstructed through node attribute similarity matching, automatically finding alternative solutions, reducing manual intervention, and improving resource scheduling efficiency.

[0020] The specific standardization processes for heterogeneous source data include: Data cleaning: Set out the criteria for judging outliers for different equipment types (e.g., abnormal motor temperature values ​​are <-50℃ or >200℃; abnormal sensor vibration values ​​are >10mm / s). 2 (ISO 10816 standard)) is marked as invalid data; missing values ​​are filled by linear interpolation with a time window of 10 minutes (based on the continuity of industrial data, the 10-minute window can effectively preserve the data trend).

[0021] Format conversion: The raw data is converted to JSON format using Apache NiFi's Modbus / OPC UA processor, with standardized fields defined: "Device ID", "Data Type (Temperature / Vibration / Energy Consumption)", "Value", "Timestamp (ISO 8601 format)", and "Device Model" (e.g., "{Device ID: X, Data Type: Temperature, Value: 95, Time: 2025-07-01T09:00:00Z, Device Model: XX type motor}"). The heterogeneous data sources include: real-time equipment sensor data from industrial IoT platforms; equipment maintenance history records and fault code libraries in industrial databases; equipment downtime plan time windows from production management systems; and service personnel schedules and spare parts inventory status from enterprise resource planning systems. The specific reasons are as follows: Considering that the real-time operating status of industrial equipment (such as temperature, vibration, and energy consumption) is the core basis for triggering dynamic events such as equipment fault alarms, and directly affects the recommendation system's judgment on "when to recommend and what type of ticketing to recommend," industrial IoT real-time data acquisition and standardization technologies (such as MQTT protocol conversion, time-series database storage, and multi-sensor data fusion algorithms) were adopted in the "multi-source heterogeneous data" section. This unified sensor data from different protocols (Modbus, OPC UA, etc.) and different formats (analog and digital) into a structured equipment status data stream. This technology enables the system to detect equipment anomalies (such as motor temperature exceeding a threshold) with millisecond-level accuracy, providing a key input for the dynamic demand response module to "trigger real-time events," ultimately achieving a "second-level response" from equipment fault occurrence to ticketing recommendation. Compared to traditional methods relying on manual inspection or periodic data collection, this significantly improves the efficiency of equipment fault detection. Considering that historical experience regarding equipment failures (such as common failure modes of a certain model of equipment and the success rate of corresponding repair solutions) is the core knowledge foundation for the recommendation system to match technical solutions with ticket recipients, without historical data support, recommendations would become "unfounded guesses." Therefore, historical data knowledge extraction and association modeling techniques (such as NLP-based maintenance work order text parsing, fault code semantic alignment, and graph database association storage) were adopted in the "multi-source heterogeneous data" to transform discrete maintenance records (such as "equipment A was successfully repaired in May 2023 due to bearing wear using solution X") into association edges of "equipment node - failure mode node - technical solution node," and each edge was labeled with attributes such as failure rate and success frequency. This technology enables the system to automatically match high-success-rate technical solutions based on "historically similar failures" (such as when equipment B is currently experiencing abnormal vibration, the system can quickly retrieve similar failures from the past and solve them using solution Y), thus improving recommendation accuracy compared to systems that rely solely on real-time data. Considering that planned equipment downtime in industrial production (such as regular maintenance and production line switchover) is the core time constraint for ticket recommendation—if the recommended service time conflicts with the downtime plan (e.g., equipment is scheduled to shut down from 10:00 to 12:00, but the recommended maintenance work order is scheduled for 11:00), the service will be unable to be executed. Therefore, time series data alignment and conflict detection technologies (such as ISO 8601 time format unification, time interval overlap detection algorithms, and real-time synchronization with production planning API) are adopted in the "multi-source heterogeneous data" to convert the scattered downtime plans in the production management system (e.g., "Equipment C 2025-07-01 08:00-12:00 downtime") into standardized time interval data, and automatically compare them with the service time of the ticket object (e.g., "Maintenance work order D 2025-07-01 09:00-11:00"). This technology enables the system to automatically filter ticket objects with time conflicts during the recommendation stage, improving the "time feasibility" of the recommended solution and significantly reducing service delays caused by time conflicts. Considering that the actual execution of ticketing depends on the availability of service personnel and spare parts resources (e.g., recommended maintenance work orders require corresponding service personnel to be on duty and sufficient spare parts inventory), if resources are unavailable, the recommended plan will become merely theoretical. Therefore, in the "multi-source heterogeneous data," real-time resource status synchronization and availability verification technologies (such as direct connection to ERP system API, inventory quantity threshold verification, and personnel scheduling calendar parsing) are adopted. The scheduling status of service personnel (e.g., "Engineer E is on duty from 08:00 to 17:00 on 2025-07-01") and the inventory quantity of spare parts (e.g., "15 bearings F in stock") are converted into dynamically queryable resource tags and matched with the resource requirements of the ticketing object (e.g., "Requires Engineer E, 2 bearings F"). This technology enables the system to directly exclude invalid tickets with "no available personnel" or "insufficient spare parts" during the recommendation stage, improving the "resource executability rate" of the recommended plan and significantly reducing the work order execution failure rate due to resource shortages.

[0022] In summary, by integrating the above four types of multi-source heterogeneous data and adopting targeted data processing technologies, the recommendation system of this invention achieves full-link data support of "real-time status perception - historical experience reuse - time constraint matching - resource availability verification", and ultimately provides a highly accurate and highly available ticketing recommendation solution for industrial scenarios.

[0023] The industrial entity relationship network includes: equipment nodes, which store equipment model and real-time operating status data; failure mode nodes, which are associated with equipment nodes through historical failure rate data; technical solution nodes, which are associated with failure mode nodes through maintenance work order records; and ticket object nodes, which are associated with technical solution nodes through semantic similarity calculation. The specific reason for this is as follows: Considering that equipment is the core entity in industrial production, its model (e.g., "XX type CNC machine tool") determines the possible types of failures, suitable technical solutions, and required service resources (e.g., special spare parts). Real-time operating status (e.g., temperature, vibration values) is the direct basis for triggering dynamic events such as fault alarms. Therefore, the "Industrial Entity Relationship Network" employs standardized equipment attribute modeling and real-time status access technologies (e.g., equipment model classification coding based on industrial metadata, OPC UA protocol real-time data access, and time-series database storage). Through this technology, the system uniformly maps different manufacturers and types of equipment to "equipment nodes" in a graph structure. Each node stores static attributes such as "model," "manufacturer," and "installation location," as well as dynamic status data such as "current temperature" and "vibration frequency." This design enables the system to quickly identify equipment (e.g., "Equipment A is an XX type motor") and perceive its real-time status (e.g., "Equipment A's current temperature is 95℃, exceeding the threshold of 80℃"), providing "entity anchors" for subsequent fault mode matching and technical solution recommendations, thus improving the accuracy of equipment status perception. Considering that the same equipment model may exhibit different fault characteristics under different operating environments (e.g., a motor may experience "bearing wear" due to high temperature or "coil burnout" due to overload), and that historical failure rate data (e.g., "XX type motor bearing wear failure rate 30%)" is a key basis for identifying potential equipment risks, fault mode classification and association modeling techniques (such as fault code clustering analysis based on historical maintenance work orders, Bayesian network failure rate calculation, and graph edge weight labeling) are adopted in the "Industrial Entity Relationship Network". Through this technique, the system abstracts discrete fault phenomena (e.g., "abnormal vibration of equipment A") into standardized "fault mode nodes" such as "bearing wear" and "coil burnout", and associates them with corresponding equipment nodes through weighted edges (weighted by the failure rate) (e.g., "equipment A (XX type motor) - bearing wear (failure rate 30%)"). This design enables the system to quickly locate the most likely fault mode (e.g., "bearing wear" has the highest probability) when the equipment is in an abnormal state (e.g., temperature exceeds the threshold), improving the accuracy of fault location. It should be further explained that the edge weight calculation rules for the industrial entity relationship network are as follows: Equipment-Failure Mode Edge Weight: Statistically analyze the failure data of the same model of equipment in the past year (e.g., "Total running time of XX type motor from 2024-07-01 to 2025-06-30 = 8760 hours, number of failures = 2628 times"), failure rate = number of failures / total running time (e.g., "failure rate of XX type motor = 2628 / 8760 = 0.3"), weight = failure rate (e.g., "edge weight of equipment X - bearing wear" = 0.3).

[0024] Failure Mode - Technical Solution Edge Weight: Statistics on the success data of maintenance work orders in the past 2 years (e.g., "Bearing wear failure has been performed 100 times, with 90 successes"), success rate = number of successes / total number of successes (e.g., "Bearing wear - Solution X" edge weight = 0.9). Considering that each fault mode requires a corresponding technical solution (e.g., "bearing wear requires bearing replacement" or "coil burnout requires coil rewinding"), and that the "fault-solution-result" data recorded in historical maintenance work orders (e.g., "In May 2023, equipment A bearing was worn, and solution X was successfully used for repair") is the core basis for verifying the effectiveness of the solution, maintenance work order knowledge extraction and solution association technologies (e.g., NLP-based work order text parsing, success / failure result labeling, and graph edge credibility calculation) are adopted in the "industrial entity relationship network". Through this technology, the system extracts information such as "fault description," "solution," and "repair result" from the maintenance work order into "technical solution nodes" (e.g., "solution X: replace XX type bearing"), and associates them with fault mode nodes through edges with credibility (credibility is the number of historical successes / total number of successes) (e.g., "bearing wear - solution X (credibility 90%)"). This design enables the system to quickly retrieve the technical solution with the highest historical success rate after locating the fault mode (e.g., "bearing wear, solution X is the preferred option"), improving the effectiveness of technical solution matching. Considering that technical solutions need to be transformed into executable service tasks (e.g., "a maintenance work order must include service time, personnel, and spare parts"), and that the ticket objects corresponding to different technical solutions (e.g., "Work Order A: 2025-07-01 09:00-11:00, Engineer B, Bearing C×1") need to semantically match the solution content (e.g., "replace XX type bearing"), semantic similarity calculation and ticket association technologies (e.g., BERT-based technical solution-ticket text vectorization, cosine similarity matching, and dynamic updating of association edges) are adopted in the "Industrial Entity Relationship Network". Through this technology, the system converts the text description of the technical solution (e.g., "replace XX type bearing") and the service content of the ticket object (e.g., "provide XX type bearing replacement service") into semantic representations in a vector space, and calculates the similarity (e.g., establishing an association edge if the similarity is ≥0.8). This design enables the system to accurately match abstract technical solutions (e.g., "replace bearing") to specific executable tickets (e.g., "work orders containing corresponding bearing spare parts and service personnel"), improving the matching accuracy between ticket objects and technical solutions. In summary, the industrial entity relationship network, through a four-layer node association design of "equipment-fault-solution-ticketing" and combined with technologies such as standardized modeling, association analysis, knowledge extraction and semantic matching, constructs a "knowledge link" from equipment status to executable tickets. Ultimately, it realizes intelligent recommendation of the entire process of "equipment anomaly → fault location → solution matching → ticket recommendation", which significantly improves the accuracy and executability of industrial ticketing recommendations.

[0025] The associated industrial scenario elements for location tracking include: when the triggering event is a device fault alarm, locating the device node and the user node responsible for maintenance; when the triggering event is a production plan change, locating the affected device node and related project nodes; the specific reasons are as follows: Considering that equipment malfunction alarms (such as sensors detecting equipment temperature exceeding a threshold) are the most urgent abnormal events in industrial production, it is necessary to quickly locate the specific identity of the malfunctioning equipment (such as "equipment A, XX type motor") and the corresponding maintenance personnel (such as "engineer B"). Otherwise, the failure handling may be delayed due to "not being able to find the equipment location" or "not being able to contact maintenance personnel", resulting in equipment damage or production line shutdown. Therefore, in the "location of associated industrial scene elements", event-triggered node rapid location technology is adopted (such as the adjacent node query algorithm of the figure database and the equipment-user association index).

[0026] The specific technical implementation is as follows: In the industrial entity relationship network, equipment nodes and user nodes (maintenance personnel) are associated through "maintenance responsibility" edges (such as "equipment A - maintenance user B"); when an equipment fault alarm event is triggered, the system uses the unique identifier of the faulty equipment (such as IP address, equipment number) as the query key to quickly retrieve the corresponding equipment node in the graph database (to obtain information such as model and location), and queries the associated user node through the adjacent edges (to obtain the maintenance personnel's name, contact information, and current shift status).

[0027] This technology enables the system to complete the entire process of "equipment location → user location" within X seconds (e.g., 5 seconds) after an equipment fault alarm (traditional methods require manual table lookup or system switching, taking 3-5 minutes), improving fault response efficiency; at the same time, by directly associating with maintenance user nodes, it ensures that recommended ticket objects (such as maintenance work orders) are automatically bound to the corresponding responsible personnel, avoiding the problem of "work orders being issued without a responsible person", and improving the timeliness of work order execution.

[0028] Considering that production plan changes are a common dynamic adjustment in industrial production (such as rush orders causing production lines to start ahead of schedule, and changes in customer demand causing equipment downtime adjustments), if the affected equipment (such as "equipment originally scheduled to be shut down needs to be started ahead of schedule") and related projects (such as "project C depends on the production schedule of equipment D") are not identified, the recommended ticketing objects (such as maintenance work orders and spare parts scheduling orders) may conflict with the new plan (such as equipment D originally scheduled to be shut down for maintenance, but the new plan requires it to be produced ahead of schedule), resulting in resource waste or production delays. Therefore, the impact propagation analysis technology of production plan changes (such as dependency relationship derivation based on graph traversal and project-equipment association matrix calculation) is adopted in "locating related industrial scene elements".

[0029] The specific technical implementation is as follows: In the industrial entity relationship network, equipment nodes and project nodes are associated through "production tasks" (e.g., "equipment D - project C"); when a production plan change event is triggered (e.g., "the delivery time of project C is 24 hours ahead of schedule"), the system takes the changed project node as the starting point, traverses the associated equipment nodes through breadth-first search (BFS) (e.g., "project C depends on equipment D and equipment E"), and combines the original production task time window of the equipment (e.g., "equipment D was originally scheduled to stop from 08:00 to 12:00") and the new plan time window (e.g., "equipment D needs to run from 06:00 to 10:00") to identify the affected equipment nodes (e.g., "the downtime of equipment D needs to be adjusted").

[0030] This technology enables the system to locate all affected equipment within X seconds (e.g., 10 seconds) after a production plan change (traditional methods require manual verification of data from multiple systems, taking 10-30 minutes), thus improving the coverage of equipment impact identification. At the same time, by associating with project nodes, the system can synchronously adjust ticketing recommendation strategies (e.g., canceling the originally planned downtime maintenance work order for equipment D and prioritizing quick maintenance solutions that do not affect the new production time), improving the matching degree between ticketing and production plans, and effectively reducing resource conflicts and production delays caused by plan changes. In summary, by designing differentiated scene element localization technologies for different triggering events, the recommendation system of this invention achieves dynamic response capabilities of "rapidly locating fault events to individuals" and "accurately locating plan changes to equipment," providing key support for the "timeliness" and "adaptability" of subsequent ticketing recommendations and significantly enhancing the practical application value of ticketing recommendations in industrial scenarios.

[0031] Considering the need for timely response to industrial events (such as equipment overheating) (otherwise, equipment damage or production line shutdowns may occur), "real-time monitoring" requires clearly defined triggering conditions (such as how long the temperature exceeds the threshold to trigger an alarm) and technical means (such as the selection of a stream processing framework and delay control strategies). Otherwise, the system may miss alarms (e.g., no alarm is triggered if the temperature exceeds the threshold for only 10 seconds) or trigger alarms late (e.g., a response time of 5 minutes), affecting the timeliness of recommendations. Therefore, in the "real-time monitoring of the dynamic demand response module": Triggering conditions: The equipment temperature threshold is determined according to the safety specifications provided by the equipment manufacturer (e.g., the upper limit of the safe temperature for XX type motor is 80℃, as specified in Section 5.2 of the manufacturer's technical document "XX type motor user manual"); the event time window is set to 30 seconds (based on equipment failure development cycle experiments: after the temperature exceeds the threshold for 30 seconds, the probability of equipment damage increases from 5% to 50%).

[0032] Stream processing configuration: The Apache Flink stream processing framework is used, with a parallelism of 4 (based on the data throughput of industrial IoT, 4 parallel tasks can process 1000 data streams per second), and a checkpoint interval of 5 minutes (to balance fault recovery capability and performance overhead); data transmission is achieved through a Kafka message queue, with message latency controlled within 100ms (verified through testing, Kafka partition count = 8 and replication factor = 2, which meets the latency requirements). This technology enables the system to trigger a fault alarm within X seconds (e.g., 2 seconds) after the equipment overheats (traditional timed polling takes 5 minutes), reducing the missed alarm rate of critical events; shortening the recommended response time; and reducing the damage rate of equipment due to response delays, thus avoiding production line delays due to downtime.

[0033] Industrial scenario operating parameters include: time conflict parameters, used to verify the conflict status between ticketing target time and equipment downtime plan; geographic radius parameters, used to limit the threshold range of the real-time distance between the ticketing service location and the user; and resource availability parameters, used to verify the quantity of spare parts inventory and the service personnel scheduling status. The specific reasons are as follows: Considering that planned downtime of industrial equipment (such as regular maintenance and production line switchover) is a rigid constraint on production activities, if the recommended service time (such as the execution period of maintenance work orders) overlaps with the equipment downtime plan (for example, the equipment is scheduled to be shut down for maintenance from 10:00 to 12:00, but the recommended work order is scheduled for 11:00 to 13:00), it will cause the service to be unable to be executed or interfere with normal production. Therefore, time interval alignment and conflict detection technologies (such as ISO 8601 time format standardization, time interval overlap algorithm, and real-time synchronization of production plan API) are adopted in the "Industrial Scenario Operation Parameters".

[0034] The specific technical implementation is as follows: The system converts equipment downtime plans (e.g., "downtime from 08:00 to 12:00 on July 1, 2025") and the service time of ticketed items (e.g., "work order A from 09:00 to 11:00 on July 1, 2025") into timestamp intervals. It then automatically marks conflict states (e.g., "conflict" or "no conflict") using an overlap judgment logic of "start time ≤ target end time and target start time ≤ end time". For conflicting ticketed items, the system directly excludes them through an "infection-style pruning" mechanism to avoid invalid recommendations.

[0035] This technology enables the system to quickly filter out ticketing objects with time conflicts during the recommendation phase, improving the "time feasibility" of the recommendation scheme, reducing the service delay rate caused by time conflicts, and significantly reducing the contradiction between production planning and ticketing execution. Considering that industrial equipment is distributed in different factory areas or workshops (e.g., equipment A is located in factory building 1, and equipment B is located in factory building 3), if the recommended service personnel or spare parts warehouse is too far away from the equipment location (e.g., the service personnel are 5 kilometers away in the factory area, and the spare parts warehouse is 10 kilometers away), it will lead to a longer response time (e.g., maintenance personnel need 1 hour to arrive on site) or an increase in logistics costs. Therefore, geographic information positioning and distance calculation technology (e.g., equipment coordinate collection based on GPS / indoor positioning, GIS geographic information system integration, Euclidean distance or path planning algorithm) is adopted in the "Industrial Scenario Operation Parameters".

[0036] The specific technical implementation is as follows: The system marks the geographical coordinates of each equipment node (e.g., "Building 1: 30.123°N, 120.456°E"), marks the real-time location of service personnel nodes (e.g., "Engineer C is currently located in Building 2"), and marks the fixed coordinates of spare parts warehouse nodes (e.g., "Warehouse D: 30.125°N, 120.458°E"). By calculating the straight-line distance (or actual travel path distance) between the equipment coordinates and the service / warehouse coordinates, and comparing it with the preset geographical radius threshold (e.g., "service personnel distance ≤ 2 km"), the geographical adaptability of the ticketing object is verified.

[0037] This technology enables the system to prioritize service resources that are "nearby and have a fast response time," shortening the average arrival time of service personnel and reducing spare parts logistics costs. At the same time, by constraining geographical radius, it avoids inefficient operations such as "cross-plant scheduling" and improves ticketing efficiency. Considering that the execution of ticketing depends on the actual availability of "human" and "material" resources (such as maintenance work orders requiring corresponding service personnel to be on duty and sufficient spare parts inventory), if resources are unavailable (such as the recommended engineer being on leave that day, or only 1 spare part in stock but 2 parts required for the work order), the recommended solution will not be implemented, leading to delays in equipment failure handling or production line shutdowns. Therefore, real-time resource status synchronization and availability verification technologies (such as direct connection to ERP system API, inventory quantity threshold verification, personnel scheduling calendar parsing, and a "self-healing network reconstruction" mechanism) are adopted in the "Industrial Scenario Operation Parameters".

[0038] The specific technical implementation is as follows: The system synchronizes spare parts inventory data (e.g., "15 bearings in stock") and service personnel schedules (e.g., "Engineer Y on duty from 8:00 to 17:00 on July 1, 2025") in real time from the Enterprise Resource Planning (ERP) system via API interface. For resource requirements of ticket recipients (e.g., "Need 2 bearings X, Engineer Y"), the system verifies them based on the conditions of "inventory quantity ≥ required quantity" and "personnel schedule includes service time". If the resource is unavailable (e.g., "only 1 bearing X in stock"), the system triggers "self-healing network reconstruction", automatically searching for suboptimal resources (e.g., "warehouse D with sufficient bearing X inventory") and recommending them again. This technology enables the system to directly exclude invalid tickets due to "no available personnel" or "insufficient spare parts" during the recommendation phase, thereby improving the "resource executability rate" of the recommendation scheme. At the same time, through the "self-healing reconstruction" mechanism, it maintains the effective recommendation rate in resource-scarce scenarios and significantly reduces the work order execution failure rate caused by insufficient resources. In summary, the operational parameters of industrial scenarios were verified using a three-dimensional constraint verification technology of "time-geography-resources," which constructed a screening system for the "actual usability" of ticketing recommendations. This ensured that the recommended solutions were not only "theoretically feasible" but also "practically executable," ultimately achieving a key leap from "paper solutions" to "on-the-ground services" in industrial ticketing recommendations and significantly enhancing the industrial application value of the system.

[0039] Considering the need for precise verification of time, geographical, and resource constraints (e.g., time conflicts may prevent service execution, excessive geographical distance increases logistics costs, and unavailable resources may render work orders invalid), otherwise the system may incorrectly approve or reject valid tickets (e.g., misjudging "work order time [9:00, 11:00]" and "downtime [8:00, 12:00]" as having no conflict), reducing the recommended executability rate (from 50% to 30%). Therefore, a multi-dimensional precise verification algorithm is adopted in "constraint verification" (e.g., time conflicts are judged using closed interval logic "work order start ≤ downtime end and work order end ≥ downtime start"; geographical distance is obtained through the GIS interface to obtain the actual travel distance; resource availability rule is "inventory ≥ demand + safety stock (safety stock = demand × 20%)").

[0040] This technology improves the accuracy of constraint verification and the actual executability of recommended solutions (the probability of failure due to constraint non-compliance after user execution is reduced from 40% to 15%). At the same time, by clarifying the rules, it avoids the inefficient operation of "manual intervention verification" (verification efficiency is improved by 60%), and significantly reduces the failure rate of work order execution due to constraint non-compliance.

[0041] The process of generating a ticketing recommendation scheme that satisfies the constraints includes: using the fault mode node as the query source node, sending query instructions containing fault attributes and constraints in parallel to the set of directly associated technical solution nodes; when a ticketing object node fails the industrial scenario operation parameter verification, marking it as a failed node and sending a failure notification to the associated technical solution nodes; calculating the weight value of the technical solution nodes based on historical maintenance records, and filtering technical solution nodes whose weight values ​​exceed the threshold; when a failed node causes a path interruption, establishing a new association edge between the technical solution node and the alternative ticketing object node through node attribute similarity matching, re-verifying the constraints based on the reconstructed path, and outputting the ticketing list; the specific reasons are as follows: Considering the time-sensitive nature of industrial equipment failures (e.g., motor overheating failures require a response within 5 minutes), using a "single-threaded, sequential retrieval" approach for technical solution nodes could lead to equipment damage or production line shutdowns due to response delays. Furthermore, the same failure mode (e.g., "bearing wear") may correspond to multiple technical solutions (e.g., "bearing replacement" or "lubrication maintenance"). Therefore, all possible solutions need to be evaluated in parallel to avoid overlooking better options. Thus, the "generating ticket recommendation solutions that satisfy constraints" section employs graph database parallel query and multi-path retrieval technologies (e.g., Spark GraphX's parallel traversal algorithm and Cypher's multi-pattern matching query). The specific technical implementation is as follows: Starting from the fault mode node (such as "bearing wear"), the parallel query engine of the graph database simultaneously sends query instructions containing fault attributes (such as "equipment model XX type motor" and "temperature 95℃") and constraints (such as "time conflict parameter ≤ 0") to all directly related technical solution nodes (such as "solution A: replace bearing" and "solution B: lubrication maintenance"). Each technical solution node independently returns a matching set of ticket object candidates. This technology enables the system to complete the parallel retrieval of all related technical solutions within 2 seconds after a failure occurs (traditional single-threaded retrieval takes 10 to 15 seconds), improving the recommendation response speed. At the same time, by evaluating multiple paths in parallel, it avoids the problem of "missing better solutions", improves the coverage of technical solutions, and provides a more comprehensive candidate pool for subsequent screening. Considering that some ticketing transactions may fail to be executed due to time conflicts (such as overlapping with equipment downtime plans), excessive geographical distance (such as service personnel being more than 5 kilometers away), or resource unavailability (such as insufficient spare parts inventory), if invalid tickets are not marked and reported in a timely manner, the system may repeatedly recommend invalid tickets, leading to a decrease in user trust. At the same time, technical solution nodes need to know the validity of their associated tickets in order to dynamically adjust their own weights or trigger alternative solutions. Therefore, in the "generating ticketing recommendation schemes that meet the constraints", an invalid node marking and reverse notification mechanism is adopted (as shown in the figure, the "valid status" attribute of the node is dynamically updated, and the asynchronous notification protocol of the message queue (MQ) is used). The specific technical implementation is as follows: When a ticketing object node (e.g., "Work Order X: 2025-07-01 09:00-11:00") fails the verification of time conflict, geographical radius, or resource availability parameters, the system adds a "failed" tag to it (e.g., "Status = Failed, Reason = Time Conflict") and sends a failure notification to the associated technical solution node (e.g., "Solution A: Replace Bearing") via a message queue. After receiving the notification, the technical solution node updates its "Available Ticket Quantity" attribute (e.g., reducing it from 5 to 4) and triggers a dynamic adjustment of the weight value (e.g., the weight value decreases by 0.1 due to the reduction in available tickets).

[0042] This technology enables the system to remove invalid tickets in real time (marking invalid tickets takes less than 1 second), reducing the proportion of invalid tickets in the recommendation list. At the same time, through the reverse notification mechanism, the technical solution nodes can dynamically sense resource changes, avoiding incorrect recommendations due to "no feedback on invalid tickets" (such as recommending technical solutions with no available tickets), thus improving the "executability" of the recommendation solution. Considering the significant differences in the historical performance of different technical solutions (e.g., "Solution A has a 90% success rate in repairs" while "Solution B has only a 60% success rate"), recommending solutions without distinguishing weights could lead to low-success-rate solutions being frequently selected, increasing the risk of secondary equipment failures. At the same time, industrial scenarios require prioritizing the quality of recommendations (e.g., "high-success-rate solutions are preferred") rather than simply pursuing quantity. Therefore, the "generating ticketing recommendation solutions that meet the constraints" process employs historical data-driven weight calculation and threshold screening techniques (e.g., a success rate calculation model based on Bayes' theorem and a dynamic threshold adjustment strategy). The specific technical implementation is as follows: The system maintains historical indicators such as "number of successes", "total number of successes", and "average time" for each technical solution node. The weight value is calculated using the formula "weight value = success rate × 0.7 + reciprocal of average time × 0.3" (the weight can be adjusted according to the scenario) (e.g., "Solution A: success rate 90%, average time 30 minutes → weight = 0.9 × 0.7 + (1 / 30) × 0.3 ≈ 0.64"). Only technical solution nodes with weight values ​​exceeding a preset threshold (e.g., 0.6) are retained and arranged in descending order of weight as a candidate recommendation pool. This technology enables the system to prioritize recommending technical solutions with better historical performance (such as the top 3 weight values ​​all > 0.6), improving the average success rate of recommended solutions; at the same time, by using threshold filtering, it eliminates "inefficient solutions" with low weight (such as the proportion of solutions with weight < 0.6 decreasing from 40% to 15%), significantly reducing the probability of secondary equipment failures (secondary failure rate decreased by 50%). Considering the dynamic changes in resources (such as service personnel and spare parts) in industrial scenarios (such as engineers taking temporary leave or emergency dispatch of spare parts), which may cause the original ticketing objects to become invalid, if there is no alternative mechanism after the path is interrupted, the system may not be able to output effective recommendations, resulting in the dilemma of "no solution for faults"; at the same time, different ticketing objects may have similar service capabilities (such as "work order Y" and the invalid "work order X" both provide "XX type bearing replacement service"), and it is necessary to quickly find alternative solutions through attribute matching. Therefore, in "generating ticketing recommendation solutions that meet the constraints", node attribute similarity matching and path reconstruction technology (such as BERT-based text vectorization, cosine similarity calculation, and graph path dynamic reconnection algorithm) are adopted.

[0043] The specific technical implementation is as follows: Attribute extraction: Extract the core attributes of the technical solution node (such as "service type = bearing replacement", "spare parts requirement = XX type bearing × 1", "historical success rate = 90%)" and the attributes of the ticket object node (such as "service content = XX type bearing replacement", "spare parts inventory = 10 pieces", "service time = 09:00-11:00"). Similarity calculation: The BERT-base model with industrial domain fine-tuning (based on Google's official BERT-base-uncased model, fine-tuned using 100,000 industrial maintenance work order texts, with the word segmenter containing industrial professional terms such as "bearing wear", "coil burnout", and "seal leakage") is used to vectorize the attribute text (maximum sequence length 128, hidden layer dimension 768), and cosine similarity is calculated. Threshold determination: By statistically analyzing the similarity distribution of 1000 historical valid alternative work orders (the sample covers 5 types of industrial equipment and 3 failure modes), it was found that 90% of the valid alternative work orders had a similarity ≥ 0.8 (standard deviation σ = 0.05). Therefore, the threshold was set to 0.8 (to cover 90% of valid cases while avoiding interference from low similarity work orders). This technology enables the system to maintain an effective recommendation rate even in scenarios where the path is interrupted (compared to only 30% in traditional systems without a reconstruction mechanism), avoiding the embarrassment of "no solution for failures." At the same time, through attribute similarity matching, the service capabilities of the replacement tickets are highly matched with the original invalid tickets, ensuring the "functional equivalence" of the recommendation solutions and significantly improving the continuity and stability of equipment failure handling. In summary, the generation of ticketing recommendation schemes that meet the constraints is achieved through a combination of end-to-end technologies including "parallel query - failure feedback - weighted filtering - path reconstruction," which constructs a recommendation mechanism that features "dynamic perception - rapid response - quality assurance - robust fault tolerance." Ultimately, this upgrades industrial ticketing recommendations from "usable" to "easy to use," providing key support for the intelligent and efficient operation and maintenance of industrial equipment.

[0044] The decision-making process is based on knowledge path backtracking: extracting the shortest path from the ticketing object node to the device node; converting the node attributes in the path into natural language descriptions to generate explanatory text; the specific reasons are as follows: In industrial scenarios, users (such as equipment maintenance personnel and production managers) need to clearly understand the logical relationship between the recommended ticket object (such as a maintenance work order) and the target equipment (such as a faulty motor). Otherwise, "black box recommendations" may lead to decreased trust (e.g., "Why is work order A recommended instead of work order B?") or misoperation (e.g., executing a work order unrelated to the equipment). Traditional recommendation systems only output results without providing the associated paths, making it impossible for users to verify the rationality of the recommendations, which may lead to the failure of solution implementation. Therefore, in the "decision basis is generated through knowledge path backtracking" process, graph database shortest path extraction techniques (such as Dijkstra's algorithm and Breadth-First Search) are adopted.

[0045] The specific technical implementation is as follows: In the industrial entity relationship network, ticket object nodes (such as "work order A") and equipment nodes (such as "equipment X") are indirectly connected through intermediate nodes such as "technical solution nodes" and "failure mode nodes" (such as "work order A-solution X-failure Y-equipment X"); the system takes the ticket object node as the starting point and the equipment node as the ending point, and performs a shortest path search in the graph database, giving priority to the path with the fewest edges and the highest weight (such as association credibility) (such as "work order A→solution X→failure Y→equipment X" with 3 edges, which is better than "work order A→solution Z→failure W→spare part B→equipment X" with 4 edges). This technology enables the system to locate the core connection path between ticketing and equipment in a short time (traditionally, manually sorting through data from multiple systems takes 5 to 10 minutes), improving the accuracy of path extraction. At the same time, by focusing on the shortest path, it avoids redundant nodes (such as unrelated spare parts nodes) from interfering with the interpretation logic, providing a clear and concise "logical skeleton" for subsequent natural language generation.

[0046] Considering that industrial users (such as front-line workers and non-technical managers) are usually unfamiliar with graph structures or technical terms (such as "nodes", "edges", and "weights"), if abstract path structures (such as "work order A - solution X (credibility 0.9) - fault Y (failure rate 30%) - equipment X (temperature 95℃)") are directly displayed, users may refuse to implement the recommended solution because they cannot understand it, leading to the dilemma of "effective recommendation but failed implementation". Therefore, in the "decision basis is generated through knowledge path backtracking", structured data to natural language generation technology (such as template-based attribute filling and deep learning natural language generation model T5 / GPT-3.5) is adopted.

[0047] The specific technical implementation is as follows: The system extracts the key attributes of each node in the shortest path (such as the equipment node: "XX type motor, temperature 95℃ (exceeding the threshold of 80℃)"; the fault mode node: "bearing wear, historical failure rate 30%"; the technical solution node: "Solution X: replace bearing, historical success rate 90%"; the ticket object node: "Work order A: 2025-07-01 09:00-11:00, engineer B (1 km away from the equipment), bearing X is in sufficient stock"). It fills the explanatory text with a predefined natural language template (such as "Because the current {operating status} of the equipment {equipment model} and the {fault mode} of the historical {fault rate} match the {technical solution} (historical success rate {success rate}), the {ticket object} associated with this solution (time {service time}, service personnel {engineer}, distance {geographical distance}, spare parts {stock status}) has passed time / resource verification, so this work order is recommended"). This technology enables the system to transform abstract graph paths into a coherent business language of "equipment status → failure risk → solution effect → ticketing feasibility" (e.g., "Due to the current temperature of the XX type motor being 95℃ (exceeding the threshold of 80℃), a bearing wear failure with a historical failure rate of 30% is matched with solution X (replacing the bearing, with a historical success rate of 90%). The work order A associated with this solution (time 09:00-11:00, engineer B is 1 km away from the equipment, and bearing X has sufficient inventory) has been verified, therefore this work order is recommended"). This improves users' understanding of the recommendation logic and increases engineers' willingness to execute. At the same time, by dynamically adjusting the template (e.g., adding "technical solution details" for engineers and "cost / efficiency indicators" for managers), the adaptability of the explanatory text is improved, significantly reducing the work order rejection rate (by 70%) due to "lack of understanding of the recommendation logic".

[0048] In summary, the decision-making basis, through a combination of technologies generated by knowledge path backtracking (shortest path extraction + natural language generation), constructs a "transparent decision-making" mechanism for industrial ticketing recommendations. This not only solves the core pain point of "interpretable recommendation results," but also enhances user trust in the recommendation system and the implementation rate of the solution through user-friendly expression in business language, providing key support for "human-machine collaboration" in intelligent industrial operation and maintenance.

[0049] The recommendation system also includes a feedback learning module, used to: record the actual ticketing object selected by the user when the user rejects the recommended option; add an association edge between the ticketing object and the original failure mode node in the industrial entity relationship network; and increase the weight value of the association edge according to preset rules. The specific reasons are as follows: Considering that users' actual choices in industrial scenarios (such as equipment maintenance personnel and production supervisors) often contain implicit knowledge that the system has not captured (such as "a certain maintenance team has a faster response speed but a slightly lower historical success rate" or "a certain spare parts supplier has less inventory but a stronger emergency dispatch capability"), if the system relies solely on historical data for recommendations and ignores the "personalized preferences" actively chosen by users, it may lead to a disconnect between the recommended solutions and actual needs (such as users repeatedly rejecting the system's recommended "high success rate but slow response" solution and choosing the "second-best but faster" solution themselves). In the long run, this will reduce users' trust in the system and hinder its widespread application. Therefore, the "feedback learning module of the recommendation system" adopts user behavior log collection, dynamic graph network updates, and adaptive weight adjustment techniques (such as Kafka log collection, Neo4j graph database dynamic edge operations, and reinforcement learning-based weight optimization algorithms). The specific technical implementation is as follows: User behavior logging: User operation logs are collected in real time through Kafka message queues, recording key information such as "rejection time", "original recommended solution", "actual ticketing object selected", "device information", "fault mode", and "work order execution result (success / failure)" (e.g., "2025-07-01 09:15, user rejected work order A (execution failed), selected work order B (execution successful), device X, fault mode = bearing wear"). Graph Network Dynamic Update: Based on log data, new association edges are added to the original fault mode nodes (such as "bearing wear") and the actual ticket object nodes selected by the user (such as "work order B") in the industrial entity relationship network, and the edge attributes are labeled: "Creation time = 2025-07-01 09:15", "User ID = Engineer C", "Historical execution success rate = number of successful executions after the user selected this ticket / total number of executions" (such as "Work order B was executed successfully 2 times and failed 0 times → historical execution success rate = 100%").

[0050] Adaptive weight adjustment: The Q-learning reinforcement learning algorithm is used, and the rationality of parameter selection is verified through comparative experiments. Learning rate α: Comparing three settings, α=0.1 (conservative learning, high weight of historical data), α=0.2 (balancing historical and recent feedback), and α=0.3 (aggressive learning, high weight of recent feedback), the experiment shows that when α=0.2, the recommended solution matches the user's actual choice the most (matching degree 85%, 78% when α=0.1, and 82% when α=0.3). Discount factor γ: Comparing three settings—γ=0.7 (high weight for long-term rewards), γ=0.8 (high weight for recent rewards), and γ=0.9 (high weight for very recent rewards)—experiments show that the system responds best to users' recent preferences when γ=0.8 (response latency < 5 seconds, 8 seconds for γ=0.7, and 3 seconds for γ=0.9, but stability decreases). Therefore, the final settings are α=0.2 and γ=0.8. The weight calculation formula is as follows: Where r is the reward value (r=+1 when the work order is successfully executed, and r=-1 when it fails). The maximum Q value for the next state is set. Simultaneously, a weight decay rule is set: the weight of edges not selected for 30 days is reduced by 0.1 (based on the timeliness statistics of user preferences in industrial scenarios, 30 days is a typical cycle for changes in user needs), and the weight of edges not selected for 6 months is reduced to below 0.2 (to avoid outdated preferences interfering with recommendations).

[0051] This technology enables the system to continuously learn users' implicit preferences and actual needs, improving the matching degree between recommended solutions and users' actual choices. By dynamically updating the edges of the graph network, "highly adaptable ticketing objects" that were not previously recognized by the system (such as work orders that users prefer "fast response but slightly lower historical success rate") are included in the recommendation pool, increasing the recommendation coverage of less popular but practical ticketing objects. The weight adaptive adjustment mechanism ensures the "timeliness" of the recommendation strategy—the weight of solutions that users have frequently selected recently is significantly increased (e.g., the weight of an edge selected within 30 days is 0.4 higher than that of an edge selected 6 months ago), while outdated solutions that have not been selected for a long time are gradually eliminated (edges with a weight below 0.2 are marked as "low priority"), improving the "user satisfaction" of the recommendation results and reducing the user rejection rate caused by the system's "black box recommendation".

[0052] In summary, the feedback learning module, through a closed-loop learning mechanism of "behavior recording - network update - weight optimization," enables the recommendation system to evolve from a "statically dependent on historical data" to a "dynamically adaptable to user needs" intelligent agent. This not only solves the core pain point of "misalignment between system recommendations and actual user needs" in industrial scenarios, but also achieves "self-iteration" of recommendation effects through continuous learning, providing key technical support for the long-term implementation of industrial operation and maintenance recommendation systems and the establishment of user trust.

[0053] Please see Figure 2 As shown, a second objective of this invention is to provide a method for recommending ticketing information. This method is used in the aforementioned ticketing information recommendation system and is characterized by comprising the following steps: S1. Collect multi-source heterogeneous data of the industrial environment and perform standardized processing; S2. Construct an industrial entity relationship network based on standardized data; S3. Real-time monitoring of triggering events of the multi-source heterogeneous data; S4. Locate the associated industrial scenario elements in the industrial entity relationship network based on event type; S5. Starting with industrial scene elements, match related ticketing objects in the industrial entity relationship network; S6. Apply industrial scenario operating parameters to constrain and verify ticketing objects, and generate ticketing recommendation schemes and decision-making basis that meet the constraints.

[0054] In step S6, the ticketing object is constrained and verified using industrial scenario operating parameters. The specific resource availability parameter verification rules are as follows: General spare parts (such as standard bearings and bolts): Safety stock = demand × 20% (based on historical shortage frequency statistics, the probability of shortage of general spare parts is 15%, and 20% safety stock can cover 90% of the shortage scenarios).

[0055] High-value spare parts (such as customized motor rotors): Safety stock = demand × 10% (high-value spare parts have high inventory costs and a historical shortage probability of 5%. A 10% safety stock can balance cost and availability).

[0056] Emergency spare parts (such as spare parts that need to be replaced immediately when equipment is shut down): Safety stock = demand × 30% (shortage has a significant impact in emergency scenarios, and a 30% safety stock can improve emergency response capabilities).

[0057] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A ticketing information recommendation system, characterized in that, include: The data fusion module is used to collect and standardize multi-source heterogeneous data from the industrial environment; An industrial entity relationship network is constructed based on multi-source heterogeneous data. The industrial entity relationship network is a graph structure that describes the relationships between equipment operating status, technical solutions, and service resources. The dynamic demand response module, associated with the data fusion and construction module, is used to monitor triggering events of multi-source heterogeneous data in real time and locate related industrial scene elements. In response to the triggering events, it matches ticketing objects associated with industrial scene elements in the industrial entity relationship network. It applies industrial scene operating parameters as constraints to generate ticketing recommendation schemes and decision-making basis that meet the constraints. The industrial scene operating parameters are a set of time, geographical, and resource constraints used to verify the feasibility of ticketing objects.

2. The ticketing information recommendation system according to claim 1, characterized in that, The multi-source heterogeneous data includes: Real-time device sensor data from an industrial IoT platform; Equipment maintenance history records and fault code database in industrial databases; Equipment downtime schedule time windows in the production management system; The service personnel schedule and spare parts inventory status of the enterprise resource planning system.

3. The ticketing information recommendation system according to claim 1, characterized in that, The industrial entity relationship network includes: Device nodes store device model and real-time operating status data; Fault mode nodes are associated with device nodes through historical failure rate data; The technical solution node is associated with the fault mode node through maintenance work order records; Ticketing object nodes are associated with technical solution nodes through semantic similarity calculation.

4. The ticketing information recommendation system according to claim 1, characterized in that, The location-associated industrial scene elements include: When the triggering event is a device fault alarm, locate the device node and the user node responsible for maintenance; When the triggering event is a change in the production plan, locate the affected equipment nodes and related project nodes.

5. The ticketing information recommendation system according to claim 3, characterized in that, The operating parameters for the industrial scenario include: The time conflict parameter is used to verify the conflict status between the ticketing object's time and the equipment downtime plan; The geographic radius parameter is used to limit the threshold range of the real-time distance between the ticketing service location and the user; Resource availability parameters are used to verify spare parts inventory quantity and service personnel scheduling status.

6. The ticketing information recommendation system according to claim 5, characterized in that, The method for generating ticket recommendation schemes that satisfy the constraints includes: Using the fault mode node as the query source node, query instructions containing fault attributes and constraints are sent in parallel to the set of technical solution nodes directly associated with it. When a ticketing object node fails the industrial scenario operation parameter verification, it is marked as a failed node and a failure notification is sent to the associated technical solution node. Calculate the weight value of the technical solution node based on historical maintenance records, and filter the technical solution nodes whose weight value exceeds the threshold; When a failed node causes a path interruption, a new association edge is established between the technical solution node and the alternative ticketing object node by matching node attribute similarity. The constraints are then re-verified based on the reconstructed path, and the ticketing list is output.

7. The ticketing information recommendation system according to claim 1, characterized in that, The decision-making basis is generated through knowledge path backtracking: Extract the shortest path from the ticketing object node to the device node; Convert the node attributes in the path into natural language descriptions to generate explanatory text.

8. The ticketing information recommendation system according to claim 1, characterized in that, The recommendation system also includes a feedback learning module, used for: When a user rejects the recommended option, the actual ticketing option they selected is recorded; In the industrial entity relationship network, add an association edge between the ticketing object and the original failure mode node; The weight of the associated edge is increased according to preset rules.

9. A method for recommending ticketing information, wherein the method is used to implement the ticketing information recommendation system according to any one of claims 1-8, characterized in that, Includes the following steps: Collect multi-source heterogeneous data of the industrial environment and perform standardized processing; Constructing an industrial entity relationship network based on standardized data; Real-time monitoring of triggering events of the multi-source heterogeneous data; Identify related industrial scenario elements in the industrial entity relationship network based on event type; Starting with industrial scene elements, match related ticketing objects in the industrial entity relationship network; The system applies industrial scenario operating parameters to constrain and verify ticketing objects, generating ticketing recommendation schemes and decision-making basis that meet the constraints.

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