Modern mobile informatization equipment management platform construction method and system

Abstract

The invention discloses a modern mobile informatization equipment management platform construction method and system, and relates to the technical field of informatization equipment management.The method comprises the steps that a management platform based on the full life cycle of equipment is used, and equipment life cycle nodes are associated; optimizing an equipment inspection flow, formulating an inspection standard and a dynamic task, and uploading data in real time in combination with a mobile terminal; based on multi-source data fusion analysis, equipment operation parameters are optimized, and decision support and abnormity early warning are provided. According to the method disclosed by the invention, the systematicness and comprehensiveness of equipment management are improved by establishing the full-life-cycle management platform and associating the equipment nodes; an embedded monitoring module is combined with a prediction algorithm, so that the equipment failure rate and the non-planned downtime are reduced, and the high efficiency and the reliability of equipment management are ensured; the abnormal early warning and decision support functions provide comprehensive optimization strategies and emergency response means for managers, so that the management efficiency and the equipment operation safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information equipment management, and in particular to a method and system for building a modern mobile information equipment management platform. Background Art

[0002] With the continuous improvement of modern industry and information level, equipment management technology is gradually developing towards automation, intelligence and informationization. Traditional equipment management methods usually rely on manual records and scattered management tools, which are difficult to adapt to complex industrial environments. In recent years, equipment management platforms based on the Internet of Things (IoT) have gradually emerged. Through the combination of sensors, communication modules and data platforms, real-time monitoring and data management of equipment status have been realized. At the same time, the introduction of big data analysis and artificial intelligence technology has made it possible to predict and optimize the operating status of equipment, providing new solutions for the management of the entire life cycle of equipment. However, current equipment management technology still has many limitations in practical applications, especially in terms of multi-device interconnection, inspection process optimization and abnormal warning capabilities, which cannot fully meet the needs of modern enterprises for efficient, safe and intelligent management.

[0003] Although the existing equipment management technology has developed to a certain extent, it still has obvious shortcomings in equipment life cycle management, inspection process optimization and data analysis capabilities. First, most equipment management platforms lack a unified full life cycle data management mechanism, making it difficult to achieve comprehensive collection and association of equipment data from planning, installation to scrapping, resulting in the inability to form a closed loop of equipment management. Secondly, the current inspection process mostly adopts fixed cycles and static task allocation, lacks a dynamic adjustment mechanism, and cannot generate personalized inspection tasks based on changes in equipment operating status. In addition, the existing mobile terminals are relatively single in function and cannot efficiently support multiple types of data collection (such as pictures, videos, sensor data) and real-time uploading. Finally, the multi-source data fusion analysis capabilities are limited, and the existing platforms are usually unable to conduct comprehensive evaluations of data from multiple sources, resulting in low accuracy of abnormal warnings and effectiveness of decision support. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing information equipment management methods lack a full life cycle data management mechanism, the inspection process cannot be dynamically adjusted, the mobile terminal has a single function, and how to achieve equipment operation status optimization and abnormal warning based on multi-source data fusion.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for building a modern mobile information equipment management platform, including using a management platform based on the entire life cycle of the equipment to associate equipment life cycle nodes; optimizing the equipment inspection process, formulating inspection standards and dynamic tasks, and combining real-time data upload from mobile terminals; optimizing equipment operating parameters based on multi-source data fusion analysis, and providing decision support and abnormal warnings.

[0007] As a preferred solution of the method for building a modern mobile information equipment management platform described in the present invention, the management platform based on the entire life cycle of the equipment includes automatically detecting the equipment life cycle stage, collecting equipment operation data through an embedded monitoring module, establishing equipment files, dynamically updating data and recording parameters according to the planning, installation, operation, and maintenance stages. During the equipment installation stage, the platform collects the equipment location and performance parameters in real time, and performs position calibration through GPS and sensors. During the operation stage, the platform records real-time operation data of temperature, pressure, and vibration, and generates a maintenance plan using historical data analysis. When the equipment is scrapped, sensitive information is deleted through the data cleaning module and historical records are archived.

[0008] As a preferred solution of the method for building a modern mobile information equipment management platform described in the present invention, the associated equipment life cycle nodes include predicting the life cycle stages of different equipment through a data analysis engine, identifying potential faults using a machine learning algorithm, analyzing historical data during equipment debugging and operation, and generating optimization plans. When the system detects an abnormality reaching a critical threshold, it automatically triggers an early warning through the platform and pushes repair suggestions to the operating terminal. The monitoring data of key nodes of the equipment are uploaded through different interface protocols for unified access of devices of different brands.

[0009] As a preferred solution of the method for building a modern mobile information equipment management platform described in the present invention, the optimized equipment inspection process includes formulating inspection standards, clarifying the type, frequency, inspection items and detection methods of the inspection equipment, using QR code labels to divide the inspection area, and associating the inspection routes. The system generates a task order based on the inspection task and distributes it to the mobile terminal in real time. The operating personnel obtain the equipment history records and inspection standards by scanning the QR code on site. After the inspection is completed, the system automatically uploads the task results, including inspection records, photos and abnormality reports. The system automatically adjusts the inspection frequency based on historical data.

[0010] As a preferred solution for the method of building a modern mobile information equipment management platform described in the present invention, the real-time data upload combined with the mobile terminal includes dynamically monitoring the work routes of the inspection personnel through satellite positioning technology to conduct full inspection coverage. The inspection system dynamically adjusts the inspection tasks according to the equipment operating status. When an abnormal equipment operating temperature is detected, the system generates an emergency inspection task. The system provides a trend analysis function for the inspection data, generates equipment historical trend charts and automatically marks abnormal data. According to the completion of the inspection tasks, the system generates an inspection performance report, and evaluates the operating personnel according to the inspection completion rate and problem discovery rate.

[0011] As a preferred solution of the method for building a modern mobile information equipment management platform described in the present invention, the optimization of equipment operating parameters includes real-time collection of equipment operating data through sensors, including temperature, pressure, and flow, and comprehensive analysis in combination with inspection data and test data, using artificial intelligence algorithms to identify anomalies in multi-source data, and providing tuning suggestions for vibration frequency and pressure fluctuations. The system dynamically adjusts equipment operating parameters based on data analysis results, automatically reduces operating load when it detects that the equipment operating temperature exceeds the standard, and realizes cross-regional comparison of multi-device operating data through a cloud computing platform to optimize overall equipment operating efficiency.

[0012] As a preferred solution of the method for building a modern mobile information equipment management platform described in the present invention, the provision of decision support and abnormal warning includes that in abnormal situations, the system gives priority to photovoltaic power supply to ensure continuous operation of the equipment. When it is detected that the equipment parameters are continuously abnormal and exceed the fault threshold, the system automatically switches to the backup power supply and notifies the operator. The data analysis module provides an operation optimization strategy based on weather, temperature and equipment load conditions. All optimization results are presented in real time through the mobile terminal for reference and manual adjustment by managers.

[0013] Another object of the present invention is to provide a modern mobile information equipment management platform construction system, which can solve the problem of low reliability of current information equipment management technology by optimizing equipment inspection processes, formulating inspection standards and dynamic tasks, and combining real-time data upload from mobile terminals.

[0014] As a preferred solution for the modern mobile information equipment management platform construction system described in the present invention, it includes: a node association module, a process optimization module, and an analysis and early warning module; the node association module is used to use a management platform based on the entire life cycle of the equipment to associate equipment life cycle nodes; the process optimization module is used to optimize the equipment inspection process, formulate inspection standards and dynamic tasks, and combine real-time data upload from mobile terminals; the analysis and early warning module is used to optimize equipment operating parameters based on multi-source data fusion analysis, and provide decision support and abnormal warnings.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for building a modern mobile information equipment management platform.

[0016] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for building a modern mobile information equipment management platform.

[0017] Beneficial effects of the present invention: The modern mobile information equipment management platform construction method provided by the present invention improves the systematicness and comprehensiveness of equipment management by establishing a full life cycle management platform and associating equipment nodes, avoiding the problem of information loss caused by data isolation in traditional methods; the combination of embedded monitoring modules and prediction algorithms realizes early identification of potential problems and generation of optimization suggestions, thereby reducing equipment failure rate and unplanned downtime, and ensuring the efficiency and reliability of equipment management; through multi-source data fusion analysis, the equipment operating parameters are optimized, the intelligence and stability of equipment operation are improved, and the energy consumption or equipment damage risk caused by parameter abnormalities is reduced; the introduction of cloud computing platforms enhances the macro-optimization capabilities of equipment management and ensures operating efficiency in complex environments; abnormal warning and decision support functions provide managers with comprehensive optimization strategies and emergency response measures, thereby improving management efficiency and equipment operation safety. The present invention achieves better results in terms of flexibility, efficiency and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 An overall flow chart of a method for constructing a modern mobile information equipment management platform provided for the first embodiment of the present invention.

[0020] Figure 2 An overall flow chart of a modern mobile information equipment management platform construction system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0022] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for building a modern mobile information equipment management platform, including:

[0023] S1: Use a management platform based on the entire life cycle of the device to associate device life cycle nodes.

[0024] Furthermore, the management platform based on the entire life cycle of the equipment includes automatic detection of the equipment life cycle stages, collecting equipment operation data through embedded monitoring modules, establishing equipment files, dynamically updating data and recording parameters according to the planning, installation, operation, and maintenance stages. During the equipment installation stage, the platform collects equipment location and performance parameters in real time, and performs position calibration through GPS and sensors. During the operation stage, the platform records real-time operating data of temperature, pressure, and vibration, and uses historical data analysis to generate maintenance plans. When the equipment is scrapped, sensitive information is deleted through the data cleaning module and historical records are archived.

[0025] It should be noted that the associated equipment life cycle nodes include predicting the life cycle stages of different equipment through a data analysis engine, identifying potential faults using machine learning algorithms, analyzing historical data during equipment debugging and operation, and generating optimization plans. When the system detects an abnormality that reaches a critical threshold, it automatically triggers an early warning through the platform and pushes repair suggestions to the operating terminal. The monitoring data of key equipment nodes is uploaded through different interface protocols for unified access to equipment of different brands.

[0026] It should also be noted that the calculation of key thresholds can include the following preferred solutions: first, use the data analysis engine to collect equipment operating parameters (such as temperature, pressure, vibration frequency, etc.) in real time, and use machine learning algorithms combined with historical data to calculate dynamic thresholds, such as adjusting baseline thresholds according to equipment load and environmental conditions; when real-time data continues to exceed the threshold (such as abnormalities in three consecutive sampling cycles), the system triggers a graded warning, including a first-level warning to prompt the operator to pay attention, a second-level warning to recommend inspection, and a third-level warning to immediately shut down for protection and notify the maintenance team; the system pushes warning information in real time through mobile terminals, provides abnormal parameters, historical trend charts and repair suggestions, and generates a task order to clarify maintenance steps, required tools and reference documents; at the same time, according to the warning level, the system can automatically perform corresponding operations, such as reducing equipment load, switching to backup equipment or shutting down for protection; unified access and data analysis of equipment of different brands are achieved through standardized interface protocols, ensuring the accuracy of abnormality detection and the efficiency of management, and improving the safety and intelligence level of equipment operation.

[0027] It should also be noted that the equipment management platform collects and dynamically updates equipment operation data through embedded monitoring modules. For example, it calibrates the equipment location through GPS and sensors during the installation phase, and records real-time data such as temperature, pressure, and vibration during the operation phase. This full life cycle data collection and management method enables equipment files to dynamically reflect their status changes and provides detailed data support for subsequent stages (such as maintenance and optimization). It associates equipment life cycle nodes, predicts possible equipment failures through data analysis engines and machine learning algorithms, and provides optimization solutions. The system enables access to equipment of different brands through a unified interface at key nodes.

[0028] S2: Optimize the equipment inspection process, formulate inspection standards and dynamic tasks, and combine real-time data upload from mobile terminals.

[0029] Furthermore, optimizing the equipment inspection process includes formulating inspection standards, clarifying the type, frequency, inspection items and detection methods of inspection equipment, using QR code labels to divide the inspection area, and associating inspection routes. The system generates task orders based on inspection tasks and distributes them to mobile terminals in real time. Operators scan QR codes on site to obtain equipment history records and inspection standards. After the inspection is completed, the system automatically uploads the task results, including inspection records, photos and abnormality reports. The system automatically adjusts the inspection frequency based on historical data.

[0030] It should be noted that, in combination with real-time data upload from mobile terminals, including dynamic monitoring of the inspection personnel's work routes through satellite positioning technology, full inspection coverage is achieved. The inspection system dynamically adjusts the inspection tasks according to the equipment operating status. When abnormal equipment operating temperature is detected, the system generates an emergency inspection task. The system provides a trend analysis function for inspection data, generates equipment historical trend graphs and automatically marks abnormal data. According to the completion of the inspection tasks, the system generates an inspection performance report and evaluates the operating personnel according to the inspection completion rate and problem discovery rate.

[0031] It should also be noted that by clarifying the type, frequency and inspection method of inspection equipment, dividing the inspection area in combination with QR code identification, and generating task orders in real time based on inspection tasks, the inspection process has been standardized and digitized; after the inspection is completed, the system can dynamically adjust the inspection frequency based on historical data; for example, when an abnormal equipment operating status is found, the system generates an emergency inspection task and dynamically monitors the inspection personnel's route through satellite positioning to ensure full coverage of the task; in addition, through real-time data uploading, the system can realize trend analysis of inspection data, including generating historical trend graphs and marking abnormal data.

[0032] S3: Based on multi-source data fusion analysis, optimize equipment operating parameters and provide decision support and abnormal warning.

[0033] Furthermore, optimizing equipment operating parameters includes collecting equipment operating data in real time through sensors, including temperature, pressure, and flow, and conducting comprehensive analysis in combination with inspection data and test data. Artificial intelligence algorithms are used to identify anomalies in multi-source data, and to provide tuning suggestions for vibration frequency and pressure fluctuations. The system dynamically adjusts equipment operating parameters based on data analysis results, automatically reduces operating load when it detects that the equipment operating temperature exceeds the standard, and realizes cross-regional comparison of multi-device operating data through the cloud computing platform to optimize overall equipment operating efficiency.

[0034] It should be noted that decision support and abnormal warning are provided, including that in abnormal situations, the system gives priority to photovoltaic power supply to ensure continuous operation of the equipment. When it is detected that the equipment parameters are continuously abnormal and exceed the fault threshold, the system automatically switches to the backup power supply and notifies the operator. The data analysis module provides operation optimization strategies based on weather, temperature and equipment load conditions. All optimization results are presented in real time through mobile terminals for managers to refer to and manually adjust.

[0035] It should also be noted that the specific operations of using artificial intelligence algorithms to identify anomalies in multi-source data may include: collecting equipment operation data (such as vibration frequency, pressure, temperature, etc.) in real time through sensors and combining it with inspection and test data, using feature extraction algorithms (such as fast Fourier transform FFT) to perform frequency domain analysis on vibration signals to identify abnormal patterns within a specific frequency range, and using time series analysis models of pressure data (such as ARIMA) to detect abnormal trends in pressure fluctuations; secondly, the system trains anomaly recognition classifiers through machine learning models (such as random forests, support vector machines SVM) to divide abnormal signals into possible fault types (such as mechanical imbalance, bearing damage, etc.); based on anomaly recognition, the system calls predefined adjustment methods according to the classification results. The optimization rule base, for example, generates specific tuning suggestions for reducing the equipment speed or adjusting the load distribution when abnormal vibration frequency is detected; when the pressure fluctuation is abnormal, it is recommended to check the blockage in the fluid circuit or adjust the valve opening; further, combined with the cross-regional comparison function of the cloud computing platform, the system conducts a comprehensive analysis of the operating data of the same type of equipment, compares the operating modes of different equipment through clustering algorithms (such as K-means), and optimizes the applicability and versatility of the tuning parameters; finally, all tuning suggestions are pushed to the operator in real time through the mobile terminal, and recorded in the equipment operation log for subsequent analysis and improvement; not only does it achieve accurate identification of anomalies, but it also improves the pertinence and execution efficiency of tuning suggestions, and enhances the intelligence and reliability of equipment operation management.

[0036] It should also be noted that by collecting sensor data (such as temperature, pressure, and vibration) in real time and combining it with inspection and test data, the system uses artificial intelligence algorithms to fuse and analyze the data and identify anomalies. For example, when the vibration frequency is abnormal, the system generates targeted tuning suggestions and dynamically adjusts the equipment operating parameters, automatically reducing the operating load when it detects that the temperature exceeds the standard. In addition, cross-regional multi-device data comparison is achieved through the cloud computing platform, providing a global perspective for equipment operation optimization. In abnormal situations, the system will switch to photovoltaic power supply first to ensure the continuous operation of the equipment. When the equipment parameters continue to be abnormal and exceed the threshold, the system will switch to the backup power supply and present the optimization results to the mobile terminal in real time.

[0037] Embodiment 2 is an embodiment of the present invention, which provides a method for constructing a modern mobile information equipment management platform. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0038] First, the equipment operating parameters were collected, analyzed and optimized based on the equipment life cycle management platform to verify the effectiveness of the platform in actual applications. The test subjects were six typical industrial equipment, which were operated under different load and environmental conditions. The platform embedded sensors collected key parameters (including temperature, pressure, vibration frequency and flow) in real time, and combined with inspection and historical test data, abnormal detection and optimization suggestions were generated for the equipment operation status. The equipment collected basic parameters under normal operating conditions, and calculated key thresholds through machine learning algorithms combined with historical data. For example, based on historical data analysis, the critical temperature threshold of the equipment was set to 100°C. When the real-time temperature of a certain equipment exceeded the threshold and lasted for three sampling cycles, the system triggered a secondary warning and recommended load reduction. In addition, for abnormal detection of vibration frequency, the platform analyzed the signal spectrum through fast Fourier transform and generated optimization suggestions for equipment with excessive frequency range, such as reducing the speed or adjusting the load distribution. The equipment data was uploaded in real time through the cloud platform, and the tuning parameters were optimized in combination with cross-device data comparison. During the experiment, detailed data of abnormal equipment was pushed through mobile terminals, including abnormal type, comparison of current parameters with historical thresholds, and repair suggestions, to provide maintenance personnel with accurate operation guidance.

[0039] By combining historical data with real-time environmental conditions, the device management platform can generate unique dynamic thresholds for each device. This capability is superior to the existing fixed threshold judgment mode. The system integrates data from multiple sources and enables cross-device comparison through the cloud platform, greatly improving the adaptability of optimization strategies. The system pushes tuning suggestions in real time and automatically generates task orders, reducing the errors and lags of manual judgment.

[0040] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a modern mobile information equipment management platform construction system, including a node association module, a process optimization module, and an analysis and early warning module.

[0041] The node association module is used to associate equipment life cycle nodes using a management platform based on the entire life cycle of the equipment; the process optimization module is used to optimize the equipment inspection process, formulate inspection standards and dynamic tasks, and combine real-time data upload from mobile terminals; the analysis and early warning module is used to optimize equipment operating parameters based on multi-source data fusion analysis, and provide decision support and abnormal warnings.

[0042] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0043] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0044] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0045] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0046] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for building a modern mobile information equipment management platform, characterized in that: include: Use a management platform based on the entire life cycle of the equipment to associate equipment life cycle nodes; Optimize equipment inspection process, formulate inspection standards and dynamic tasks, and combine real-time data upload from mobile terminals; Based on multi-source data fusion analysis, equipment operating parameters are optimized, and decision support and abnormal warnings are provided.

2. The method for constructing a modern mobile information equipment management platform according to claim 1, characterized in that: The management platform based on the entire life cycle of the equipment includes automatically detecting the equipment life cycle stage, collecting equipment operation data through the embedded monitoring module, establishing equipment files, dynamically updating data and recording parameters according to the planning, installation, operation and maintenance stages. During the equipment installation stage, the platform collects the equipment location and performance parameters in real time, and calibrates the position through GPS and sensors. During the operation stage, the platform records the real-time operation data of temperature, pressure, and vibration, and generates a maintenance plan using historical data analysis. When the equipment is scrapped, the data cleaning module deletes sensitive information and archives historical records.

3. The method for constructing a modern mobile information equipment management platform as claimed in claim 2, characterized in that: The associated equipment life cycle nodes include predicting the life cycle stages of different equipment through a data analysis engine, identifying potential faults using machine learning algorithms, analyzing historical data and generating optimization plans during equipment debugging and operation. When the system detects an abnormality at a critical threshold, it automatically triggers an early warning through the platform and pushes repair suggestions to the operating terminal. The monitoring data of key equipment nodes is uploaded through different interface protocols for unified access to equipment of different brands.

4. The method for constructing a modern mobile information equipment management platform as claimed in claim 3, characterized in that: The optimized equipment inspection process includes formulating inspection standards, clarifying the type, frequency, inspection items and detection methods of inspection equipment, using QR code labels to divide the inspection area, and associating inspection routes. The system generates task orders based on inspection tasks and distributes them to mobile terminals in real time. Operators obtain equipment history records and inspection standards on site by scanning QR codes. After the inspection is completed, the system automatically uploads the task results, including inspection records, photos and abnormality reports. The system automatically adjusts the inspection frequency based on historical data.

5. The method for constructing a modern mobile information equipment management platform as claimed in claim 4, characterized in that: The combination of real-time data upload by mobile terminals includes dynamically monitoring the work routes of inspection personnel through satellite positioning technology to conduct full coverage of inspections. The inspection system dynamically adjusts the inspection tasks according to the operating status of the equipment. When abnormal operating temperature of the equipment is detected, the system generates an emergency inspection task. The system provides a trend analysis function for inspection data, generates equipment historical trend charts and automatically marks abnormal data. According to the completion of the inspection tasks, the system generates an inspection performance report and evaluates the operating personnel according to the inspection completion rate and problem discovery rate.

6. The method for constructing a modern mobile information equipment management platform as claimed in claim 5, characterized in that: The optimization of equipment operating parameters includes real-time collection of equipment operating data, including temperature, pressure, and flow, through sensors, and comprehensive analysis combined with inspection data and test data. Artificial intelligence algorithms are used to identify anomalies in multi-source data, and tuning suggestions are provided for vibration frequency and pressure fluctuations. The system dynamically adjusts equipment operating parameters based on data analysis results, and automatically reduces operating load when it detects that the equipment operating temperature exceeds the standard. Cross-regional comparison of multi-device operating data is achieved through a cloud computing platform to optimize overall equipment operating efficiency.

7. The method for constructing a modern mobile information equipment management platform according to claim 6, characterized in that: The decision support and abnormal warning provided include that in abnormal situations, the system gives priority to photovoltaic power supply to ensure continuous operation of the equipment. When it is detected that the equipment parameters are continuously abnormal and exceed the fault threshold, the system automatically switches to the backup power supply and notifies the operator. The data analysis module provides operation optimization strategies based on weather, temperature and equipment load conditions. All optimization results are presented in real time through mobile terminals for managers to refer to and manually adjust.

8. A system using the method for building a modern mobile information equipment management platform as claimed in any one of claims 1 to 7, characterized in that: Including node association module, process optimization module, analysis and early warning module; The node association module is used to associate device life cycle nodes using a management platform based on the device's full life cycle; The process optimization module is used to optimize the equipment inspection process, formulate inspection standards and dynamic tasks, and combine real-time data upload from mobile terminals; The analysis and warning module is used to optimize equipment operating parameters based on multi-source data fusion analysis and provide decision support and abnormal warning.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for building a modern mobile information equipment management platform described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for building a modern mobile information equipment management platform described in any one of claims 1 to 7 are implemented.

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