A campus hardware and software integrated management method and system supporting remote operation via mobile devices

Abstract

This invention provides a campus hardware and software integrated management method and system supporting remote operation via mobile devices. It collects status parameters and business data from heterogeneous devices in the campus teaching, security, and logistics domains, synchronizes them with a hybrid clock, and pushes them to a campus digital twin virtual model incorporating geographic information, achieving full-domain resource virtual-real mapping and 3D visualization. A sliding time window combined with a business weight correction mechanism is used to determine adaptive warning thresholds and cross-device linkage strategies based on device operating time and business scenarios. Mobile devices access the system via an end-to-end encrypted link, enabling remote hierarchical handling based on dynamic hierarchical permissions. Hierarchical warnings are generated when a single parameter exceeds limits or multi-dimensional correlation anomalies are detected. If remote handling is ineffective, maintenance work orders are automatically generated and resources are intelligently scheduled, forming a closed-loop management process. This solution breaks down campus hardware and software data silos, enabling deep mobile control and intelligent operation and maintenance, significantly improving campus management efficiency and emergency response capabilities.

Description

Technical Field

[0001] This invention relates to the field of intelligent campus management technology, and in particular to an integrated campus hardware and software management method and system that supports remote operation via mobile devices. Background Technology

[0002] With the continuous advancement of smart campus construction, a large number of hardware and software devices have been deployed on campus, covering multimedia, smart blackboards, lighting, and air conditioning in the teaching domain; access control, monitoring, and alarm systems in the security domain; and water and electricity metering, elevators, and fire-fighting equipment in the logistics domain. The stable operation and efficient management of these devices are the foundation for ensuring the normal teaching and living order on campus.

[0003] Currently, the main problems in campus hardware and software management are as follows: First, hardware and software management are separated, with each system operating independently, forming serious data silos and making it impossible to achieve cross-system linkage and collaboration. For example, classroom access control and multimedia, lighting and air conditioning cannot automatically link according to class schedule information, and security monitoring and alarm systems cannot achieve intelligent linkage. Second, management methods are outdated, mostly relying on manual inspections and on-site operations, resulting in low maintenance efficiency, delayed fault detection, and the inability to achieve remote control. When equipment malfunctions, maintenance personnel must be present to handle it, affecting teaching and living order. Third, most existing mobile applications only support information viewing and simple repair submissions, lacking in-depth remote control capabilities and hierarchical permission management, failing to meet the operational needs of users with different roles, and posing data security and operational security risks. Fourth, fault early warning mechanisms are imperfect, mostly using fixed threshold alarms, which cannot adapt to equipment aging and changes in business scenarios, easily generating false alarms and missed alarms, and failing to identify complex fault modes such as multi-device collaborative anomalies. Fifth, there is a lack of a closed-loop management mechanism covering the entire process, with disconnects between fault discovery, reporting, handling and evaluation, making it impossible to achieve the accumulation and optimization of maintenance data. Therefore, how to achieve integrated management of campus hardware and software, support deep remote operation via mobile devices, and build an intelligent fault early warning and closed-loop operation and maintenance system has become an urgent problem to be solved in the construction of smart campuses. Summary of the Invention

[0004] Based on this, the present invention provides a campus hardware and software integrated management method and system that can break down campus hardware and software data silos, support deep remote control via mobile terminals, and realize intelligent early warning and full-process closed-loop management, supporting remote operation via mobile terminals.

[0005] In a first aspect, the present invention provides a campus hardware and software integrated management method that supports remote operation via mobile devices, comprising the following steps: Collect status parameters and campus business data of heterogeneous hardware and software devices in the campus teaching domain, security domain, and logistics domain; All collected multi-source data are time-stamped and synchronized using a hybrid clock. Then, all the time-stamped data is pushed to the campus digital twin virtual model that integrates the geographic information system to achieve full-domain mapping and three-dimensional visualization of all campus hardware and software resources in the virtual space. Based on historical health operation data stored in a time-series database associated with a digital twin virtual model, and combined with a campus business rule base, a sliding time window combined with a business weight correction mechanism is used to determine the early warning threshold and cross-device linkage strategy for each device that adapts to the running time and business scenario. Mobile devices access the system through an end-to-end encrypted link, and obtain corresponding operation permissions and customized visual interfaces based on dynamic hierarchical permissions according to user roles and current business scenarios. Real-time monitoring of the status parameters and business data of various devices on campus; when a single parameter exceeds the limit continuously or multiple dimensions are abnormal, a graded warning signal is generated and pushed to the mobile terminal with corresponding permissions, supporting remote execution of graded handling operations on the mobile terminal. When remote handling via mobile terminal is ineffective or the anomaly level reaches a preset threshold, an operation and maintenance work order is automatically generated and operation and maintenance resources are intelligently scheduled. The work order execution progress is tracked and the operation and maintenance effect is evaluated, forming a closed-loop management of the entire process.

[0006] In some embodiments, timestamp alignment for hybrid clock synchronization of all acquired multi-source data specifically includes: Obtain structured data packets with local timestamps corresponding to all status parameters and campus business data, where the status parameters come from heterogeneous devices that support different communication protocols; The core security and teaching equipment are synchronized using the IEEE 1588 precision clock protocol, while the general logistics equipment is synchronized using the network time protocol. The edge gateway completes the unified reference conversion of the hybrid clock sources. By setting a fixed time alignment period, data from all devices within the same period are aggregated, and missing data is filled using interpolation based on business rules, resulting in a multi-source dataset with timestamp alignment.

[0007] In some embodiments, pushing all timestamped data to a campus digital twin virtual model integrated with a geographic information system to achieve full-domain mapping and 3D visualization of all campus hardware and software resources in virtual space specifically includes: The timestamp-aligned multi-source dataset is sent to the data access layer of the campus digital twin virtual model via an industrial communication protocol; Based on the device geocode and device identifier in the data, the corresponding virtual device entity is located in the 3D virtual campus scene integrated with the geographic information system, and its status attributes and spatial location information are updated. The visualization engine that drives the digital twin virtual model renders and displays the operational status of all campus hardware and software resources based on real-time changes in status attributes, and supports layered visualization and roaming viewing by region and device type.

[0008] In some embodiments, based on historical health operation data stored in a time-series database associated with a digital twin virtual model, and combined with a campus business rule base, a sliding time window combined with a business weight correction mechanism is used to determine the adaptive warning threshold and cross-device linkage strategy for each device as it operates over time and in relation to business scenarios. Specifically, this includes: Extract historical health operation data of devices within a specified time range from the time series database, and extract class scheduling information, work and rest time, holiday arrangements, and personnel density rules from the campus business rule base; For each device, the sliding time window data sequence is extracted according to the set window length and sliding step size, and its exponentially weighted moving mean and standard deviation are calculated; The calculation results are weighted and adjusted according to the current business scenario to obtain an early warning threshold that adapts to the running time and business scenario. Based on the business relationships between devices, corresponding cross-device linkage strategies are generated and stored in the linkage strategy library.

[0009] In some embodiments, the mobile device accesses the system via an end-to-end encrypted link and obtains corresponding operation permissions and a customized visual interface based on dynamic hierarchical permissions according to user roles and the current business scenario. Specifically, this includes: The mobile device sends an access request to the system. After identity authentication, an end-to-end encrypted link based on the transport layer security protocol is established, and a one-time token mechanism is used to ensure the security of each session. The system dynamically assigns corresponding operation permissions based on user roles and current business scenarios. Operation permissions are divided into viewing permissions, basic control permissions, advanced operation and maintenance permissions, and system management permissions. Based on the assigned permissions, push customized visual interfaces and operation controls to mobile devices, and hide functional modules and data information that are not authorized.

[0010] In some embodiments, real-time monitoring of the correlation changes between the status parameters of various campus devices and business data, and when a single parameter continuously exceeds the limit or multiple dimensions of correlation anomalies are detected, generating a graded early warning signal and pushing it to the mobile terminal with corresponding permissions, specifically includes: Real-time acquisition of timestamp-aligned device status parameters and business data; query and obtain adaptive warning thresholds for the corresponding device in the current business scenario. The system detects whether a single parameter exceeds the warning threshold within a continuous sampling period, or whether there are multi-dimensional correlation anomalies, including mismatch between device status and business rules, and abnormal coordination of multiple device statuses. When an anomaly is detected, a level 1, level 2, or level 3 warning signal is generated based on the anomaly type and the scope of its impact. The warning signal is then pushed to mobile devices with the corresponding permissions and visualized in the digital twin virtual model.

[0011] In some embodiments, supporting remote execution of tiered handling operations via mobile devices specifically includes: After receiving the warning signal, the mobile device displays the corresponding remote handling options according to the user's permissions. The handling options include one-click start / stop, parameter adjustment, remote restart, firmware upgrade, and remote diagnostics. The mobile device sends a processing command to the system. The system verifies the legality and permissions of the command. Once the verification is successful, the command is sent to the edge controller of the corresponding device. The edge controller executes instructions and feeds back the results to the system. The system then updates the status attributes of the device in the digital twin virtual model and pushes the results to the mobile device, while also recording a complete operation log.

[0012] In some embodiments, when remote handling via mobile terminal is ineffective or the anomaly level reaches a preset threshold, an operation and maintenance work order is automatically generated and operation and maintenance resources are intelligently scheduled. The work order execution progress is tracked and the operation and maintenance effect evaluation is completed. Specifically, this includes: When remote handling fails or the anomaly level reaches level two or above, the system automatically extracts information about the abnormal device, its location, the type of the anomaly, and historical maintenance records to generate a standardized maintenance work order. Based on the real-time location, skill tags, and current work order load of maintenance personnel, an intelligent matching algorithm is used to dispatch work orders to the most suitable maintenance personnel and push them to the maintenance personnel's mobile devices. The system tracks the progress of work orders in real time. After completing the handling, maintenance personnel submit the handling results and the cause of the fault on the mobile device. The system automatically verifies the equipment operating status and completes the maintenance effect evaluation. Operational data is fed back to the time-series database and business rule base to optimize subsequent early warning thresholds and linkage strategies.

[0013] In some embodiments, the status parameters include device voltage, current, operating temperature, operating time, switch status, and signal strength, and the campus business data includes course scheduling information, timetable, personnel attendance data, repair records, and holiday arrangements.

[0014] Secondly, the present invention provides a campus hardware and software integrated management system that supports remote operation via mobile devices, comprising: The data acquisition module is used to collect status parameters and campus business data from heterogeneous hardware and software devices in the campus teaching domain, security domain, and logistics domain. The processing module is used to perform timestamp alignment of all collected multi-source data with mixed clock synchronization, and then push all the timestamp aligned data to the campus digital twin virtual model of the integrated geographic information system to realize the full-domain mapping and three-dimensional visualization of all campus hardware and software resources in the virtual space. The processing module is also used to determine the early warning threshold and cross-device linkage strategy for each device based on the historical health operation data stored in the time-series database associated with the digital twin virtual model, combined with the campus business rule base, using a sliding time window combined with a business weight correction mechanism. The mobile interaction module is used to access the system through an end-to-end encrypted link, obtain corresponding operation permissions and customized visual interfaces based on the dynamic hierarchical permissions of user roles and current business scenarios, and support remote execution of hierarchical processing operations. The early warning module is used to monitor the correlation changes between the status parameters of various devices on campus and business data in real time. When a single parameter exceeds the limit continuously or multiple dimensions are abnormal, a graded early warning signal is generated and pushed to the mobile terminal with the corresponding permissions. The closed-loop management module is used to automatically generate maintenance work orders and intelligently schedule maintenance resources when remote handling by the mobile terminal is ineffective or the abnormal level reaches a preset threshold, track the execution progress of the work orders, and complete the maintenance effect evaluation.

[0015] Compared with the prior art, the present invention has the following advantages: The present invention provides a campus hardware and software integrated management method and system supporting remote operation via mobile terminals. First, it collects status parameters and campus business data from heterogeneous hardware and software devices across three major domains: teaching, security, and logistics. After aligning the timestamps of the multi-source data using a mixed clock synchronization, it pushes the data to a campus digital twin virtual model that integrates geographic information. This achieves virtual-real mapping and 3D visualization of all campus hardware and software resources. This step solves the problem of inconsistent data across heterogeneous devices in multiple domains, establishing a fully covered and spatiotemporally consistent digital twin campus foundation, providing a reliable data foundation and visualization carrier for subsequent integrated management. Second, based on historical healthy operation data and campus business rules, a sliding time window combined with a business weight correction mechanism is used to determine the adaptive warning threshold and cross-device linkage strategy for each device based on operating time and business scenarios. This step breaks through the limitations of traditional fixed threshold warnings, dynamically adjusting warning standards according to device aging and campus business scenarios, while simultaneously achieving intelligent cross-device linkage, significantly improving the accuracy of warnings and the system's intelligence level. Subsequently, the mobile device accesses the system via an end-to-end encrypted link, obtaining corresponding operation interfaces and control permissions based on user roles and dynamic hierarchical permissions for the current business scenario. This step enables deep remote management and control of the mobile device, while ensuring data and operational security through encrypted links and dynamic permission management, meeting the differentiated needs of users with different roles. Next, the system monitors the correlation changes between device status and business data in real time. When a single parameter exceeds the limit or a multi-dimensional correlation anomaly is detected, a hierarchical warning is generated and pushed to the mobile device with the corresponding permissions, supporting remote execution of hierarchical handling operations by the mobile device. This step can promptly detect various anomalies, especially multi-dimensional correlation anomalies that traditional methods cannot identify, and supports remote handling by maintenance personnel anytime, anywhere via the mobile device, significantly shortening fault response time. Finally, when remote handling is ineffective, a maintenance work order is automatically generated and resources are intelligently scheduled, forming a closed-loop management process from warning, handling to evaluation. This step realizes the automation and intelligence of campus maintenance, and continuously optimizes the system's warning and linkage capabilities through the accumulation and feedback of maintenance data. In summary, the solution of this invention can break down the data silos of campus hardware and software, realize in-depth remote control and intelligent operation and maintenance in all scenarios via mobile terminals, and significantly improve campus management efficiency and emergency response capabilities. Attached Figure Description

[0016] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is an exemplary flowchart of a campus hardware and software integrated management method that supports remote operation via mobile terminal, as shown in some embodiments of the present invention. Figure 2This is a schematic diagram illustrating an application scenario of the integrated campus hardware and software management system according to some embodiments of the present invention; Figure 3 This is a schematic diagram of the process for generating tiered early warnings and remote handling according to some embodiments of the present invention; Figure 4 This is a schematic diagram of a campus hardware and software integrated management system that supports remote operation via mobile terminal, according to some embodiments of the present invention. Figure 5 This is a schematic diagram of the structure of a computer device that implements a campus hardware and software integrated management method supporting remote operation via mobile terminals, according to some embodiments of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] The present invention will be further described in detail below with reference to the accompanying drawings.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a campus hardware and software integrated management method supporting remote operation via mobile terminal, according to some embodiments of the present invention. This campus hardware and software integrated management method supporting remote operation via mobile terminal mainly includes the following steps: In step 101, status parameters and campus business data of heterogeneous hardware and software devices in the campus teaching domain, security domain, and logistics domain are collected.

[0020] In practical implementation, the collection of status parameters and campus business data from heterogeneous hardware and software devices in the campus teaching, security, and logistics domains can be achieved in the following way: First, deploy edge computing gateways in various areas of the campus, configuring corresponding protocol conversion modules for different types of heterogeneous devices, supporting multiple industrial and civilian communication protocols such as Modbus, OPCUA, ONVIF, Wiegand, RS232, and HDMI-CEC; for multimedia devices, smart blackboards, lighting, and air conditioning in the teaching domain, collect their on / off status, operating parameters, and energy consumption data through corresponding communication interfaces; for access control devices, surveillance cameras, and alarm systems in the security domain, collect their card swipe records, etc. The system collects video stream data, alarm signals, and equipment online status; for water and electricity metering equipment, elevators, and fire-fighting equipment in the logistics domain, it collects their metering data, operating status, and fault codes; simultaneously, by connecting to the campus academic affairs system, attendance system, and repair system, it obtains campus business data such as course scheduling information, timetables, personnel attendance data, repair records, and holiday arrangements; all collected status parameters and business data are encapsulated into structured data packets with device identifiers, geocoding, and local timestamps, and sent to the edge computing gateway for preprocessing, thereby completing the collection of multi-source data. Other methods can also be used in other embodiments, which are not limited here.

[0021] It should be noted that the status parameters in this invention refer to numerical and on / off indicators extracted from various hardware and software devices on campus, used to quantitatively characterize the operating status and performance of the devices; campus business data refers to various business information related to campus teaching, management, and life, used to provide business context for equipment management and anomaly judgment.

[0022] In some embodiments, reference Figure 2 As shown in the figure, this diagram illustrates an application scenario of the integrated campus hardware and software management system according to some embodiments of the present invention. The figure includes an edge computing gateway, a cloud server, a digital twin platform, mobile terminals, and various campus hardware and software devices. The edge computing gateway is responsible for collecting data from various devices and preprocessing it, and uploading the data to the cloud server via 5G or Ethernet. The cloud server runs the integrated campus hardware and software management system. The digital twin platform synchronizes data with the cloud server in real time to achieve three-dimensional visualization of the entire campus. The mobile terminals access the cloud server via the mobile Internet to achieve remote viewing and control.

[0023] In step 102, the timestamps of all collected multi-source data are synchronized using a hybrid clock, and then all the timestamp-aligned data is pushed to the campus digital twin virtual model of the integrated geographic information system to realize the full-domain mapping and three-dimensional visualization of all campus hardware and software resources in the virtual space.

[0024] In some embodiments, timestamp alignment for hybrid clock synchronization of all acquired multi-source data can be achieved using the following steps: Obtain structured data packets with local timestamps corresponding to all status parameters and business data, where the status parameters come from heterogeneous devices that support different communication protocols; The core security and teaching equipment are synchronized using the IEEE 1588 precision clock protocol, while the general logistics equipment is synchronized using the network time protocol. The edge gateway completes the unified reference conversion of the hybrid clock sources. By setting a fixed time alignment period, data from all devices within the same period are aggregated, and missing data is filled using interpolation based on business rules, resulting in a multi-source dataset with timestamp alignment.

[0025] In specific implementation, obtaining structured data packets with local timestamps corresponding to all status parameters and business data can be achieved in the following way: the edge computing gateway continuously receives structured data packets from various devices and business systems, performs format verification and abnormal data filtering on the data packets, stores the filtered valid data packets in the local cache queue, and waits for time synchronization processing. Other methods can also be used in other embodiments, which are not limited here.

[0026] In specific implementation, the core security and teaching equipment are synchronized using the IEEE 1588 precision clock protocol, while ordinary logistics equipment is synchronized using the network time protocol. The unified reference conversion of the hybrid clock sources by the edge gateway can be achieved in the following way: a master clock server is deployed within the campus network. The core security and teaching equipment synchronize with the master clock server via the IEEE 1588 protocol, with synchronization accuracy reaching the microsecond level; ordinary logistics equipment synchronizes with the master clock server via the network time protocol, with synchronization accuracy reaching the millisecond level; the edge computing gateway converts the local timestamp of all data packets into the unified reference timestamp of the master clock server, thereby achieving the unification of the hybrid clock sources. Other methods can also be used in other embodiments, which are not limited here.

[0027] In specific implementation, a fixed time alignment period is set, and data from all devices within the same period is collected. Missing data is filled using interpolation based on business rules. The resulting timestamp-aligned multi-source dataset can be achieved as follows: the time alignment period is set to 1 second, and the edge computing gateway collects data packets in the cache queue every 1 second, integrating the data from all devices under the same timestamp. For devices that have not uploaded data within this period, interpolation is performed based on their historical operating rules and the current business scenario. For example, classroom lights are on by default during class time and off by default during holidays, thus obtaining a complete timestamp-aligned multi-source dataset. Other methods can also be used in other embodiments, which are not limited here.

[0028] In some embodiments, pushing all timestamped data to the campus digital twin virtual model integrated with the geographic information system to achieve full-domain mapping and 3D visualization of all campus hardware and software resources in the virtual space can be achieved through the following steps: The timestamp-aligned multi-source dataset is sent to the data access layer of the campus digital twin virtual model via an industrial communication protocol; Based on the device geocode and device identifier in the data, the corresponding virtual device entity is located in the 3D virtual campus scene integrated with the geographic information system, and its status attributes and spatial location information are updated. The visualization engine that drives the digital twin virtual model renders and displays the operational status of all campus hardware and software resources based on real-time changes in status attributes, and supports layered visualization and roaming viewing by region and device type.

[0029] It should be noted that the campus digital twin virtual model integrating geographic information system in this invention refers to a high-fidelity three-dimensional model constructed in virtual space that corresponds one-to-one with the geographical environment and hardware and software equipment of the physical campus. It can not only display the operating status of the equipment, but also intuitively display the spatial location and distribution of the equipment, providing a global perspective for campus management.

[0030] In specific implementation, the timestamp-aligned multi-source dataset can be sent to the data access layer of the campus digital twin virtual model via an industrial communication protocol in the following way: the edge computing gateway sends the timestamp-aligned multi-source dataset to the data access layer of the digital twin platform on the cloud server via the MQTT protocol. After parsing and verifying the data, the data access layer stores the data in the associated industrial time-series database and synchronizes it to the digital twin simulation kernel. Other methods can also be used in other embodiments, which are not limited here.

[0031] In specific implementation, based on the device geocode and device identifier in the data, the corresponding virtual device entity is located in the 3D virtual campus scene integrated with the geographic information system, and its status attributes and spatial location information are updated. This can be achieved in the following way: the digital twin simulation kernel quickly locates the corresponding virtual device entity in the 3D virtual campus scene based on the device geocode and device identifier in the data packet; the status parameters in the data packet are assigned to the corresponding attribute variables of the virtual device entity, for example, the temperature of the air conditioner is assigned to the current temperature attribute of the virtual air conditioner, and the on / off status of the lights is assigned to the on / off status attribute of the virtual lights; for mobile devices such as inspection robots, their spatial location information is updated synchronously. Other methods can also be used in other embodiments, which are not limited here.

[0032] In practical implementation, the visualization engine driving the digital twin virtual model renders and displays the operational status of all campus hardware and software resources based on real-time changes in state attributes. Layered visualization and roaming viewing by region and device type can be achieved in the following way: the digital twin visualization engine continuously monitors the changes in the state attributes of each virtual device entity. When an attribute changes, the corresponding model in the 3D scene is updated in real time. For example, when lights are turned on, the virtual light model becomes illuminated; when a device malfunctions, the virtual device model turns red and flashes. Simultaneously, users can view the data in layers by area such as teaching buildings, dormitories, and office buildings, and filter by type such as teaching equipment, security equipment, and logistics equipment. Users can also freely roam and zoom in the 3D scene, thus gaining an intuitive and comprehensive understanding of the operational status of all campus hardware and software resources. Other methods can also be used in other embodiments, which are not limited here.

[0033] It should be noted that the above steps solved the problem of inconsistent data from heterogeneous devices across multiple domains on campus, and established a digital twin campus foundation that is fully covered and consistent in time and space, providing a reliable data foundation and visualization carrier for subsequent integrated management.

[0034] In step 103, based on the historical health operation data stored in the time-series database associated with the digital twin virtual model, and combined with the campus business rule base, a sliding time window combined with a business weight correction mechanism is used to determine the early warning threshold and cross-device linkage strategy for each device that adapts to the running time and business scenario.

[0035] In some embodiments, based on historical health operation data stored in a time-series database associated with a digital twin virtual model, and combined with a campus business rule base, a sliding time window combined with a business weight correction mechanism is used to determine the adaptive warning threshold and cross-device linkage strategy for each device as it operates over time and in relation to business scenarios. Specifically, this includes: Extract historical health operation data of devices within a specified time range from the time series database, and extract class scheduling information, work and rest time, holiday arrangements, and personnel density rules from the campus business rule base; For each device, the sliding time window data sequence is extracted according to the set window length and sliding step size, and its exponentially weighted moving mean and standard deviation are calculated; The calculation results are weighted and adjusted according to the current business scenario to obtain an early warning threshold that adapts to the running time and business scenario. Based on the business relationships between devices, corresponding cross-device linkage strategies are generated and stored in the linkage strategy library.

[0036] In specific implementation, extracting historical health operation data of equipment within a specified time range from the time-series database and extracting scheduling information, work and rest schedules, holiday arrangements, and personnel density rules from the campus business rule base can be achieved in the following way: access the industrial time-series database associated with the digital twin virtual model, extract historical data of equipment under normal operation in the past 3 months, and exclude data from fault periods and maintenance periods; at the same time, obtain the latest scheduling information, work and rest schedules, holiday arrangements, and personnel density management rules from the campus business rule base to provide a basis for subsequent business weight correction. Other methods can also be used in other embodiments, which are not limited here.

[0037] In specific implementation, for each device, the data sequence of the sliding time window is extracted according to the set window length and sliding step size. The calculation of its exponentially weighted moving average and standard deviation can be achieved in the following way: Set an independent sliding time window for each device, with a window length of 7 days and a sliding step size of 1 hour; starting from the latest data point, backtrack to extract a data sequence that meets the window length requirement, and use a recursive algorithm to calculate the exponentially weighted moving average and exponentially weighted moving standard deviation of the data sequence. The smoothing coefficient is set to 0.3, so that recent data has a higher weight and can sensitively track the slow changes in the device's operating status. Other methods can also be used in other embodiments, which are not limited here.

[0038] In specific implementation, the calculation results are weighted and adjusted according to the current business scenario to obtain an adaptive warning threshold that adapts to the running time and business scenario. This can be achieved in the following way: Based on the current time, class scheduling information, and holiday arrangements, determine the business scenario in which the device is located, such as a class session, class dismissal, exam, or holiday scenario; For different business scenarios, set corresponding business weight coefficients. For example, the warning weight coefficient for teaching equipment in a class session scenario is 1.2, and the warning threshold is more stringent; the warning weight coefficient for non-essential equipment in a holiday scenario is 0.8, and the warning threshold is appropriately relaxed; Multiply the calculated exponentially weighted moving average and standard deviation by the corresponding business weight coefficient to obtain the adaptive warning upper and lower limits for the current business scenario. Other methods can also be used in other embodiments, which are not limited here.

[0039] In specific implementation, the generation and storage of corresponding cross-device linkage strategies based on the business relationships between devices can be achieved in the following way: Analyze the business relationships between devices, such as the relationship between classroom access control, multimedia, lighting and air conditioning, and the relationship between access control card swiping and surveillance cameras; based on these relationships, generate corresponding cross-device linkage strategies, such as automatically turning on the multimedia, lighting and air conditioning in the classroom when the access control detects that a teacher has swiped their card to enter the classroom, and automatically turning off all devices when the surveillance detects that no one is in the classroom for more than 10 minutes; store the generated linkage strategies in the linkage strategy library, and automatically execute them when the trigger conditions are met. Other methods can also be used in other embodiments, which are not limited here.

[0040] It should be noted that the above steps break through the limitations of traditional fixed threshold early warning, and can dynamically adjust the early warning standards according to the aging of equipment and campus business scenarios. At the same time, it realizes intelligent linkage across devices, which greatly improves the accuracy of early warning and the intelligence level of the system.

[0041] In step 104, the mobile device accesses the system through an end-to-end encrypted link and obtains corresponding operation permissions and a customized visual interface based on the user role and the dynamic hierarchical permissions of the current business scenario.

[0042] In specific implementation, the mobile terminal accesses the system via an end-to-end encrypted link. Based on user roles and dynamic hierarchical permissions according to the current business scenario, obtaining corresponding operation permissions and a customized visual interface can be achieved in the following way: The user opens the application on the mobile terminal, enters their account and password for authentication. After successful authentication, the system establishes an end-to-end encrypted link with the mobile terminal based on Transport Layer Security (TLS) version 1.3 and generates a one-time session token, which automatically expires after the session ends. The system dynamically assigns corresponding operation permissions based on the user's role, such as administrator, teacher, maintenance personnel, or student, and the current business scenario, such as class time, exam time, or holidays. For example, a teacher has basic control permissions for multimedia, lighting, and air conditioning in their classroom during class time, but only viewing permissions during exam time; maintenance personnel have advanced maintenance permissions for all devices; and students only have permission to submit repair requests and view progress. Based on the assigned permissions, the system pushes a customized visual interface to the mobile terminal, hiding functional modules and data information that the user does not have permission to access, ensuring data security and operational security. Other methods can also be used in other embodiments, and are not limited here.

[0043] It should be noted that the above steps enable deep remote control on mobile devices, while ensuring data and operational security through encrypted links and dynamic permission management, thus meeting the differentiated needs of users with different roles.

[0044] In step 105, the status parameters and business data of various devices on campus are monitored in real time. When a single parameter exceeds the limit continuously or multiple dimensions are abnormal, a graded warning signal is generated and pushed to the mobile terminal with the corresponding permissions, supporting the mobile terminal to remotely execute graded handling operations.

[0045] In some embodiments, reference Figure 3 As shown in the figure, this is a schematic diagram of the process for generating tiered early warnings and remote handling in some embodiments of the present invention. In this embodiment, the correlation changes between the status parameters and business data of various devices on campus are monitored in real time. When a single parameter is detected to continuously exceed the limit or multiple dimensions of correlation are abnormal, a tiered early warning signal is generated and pushed to the mobile terminal with corresponding permissions. Supporting remote execution of tiered handling operations by the mobile terminal can be achieved by the following steps: In step 1051, the device status parameters and business data after timestamp alignment are obtained in real time, and the adaptive warning threshold for the current business scenario of the corresponding device is queried and obtained. In step 1052, it is detected whether a single parameter exceeds the warning threshold within a continuous sampling period, or whether there is a multi-dimensional correlation anomaly. The multi-dimensional correlation anomaly includes mismatch between device status and business rules, and anomaly in the coordination of multiple device statuses. In step 1053, when an anomaly is detected, a first-level, second-level, and third-level graded early warning signal is generated according to the anomaly type and the scope of impact. The early warning signal is pushed to the mobile terminal with the corresponding permissions and visualized in the digital twin virtual model. In step 1054, after receiving the warning signal, the mobile terminal displays the corresponding remote handling options according to the user's permissions, supporting the user to remotely perform tiered handling operations.

[0046] In specific implementation, detecting whether a single parameter exceeds the warning threshold within a continuous sampling period, or whether there are multi-dimensional correlation anomalies, can be achieved in the following way: For single-parameter anomalies, detect whether the device's status parameter exceeds the corresponding adaptive warning threshold within three consecutive sampling periods; for multi-dimensional correlation anomalies, detect whether the device status matches the business rules, such as the multimedia in the classroom not being turned on during class time, or all the lights in the teaching building being turned on during holidays, and whether the status of multiple devices is abnormally coordinated, such as multiple monitoring devices in the same area being offline at the same time, or an unidentified person being detected on the monitoring screen while the access control card is swiped abnormally; when any of the above anomalies are detected, it is determined that an anomaly exists. Other methods can also be used in other embodiments, which are not limited here.

[0047] In specific implementation, when an anomaly is detected, a level 1, level 2, or level 3 warning signal is generated based on the anomaly type and its impact scope. This warning signal is then pushed to mobile devices with corresponding permissions, and a visual alarm is displayed in the digital twin virtual model. This can be achieved in the following way: Based on the urgency and impact scope of the anomaly, the warning is divided into three levels: Level 1 warnings are for urgent anomalies, such as fire alarms or elevator entrapments, and are pushed to the mobile devices of administrators and security personnel; Level 2 warnings are for important anomalies, such as teaching equipment malfunctions or access control anomalies, and are pushed to the mobile devices of maintenance personnel and the corresponding area managers; Level 3 warnings are for general anomalies, such as excessively high equipment temperature or abnormal energy consumption, and are pushed to the mobile devices of maintenance personnel. Simultaneously, in the digital twin virtual model, the virtual model corresponding to the abnormal device is marked with the color corresponding to the warning level and flashes as a notification. Detailed anomaly information is displayed on the warning panel. Other methods can also be used in other embodiments, and are not limited here.

[0048] In specific implementation, after receiving the warning signal, the mobile device displays corresponding remote handling options based on user permissions. Supporting users to remotely execute tiered handling operations can be achieved in the following way: After receiving the warning signal, the mobile device alerts the user via pop-up window and sound. After clicking the warning information, the user enters the corresponding device details page. The system displays corresponding remote handling options based on the user's permissions. For example, maintenance personnel can see options such as remote restart, parameter adjustment, firmware upgrade, and remote diagnostics, while teachers can only see basic options such as one-click start / stop. After the user selects the corresponding handling option, the mobile device sends a handling command to the system. The system performs legality verification and permission verification on the command. After successful verification, the command is sent to the edge controller of the corresponding device. The edge controller executes the command and feeds back the execution result to the system. The system synchronously updates the device's status attributes in the digital twin virtual model and pushes the execution result to the mobile device, while recording a complete operation log, including the operator, operation time, operation content, and execution result. Other methods can also be used in other embodiments, which are not limited here.

[0049] It should be noted that the above steps can promptly detect various anomalies, especially multi-dimensional related anomalies that traditional methods cannot identify, and support maintenance personnel to remotely handle them anytime and anywhere via mobile devices, significantly shortening fault response time.

[0050] In step 106, when remote handling by the mobile terminal is ineffective or the abnormal level reaches a preset threshold, an operation and maintenance work order is automatically generated and operation and maintenance resources are intelligently scheduled. The work order execution progress is tracked and the operation and maintenance effect is evaluated, forming a closed-loop management of the entire process.

[0051] In practice, when remote handling via mobile terminal fails or the anomaly level reaches a preset threshold, an operation and maintenance work order is automatically generated, and operation and maintenance resources are intelligently scheduled. The work order execution progress is tracked, and the operation and maintenance effect is evaluated to form a closed-loop management process. This can be achieved in the following way: When remote handling fails or the anomaly level reaches level two or above, the system automatically triggers the work order generation process, extracts information such as the device identifier, location information, anomaly type, and historical operation and maintenance records of the abnormal device, and generates a standardized operation and maintenance work order. Based on the real-time location, skill tags, and current work order load of the operation and maintenance personnel, the system uses a genetic algorithm for intelligent matching, and dispatches the work order to the nearest, skill-matched, and lightly loaded operation and maintenance personnel. The system sends work order information to maintenance personnel's mobile devices. After receiving the work order, the system tracks its progress in real time, including statuses such as "accepted," "on-site," "in progress," and "completed." After completing the task, the maintenance personnel submit the results, cause of the fault, and repair costs on their mobile devices. The system automatically verifies the equipment's operating status remotely. Once the fault is confirmed to be resolved, the work order is completed, and the maintenance effectiveness is evaluated. Finally, the maintenance data is fed back to the time-series database and business rule base to optimize subsequent warning thresholds and linkage strategies, thus forming a closed-loop management system from warning and handling to evaluation. Other methods can also be used in other embodiments, which are not limited here.

[0052] It should be noted that the above steps have achieved automation and intelligence in campus operation and maintenance. At the same time, through the accumulation and feedback of operation and maintenance data, the system's early warning and linkage capabilities are continuously optimized.

[0053] Furthermore, in another aspect, in some embodiments, the present invention provides a campus hardware and software integrated management system that supports remote operation via mobile devices, see reference. Figure 4 The figure is a schematic diagram of the structure of a campus integrated hardware and software management system supporting remote operation via mobile terminal, according to some embodiments of the present invention. This integrated hardware and software management system includes: a data acquisition module, a processing module, a mobile terminal interaction module, an early warning module, and a closed-loop management module, which are described below: The data acquisition module in this invention is mainly used to collect status parameters and campus business data of heterogeneous hardware and software devices in the campus teaching domain, security domain, and logistics domain. The processing module in this invention is mainly used to perform timestamp alignment of all collected multi-source data with mixed clock synchronization, and then push all the timestamp aligned data to the campus digital twin virtual model of the integrated geographic information system to realize the full-domain mapping and three-dimensional visualization of all campus hardware and software resources in the virtual space. The processing module is also used to determine the early warning threshold and cross-device linkage strategy for each device based on the historical health operation data stored in the time-series database associated with the digital twin virtual model, combined with the campus business rule base, using a sliding time window combined with a business weight correction mechanism. The mobile terminal interaction module in this invention is mainly used to access the system through an end-to-end encrypted link, obtain corresponding operation permissions and customized visual interfaces based on the dynamic hierarchical permissions of user roles and current business scenarios, and support remote execution of hierarchical processing operations. The early warning module in this invention is mainly used to monitor the correlation changes between the status parameters and business data of various devices on campus in real time. When a single parameter is detected to exceed the limit continuously or a multi-dimensional correlation anomaly is detected, a graded early warning signal is generated and pushed to the mobile terminal with corresponding permissions. The closed-loop management module in this invention is mainly used to automatically generate maintenance work orders and intelligently schedule maintenance resources when remote handling by the mobile terminal is ineffective or the abnormal level reaches a preset threshold, track the execution progress of the work orders, and complete the maintenance effect evaluation.

[0054] The various modules in the aforementioned campus hardware and software integrated management system that supports remote operation via mobile devices can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the computer device's memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0055] In another embodiment, the present invention provides a computer device, which may be a cloud server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores campus hardware and software operation data, business data, maintenance data, and digital twin model data. The network interface communicates with external edge gateways and mobile terminals via a network connection. When the computer program is executed by the processor, it implements an integrated campus hardware and software management method that supports remote operation via mobile devices.

[0056] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0057] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above embodiment of the campus hardware and software integrated management method supporting remote operation of mobile terminals.

[0058] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps described in the embodiment of the campus hardware and software integrated management method supporting remote operation of mobile devices.

[0059] In one embodiment, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps described in the embodiment of the campus hardware and software integrated management method supporting remote mobile operation.

[0060] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0061] The technical solutions provided by the embodiments disclosed in this invention have the following beneficial effects: The present invention provides a campus hardware and software integrated management method and system supporting remote operation via mobile terminals. First, it collects status parameters and campus business data from heterogeneous hardware and software devices across three major domains: teaching, security, and logistics. After aligning the timestamps of the multi-source data using a mixed clock synchronization, it pushes the data to a campus digital twin virtual model that integrates geographic information. This achieves virtual-real mapping and 3D visualization of all campus hardware and software resources. This step solves the problem of inconsistent data across heterogeneous devices in multiple domains, establishing a fully covered and spatiotemporally consistent digital twin campus foundation, providing a reliable data foundation and visualization carrier for subsequent integrated management. Second, based on historical healthy operation data and campus business rules, a sliding time window combined with a business weight correction mechanism is used to determine the adaptive warning threshold and cross-device linkage strategy for each device based on operating time and business scenarios. This step breaks through the limitations of traditional fixed threshold warnings, dynamically adjusting warning standards according to device aging and campus business scenarios, while simultaneously achieving intelligent cross-device linkage, significantly improving the accuracy of warnings and the system's intelligence level. Subsequently, the mobile device accesses the system via an end-to-end encrypted link, obtaining corresponding operation interfaces and control permissions based on user roles and dynamic hierarchical permissions for the current business scenario. This step enables deep remote management and control of the mobile device, while ensuring data and operational security through encrypted links and dynamic permission management, meeting the differentiated needs of users with different roles. Next, the system monitors the correlation changes between device status and business data in real time. When a single parameter exceeds the limit or a multi-dimensional correlation anomaly is detected, a hierarchical warning is generated and pushed to the mobile device with the corresponding permissions, supporting remote execution of hierarchical handling operations by the mobile device. This step can promptly detect various anomalies, especially multi-dimensional correlation anomalies that traditional methods cannot identify, and supports remote handling by maintenance personnel anytime, anywhere via the mobile device, significantly shortening fault response time. Finally, when remote handling is ineffective, a maintenance work order is automatically generated and resources are intelligently scheduled, forming a closed-loop management process from warning, handling to evaluation. This step realizes the automation and intelligence of campus maintenance, and continuously optimizes the system's warning and linkage capabilities through the accumulation and feedback of maintenance data. In summary, the solution of this invention can break down the data silos of campus hardware and software, realize in-depth remote control and intelligent operation and maintenance in all scenarios via mobile terminals, and significantly improve campus management efficiency and emergency response capabilities.

[0062] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A campus hardware and software integrated management method supporting remote operation via mobile terminal, characterized in that, Includes the following steps: Collect status parameters and campus business data of heterogeneous hardware and software devices in the campus teaching domain, security domain, and logistics domain; All collected multi-source data are time-stamped and synchronized using a hybrid clock. Then, all the time-stamped data is pushed to the campus digital twin virtual model that integrates the geographic information system to achieve full-domain mapping and three-dimensional visualization of all campus hardware and software resources in the virtual space. Based on historical health operation data stored in a time-series database associated with a digital twin virtual model, and combined with a campus business rule base, a sliding time window combined with a business weight correction mechanism is used to determine the early warning threshold and cross-device linkage strategy for each device that adapts to the running time and business scenario. Mobile devices access the system through an end-to-end encrypted link, and obtain corresponding operation permissions and customized visual interfaces based on dynamic hierarchical permissions according to user roles and current business scenarios. Real-time monitoring of the status parameters and business data of various devices on campus; when a single parameter exceeds the limit continuously or multiple dimensions are abnormal, a graded warning signal is generated and pushed to the mobile terminal with corresponding permissions, supporting remote execution of graded handling operations on the mobile terminal. When remote handling via mobile terminal is ineffective or the anomaly level reaches a preset threshold, an operation and maintenance work order is automatically generated and operation and maintenance resources are intelligently scheduled. The work order execution progress is tracked and the operation and maintenance effect is evaluated, forming a closed-loop management of the entire process.

2. The campus hardware and software integrated management method supporting remote operation via mobile terminal as described in claim 1, characterized in that, The timestamp alignment for hybrid clock synchronization of all collected multi-source data specifically includes: Obtain structured data packets with local timestamps corresponding to all status parameters and campus business data, where the status parameters come from heterogeneous devices that support different communication protocols; The core security and teaching equipment are synchronized using the IEEE 1588 precision clock protocol, while the general logistics equipment is synchronized using the network time protocol. The edge gateway completes the unified reference conversion of the hybrid clock sources. Set a fixed time alignment period, collect data from all devices within the same period, and fill in missing data using interpolation based on business rules to obtain a multi-source dataset with timestamp alignment.

3. The integrated campus hardware and software management method supporting remote operation via mobile terminal as described in claim 1, characterized in that, All timestamp-aligned data is pushed to the campus digital twin virtual model integrated with the geographic information system to achieve full-domain mapping and 3D visualization of all campus hardware and software resources in the virtual space. Specifically, this includes: The timestamp-aligned multi-source dataset is sent to the data access layer of the campus digital twin virtual model via an industrial communication protocol; Based on the device geocode and device identifier in the data, the corresponding virtual device entity is located in the 3D virtual campus scene integrated with the geographic information system, and its status attributes and spatial location information are updated. The visualization engine that drives the digital twin virtual model renders and displays the operational status of all campus hardware and software resources based on real-time changes in status attributes, and supports layered visualization and roaming viewing by region and device type.

4. The campus hardware and software integrated management method supporting remote operation via mobile terminal as described in claim 1, characterized in that, Based on historical health operation data stored in a time-series database associated with a digital twin virtual model, and combined with a campus business rule base, a sliding time window combined with a business weight correction mechanism is used to determine the adaptive warning threshold and cross-device linkage strategy for each device based on operating time and business scenario. Specifically, this includes: Extract historical health operation data of devices within a specified time range from the time series database, and extract class scheduling information, work and rest time, holiday arrangements, and personnel density rules from the campus business rule base; For each device, the sliding time window data sequence is extracted according to the set window length and sliding step size, and its exponentially weighted moving mean and standard deviation are calculated; The calculation results are weighted and adjusted according to the current business scenario to obtain an early warning threshold that adapts to the running time and business scenario. Based on the business relationships between devices, corresponding cross-device linkage strategies are generated and stored in the linkage strategy library.

5. The campus hardware and software integrated management method supporting remote operation via mobile terminal as described in claim 1, characterized in that, Mobile devices access the system via an end-to-end encrypted link, and obtain corresponding operation permissions and customized visual interfaces based on dynamic hierarchical permissions according to user roles and current business scenarios. Specifically, this includes: The mobile device sends an access request to the system. After identity authentication, an end-to-end encrypted link based on the transport layer security protocol is established, and a one-time token mechanism is used to ensure the security of each session. The system dynamically assigns corresponding operation permissions based on user roles and current business scenarios. Operation permissions are divided into viewing permissions, basic control permissions, advanced operation and maintenance permissions, and system management permissions. Based on the assigned permissions, push customized visual interfaces and operation controls to mobile devices, and hide functional modules and data information that are not authorized.

6. The campus hardware and software integrated management method supporting remote operation via mobile terminal as described in claim 1, characterized in that, Real-time monitoring of the correlation between the status parameters of various campus devices and business data; when a single parameter continuously exceeds the limit or multiple dimensions of correlation anomalies are detected, a tiered early warning signal is generated and pushed to the mobile device with the corresponding permissions. Specifically, this includes: Real-time acquisition of timestamp-aligned device status parameters and business data; query and obtain adaptive warning thresholds for the corresponding device in the current business scenario. The system detects whether a single parameter exceeds the warning threshold within a continuous sampling period, or whether there are multi-dimensional correlation anomalies, including mismatch between device status and business rules, and abnormal coordination of multiple device statuses. When an anomaly is detected, a level 1, level 2, or level 3 warning signal is generated based on the anomaly type and the scope of its impact. The warning signal is then pushed to mobile devices with the corresponding permissions and visualized in the digital twin virtual model.

7. The integrated campus hardware and software management method supporting remote operation via mobile terminal as described in claim 1, characterized in that, Support for remote execution of tiered handling operations via mobile devices specifically includes: After receiving the warning signal, the mobile device displays the corresponding remote handling options according to the user's permissions. The handling options include one-click start / stop, parameter adjustment, remote restart, firmware upgrade, and remote diagnostics. The mobile device sends a processing command to the system. The system verifies the legality and permissions of the command. Once the verification is successful, the command is sent to the edge controller of the corresponding device. The edge controller executes instructions and feeds back the results to the system. The system then updates the status attributes of the device in the digital twin virtual model and pushes the results to the mobile device, while also recording a complete operation log.

8. The campus hardware and software integrated management method supporting remote operation via mobile terminal as described in claim 1, characterized in that, When remote handling via mobile terminal is ineffective or the anomaly level reaches a preset threshold, an operation and maintenance work order is automatically generated, and operation and maintenance resources are intelligently scheduled. The work order execution progress is tracked, and an operation and maintenance effectiveness evaluation is completed. Specifically, this includes: When remote handling fails or the anomaly level reaches level two or above, the system automatically extracts information about the abnormal device, its location, the type of the anomaly, and historical maintenance records to generate a standardized maintenance work order. Based on the real-time location, skill tags, and current work order load of maintenance personnel, an intelligent matching algorithm is used to dispatch work orders to the most suitable maintenance personnel and push them to the maintenance personnel's mobile devices. The system tracks the progress of work orders in real time. After completing the handling, maintenance personnel submit the handling results and the cause of the fault on the mobile device. The system automatically verifies the equipment operating status and completes the maintenance effect evaluation. Operational data is fed back to the time-series database and business rule base to optimize subsequent early warning thresholds and linkage strategies.

9. The campus hardware and software integrated management method supporting remote operation via mobile terminal as described in claim 1, characterized in that, The status parameters include equipment voltage, current, operating temperature, operating time, switch status, and signal strength. The campus business data includes course scheduling information, timetable, personnel attendance data, repair records, and holiday arrangements.

10. A campus hardware and software integrated management system supporting remote operation via mobile terminal, characterized in that, include: The data acquisition module is used to collect status parameters and campus business data from heterogeneous hardware and software devices in the campus teaching domain, security domain, and logistics domain. The processing module is used to perform timestamp alignment of all collected multi-source data with mixed clock synchronization, and then push all the timestamp aligned data to the campus digital twin virtual model of the integrated geographic information system to realize the full-domain mapping and three-dimensional visualization of all campus hardware and software resources in the virtual space. The processing module is also used to determine the early warning threshold and cross-device linkage strategy for each device based on the historical health operation data stored in the time-series database associated with the digital twin virtual model, combined with the campus business rule base, using a sliding time window combined with a business weight correction mechanism. The mobile interaction module is used to access the system through an end-to-end encrypted link, obtain corresponding operation permissions and customized visual interfaces based on the dynamic hierarchical permissions of user roles and current business scenarios, and support remote execution of hierarchical processing operations. The early warning module is used to monitor the correlation changes between the status parameters of various devices on campus and business data in real time. When a single parameter exceeds the limit continuously or multiple dimensions are abnormal, a graded early warning signal is generated and pushed to the mobile terminal with the corresponding permissions. The closed-loop management module is used to automatically generate maintenance work orders and intelligently schedule maintenance resources when remote handling by the mobile terminal is ineffective or the abnormal level reaches a preset threshold, track the execution progress of the work orders, and complete the maintenance effect evaluation.

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