Warning device boot techniques

Abstract

The invention relates to the technical field of data center operation and maintenance management, and discloses an alarm equipment guiding technology, which comprises a fault alarm generation module used for collecting equipment operation state data through a sensor network, triggering an alarm based on anomaly detection and positioning target equipment; the dynamic path planning module is used for generating an optimal path from the entrance to the target equipment according to the position of the target equipment and the layout of the data center, and adjusting path planning in real time; the task distribution module is used for distributing tasks according to the alarm priority and the maintenance personnel state; and the multi-mode guiding module is used for guiding the maintenance personnel to arrive at the target equipment through ground dynamic guidance, augmented reality navigation and voice prompt. According to the method, the problems of incomplete equipment fault monitoring, lack of dynamic adjustment of path planning, inflexible task allocation and non-visual navigation guidance in the prior art are solved, and the fault response efficiency and the operation and maintenance accuracy of the data center are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance management of data centers, specifically to alarm device guidance technology. Background Art

[0002] With the continuous expansion of the scale of data centers, the number of devices has increased sharply, and its complex multi-entry and multi-channel layout has greatly increased the difficulty of operation and maintenance management. The stable operation of data centers is crucial for services such as cloud computing and big data, and the efficient monitoring and rapid maintenance of devices have become key links in the operation and maintenance of data centers.

[0003] Existing technologies have made certain progress in the device monitoring and operation and maintenance management of data centers. Some technical solutions use sensor networks to monitor the status of devices in real time, can capture the basic operation information of devices in a timely manner, and achieve basic abnormal alarm functions. In addition, some navigation technologies based on floor plans and text information have also been applied in the operation and maintenance of data centers. These technologies can provide preliminary guidance on the location of devices for maintenance personnel, and to a certain extent reduce the workload of manual memory and query paths.

[0004] Existing technologies still have the following deficiencies. First, the existing single-sensor monitoring scheme is difficult to cope with the complexity of the device operation environment, resulting in insufficient coverage of fault detection and low accuracy of abnormal identification. Second, path planning technologies often rely on static layouts and lack the ability to respond in real time to dynamic changes in the internal environment of data centers, such as channel congestion or temporary closure of device areas, which seriously restricts the navigation efficiency. In addition, traditional task allocation methods usually rely on simple rules and cannot dynamically adjust in combination with multiple factors such as alarm priority, maintenance personnel status, and skill level, resulting in uneven resource allocation and delayed response to key tasks. Finally, the existing navigation guidance schemes lack intuitiveness, and the methods relying on floor plans or single voice guidance cannot meet the requirements of multi-path and multi-scene in complex multi-entry environments. Summary of the Invention

[0005] In view of the deficiencies of the existing technologies, the present invention provides alarm device guidance technology, which solves the problems of insufficient coverage of device fault monitoring, low accuracy of abnormal identification, inability to dynamically adjust path planning, lack of flexibility in task allocation, and non-intuitive and non-adaptive navigation guidance methods in complex environments in the existing technologies.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: Alarm device guidance technology, including: A fault alarm generation module, configured to collect device operation status data through a sensor network, trigger an alarm based on anomaly detection, and locate the target device; A dynamic path planning module, which is used to generate the optimal path from the entrance to the target device according to the location of the target device and the layout of the data center, and adjust the path planning in real time; A task assignment module, which is used to assign tasks according to the alarm priority and the status of maintenance personnel; A multimodal guidance module, which is used to guide maintenance personnel to the target device through ground dynamic guidance, augmented reality navigation and voice prompts; A data prediction and optimization module, which is used to predict the device status based on historical data and optimize the device layout and operation and maintenance processes.

[0007] Preferably, the fault alarm generation module includes: A multi-sensor network, which is used to collect device environment parameters; A dynamic anomaly detection unit, which is used to judge whether the device status is abnormal based on the dynamic threshold algorithm and trigger an alarm signal; A device positioning unit, which is used to locate the physical position of the target device in combination with the three-dimensional coordinate system and generate a unique device identifier.

[0008] Preferably, the dynamic path planning module includes: A data center modeling unit, which is used to abstract the data center into a weighted graph model composed of nodes and edges; A path calculation unit, which is used to calculate the optimal path based on an improved path planning algorithm; A path adjustment unit, which is used to update the path weight in real time according to the channel status and congestion information and re-plan the path.

[0009] Preferably, the task assignment module includes: An alarm priority calculation unit, which is used to calculate the priority of the alarm task according to the influence range of the alarm device and the criticality of the device; A personnel status monitoring unit, which is used to obtain the location information, task status and skill level of maintenance personnel in real time; A dynamic task assignment unit, which is used to dynamically assign tasks according to the current location, task load and skill level of maintenance personnel.

[0010] Preferably, the multimodal guidance module includes: A ground dynamic guidance unit, which is used to display the path direction and the remaining distance through ground fluorescent arrows; An augmented reality navigation unit, which is used to overlay path guidance information in real time through augmented reality devices; A voice prompt unit, which is used to provide real-time voice prompts of the target device number, direction and status according to the path planning result.

[0011] Preferably, the multimodal guidance module further includes: A display prompt unit, which is used to display device failure information and a global path planning map at the entrance of the data center, providing an overview of the path. A dynamic instruction generation unit, which is used to generate step-by-step navigation instructions based on the specific environment in combination with real-time path adjustment, and execute synchronously with multi-modal guiding devices.

[0012] Preferably, the data prediction and optimization module includes: A failure prediction unit, which is used to predict the operating state of the device through a time series analysis model. A data analysis unit, which is used to analyze historical failure data and path usage frequency. An optimization suggestion unit, which is used to optimize the sensor layout and resource allocation of the guiding path based on the analysis results.

[0013] Preferably, the multi-sensor network includes: A temperature sensor, which is used to obtain temperature data of the device and its surrounding environment. A humidity sensor, which is used to detect the humidity level of the device's surrounding environment. A current sensor, which is used to collect current data during the operation of the device. A vibration sensor, which is used to record vibration parameters of the device during operation.

[0014] Preferably, the dynamic path planning module further includes a path segment planning unit, which is used to divide the path planning into two parts: a global path from the entrance to the partition and a local path within the partition, and provide independent path calculation and dynamic adjustment functions respectively.

[0015] Preferably, the task allocation module further includes a task interruption and switching unit, which is used to dynamically interrupt the current task and switch to a new high-priority task when the alarm priority is adjusted or the status of the maintenance personnel changes, and at the same time re-plan the allocation scheme of the remaining tasks.

[0016] The present invention provides an alarm device guiding technology. It has the following beneficial effects: 1. The present invention adopts a technical solution of a failure alarm generation module combined with a multi-sensor network and dynamic threshold judgment, which can monitor the operating state of the device in real time and accurately locate the faulty device. Compared with the single-sensor monitoring or manual inspection methods in the prior art, this design solves the problems of untimely fault location and insufficient monitoring coverage, and significantly improves the response efficiency and accuracy of the data center.

[0017] 2. Through the technical solution of combining dynamic path planning module with real-time weight adjustment and segmented path planning, the present invention achieves the goal of quickly generating the optimal path in a complex environment. It can dynamically sense environmental changes such as path congestion and obstacles, and adjust the planning results in real time. Compared with the existing static path planning technology, this design solves the problems of poor path flexibility and insufficient dynamic adaptability.

[0018] 3. The present invention innovatively adopts the technical solution of combining task assignment module with alarm priority calculation and maintenance personnel status monitoring, which can dynamically assign tasks and optimize resource scheduling. Different from the existing manual assignment or single-rule assignment methods, this design greatly improves the task response speed and solves the industry pain points of unreasonable resource scheduling and untimely response to priority tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is the module architecture diagram of the present invention; Figure 2 is the module architecture diagram for fault alarm production of the present invention; Figure 3 is the module architecture diagram for dynamic path planning of the present invention; Figure 4 is the module architecture diagram for task assignment of the present invention; Figure 5 is the module architecture diagram for modal guidance of the present invention; Figure 6 is the module architecture diagram for data prediction and optimization of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to the attached Figure 1 - attached Figure 6 , the embodiment of the present invention provides alarm device guidance technology, including: A fault alarm generation module, configured to collect device operation status data through a sensor network, trigger an alarm based on anomaly detection, and locate the target device; Through the dynamic collection and analysis of device data, this module can provide accurate fault location information and status information for subsequent path planning and task assignment, which is the basis and prerequisite for the normal operation of the entire system.

[0022] Specifically, this module first collects the operation data of the device and its surrounding environment through a multi-sensor network. After the data is analyzed by the dynamic anomaly detection unit, if the anomaly conditions are met, an alarm signal is triggered. Subsequently, the device positioning unit determines the spatial position of the target device through a three-dimensional coordinate system and generates a unique identifier for subsequent modules to call. In some embodiments, this module can also improve the alarm accuracy through the joint analysis of multi-sensor data and effectively troubleshoot possible false alarms of the system.

[0023] In this embodiment, the implementation of the fault alarm generation module is divided into three parts: a multi-sensor network, a dynamic anomaly detection unit, and a device positioning unit.

[0024] Generally, the multi-sensor network consists of various sensors arranged at key positions in the data center and is used to collect the operation data of the device and the environment. Specifically, the sensors include but are not limited to the following types: Temperature sensors, used to monitor the temperature level during device operation; Humidity sensors, used to record the humidity of the device's surrounding environment; Current sensors, used to collect data on changes in device current; Vibration sensors, used to detect the vibration state of the device's mechanical components.

[0025] As an option, these sensors are connected to the central control unit through wireless or wired communication protocols and upload the collected real-time data to the system every second. Specifically, the data stream collected by the sensors can be expressed as: where represents the temperature data collected by the temperature sensor at time ; represents the humidity data collected by the humidity sensor at time ; represents the current data collected by the current sensor at time ; represents the vibration data collected by the vibration sensor at time .

[0026] In some embodiments, the data collection frequency of the sensors can be adjusted according to the actual needs of the data center. For example, when the system is in a high-load state, the collection frequency can be increased to obtain more detailed device operation information.

[0027] The dynamic anomaly detection unit is used to analyze the real-time data uploaded by the sensor network and determine whether the device is in an abnormal state. Specifically, this unit uses a dynamic threshold algorithm to automatically adjust the threshold in combination with historical data and the real-time operating environment to ensure the accuracy of alarm triggering.

[0028] In a possible implementation, the exception trigger condition can be expressed as: Wherein, represents the fault alarm trigger status, and when it is 1, the alarm is triggered; represents the sensor at time The real-time data collected at the moment; represents the sensor at time The dynamic threshold.

[0029] Specifically, when the collected value of any sensor exceeds its dynamic threshold , the system determines that the device corresponding to the sensor is in an abnormal state. For example, if the temperature sensor detects , and at the same time the current sensor detects , then it can be further determined that the device may be in a high-load abnormal state. In some embodiments, the false alarm rate can be reduced through joint analysis of multiple sensors. For example, a single increase in temperature may be just an environmental factor, but if accompanied by current fluctuations, it can be confirmed that there is indeed an abnormality in the device; The device location unit is used to determine the spatial location of the faulty device in the data center and generate a unique identifier for it. Generally, the device location in the data center is represented by a three-dimensional coordinate system, specifically:

[0030] Wherein, respectively represent the row, column and height coordinates of the device in the plane layout of the data center.

[0031] In some embodiments, the device location unit will also combine the cabinet number where the device is located and the specific row and column positions to generate a unique identifier for the faulty device. For example, if the device is located in cabinet number R3, column number C5, and row number 002, its identifier can be "R3C5-002".

[0032] As a possible implementation, the device location information will be shared in real time through the central system with the path planning module to provide support for subsequent path calculation and task allocation.

[0033] In an extended scheme, the fault alarm generation module can also perform abnormal analysis in combination with the device operation mode. For example, the system can identify whether the device is currently in a high-performance mode or an energy-saving mode and adjust the threshold strategy based on different modes. In addition, in order to improve the overall reliability of the module, a redundant sensor network can be configured for key devices to ensure that a single point of failure will not affect the accuracy of abnormal detection and device location; The dynamic path planning module is used to generate the optimal path from the entrance to the target device according to the location of the target device and the layout of the data center, and adjust the path planning in real time. This module receives the accurate location information of the target device provided by the fault alarm generation module, combines the physical layout and real-time status of the data center, and generates the optimal path from the current location or entrance of the maintenance personnel to the target device. Through real-time path calculation and dynamic adjustment, this module provides path instructions for the subsequent multimodal guidance module and clear and efficient navigation support for the actions of the maintenance personnel.

[0034] Generally, the data center has a complex layout, dense equipment, multiple entrances and channels, and the operating environment will be dynamically affected by personnel flow, equipment layout or emergencies. Therefore, this module needs to build a path planning model, dynamically update the channel status, and adjust the path weights in real time according to the feedback of the sensor network. In some embodiments, to meet the requirements of partition management, the path planning can be further divided into two parts: global path and local path. The finally realized path planning result has high flexibility and accuracy, and can effectively handle various complex scenarios.

[0035] In this embodiment, the implementation of the dynamic path planning module includes the following core units: the data center modeling unit, the path calculation unit, and the path adjustment unit.

[0036] Generally, the physical layout of the data center consists of multiple equipment areas, channels and entrances. To facilitate path planning, this unit abstracts the data center as a weighted undirected graph , where: is the set of nodes, including the entrance points, equipment locations and turning points of the channels in the data center; is the set of edges, representing the connection paths between nodes; As an option, the weight has an initial value of the actual physical distance between node and node . When the channel is congested, blocked or has other restrictions, the weight will be dynamically adjusted. For example, when the travel time of a certain path is extended due to a large number of maintenance personnel, the weight will increase to reflect the change of the channel status.

[0037] Specifically, the initial weight can be defined by the following formula:

[0038] where, is the initial weight of; is the actual path length between node and node .

[0039] In a possible implementation, the data center modeling unit will also add additional weights based on the importance of the node. For example, for a main channel connected to multiple key devices, its weight will be added with an adjustment factor during the initial modeling to reflect its priority of passage; Path computation unit for weighted undirected graph model in data center Find the starting point node To the target device node As an alternative, the unit adopts an improved algorithm to reduce the computational complexity through a heuristic method and ensure the rationality of the path selection.

[0040] In one possible implementation, the path calculation goal is to minimize the total cost from the starting point to the target:

[0041] in, Indicates starting point To the target node An estimate of the total cost; Represents the distance from the starting point to the current node the actual cost of Indicates that from the current node To the target node The heuristic cost of .

[0042] Specifically, the heuristic cost The three-dimensional Euclidean distance can be calculated: in, , , , is the current node The three-dimensional coordinates of , , , is the target node The three-dimensional coordinates of .

[0043] In some embodiments, the path calculation unit selects the path based on the priority of the area where the device is located. For example, for the core computer room area, the path calculation will give priority to the main channel to reduce possible delays.

[0044] The path adjustment unit is used to dynamically adjust the path according to the real-time environmental data after the path is calculated. Generally, the real-time environmental data is provided by the sensor network of the data center, including the congestion status of the channel, traffic obstacle information, etc. The core of dynamic adjustment is to update the path weight to ensure the feasibility and efficiency of the path.

[0045] As an implementation method, the adjusted path weight can be expressed as: Among them, is the updated path weight; is the initial path weight; represents the channel congestion cost.

[0046] Specifically, when the pedestrian flow density in a certain channel exceeds the set threshold the congestion cost will increase dynamically according to the density.

[0047] In a possible implementation, the path adjustment unit also supports segmented path adjustment. Specifically, when the path from the entrance to the target path is divided into two parts: the global path (from the entrance to the partition) and the local path (from within the partition to the device), the system will first adjust the local path to reduce the impact on the global path. For example, when the channel near a certain device in the partition is blocked, the local path will be re-planned to the backup channel of the device, while the global path remains unchanged.

[0048] In some extended embodiments, the path adjustment unit can also dynamically update the path in combination with the fault area. For example, when a certain device area is closed due to a fault, the path planning will automatically bypass this area. At the same time, to improve the fault tolerance of the system, this module supports the generation of multiple alternative paths. When the main path becomes impassable due to unforeseen circumstances, the system will quickly switch to the sub-optimal path to ensure the timeliness of maintenance tasks; The task assignment module is used to assign tasks according to the alarm priority and the status of maintenance personnel; This module conducts comprehensive analysis based on various factors such as alarm priority, the current location of maintenance personnel, task load, and skill level to ensure the rationality and efficiency of task assignment. At the same time, this module closely cooperates with the fault alarm generation module and the dynamic path planning module, and uses real-time device status data and path information to optimize the task assignment process.

[0049] Generally, the operating environment of the data center is complex, and the distribution and task status of maintenance personnel change at any time. Therefore, task assignment needs to be dynamic and flexible. In some embodiments, this module can also handle the dynamic adjustment of alarm priority and the situation of task interruption and switching, so as to ensure that emergency tasks can be responded to first. Finally, this module provides guarantee for the efficient operation and maintenance of the data center through intelligent scheduling algorithms.

[0050] In this embodiment, the task assignment module includes three main units: the alarm priority calculation unit, the personnel status monitoring unit, and the dynamic task assignment unit.

[0051] The alarm priority calculation unit Generally, the alarm priority is jointly determined by the impact scope of the device and the importance of the device. Devices with a larger impact scope, such as core servers or data exchange devices, usually have a higher priority. Specifically, the alarm priority is calculated as follows: where, represents the alarm priority; represents the impact scope of the device, which can be measured by the number of affected devices or the number of service connections; represents the importance of the device, which is assigned according to the role of the device in the overall system; , is the weight coefficient, which adjusts the influence ratio of and respectively; As a possible implementation, the value of the core device (such as the database server) can be set to a relatively high constant, such as 10, while the value of the auxiliary device is set to a relatively low constant, such as 3. The weight coefficients and can be adjusted according to the actual operation strategy of the data center. For example, for a system that pays more attention to the importance of the device, can be set to 0.7, while is set to 0.3.

[0052] In some embodiments, the unit also makes dynamic adjustments in combination with the alarm type. For example, for an alarm of hardware failure, its priority may be higher than that of an alarm of performance degradation.

[0053] The personnel status monitoring unit is responsible for obtaining relevant information of maintenance personnel in real time, including the current location, task status, skill level, etc. This information is obtained through positioning sensors, task scheduling systems, etc. within the data center and is updated in real time.

[0054] Specifically, the status of each maintenance personnel can be represented by the following triple:

[0055] where, represents the current location of the maintenance personnel , represented in the form of three-dimensional coordinates: ; represents the task status of the maintenance personnel , and the values include idle, in process, completed, etc.; represents the skill level of the maintenance personnel , which is evaluated according to their experience or professional field; As an option, the personnel status monitoring unit updates the personnel status data from the sensor or the task management system every 5 seconds to ensure the timeliness of the information on which the task assignment is based; The dynamic task assignment unit dynamically assigns tasks based on the alarm priority and the status of maintenance personnel, combined with the specific location of the faulty device. Generally, the goal of this unit is to assign tasks to the most suitable maintenance personnel. The specific objective function is as follows: where, represents the cost of assigning the task to maintenance personnel ; represents the current location of the maintenance personnel and the location of the faulty device The path cost between them can be calculated by the dynamic path planning module; represents the maintenance personnel 's skill level and the skills required for the faulty task The gap between them; is the weight of the skill level gap, used to adjust the relative influence of the path cost and the skill gap.

[0056] As an implementation method, when the task involves the fault handling of complex equipment (such as a high-load server), can be set to a higher value to preferentially assign personnel with higher skill levels.

[0057] Specifically, the dynamic task assignment process can be divided into the following steps: First, determine the urgency of the task according to the alarm priority ; Then, screen the maintenance personnel who are currently idle or about to complete tasks; Finally, calculate the cost of each candidate personnel and select the personnel with the minimum cost to execute the task.

[0058] In some embodiments, this unit also supports the task interruption and switching functions. When a high-priority task appears, the maintenance personnel who are executing low-priority tasks will be interrupted and re-assigned to the high-priority task, and the unfinished low-priority tasks will be re-queued.

[0059] As an extended implementation method, the task assignment module can further optimize the task assignment in combination with the results of real-time path planning. For example, when the cost difference between maintenance personnel is small, the personnel with less affected paths by congestion can be preferentially selected, thereby further improving the task execution efficiency. In addition, the system can also support multi-task parallel scheduling. When the task volume is large, using the personnel grouping strategy, multiple related tasks can be assigned to different maintenance teams; A multimodal guidance module for guiding maintenance personnel to the target device through ground dynamic guidance, augmented reality navigation, and voice prompts; This module provides comprehensive support for maintenance personnel to locate the target device in a complex data center through multimodal guidance means such as ground dynamic guidance, augmented reality navigation, and voice prompts. Its design purpose is to enhance the intuitiveness and real-time nature of the guidance, thereby reducing path deviation during maintenance.

[0060] Generally, the data center environment is complex, and the layout characteristics of multiple entrances and multiple devices determine that traditional single navigation means are difficult to meet the needs of efficient operation and maintenance. Therefore, this module combines multimodal methods and enables maintenance personnel to quickly locate the device and successfully complete the fault handling task through multi-dimensional guidance strategies. In some embodiments, this module also supports providing a global path planning map or an overview of task distribution at the entrance, so that maintenance personnel can understand path and device location information before entering the data center.

[0061] In this embodiment, the multimodal guidance module includes three main units: a ground dynamic guidance unit, an augmented reality navigation unit, and a voice prompt unit.

[0062] Ground dynamic guidance unit Generally, the ground dynamic guidance unit provides path indication for maintenance personnel through a fluorescent arrow system arranged on the main channels of the data center. Specifically, this unit includes a series of LED lamp arrays, each of which can be independently controlled to display direction indication information, remaining path length, and other content.

[0063] As an option, the calculation of the arrow direction is based on the path information generated by the dynamic path planning module. Assuming the path is represented by a series of nodes The arrow direction can be calculated by the following formula:

[0064] where is the direction angle of the arrow; , is the coordinate of the current node ; , is the coordinate of the next node .

[0065] As an implementation method, when the maintenance personnel approach the target device along the path, the arrow color will change. For example, when within 10 meters of the target device, the arrow can switch to a red flashing state to further remind the maintenance personnel of approaching the target.

[0066] In some embodiments, the ground dynamic guidance unit can also display the remaining distance. Assuming the total path length is, the remaining distance between the current position of the maintenance personnel and the target node can be expressed as: Wherein, represents the remaining distance; represents the set of edges of the path where the maintenance personnel are currently located; represents the path edge The weight of.

[0067] Specifically, the path weight is provided by the dynamic path planning module and can be adjusted in real time according to the channel status (such as congestion); The augmented reality navigation unit superimposes virtual navigation information through an AR device (such as smart glasses or a mobile device) to achieve a more intuitive path guidance. Generally, this unit combines the path information of the dynamic path planning module and the personnel status data of the task assignment module to display the target direction, path nodes, distance, etc. in real time on the device screen.

[0068] Specifically, the navigation information in the AR device is presented in a three-dimensional form, and the arrow in the current direction is matched with the three-dimensional coordinates of the device in real time. For example, when the position of the target device is ( , , ), and the current position of the maintenance personnel is ( , , ), the displayed direction arrow should point to the following direction vector: Wherein, is the direction vector; , , are the three-dimensional coordinates of the target device; , , are the three-dimensional coordinates of the maintenance personnel.

[0069] In a possible implementation manner, the augmented reality navigation unit can also update the path node information in real time. For example, when the maintenance personnel deviate from the path, the AR device will guide them back to the nearest correct node through a virtual arrow.

[0070] In some embodiments, this unit also supports a path scaling function, enabling the maintenance personnel to view the detailed information of the global path and the local path on the device screen, thereby improving the comprehensibility of path planning in complex scenarios.

[0071] The voice prompt unit provides real-time voice instructions for path guidance through the system speaker or the terminal device carried by the maintenance personnel. Generally, this unit generates voice navigation information based on the path nodes and device numbers provided by the dynamic path planning module.

[0072] As an option, the content of the voice prompt includes but is not limited to the following information: The number and location information of the target device; The turning instruction of the current path; The remaining distance to the target device.

[0073] In a possible implementation, the voice prompt unit generates voice instructions through a preset template. For example, when the maintenance personnel need to turn left at the next node, the voice prompt content can be expressed as: "Arrive at the intersection 15 meters ahead, please turn left and continue straight for 30 meters to reach the device number R3C5-002." As an extended function, this unit also supports real-time dynamic updates. When the path is adjusted due to congestion or obstacles, the voice prompt will be changed synchronously. For example, if the front path is closed due to a fault, the voice prompt can be changed to: "The front passage is temporarily unavailable, please detour to the right to the alternative path." In some extended embodiments, the multi-modal guidance module can also dynamically adjust the guidance method in combination with the ambient light conditions. For example, in areas with relatively dim light, the brightness of the ground dynamic guidance unit will be automatically enhanced to ensure the visibility of the path guidance. In addition, to further improve the fault tolerance of the system, this module supports multi-mode switching. For example, when the AR device of the maintenance personnel is unavailable, the system will give priority to activating the ground dynamic guidance unit and the voice prompt unit to ensure the continuity of navigation; The data prediction and optimization module is used to predict the device status based on historical data and optimize the device layout and operation and maintenance processes; Based on the device monitoring data, this module provides accurate early warning information and optimization suggestions for the operation and maintenance personnel through the comprehensive application of prediction models and optimization algorithms. It not only plays a key role in reducing the device failure rate and improving the operation and maintenance efficiency, but also provides a long-term optimization basis for device layout and resource allocation.

[0074] Generally, the operating status of the devices in the data center will be affected by various environmental factors and internal load changes, resulting in obvious dynamics and uncertainties in the operating data. Therefore, the introduction of real-time prediction and optimization strategies can effectively make up for the limitations of traditional passive warning methods. In some embodiments, this module can also combine the operating mode and environmental conditions of the devices to dynamically adjust the prediction strategy, thereby improving the accuracy of prediction and the rationality of optimization suggestions.

[0075] In this embodiment, the data prediction and optimization module includes a fault prediction unit, a data analysis unit, and an optimization suggestion unit.

[0076] Generally, the fault prediction unit analyzes historical data and real-time monitoring data to predict possible abnormal states or fault trends of the device. Specifically, this unit uses a deep learning model based on time series analysis, such as LSTM (Long Short-Term Memory Network), to construct a prediction model for the device state.

[0077] In a possible implementation, the input of the prediction model is the historical data sequence { , , } of the device operation, and the output of the prediction model is the state value at a future time point. The specific formula is: Where represents the operation state of the device at the prediction time ; represents the device state data at time , including multiple monitoring parameters; is the time window size of the historical data; is the prediction time span.

[0078] As an option, the training data of the model consists of the historical operation records of the data center, and the model parameters are optimized through supervised learning. In actual operation, the prediction results can be used to trigger maintenance operations in advance. For example, if the predicted temperature value exceeds the set threshold, an alarm is sent to the maintenance personnel in advance.

[0079] In some embodiments, this unit can also correct the prediction results of the model by combining external environmental factors (such as seasonal temperature changes), thereby improving the applicability of the model.

[0080] The main function of the data analysis unit is to perform statistical analysis on the historical records and real-time data of the device operation, so as to discover potential laws that may affect the device performance and provide data support for formulating optimization strategies. Generally, this unit uses data mining techniques such as clustering analysis and frequency statistics to analyze the operation characteristics and fault distributions of devices in different regions of the data center.

[0081] Specifically, the operation characteristics of the device can be quantified by the following formula: Where represents the average operation state of the sensor ; represents the state fluctuation range of the sensor ; represents the sensor at time operating data; is the total number of statistical time points.

[0082] As an option, the unit can divide the device into different operating modes based on these statistical characteristics. For example, there may be significant characteristic differences between the high-load operating mode and the low-load operating mode of the device, and this mode information can be used to guide the dynamic management strategy of the device.

[0083] In some embodiments, the unit also supports the analysis of path usage frequency. For example, by counting the historical planning records of the dynamic path planning module, identifying frequently used path nodes, and marking them as key nodes, so as to prioritize the resource allocation in these areas in the optimization suggestions.

[0084] The optimization suggestion unit generates specific suggestions for the overall optimization of the data center based on the results of the fault prediction unit and the data analysis unit. Generally, these suggestions include but are not limited to sensor layout optimization, device resource reconfiguration, and path planning strategy adjustment, etc.

[0085] Specifically, the sensor layout optimization can be adjusted according to the distribution of device failures. For example, for areas with frequent failures, it is recommended to increase the density of temperature sensors and vibration sensors, so as to improve the monitoring accuracy and coverage. In addition, in the maintenance path planning, the optimization suggestion unit can combine the path usage frequency analysis results to re-plan the priorities of the main channel and the standby channel.

[0086] In a possible implementation, the optimization suggestion unit can also propose improvement suggestions for the device operation strategy. For example, for devices that have been in a high-load state for a long time, it is recommended to introduce a load balancing mechanism to reasonably allocate tasks to extend the service life of the device.

[0087] In some embodiments, the unit supports the generation of detailed optimization reports, including the fault distribution map of key areas, the path usage statistics table, and the improvement plan for sensor deployment. These reports can be directly presented through the central control panel for administrators to review and make decisions.

[0088] As an extended solution, the data prediction and optimization module can also perform dynamic optimization in combination with external factors. For example, during special events (such as large-scale data migration or emergency fault repair), the module can adjust the optimization strategy in real time according to the task distribution and resource requirements. In addition, to improve the applicability of the system, the optimization suggestion unit can also support the switching of multiple scenarios, such as generating optimization plans for high-load operating scenarios and low-load operating scenarios respectively.

[0089] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Alarm device guidance technology, characterized in that: include: Fault alarm generation module, used to collect equipment operation status data through sensor networks, trigger alarms based on anomaly detection and locate target equipment; Dynamic path planning module, which is used to generate the optimal path from the entrance to the target device according to the location of the target device and the layout of the data center, and adjust the path planning in real time; Task allocation module, used to allocate tasks according to alarm priority and maintenance personnel status; A multi-modal guidance module, which is used to guide maintenance personnel to the target equipment through ground dynamic guidance, augmented reality navigation and voice prompts; The data prediction and optimization module is used to predict equipment status based on historical data and optimize equipment layout and operation and maintenance processes.

2. The alarm device guidance technology according to claim 1, characterized in that: The fault alarm generation module comprises: Multi-sensor network for collecting equipment environment parameters; A dynamic anomaly detection unit, used to determine whether the device status is abnormal based on a dynamic threshold algorithm and trigger an alarm signal; The device positioning unit is used to locate the physical position of the target device in combination with the three-dimensional coordinate system and generate a unique device identifier.

3. The alarm device guidance technology according to claim 1, characterized in that: The dynamic path planning module includes: Data center modeling unit, used to abstract the data center into a weighted graph model consisting of nodes and edges; A path calculation unit, used for calculating an optimal path based on an improved path planning algorithm; The path adjustment unit is used to update the path weight in real time and re-plan the path according to the channel status and congestion information.

4. The warning device guidance technology according to claim 1, characterized in that: The task allocation module includes: An alarm priority calculation unit is used to calculate the priority of the alarm task according to the impact range of the alarm device and the criticality of the device; Personnel status monitoring unit, used to obtain the location information, task status and skill level of maintenance personnel in real time; The dynamic task allocation unit is used to dynamically allocate tasks according to the current location, task load and skill level of the maintenance personnel.

5. The warning device guidance technology according to claim 1, characterized in that: The multimodal guidance module comprises: A ground dynamic guidance unit for displaying the path direction and remaining distance through fluorescent arrows on the ground; An augmented reality navigation unit, used to overlay path guidance information in real time through an augmented reality device; The voice prompt unit is used to provide real-time voice prompts of the target device number, direction and status according to the path planning results.

6. The warning device guidance technology according to claim 5, characterized in that: The multimodal guidance module also includes: Display prompt unit, used to display equipment fault information and global path planning map at the entrance of the data center, providing a path overview; The dynamic instruction generation unit is used to generate step-by-step navigation instructions based on the specific environment in combination with real-time path adjustment, and execute them synchronously with the multimodal guidance device.

7. The warning device guidance technology according to claim 1, characterized in that: The data prediction and optimization module includes: A fault prediction unit, used to predict the equipment operation status through a time series analysis model; Data analysis unit, used to analyze historical fault data and path usage frequency; The optimization suggestion unit is used to optimize the resource configuration of sensor layout and guidance path based on the analysis results.

8. The warning device guidance technology according to claim 2, characterized in that: The multi-sensor network comprises: Temperature sensor, used to obtain temperature data of the device and its surrounding environment; Humidity sensor, used to detect the humidity level of the device's surrounding environment; Current sensor, used to collect current data during equipment operation; Vibration sensor, used to record vibration parameters of the equipment under operating conditions.

9. The warning device guidance technology according to claim 3, characterized in that: The dynamic path planning module also includes a path segment planning unit, which is used to divide the path planning into two parts: a global path from the entrance to the partition and a local path within the partition, and provide independent path calculation and dynamic adjustment functions respectively.

10. The warning device guidance technology according to claim 4, characterized in that: The task allocation module also includes a task interruption and switching unit, which is used to dynamically interrupt the current task and switch to a new high-priority task when the alarm priority is adjusted or the maintenance personnel status changes, and re-plan the allocation plan of the remaining tasks.

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