Equipment maintenance management system and method based on Internet of Things cloud platform

Abstract

The invention provides an equipment maintenance management system and method based on an Internet of Things cloud platform. The system comprises a data acquisition module, an Internet of Things cloud platform module and a decision module, the data acquisition module acquires real-time data of an equipment site and uploads the real-time data to the Internet of Things cloud platform module; the Internet of Things cloud platform module stores and analyzes the real-time data, and uploads an analysis result to the decision module; the decision-making module performs optimization decision-making on the maintenance strategy of the equipment site based on the decision-making result; wherein the Internet of Things cloud platform module also performs predictive maintenance on an equipment site. According to the equipment maintenance management system and method based on the Internet of Things cloud platform, through predictive maintenance, the equipment fault and downtime are reduced, the maintenance cost is reduced, the operation efficiency of the equipment is improved, and more intelligent operation and maintenance management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment maintenance management, and in particular to an equipment maintenance management system and method based on an Internet of Things cloud platform. Background Art

[0002] In modern industrial and manufacturing environments, efficient operation and maintenance management of equipment is the key to ensuring production continuity and reducing operating costs. Traditional equipment maintenance management mainly relies on regular inspections and manual records, and usually adopts a preventive maintenance strategy. Although this method can reduce the probability of failure to a certain extent, it has the following shortcomings: 1. Data collection is not timely: Traditional maintenance management often relies on manual records and regular inspections, resulting in insufficient real-time information on equipment status.

[0003] 2. Lack of scientific basis for maintenance decisions: Maintenance decisions based on manual experience and regular maintenance are prone to arbitrariness. The actual operating status and maintenance needs of the equipment are not evaluated in a timely and accurate manner, which may lead to excessive maintenance or lack of necessary maintenance.

[0004] 3. High operating costs: Traditional methods are inefficient in resource allocation and maintenance arrangements, resulting in waste of human and material resources and affecting the overall benefits of the enterprise.

[0005] 4. Information island phenomenon: Data between various devices and systems often cannot be effectively integrated, resulting in the formation of information islands. The lack of a global perspective makes it difficult for managers to optimize equipment maintenance strategies as a whole and achieve collaborative management.

[0006] Therefore, a solution is urgently needed. Summary of the invention

[0007] One of the purposes of the present invention is to provide an equipment maintenance and management system based on the Internet of Things cloud platform to solve the above-mentioned shortcomings.

[0008] An embodiment of the present invention provides an equipment maintenance management system based on an Internet of Things cloud platform, comprising: Data collection module, IoT cloud platform module and decision-making module; The data acquisition module collects real-time data from the equipment site and uploads it to the IoT cloud platform module; The IoT cloud platform module stores and analyzes real-time data and uploads the analysis results to the decision-making module; The decision-making module optimizes the maintenance strategy of the equipment site based on the decision results; Among them, the IoT cloud platform module also performs predictive maintenance on equipment on site.

[0009] Optionally, the data acquisition module specifically performs the following operations: Acquire real-time data by communicating with equipment on site; Use edge computing devices to process real-time data; The processed data is uploaded to the IoT cloud platform module via wireless communication technology.

[0010] Optionally, the IoT cloud platform module specifically performs the following operations: Use database to store real-time data; Use big data technology and artificial intelligence (AI) technology to analyze data, generate real-time monitoring reports and predict faults.

[0011] Optionally, the decision module specifically performs the following operations: Generate maintenance recommendations and decision support based on analysis results to optimize maintenance strategies.

[0012] Optionally, the IoT cloud platform module further specifically performs the following operations: Based on historical data and industry standards, establish warning threshold ranges according to key indicators of equipment; When the device parameters in the real-time data exceed the corresponding warning threshold range, the warning is triggered immediately; When warning is issued, it is issued through on-site alarm and mobile application push alarm.

[0013] Optionally, the IoT cloud platform module further specifically performs the following operations: Based on the pre-built fault prediction model and real-time data, equipment failure prediction is performed on-site; The steps for pre-building the fault prediction model are as follows: Preprocess a large amount of historical equipment failure data, including at least: outlier detection, missing value filling, normalization, data cleaning and conversion; Based on the machine learning algorithm, the candidate fault prediction model is trained according to the pre-processed historical equipment failure data; Based on the cross-validation method and the hyperparameter tuning method, the optimal candidate fault prediction model is verified and selected from the candidate fault prediction models as the fault prediction model.

[0014] Optionally, the IoT cloud platform module further specifically performs the following operations: Define key performance indicators, including at least: equipment operating load, temperature, and vibration; Based on key performance indicators, collect key performance parameters of equipment on site; Dynamically adjust the monitoring strategy of the equipment site according to the equipment characteristics, operating conditions and external environment changes at the equipment site; the monitoring strategy at least includes: collection frequency and monitoring parameters.

[0015] Optionally, the IoT cloud platform module further specifically performs the following operations: Based on the equipment failure prediction results of the fault prediction model, a predictive maintenance plan is generated and executed according to the importance of the equipment at the equipment site, maintenance time and resource availability.

[0016] Optionally, the IoT cloud platform module further specifically performs the following operations: Use data analysis to predict component requirements for different equipment failure risks within the equipment site; Based on the inventory management model, spare parts inventory is configured according to the component requirements of different equipment failure risks.

[0017] An embodiment of the present invention provides a device maintenance management method based on an Internet of Things cloud platform, which is characterized by comprising: Collect real-time data from the equipment site through the data acquisition module and upload it to the IoT cloud platform module; The real-time data is stored and analyzed through the IoT cloud platform module, and the analysis results are uploaded to the decision-making module; Based on the decision-making results, the decision-making module optimizes the maintenance strategy of the equipment site; Among them, predictive maintenance of equipment on site is also carried out through the Internet of Things cloud platform module.

[0018] The present invention has achieved the following beneficial effects: 1. Through predictive maintenance, reduce equipment failures and downtime, reduce maintenance costs, improve equipment operating efficiency, and achieve smarter operation and maintenance management.

[0019] 2. Through data collection and analysis, real-time monitoring and management of equipment can be achieved, and potential faults can be discovered in a timely manner.

[0020] 3. Use the Internet of Things, big data and artificial intelligence technologies to realize automated maintenance suggestions and decisions, and reduce manual intervention.

[0021] 4. On-site data is collected automatically in real time, equipment is interconnected, and data is shared, providing reliable data support for big data decision-making, production management systems, etc., breaking down data silos.

[0022] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0023] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 Schematic diagram of an equipment maintenance management system based on an Internet of Things cloud platform in an embodiment of the present invention; Figure 2 Schematic diagram of an equipment maintenance management method based on an Internet of Things cloud platform in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0026] The embodiment of the present invention provides an equipment maintenance management system based on the Internet of Things cloud platform, such as Figure 1 As shown, including: Data collection module 1, IoT cloud platform module 2 and decision-making module 3; The data acquisition module 1 collects real-time data from the equipment site and uploads it to the IoT cloud platform module 2; The IoT cloud platform module 2 stores and analyzes the real-time data and uploads the analysis results to the decision-making module 3; Decision module 3 optimizes the maintenance strategy of the equipment site based on the decision results; Among them, the IoT cloud platform module 2 also performs predictive maintenance on equipment on site.

[0027] The working principle and beneficial effects of the above technical solution are: The system workflow includes: Data collection: Sensors communicate with devices to continuously monitor device status, collect and upload data to the IoT cloud platform at regular intervals.

[0028] Data storage and processing: The cloud platform receives and stores data for real-time processing and historical data analysis.

[0029] Intelligent analysis and decision-making: Based on the analysis results, the decision module generates maintenance recommendations and sends alerts through the rule engine.

[0030] User feedback and operation: Maintenance personnel view equipment status, alarms, and maintenance recommendations through the user interface, perform corresponding operations, and record them.

[0031] Maintenance record management: All maintenance records and equipment status are stored for subsequent analysis and decision-making.

[0032] This application uses predictive maintenance to reduce equipment failures and downtime, reduce maintenance costs, improve equipment operating efficiency, and achieve smarter operation and maintenance management.

[0033] Through data collection and analysis, real-time monitoring and management of equipment can be achieved, and potential faults can be discovered in time.

[0034] Automated maintenance recommendations and decisions are made to reduce manual intervention.

[0035] On-site data is collected automatically in real time, equipment is interconnected, and data is shared, providing reliable data support for big data decision-making, production management systems, etc., breaking down data silos.

[0036] In one embodiment, the data acquisition module 1 specifically performs the following operations: Acquire real-time data by communicating with equipment on site; Use edge computing devices to process real-time data; The processed data is uploaded to the IoT cloud platform module 2 via wireless communication technology.

[0037] The working principle and beneficial effects of the above technical solution are: Get real-time data by communicating with devices; Use edge computing devices (such as gateways) to process some data and reduce the burden of network transmission; During communication, the data is uploaded to the cloud platform through wireless communication technology (such as Wi-Fi, LoRa, NB-IoT, etc.).

[0038] In one embodiment, the Internet of Things cloud platform module 2 specifically performs the following operations: Use database to store real-time data; Use big data technology and artificial intelligence (AI) technology to analyze data, generate real-time monitoring reports and predict faults.

[0039] The working principle and beneficial effects of the above technical solution are: Use a database (such as MongoDB, MySQL, etc.) to store device data and history.

[0040] Use big data technologies (such as Apache Spark and Hadoop) and artificial intelligence (AI) technologies to analyze data, generate real-time monitoring reports, and predict faults.

[0041] In one embodiment, the decision module 3 specifically performs the following operations: Generate maintenance recommendations and decision support based on analysis results to optimize maintenance strategies.

[0042] In one embodiment, the Internet of Things cloud platform module 2 further specifically performs the following operations: Based on historical data and industry standards, establish warning threshold ranges according to key indicators of equipment; When the device parameters in the real-time data exceed the corresponding warning threshold range, the warning is triggered immediately; When warning is issued, it is issued through on-site alarm and mobile application push alarm.

[0043] The working principle and beneficial effects of the above technical solution are: Set warning thresholds based on key indicators of the equipment (such as load, offset, temperature, vibration, etc.). By analyzing historical data and industry standards, set reasonable thresholds. Once the equipment parameters exceed the set threshold range, the system will immediately trigger a warning. Provide multiple notification methods such as on-site alarms and mobile application push to ensure that maintenance personnel can receive fault alarms in a timely manner.

[0044] In one embodiment, the Internet of Things cloud platform module 2 further specifically performs the following operations: Based on the pre-built fault prediction model and real-time data, equipment failure prediction is performed on-site; The steps for pre-building the fault prediction model are as follows: Preprocess a large amount of historical equipment failure data, including at least: outlier detection, missing value filling, normalization, data cleaning and conversion; Based on the machine learning algorithm, the candidate fault prediction model is trained according to the pre-processed historical equipment failure data; Based on the cross-validation method and the hyperparameter tuning method, the optimal candidate fault prediction model is verified and selected from the candidate fault prediction models as the fault prediction model.

[0045] The working principle and beneficial effects of the above technical solution are: The collected data is pre-processed in detail, including outlier detection, missing value filling, normalization, etc., to ensure the accuracy and consistency of the data. In addition, data cleaning and conversion are implemented to eliminate noise and improve the efficiency of model training.

[0046] Use a variety of machine learning algorithms (such as decision trees, random forests, support vector machines, deep learning, etc.) to train fault prediction models. Use cross-validation, hyperparameter tuning and other techniques to compare the prediction effects of different models and select the model with the best performance. At the same time, continue to track the prediction accuracy of the model and perform necessary model retraining and updates.

[0047] In one embodiment, the Internet of Things cloud platform module 2 further specifically performs the following operations: Define key performance indicators, including at least: equipment operating load, temperature, and vibration; Based on key performance indicators, collect key performance parameters of equipment on site; Dynamically adjust the monitoring strategy of the equipment site according to the equipment characteristics, operating conditions and external environment changes at the equipment site; the monitoring strategy at least includes: collection frequency and monitoring parameters.

[0048] The working principle and beneficial effects of the above technical solution are: Define key performance indicators (KPIs), such as equipment operating load, temperature, vibration, etc., and collect data in real time. Dynamically adjust data collection frequency and monitoring parameters based on equipment characteristics, operating conditions, and external environmental changes. Automatically optimize monitoring strategies to maximize monitoring effectiveness and accuracy.

[0049] In one embodiment, the Internet of Things cloud platform module 2 further specifically performs the following operations: Based on the equipment failure prediction results of the fault prediction model, a predictive maintenance plan is generated and executed according to the importance of the equipment at the equipment site, maintenance time and resource availability.

[0050] The working principle and beneficial effects of the above technical solution are: Data-based dynamic maintenance planning: Based on the results of the failure prediction model, the maintenance plan is automatically generated and adjusted by comprehensively considering the importance of the equipment, maintenance time and resource availability to ensure timely maintenance before equipment failure occurs.

[0051] In one embodiment, the Internet of Things cloud platform module 2 further specifically performs the following operations: Use data analysis to predict component requirements for different equipment failure risks within the equipment site; Based on the inventory management model, spare parts inventory is configured according to the component requirements of different equipment failure risks.

[0052] The working principle and beneficial effects of the above technical solution are: Use data analysis to predict demand for parts with high failure risks and rationally allocate spare parts inventory. By building an inventory management model, dynamically adjust spare parts procurement and storage strategies to avoid equipment downtime due to spare parts shortages. At the same time, predictive analysis can be used to optimize supply chain management and improve spare parts turnover efficiency.

[0053] Predictive maintenance can not only improve the operating efficiency of equipment, but also significantly reduce maintenance costs and downtime, and achieve smarter operation and maintenance management.

[0054] In one embodiment, the equipment maintenance management system based on the Internet of Things cloud platform also includes: Interactive module, used to interactively assist management personnel in equipment maintenance management on site; The interactive module interactively assists the management personnel to perform equipment maintenance management on the equipment site, specifically performing the following operations: A1. Based on the real-time data stored in the IoT cloud platform module and the on-site 3D map of the equipment site, a management interaction model is constructed; In step A1, the on-site three-dimensional map of the equipment site is a three-dimensional model map of the equipment site, on which there are three-dimensional models corresponding to various equipment, infrastructure, etc. at the equipment site; firstly, the real-time data of the equipment site, including the operating status and environmental conditions of the equipment, etc., are collected and stored using the Internet of Things cloud platform, and a management interaction model is constructed based on the on-site three-dimensional map. Specifically, the real-time data can be represented on the on-site three-dimensional map, so as to construct a management interaction model that dynamically displays the real-time data in real time, which is convenient for subsequent operation and management; A2. Display the management interaction model to managers; In step A2, the management interaction model will be displayed on the manager's display screen, and the manager can intuitively see the 3D model of the equipment site and related real-time information, so as to clearly understand the site conditions and provide a basis for subsequent operations; A3. Control the drone to enter the equipment site; In step A3, after the drone enters the equipment site, it can monitor the on-site situation in real time and obtain key audio-visual information to assist equipment maintenance work; A4. Determine the target position and logical expectation of the UAV based on the interaction information between the manager and the management interaction model in the first period; wherein the start time of the first period is the time when the manager and the management interaction model start to interact, and the duration of the first period is the first threshold; In step A4, the interaction information at least includes: the operation type, operation time, operation position, etc. of the management interaction model operated by the manager; the interaction information reflects to a certain extent how the manager wants to perform equipment maintenance management on the equipment site this time, and based on it, it can be determined how the drone should be positioned so that its flight shooting pictures can assist the manager, that is, the target position, and it can also be determined based on it how to control the drone to obtain corresponding assistance, that is, logical expectation; A5. Control the drone to enter the target position; In step A5, the target position includes the flight position and the shooting posture; controlling the UAV to enter the target position is to control the UAV to maintain the flight position and the shooting posture; A6. Display the virtual control panel of the drone and the real-time flight footage to the management personnel; In step A6, the virtual control panel of the drone is a virtual operation interface for controlling the drone to perform flight shooting, on which there are multiple parameters for controlling the drone's flight shooting, such as: forward, descending, shooting direction adjustment, etc.; the drone will send back real-time flight shooting images during flight shooting; A7. When the operation logic of the manager's operation of the virtual operation panel in the second time period is inconsistent with the logical expectation, based on the logical expectation, the manager's operation of the virtual operation panel is guided in a constrained manner in the third time period; wherein the second time period is after the first time period, the start time of the second time period is the time when the manager starts to operate the virtual operation panel, and the duration of the second time period is the second threshold; the third time period is after the second time period and is adjacent to the second time period, and the duration of the third time period is the third threshold; the third threshold is greater than the first threshold and greater than the second threshold; In step A7, the manager operates the virtual operation panel to generate operation logic, which is the logic for the manager to operate the virtual operation panel in sequence to control the content of the drone to fly and shoot. When the operation logic is inconsistent with the expected logic, it means that the manager needs to make corrections, and the manager is guided to operate the virtual operation panel in a constrained manner. A8. When the management personnel input the equipment maintenance management strategy, online equipment maintenance management is performed on the equipment site based on the equipment maintenance management strategy.

[0055] In step A8, when the manager has decided how to perform equipment maintenance management, he will input the equipment maintenance management strategy, and perform online equipment maintenance management on the equipment site based on the equipment maintenance management strategy.

[0056] The embodiments of the present invention achieve the following beneficial effects: Through 3D maps and management interactive models, managers can quickly and intuitively understand the on-site status of equipment without having to check each device one by one, thus significantly saving time. The introduction of drones further improves the efficiency and accuracy of on-site inspections, photography, and data collection.

[0057] Using the virtual operation panel and interactive model, managers can remotely control the drone, which is easy and intuitive to operate. The system provides intelligent guidance based on the preset operation mode, reducing operational errors and improving operational accuracy and safety.

[0058] The system automatically collects on-site data and performs equipment maintenance according to predetermined management strategies, effectively identifying potential faults in advance and avoiding the impact of equipment failures on production. At the same time, through the auxiliary inspection of drones, refined equipment management and monitoring can be achieved.

[0059] Managers can perform maintenance tasks remotely, and combined with real-time feedback, drones and virtual operation panels greatly improve the flexibility and controllability of on-site management. Even if the equipment is located in a hard-to-reach area, managers can use drones to conduct inspections and collect data, which significantly enhances management flexibility.

[0060] The use of drones effectively avoids the need for personnel to enter dangerous or complex environments, reduces the risk of accidents, and improves the safety of equipment management work.

[0061] Through intelligent constraints and guidance mechanisms, the system can identify and provide corrective suggestions in real time even if the operation is improper, ensuring that the drone operates as expected, thereby improving work quality and safety.

[0062] In one embodiment, based on the interaction information between the manager and the management interaction model during the first period, determining the target station and logical expectation of the drone includes: A401. Representing the interaction information in a management interaction model in terms of interaction time and space distribution to obtain interaction time and space distribution; In step A401, when performing the interactive spatiotemporal distribution representation, the operations performed by the administrator on the management interaction model in the interactive information are represented according to the operation position, and the operation type and operation time are marked, and the spatiotemporal relationship and spatial position relationship between the operations are marked; multiple represented contents are combined to form the interactive spatiotemporal distribution; A402, extracting fuzzy features from the interactive spatiotemporal distribution to obtain a fuzzy feature set; In step A402, the fuzzy feature set includes multiple fuzzy features, including: the operation type that the manager has operated most times in the interaction spatiotemporal distribution, the operation object that has been operated by the manager most times in a row, etc.; A403, determining a target refinement feature extraction rule from a refinement feature extraction rule base; wherein the standard fuzzy feature set of the target refinement feature extraction rule matches the fuzzy feature set; In step A403, the fuzzy feature set preliminarily reflects the equipment maintenance management intention of the manager, based on which it can be determined how the manager's deeper equipment maintenance management intention will be reflected, that is, the target refined feature extraction rule is determined. For example, if the fuzzy feature in the fuzzy feature set is that the operation type that the manager performs most times in the interactive spatiotemporal distribution is the zoom viewing operation, and the operation object that is operated by the manager most times in succession is the machine tool working motor, it means that the manager may want to perform in-depth equipment maintenance management on the machine tool working motor, then the target refined feature extraction rule is to extract all data related to the machine tool working motor in the interactive spatiotemporal distribution; there are different refined feature extraction rules in the refined feature extraction rule library, and different refined feature extraction rules are preset with corresponding standard fuzzy feature sets. When the standard fuzzy feature set matches the fuzzy feature set, the corresponding refined feature extraction rule can be adopted; A404, based on the target refined feature extraction rule, extract refined features of the interactive spatiotemporal distribution to obtain a refined feature set; In step A404, based on the target refined feature extraction rule, corresponding refined features can be extracted from the interactive spatiotemporal distribution to obtain a refined feature set; A405, determining target determination knowledge corresponding to the refined feature set from the determination knowledge base; wherein the standard refined feature set of the target determination knowledge matches the refined feature set; In step A405, after the refined feature set is determined, it can be determined based on how to determine the appropriate position of the drone and how to control the drone so that the management personnel themselves can be assisted, that is, the target determination knowledge is determined; it is determined that there are different determination knowledge in the knowledge base, and different determination knowledge is preset with standard refined feature sets. When the standard refined feature set matches the refined feature set, the corresponding determination knowledge can be adopted; A406. Analyze target determination knowledge, determine the effective target distribution division rules and target expectation formulation rules; In step A406, the target determination includes a target effective distribution division rule, which indicates how to divide the effective distribution of the range that the drone needs to shoot from the interactive spatiotemporal distribution. For example, if the refined features in the refined feature set are the operating parameters related to the machine tool working motor, then the target effective distribution division rule is to divide the relevant operation types and operation time of the machine tool working motor from the interactive spatiotemporal distribution, and to divide other operations performed before and after the operation of the machine tool working motor as the effective distribution; the target expectation formulation rule indicates how to formulate logical expectations based on the shooting range circle, for example, if the refined features in the refined feature set are the operating parameters related to the machine tool working motor, then the target expectation formulation rule is to control the drone to shoot each parameter in order from the greatest to the least important of the operating parameters; A407, based on the target effective distribution partitioning rule, divide the effective distribution from the interactive spatiotemporal distribution; A408. Based on the effective distribution, determine a shooting range circle in the management interaction model; wherein the shooting range circle is a minimum enclosing sphere that minimum encloses the effective distribution; A409, based on the shooting range circle, determining the drone model position; wherein, when the drone enters the drone model position, it can clearly shoot all the shooting range circles at the same time or change the shooting angle within a preset number of times to clearly shoot all the shooting range circles in sequence; In step A409, clear shooting means that the straight-line distance between the camera of the drone and the element to be shot does not exceed a certain value, for example, 2 meters; the preset number of times may be 3 times; changing the shooting angle within the preset number of times to be able to clearly shoot all the shooting range circles in sequence means that the drone can clearly shoot all the shooting range circles in sequence by switching the shooting angle within the preset number of times; A410. Determine the actual position corresponding to the UAV model position from the equipment site and use it as the target position; In step A410, after the drone model position is determined, it will correspond to a real position at the equipment site, which will be used as the target position; A411. Establish rules based on target expectations and formulate logical expectations based on the shooting range circle.

[0063] In step A411, finally, rules are formulated based on target expectations, and logical expectations are formulated according to the shooting range circle.

[0064] The embodiments of the present invention achieve the following beneficial effects: By capturing and analyzing the interactive information of managers (such as operation type, operation time, operation object, etc.), we can deeply understand the equipment maintenance management intentions of managers. Existing technologies usually rely on preset rules or fixed patterns, but this solution analyzes interactive information in real time and dynamically captures the actual needs and behaviors of managers, thereby more intelligently predicting their maintenance intentions. This precise intention mining can greatly improve the response speed and accuracy of the system and optimize the management decision-making process.

[0065] Traditional drone positioning is usually static, and shooting tasks are performed based on preset parameters. This solution dynamically calculates the optimal drone position through real-time interactive spatiotemporal distribution analysis, allowing the drone to shoot at the most appropriate angle and position. This method can ensure that the drone accurately covers the target area, reduce shooting blind spots, and ensure that all details required for equipment maintenance are clearly captured.

[0066] The determination of the shooting range circle is based on the effective distribution division rules of the target, so as to ensure that the drone can complete the shooting task of all target areas within the minimum number of shooting angle adjustments. This can reduce the number of times the drone frequently adjusts the shooting angle, improve shooting efficiency, reduce operation complexity, and reduce the risk of equipment failure.

[0067] By analyzing the interactive information, the key maintenance objects of the equipment (such as the working motor of the machine tool) and their related parameters are identified, and based on this information, in-depth mining is performed to generate detailed features related to equipment maintenance. This precise equipment identification and feature extraction can help managers quickly identify potential problems or impending failures of equipment, perform maintenance in advance, improve equipment operation reliability and reduce downtime.

[0068] By determining detailed features and target determination knowledge, more targeted decision support can be provided to managers, such as recommending equipment parts that need to be checked first based on real-time feedback on the equipment's operating status. This type of decision support can reduce the workload of managers, improve decision-making efficiency, and avoid judgment bias caused by human factors.

[0069] By using target-refined feature extraction rules and determining the knowledge base, different maintenance requirements are effectively mapped to the best solution in the knowledge base. This approach can provide continuously optimized maintenance management strategies through the support of machine learning and rule engines, instead of relying solely on fixed empirical rules like traditional methods. This provides more powerful decision support for drones in complex equipment maintenance tasks.

[0070] Among them, based on logical expectations, during the third period, managers are guided to operate the virtual operation panel in a constrained manner, including: A701. Serialize the logical expectation to obtain a sub-logical sequence; In step A701, when performing serialization representation, multiple sub-logics in the logical expectation are sorted according to the logical sequence; A702. When the operation sub-logic of the virtual operation disk operated by the manager in the i-th sub-period within the third time period is consistent with the j-th sub-logic in the sub-logic sequence, if i+j is less than or equal to N and the absolute value of the difference between i and j is greater than the absolute value threshold, a first operation constraint rule is generated based on other sub-logics within the first number preset after the j-th sub-logic in the sub-logic sequence, and based on the first operation constraint rule, a corresponding operation constraint is imposed on the manager's operation of the virtual operation disk in the sub-period within the second number preset after the i-th sub-period within the third time period; In step A702, the third time period is evenly divided into N sub-periods, where N is the total number of sub-logics in the sub-logic sequence; the absolute value threshold may be 4; if i+j is less than or equal to N and the absolute value of the difference between i and j is greater than the absolute value threshold, it means that one or both of the sub-periods and the sub-logics that are matched are at the front, however, the sub-periods and the sub-logics that are matched reflect that the management decision-making rhythm of the manager is inconsistent with the time trend, and it is necessary to guide it quickly based on the subsequent sub-logics to improve its management decision-making rhythm, therefore, based on the other sub-logics within the first number preset after the j-th sub-logic in the sub-logic sequence, generate the first operation constraint rule, and based on the first operation constraint rule, the first number may be 3, and the first operation constraint rule is to constrain the manager from performing operations unrelated to other sub-logics within the first number preset after the j-th sub-logic in the sub-logic sequence; however, the manager cannot be guided continuously, then based on the first operation constraint rule, in the sub-period within the second number preset after the i-th sub-period in the third time period, the manager operates the virtual operation disk correspondingly. The second number may be 2; A703. If i+j is greater than N and the absolute value of the difference between i and j is greater than the absolute value threshold, a second operation constraint rule is generated based on other sub-logics within a third number preset before and after the j-th sub-logic in the sub-logic sequence, and based on the second operation constraint rule, corresponding operation constraints are imposed on the administrator's operation of the virtual operation panel in all sub-periods after the i-th sub-period within the third time period.

[0071] If i+j is greater than N and the absolute value of the difference between i and j is greater than the absolute value threshold, it means that one or both of the sub-period and the sub-logic are behind. However, the sub-period and the sub-logic reflect that the management decision-making rhythm of the manager is inconsistent with the time trend, so it is necessary to pay attention to the current management decision, that is, to focus on the management decision, and generate the second operation constraint rule based on the other sub-logics within the third number preset before and after the j-th sub-logic in the sub-logic sequence. The second operation constraint rule is to constrain the manager from performing operations unrelated to other sub-logics within the third number preset before and after the j-th sub-logic in the sub-logic sequence; the third number can be 4; and based on the second operation constraint rule, in all sub-periods after the i-th sub-period in the third time period, the manager's operation of the virtual operation disk is subject to corresponding operation constraints to achieve continuous constraint guidance.

[0072] The embodiments of the present invention achieve the following beneficial effects: By real-time constraints and guidance on managers' operations, the solution can effectively adjust the decision-making rhythm of managers so that their operations are more in line with established logical expectations. In particular, when there is a mismatch between managers' operations and the predetermined sequence, their operation progress can be adjusted through guidance to avoid overly hasty or delayed decisions. In existing management decision-making systems, there is usually a lack of in-depth guidance on operation timing. Most systems provide static prompts or suggestions, and fail to make real-time and dynamic adjustments based on the differences between managers' current operating status and logical sequences. This technical solution can more accurately control the decision-making rhythm through dynamic adjustment of the guidance mechanism.

[0073] The third period is divided into N sub-periods, and refined guidance is provided based on the operational logic of each sub-period. This guidance mechanism ensures that managers can focus on the most important decision points at different times, thereby avoiding fragmentation or over-centralization of operations.

[0074] When the manager's operation does not conform to the predetermined sub-logic sequence, the manager's deviation can be corrected in time by dynamically generating operation constraint rules. This mechanism helps to strengthen the correctness and consistency of decision-making and avoid deviation from the goal due to overly arbitrary operations or decisions.

[0075] By presetting sub-logic intervals with different values, the solution can maintain a continuous constraint during the operation of managers. Especially for those situations where decision deviation occurs, this solution not only makes timely corrections, but also ensures that managers always stay on the correct decision path through continuous guidance.

[0076] By accurately combining logical sequences with time periods and providing operational constraint rules in real time, this solution enables managers to operate more accurately according to the predetermined decision-making route, avoid missing key decision-making opportunities or making wrong decisions, and thus improve the quality of overall management decisions.

[0077] The embodiment of the present invention provides a device maintenance management method based on the Internet of Things cloud platform, such as Figure 2 As shown, including: S1. Collect real-time data from the equipment site through the data acquisition module and upload it to the IoT cloud platform module; S2. Store and analyze real-time data through the IoT cloud platform module, and upload the analysis results to the decision-making module; S3. Make an optimization decision on the maintenance strategy of the equipment site based on the decision results through the decision module; Among them, predictive maintenance of equipment on site is also carried out through the Internet of Things cloud platform module.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An equipment maintenance management system based on the Internet of Things cloud platform, characterized in that: include: Data collection module, IoT cloud platform module and decision-making module; The data acquisition module collects real-time data from the equipment site and uploads it to the IoT cloud platform module; The IoT cloud platform module stores and analyzes real-time data and uploads the analysis results to the decision-making module; The decision-making module optimizes the maintenance strategy of the equipment site based on the decision results; Among them, the IoT cloud platform module also performs predictive maintenance on equipment on site.

2. The equipment maintenance and management system based on the Internet of Things cloud platform as claimed in claim 1, characterized in that: The data acquisition module specifically performs the following operations: Acquire real-time data by communicating with equipment on site; Use edge computing devices to process real-time data; The processed data is uploaded to the IoT cloud platform module via wireless communication technology.

3. The equipment maintenance and management system based on the Internet of Things cloud platform as claimed in claim 1, characterized in that: The IoT cloud platform module specifically performs the following operations: Use database to store real-time data; Use big data technology and artificial intelligence (AI) technology to analyze data, generate real-time monitoring reports and predict faults.

4. The equipment maintenance and management system based on the Internet of Things cloud platform as claimed in claim 1, characterized in that: The decision module specifically performs the following operations: Generate maintenance recommendations and decision support based on analysis results to optimize maintenance strategies.

5. The equipment maintenance and management system based on the Internet of Things cloud platform as claimed in claim 1, characterized in that: The IoT cloud platform module also specifically performs the following operations: Based on historical data and industry standards, establish warning threshold ranges according to key indicators of equipment; When the device parameters in the real-time data exceed the corresponding warning threshold range, the warning is triggered immediately; When warning is issued, it is issued through on-site alarm and mobile application push alarm.

6. The equipment maintenance and management system based on the Internet of Things cloud platform as claimed in claim 1, characterized in that: The IoT cloud platform module also specifically performs the following operations: Based on the pre-built fault prediction model and real-time data, equipment failure prediction is performed on-site; The steps for pre-building the fault prediction model are as follows: Preprocess a large amount of historical equipment failure data, including at least: outlier detection, missing value filling, normalization, data cleaning and conversion; Based on the machine learning algorithm, the candidate fault prediction model is trained according to the pre-processed historical equipment failure data; Based on the cross-validation method and the hyperparameter tuning method, the optimal candidate fault prediction model is verified and selected from the candidate fault prediction models as the fault prediction model.

7. The equipment maintenance and management system based on the Internet of Things cloud platform as claimed in claim 1, characterized in that: The IoT cloud platform module also specifically performs the following operations: Define key performance indicators, including at least: equipment operating load, temperature, and vibration; Based on key performance indicators, collect key performance parameters of equipment on site; Dynamically adjust the monitoring strategy of the equipment site according to the equipment characteristics, operating conditions and external environment changes at the equipment site; the monitoring strategy at least includes: collection frequency and monitoring parameters.

8. The equipment maintenance and management system based on the Internet of Things cloud platform as claimed in claim 6, characterized in that: The IoT cloud platform module also specifically performs the following operations: Based on the equipment failure prediction results of the fault prediction model, a predictive maintenance plan is generated and executed according to the importance of the equipment at the equipment site, maintenance time and resource availability.

9. The equipment maintenance and management system based on the Internet of Things cloud platform as claimed in claim 6, characterized in that: The IoT cloud platform module also specifically performs the following operations: Use data analysis to predict component requirements for different equipment failure risks within the equipment site; Based on the inventory management model, spare parts inventory is configured according to the component requirements of different equipment failure risks.

10. A device maintenance management method based on the Internet of Things cloud platform, characterized in that: include: Collect real-time data from the equipment site through the data acquisition module and upload it to the IoT cloud platform module; The real-time data is stored and analyzed through the IoT cloud platform module, and the analysis results are uploaded to the decision-making module; Based on the decision-making results, the decision-making module optimizes the maintenance strategy of the equipment site; Among them, predictive maintenance of equipment on site is also carried out through the Internet of Things cloud platform module.

Images