An IBMS-based device anomaly early warning and multi-dimensional management system

Abstract

This invention discloses an IBMS-based equipment anomaly early warning and multi-dimensional management system, belonging to the field of equipment anomaly early warning management technology. This IBMS-based equipment anomaly early warning and multi-dimensional management system includes: an equipment operation data acquisition module, a primary heat dissipation early warning determination module, an early warning management energy consumption assessment module, a secondary heat dissipation early warning management module, and an equipment information visualization management module. This invention assesses the impact of operating load fluctuations on the heat dissipation performance of power equipment using acquired early warning monitoring data to determine whether a primary heat dissipation anomaly early warning has been triggered. It then determines whether a secondary heat dissipation anomaly early warning has been triggered, and finally establishes a file information for the power equipment. This achieves more accurate heat dissipation anomaly early warning management for power equipment in smart parks, solving the problem in existing technologies where energy consumption fluctuations during the heat dissipation process are not fully considered in the heat dissipation anomaly early warning management process for power equipment in smart parks.

Description

Technical Field

[0001] This invention relates to the field of equipment anomaly early warning management technology, and in particular to an equipment anomaly early warning and multi-dimensional management system based on IBMS. Background Technology

[0002] IBMS (Integrated Building Management System) enables comprehensive monitoring and precise decision-making by integrating and managing various equipment and facilities within a smart park. In a smart park, the efficient operation of all facilities and equipment is fundamental to its normal functioning. Traditional equipment management often relies on manual inspections or periodic maintenance, which can easily overlook potential faults, leading to unexpected equipment downtime and even impacting the overall operation of the park. Furthermore, the operation of a smart park involves more than just equipment management; it also encompasses multi-dimensional collaborative management of energy, security, and environmental monitoring. These multi-dimensional management needs require the park to have timely and accurate access to various information and to respond quickly. IBMS's multi-dimensional data collection, analysis, and decision support enable park managers to manage and optimize various aspects through a unified platform, improving operational efficiency and service quality, and facilitating innovation and digital transformation in smart parks.

[0003] In smart parks, the realization of equipment anomaly early warning and multi-dimensional management relies on modern information technology, particularly the Internet of Things (IoT), big data, artificial intelligence (AI), cloud computing, and automated control systems. First, comprehensive monitoring of various devices within the park is required using IoT devices and sensors. The collected data is stored and processed via a cloud computing platform or local servers. Data processing can be performed using big data technology to provide a foundation for equipment status analysis. The collected data is then aggregated on a central platform and comprehensively analyzed through real-time data stream processing and historical data review. Machine learning and AI algorithms are used to analyze the equipment's operational data. When the early warning model identifies an equipment anomaly, the system automatically triggers an alarm and notifies relevant maintenance personnel of the anomaly information.

[0004] For example, patent application CN118941124A discloses an energy-saving management method and system based on power big data, which includes: collecting power input data, power equipment operation data, and operating environment data through a power data acquisition module, transmitting the data to a power management platform, importing the acquired power input data and power equipment operation data into a power equipment operation anomaly coefficient evaluation strategy for power equipment operation anomaly evaluation, importing the acquired operating environment data into a power environment anomaly evaluation strategy for power environment anomaly evaluation, substituting the evaluated power equipment operation anomaly coefficient and power environment operation anomaly coefficient into an energy-saving output value calculation formula to output an energy-saving output value, obtaining the obtained energy-saving output value, and inputting energy into the power equipment based on the obtained energy-saving output value.

[0005] For example, the invention patent announcement CN115423127B discloses an artificial intelligence-based method and system for on-site operation and maintenance of power equipment, which includes: dividing the power equipment site area into grids to determine the set of grid intersections; setting monitoring points based on the set of grid intersections and obtaining real-time monitoring data output by the data monitoring module; inputting the real-time monitoring data into an anomaly warning model and outputting equipment warning information; retrieving the operation and maintenance knowledge base based on the remote assistance request information of the equipment on-site operation and maintenance terminal and embedding the operation and maintenance knowledge base into the anomaly warning model; and generating an operation and maintenance plan based on the equipment warning information and the operation and maintenance knowledge base.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0007] In the construction and operation of smart parks, heat dissipation management and anomaly early warning for power equipment are crucial aspects. Over long-term use, the heat dissipation system of power equipment may experience performance degradation, leading to reduced heat dissipation efficiency. Without regular maintenance, inspections, and early warning systems, anomalies in the heat dissipation system may go undetected. Furthermore, load fluctuations and environmental changes cause constantly changing heat dissipation requirements. Therefore, the heat dissipation system needs to possess good dynamic adjustment capabilities; however, many current heat dissipation early warning processes lack effective linkage mechanisms with factors affecting heat dissipation.

[0008] It is also necessary to consider that load fluctuations of electrical equipment may cause fluctuations in the heat generated by the equipment, which in turn affects the energy consumption of the cooling system. However, the existing management system fails to adjust the working status of the cooling system in real time according to changes in equipment load, resulting in the energy consumption fluctuations not being effectively controlled. There is a problem that the energy consumption fluctuations during the cooling process are not fully considered in the process of early warning management of abnormal heat dissipation of electrical equipment in smart parks. Summary of the Invention

[0009] This application provides an IBMS-based equipment anomaly early warning and multi-dimensional management system, which solves the problem in the prior art that the energy consumption fluctuation during the heat dissipation anomaly early warning management of power equipment in smart parks is not fully considered, and achieves more accurate heat dissipation anomaly early warning management of power equipment in smart parks.

[0010] This application provides an IBMS-based equipment anomaly early warning and multi-dimensional management system, including: an equipment operation data acquisition module, a heat dissipation first-level early warning judgment module, an early warning management energy consumption assessment module, a heat dissipation second-level early warning management module, and an equipment information visualization management module; the equipment operation data acquisition module is used to collect equipment operation data within a preset time interval and store the equipment operation data in a preset database; the heat dissipation first-level early warning judgment module is used to obtain early warning monitoring data of power equipment in a smart park within a preset area, and evaluate the degree of influence of the heat dissipation performance of power equipment on the fluctuation of operating load based on the obtained early warning monitoring data, and determine whether to trigger a first-level early warning for equipment heat dissipation anomaly; the early warning management energy consumption assessment module is used to prompt a first-level early warning response after sending a first-level abnormal equipment management optimization instruction if a first-level early warning for equipment heat dissipation anomaly is triggered, otherwise it evaluates the energy consumption fluctuation during the heat dissipation process of power equipment; the heat dissipation second-level early warning management module is used to determine whether a second-level early warning for equipment heat dissipation anomaly is triggered based on the obtained heat dissipation energy consumption early warning results, and if triggered, prompt a second-level early warning response after sending a second-level abnormal equipment management optimization instruction, otherwise it sends a heat dissipation energy consumption early warning monitoring instruction; the equipment information visualization management module is used to obtain the periodic inspection early warning results of power equipment and visualize them, and at the same time establish the archive information of power equipment.

[0011] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0012] 1. By acquiring early warning monitoring data, the impact of operating load fluctuations on the heat dissipation performance of power equipment is assessed to determine whether a first-level early warning for abnormal heat dissipation is triggered. Then, it is determined whether a second-level early warning for abnormal heat dissipation is triggered. Finally, the periodic inspection early warning results of the power equipment are obtained and visualized. At the same time, the archive information of the power equipment is established, thereby realizing early warning and response optimization for the heat dissipation performance and heat dissipation energy consumption of power equipment. This enables more accurate early warning management of abnormal heat dissipation of power equipment in smart parks and effectively solves the problem that existing technologies do not fully consider the energy consumption fluctuations during the heat dissipation process in the early warning management of abnormal heat dissipation of power equipment in smart parks.

[0013] 2. By judging whether the re-acquired heat dissipation impact judgment result is qualified, if so, a first-level abnormal equipment management end prompt is sent; otherwise, the equipment heat dissipation adjustment is continuously executed until the preset number is reached, and then a first-level abnormal equipment management warning prompt is sent. This enables a more accurate judgment of the degree of influence of operating load fluctuation on the heat dissipation performance of power equipment, thereby effectively reducing the degree of influence of operating load fluctuation on the heat dissipation performance of power equipment.

[0014] 3. By obtaining the heat dissipation energy consumption warning value within the preset heat dissipation energy consumption monitoring window, further comparison is made to obtain the heat dissipation energy consumption warning result, thereby achieving a more accurate assessment of energy consumption fluctuations during the heat dissipation process of power equipment, and thus improving the reliability of energy consumption management during the heat dissipation process of power equipment. Attached Figure Description

[0015] Figure 1 A schematic diagram of the structure of an IBMS-based device anomaly early warning and multi-dimensional management system is provided in this application embodiment;

[0016] Figure 2 A module logic diagram of an IBMS-based device anomaly early warning and multi-dimensional management system provided in this application embodiment;

[0017] Figure 3 An overall architecture diagram of a device anomaly early warning and multi-dimensional management system based on IBMS provided for embodiments of this application;

[0018] Figure 4 This application provides a flowchart for obtaining the heat dissipation and energy consumption early warning results of a device anomaly early warning and multi-dimensional management system based on IBMS. Detailed Implementation

[0019] This application provides an IBMS-based equipment anomaly early warning and multi-dimensional management system, which solves the problem in existing technologies that do not fully consider energy consumption fluctuations during the heat dissipation anomaly early warning management of power equipment in smart parks. It collects equipment operation data within a preset time interval and stores the data in a preset database. Then, it acquires early warning monitoring data of power equipment in a preset area of ​​the smart park and evaluates the impact of operating load fluctuations on the heat dissipation performance of the power equipment based on the acquired data to determine whether a first-level heat dissipation anomaly early warning is triggered. If a first-level heat dissipation anomaly early warning is triggered, a first-level anomaly equipment management optimization command is sent, prompting a first-level early warning response. Otherwise, the energy consumption fluctuations during the heat dissipation process of the power equipment are evaluated. Then, based on the acquired heat dissipation energy consumption early warning results, it determines whether a second-level heat dissipation anomaly early warning is triggered. If triggered, a second-level anomaly equipment management optimization command is sent, prompting a second-level early warning response. Otherwise, a heat dissipation energy consumption early warning monitoring command is sent. Finally, the periodic inspection early warning results of the power equipment are acquired and visualized, and a file information of the power equipment is established, achieving more accurate heat dissipation anomaly early warning management of power equipment in smart parks.

[0020] The technical solution in this application embodiment addresses the problem that the energy consumption fluctuations during the heat dissipation process were not fully considered in the above-mentioned smart park power equipment heat dissipation anomaly early warning management process. The overall approach is as follows:

[0021] By obtaining heat dissipation and energy consumption early warning results to determine whether a first-level early warning for abnormal heat dissipation of equipment is triggered, and then based on the obtained heat dissipation and energy consumption early warning results to determine whether a second-level early warning for abnormal heat dissipation of equipment is triggered, and finally establishing the archive information of power equipment, a more accurate effect of early warning management of abnormal heat dissipation of power equipment in smart parks is achieved.

[0022] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0023] like Figure 1 The diagram shown is a structural schematic of an IBMS-based device anomaly early warning and multi-dimensional management system provided in this application embodiment. The IBMS-based device anomaly early warning and multi-dimensional management system provided in this application embodiment includes: a device operation data acquisition module, a heat dissipation first-level early warning judgment module, an early warning management energy consumption assessment module, a heat dissipation second-level early warning management module, and a device information visualization management module.

[0024] Specifically, the equipment operation data acquisition module is used to collect equipment operation data within a preset time interval and store the equipment operation data in a preset database. The equipment operation data represents the equipment's operating parameters, such as temperature, voltage, current, load, and the equipment's operating time.

[0025] The heat dissipation level 1 early warning judgment module is used to acquire early warning monitoring data of power equipment in the smart park within a preset area. Based on the acquired early warning monitoring data, the module evaluates the degree of impact of the heat dissipation performance of the power equipment on the fluctuation of operating load to obtain the heat dissipation impact judgment index and determines whether to trigger the level 1 early warning of abnormal heat dissipation of the equipment.

[0026] The early warning management energy consumption assessment module is used to prompt a level one early warning response after sending a level one abnormal equipment management optimization instruction if a level one early warning of equipment heat dissipation is triggered; otherwise, it assesses the energy consumption fluctuations during the heat dissipation process of power equipment.

[0027] The heat dissipation level 2 early warning management module is used to determine whether a level 2 early warning for abnormal heat dissipation of the equipment is triggered based on the obtained heat dissipation energy consumption early warning results. If triggered, it prompts for a level 2 early warning response after sending a level 2 abnormal equipment management optimization instruction; otherwise, it sends a heat dissipation energy consumption early warning monitoring instruction.

[0028] The equipment information visualization management module is used to obtain the periodic inspection and early warning results of power equipment and display them visually, while also establishing archive information for power equipment.

[0029] like Figure 2 The diagram shown is a module logic diagram of an IBMS-based equipment anomaly early warning and multi-dimensional management system provided in this application embodiment. The corresponding logic is as follows: the collected equipment operation data is stored in a preset database; the degree of influence of the heat dissipation performance of the power equipment on the fluctuation of the operating load is evaluated by the acquired early warning monitoring data to obtain a heat dissipation impact judgment index; the heat dissipation impact judgment index is compared with a preset heat dissipation anomaly impact judgment range to obtain a heat dissipation impact judgment result; an abnormal heat dissipation impact judgment indicates that the heat dissipation impact judgment index is within the preset heat dissipation anomaly impact judgment range, triggering a first-level equipment heat dissipation anomaly early warning, i.e., sending a first-level abnormal equipment management optimization command and prompting a first-level early warning response; a qualified heat dissipation impact judgment indicates that the heat dissipation impact judgment index is not within the preset heat dissipation anomaly impact judgment range, not triggering a first-level equipment heat dissipation anomaly early warning, and obtaining a heat dissipation energy consumption early warning result; when the heat dissipation energy consumption early warning result corresponds to controllable heat dissipation energy consumption, not triggering a second-level equipment heat dissipation anomaly early warning, and sending a heat dissipation energy consumption early warning monitoring command; when the heat dissipation energy consumption early warning result corresponds to abnormal heat dissipation energy consumption, triggering a second-level equipment heat dissipation anomaly early warning, i.e., sending a second-level abnormal equipment management optimization command and prompting a second-level early warning response.

[0030] In this embodiment, as the scale of the smart park continues to expand, the complexity of management and the number of devices increase, traditional equipment management models often struggle to cope with the increasing number of equipment failures, rising maintenance costs, and escalating security risks; such as Figure 3 The diagram shown illustrates the overall architecture of an IBMS-based equipment anomaly early warning and multi-dimensional management system provided in this application embodiment. The system comprises a web interface and an app interface. The web interface's main functions include: real-time data monitoring, data recording and analysis, early warning analysis, alarm monitoring, asset management, work order management, inspection management, planned maintenance, warranty management, user management, and log management. The app interface's main functions include: warranty management, inspection management, equipment monitoring management, cost management, parking management, visitor management, meeting scheduling, and cost management. In the equipment monitoring and management process within the operation and maintenance module, the detection of the operating status of power equipment is crucial.

[0031] In modern smart parks, electrical equipment is a crucial infrastructure ensuring production and daily life. Electrical equipment such as transformers, distribution cabinets, and electrical control devices generate significant amounts of heat during operation. Improper heat dissipation management can lead to overheating, causing malfunctions, equipment damage, shutdowns, fires, and other serious accidents. Therefore, early warning and management of abnormal heat dissipation in electrical equipment has become an indispensable part of smart park operations.

[0032] However, traditional heat dissipation anomaly early warning management typically focuses on equipment temperature changes, heat dissipation efficiency, and safe equipment operation, often neglecting the energy consumption fluctuations that may accompany the heat dissipation process. Energy consumption fluctuations not only affect equipment operating efficiency but can also impact the park's energy management, operating costs, and equipment lifespan. The problem of information silos between different subsystems within the park is severe. In traditional management models, the linkage between power equipment heat dissipation monitoring and the energy management system is poor, making it difficult to monitor energy consumption fluctuations in real time during heat dissipation. This results in a fragmented management system, hindering effective collaboration. IBMS can not only monitor and manage individual devices but also achieve linkage between different subsystems, ensuring efficient allocation and operation of park resources. Through automated control and scheduling, IBMS can adjust when equipment heat dissipation anomalies occur to maximize heat dissipation and reduce energy consumption fluctuations. In the process of managing abnormal heat dissipation of power equipment in smart parks, this application analyzes the correlation between the heat dissipation performance and heat dissipation energy consumption of power equipment to conduct corresponding early warnings and optimize responses. This effectively improves the operating efficiency of power equipment in the park, reduces energy consumption, and enables more accurate management of abnormal heat dissipation of power equipment in smart parks, making the operation of smart parks more intelligent and integrated.

[0033] Furthermore, based on the acquired early warning monitoring data, the degree to which the heat dissipation performance of power equipment is affected by fluctuations in operating load is assessed. The specific steps are as follows:

[0034] Q1. Obtain the early warning monitoring data within the preset early warning monitoring window. The early warning monitoring data includes the equipment load fluctuation rate, the equipment maximum load value, the equipment minimum load value, the equipment heat dissipation efficiency, and the equipment heat dissipation thermal resistance value. Among them, the early warning monitoring data has been de-unitized. The equipment load fluctuation rate is the ratio of the difference between the equipment maximum load value and the equipment minimum load value to the length of the early warning monitoring window. Specifically, the equipment load value is obtained through the power sensor integrated into IBMS and deployed at the power output end of the power equipment. The equipment maximum load value and the equipment minimum load value are obtained by statistically analyzing the equipment load value using the MAX and MIN functions in Excel. The equipment heat dissipation efficiency is obtained through the power equipment temperature control management interface integrated into IBMS. The equipment heat dissipation thermal resistance value is obtained through thermal resistance measurement tools (such as heat flow meters).

[0035] Q2, the result of the proportion of equipment load fluctuation rate (i.e., in the load fluctuation impact factor) The load fluctuation impact factor is obtained by weighting the results of the partial load fluctuation amplitude deviation percentage with the fluctuation rate weighting coefficient and fluctuation amplitude weighting coefficient obtained from the preset database, and then performing coupled calculation. The numerical expression of the load fluctuation impact factor is as follows:

[0036] ;

[0037] In the formula, This indicates the load fluctuation influencing factors within the preset early warning monitoring window. This represents the fluctuation rate weighting coefficient. This represents the weighting factor for fluctuation range. This indicates the rate of equipment load fluctuation within the preset early warning monitoring window. Indicates the reference load fluctuation rate threshold. This indicates the maximum load value of the equipment within the preset early warning monitoring window. This indicates the minimum load value of the equipment within the preset early warning monitoring window. This indicates the reference fluctuation amplitude deviation threshold.

[0038] The sum of the fluctuation rate weighting coefficient and the fluctuation amplitude weighting coefficient is 1, which are used to describe the influence of the equipment load fluctuation rate ratio result and the equipment load fluctuation amplitude deviation ratio result on the load fluctuation influence factor, respectively. The corresponding fluctuation rate weighting coefficient and fluctuation amplitude weighting coefficient are obtained by inputting the real-time equipment load fluctuation rate ratio result and the equipment load fluctuation amplitude deviation ratio result into the database and the preset mapping set of the equipment load fluctuation rate ratio result and the equipment load fluctuation amplitude deviation ratio result and their respective weighting coefficients.

[0039] The result of the equipment load fluctuation amplitude deviation ratio represents the result of a calculation that proportions the difference between the equipment's maximum load value and minimum load value to the reference fluctuation amplitude deviation threshold, i.e., the load fluctuation impact factor. Partial; the reference fluctuation amplitude deviation threshold is the difference between the collected historical equipment maximum load value and the equipment minimum load value, and the reference load fluctuation rate threshold is the average result of the collected historical equipment load fluctuation rates.

[0040] Q3, the equipment heat dissipation efficiency (i.e., the heat dissipation impact assessment index) (Partial) The percentage of thermal resistance values ​​of equipment heat dissipation (i.e., in the heat dissipation impact assessment index) The heat dissipation efficiency and thermal resistance judgment compensation values ​​obtained from a preset database are weighted and coupled, and then interacted with the load fluctuation impact factor to obtain the heat dissipation impact judgment index. The numerical expression of the heat dissipation impact judgment index is as follows:

[0041] ;

[0042] In the formula, This indicates the indicators for determining the impact of heat dissipation within the preset early warning monitoring window. This indicates the compensation value for determining heat dissipation efficiency. This indicates the compensation value for determining thermal resistance. This indicates the heat dissipation efficiency of the equipment within the preset early warning monitoring window. This indicates the thermal resistance value of the equipment within the preset early warning monitoring window. This indicates the reference thermal resistance threshold.

[0043] The heat dissipation efficiency judgment compensation value and the heat dissipation thermal resistance judgment compensation value both range from 0 to 1 and their sum is 1. The heat dissipation efficiency judgment compensation value and the heat dissipation thermal resistance judgment compensation value are used to describe the degree of influence of the equipment heat dissipation efficiency and the proportion of equipment heat dissipation thermal resistance value on the heat dissipation impact judgment index, respectively. The corresponding heat dissipation efficiency judgment compensation value and heat dissipation thermal resistance judgment compensation value are obtained by inputting the real-time equipment heat dissipation efficiency and the proportion of equipment heat dissipation thermal resistance value into the database and mapping the equipment heat dissipation efficiency and the proportion of equipment heat dissipation thermal resistance value with their respective compensation values.

[0044] The heat dissipation impact interaction processing is used to describe the interaction between the weighted coupling results of the equipment heat dissipation efficiency and the proportion of equipment heat dissipation thermal resistance and the load fluctuation impact factor; the reference heat dissipation thermal resistance threshold is the result of averaging the collected historical equipment heat dissipation thermal resistance values.

[0045] In this embodiment, the heat dissipation impact assessment index is used to quantitatively evaluate the degree to which the heat dissipation performance of power equipment is affected by fluctuations in operating load. It represents the quantitative data from early warning monitoring data used to determine the degree to which the heat dissipation performance of power equipment is affected by fluctuations in operating load. The heat dissipation impact assessment index considers the correlation and mutual influence between various parameters, and quantitatively judges the degree to which the heat dissipation performance of power equipment is affected by fluctuations in operating load. For example, rapidly changing loads bring rapid heat fluctuations, and large fluctuations require the heat dissipation system to effectively manage these fluctuations over a longer period. The radiator must have high responsiveness and strong adaptability to reduce the impact of load fluctuations. If the heat dissipation system does not respond in time, heat accumulation, local overheating, and increased thermal resistance will lead to a significant decrease in heat dissipation efficiency. That is, as the load fluctuation impact factor increases, the equipment's heat dissipation efficiency decreases accordingly, and the heat dissipation impact assessment index increases accordingly. Simultaneously, as the rate of equipment load fluctuation increases, the rate of heat generation and accumulation inside the equipment is faster, and the heat dissipation system needs to respond to temperature changes more quickly. If the cooling system does not respond promptly (e.g., fan speed or coolant flow rate adjustment lags), heat cannot be dissipated effectively and in a timely manner, thus affecting cooling efficiency. This results in a decrease in equipment cooling efficiency and an increase in the indicators for assessing the impact of cooling. Furthermore, as the rate of load fluctuation increases, the heat generated by the equipment increases rapidly, and the thermal resistance in the heat dissipation path may be relatively large, leading to a decrease in heat transfer efficiency. This increases the load fluctuation impact factor, causing the equipment's thermal resistance to increase, and consequently, the indicators for assessing the impact of cooling. By quantitatively assessing the degree to which the cooling performance of power equipment is affected by operating load fluctuations, a more accurate judgment of the impact on the cooling performance of power equipment is achieved, thereby improving the reliability of power equipment cooling performance management within the smart park.

[0046] Further, to determine whether a Level 1 warning for abnormal device heat dissipation has been triggered, the specific procedure is as follows:

[0047] First, the heat dissipation impact assessment indicators are compared with the preset heat dissipation anomaly impact assessment range obtained from a preset database to obtain the heat dissipation impact assessment result. The heat dissipation impact assessment result includes "qualified" and "abnormal" assessments; the preset heat dissipation anomaly impact assessment range is set by professionals according to industry standards.

[0048] Secondly, if the heat dissipation impact assessment result corresponds to an abnormal heat dissipation impact assessment, a Level 1 heat dissipation anomaly warning will be triggered. Specifically, an abnormal heat dissipation impact assessment result means that the heat dissipation impact assessment indicator is within the preset heat dissipation anomaly impact assessment range; a Level 1 heat dissipation anomaly warning means that a Level 1 abnormal equipment management optimization instruction is sent and a Level 1 warning response is prompted. The Level 1 abnormal equipment management optimization instruction is used to prompt the execution of Level 1 abnormal equipment management.

[0049] Furthermore, if the heat dissipation impact assessment result corresponds to "qualified," then the first-level warning for abnormal heat dissipation will not be triggered, and the heat dissipation energy consumption warning result will be obtained. Specifically, "qualified" indicates the heat dissipation impact assessment result when the heat dissipation impact assessment index is not within the preset range of abnormal heat dissipation impact assessment.

[0050] It should be added that the specific procedure for implementing Level 1 abnormal device management is as follows:

[0051] First, if the heat dissipation impact assessment result obtained again after performing equipment heat dissipation adjustment is deemed satisfactory, a Level 1 abnormal equipment management termination prompt will be sent. Otherwise, equipment heat dissipation adjustment will continue to be performed until a preset number of times is reached, at which point a Level 1 abnormal equipment management warning prompt will be sent. The preset number of times is set by professionals according to industry standards.

[0052] Among them, equipment heat dissipation regulation includes equipment load smoothing regulation, cooling fan speed regulation, and coolant flow rate regulation.

[0053] Specifically, load smoothing adjustment refers to inputting the load smoothing adjustment force into the load smoothing algorithm to smooth the fluctuations of the operating load of power equipment. The load smoothing adjustment force represents the result of mapping the deviation between the heat dissipation impact judgment index and the heat dissipation impact judgment threshold from a preset database. The heat dissipation impact judgment threshold is the minimum value of the historical heat dissipation impact judgment index collected when the heat dissipation impact judgment result is abnormal. Among them, the preset database has established a mapping relationship between the deviation between the heat dissipation impact judgment index and the heat dissipation impact judgment threshold and the load smoothing adjustment force. The deviation between the heat dissipation impact judgment index and the heat dissipation impact judgment threshold is the difference between the heat dissipation impact judgment index and the heat dissipation impact judgment threshold (wherein, the heat dissipation impact judgment index is not less than the heat dissipation impact judgment threshold).

[0054] Cooling fan speed adjustment refers to iterating the initial cooling fan speed to the adjusted cooling fan speed. The adjusted cooling fan speed is the sum of the initial cooling fan speed and the adjusted cooling fan speed value. The adjusted cooling fan speed value represents the result of a ratio calculation between the difference between the heat dissipation impact judgment index and the heat dissipation impact judgment threshold and the heat dissipation impact judgment threshold.

[0055] Coolant flow rate regulation refers to iterating the initial coolant flow rate to the regulated coolant flow rate. The regulated coolant flow rate is the sum of the initial coolant flow rate and the regulated coolant flow rate value. The regulated coolant flow rate value represents the result of a ratio calculation between the difference between the heat dissipation impact judgment index and the heat dissipation impact judgment threshold and the heat dissipation impact judgment threshold.

[0056] Furthermore, the specific procedure for implementing a Level 1 early warning response is as follows:

[0057] The length of the early warning monitoring window is iterated to the length of the first-level early warning response window. The length of the first-level early warning response window is the difference between the length of the early warning monitoring window and the adjustment value of the early warning response window. The adjustment value of the early warning response window represents the result of the ratio calculation between the difference between the heat dissipation impact judgment index and the heat dissipation impact judgment threshold and the heat dissipation impact judgment threshold.

[0058] The initial collection frequency of the early warning monitoring data is iterated to the first-level early warning response collection frequency. The first-level early warning response collection frequency is the sum of the initial collection frequency and the collection frequency adjustment value. The collection frequency adjustment value represents the result of the ratio calculation between the difference between the heat dissipation impact judgment index and the heat dissipation impact judgment threshold and the heat dissipation impact judgment threshold.

[0059] The system retrieves the heat dissipation impact assessment results within a preset warning period. If the number of times the heat dissipation impact assessment results indicate an anomaly reaches a preset maximum, a Level 1 emergency response alert is sent. Both the preset warning period and the preset maximum number of occurrences are set by professionals according to industry standards.

[0060] like Figure 4 The diagram shown is a flowchart of the heat dissipation energy consumption early warning result acquisition process of an IBMS-based equipment anomaly early warning and multi-dimensional management system provided in this application embodiment. The corresponding logic is as follows: the heat dissipation energy consumption early warning value is obtained by quantitatively evaluating the energy consumption fluctuation during the heat dissipation process of the power equipment through heat dissipation energy consumption monitoring data. The heat dissipation energy consumption early warning value is compared with the preset heat dissipation energy consumption allowable range obtained from the preset database to obtain the heat dissipation energy consumption early warning result. The heat dissipation energy consumption early warning result includes heat dissipation energy consumption controllable and heat dissipation energy consumption abnormal. When the heat dissipation energy consumption early warning value is within the preset heat dissipation energy consumption allowable range, the heat dissipation energy consumption early warning result corresponds to heat dissipation energy consumption controllable, and the secondary early warning of equipment heat dissipation abnormality is not triggered. A heat dissipation energy consumption early warning monitoring command is sent. When the heat dissipation energy consumption early warning value is not within the preset heat dissipation energy consumption allowable range, the heat dissipation energy consumption early warning result corresponds to heat dissipation energy consumption abnormality, and the secondary early warning of equipment heat dissipation abnormality is triggered.

[0061] Specifically, the process for obtaining heat dissipation and energy consumption warning results is as follows:

[0062] Obtain the heat dissipation energy consumption warning value within the preset heat dissipation energy consumption monitoring window, and compare the heat dissipation energy consumption warning value with the preset heat dissipation energy consumption allowable range obtained from the preset database to obtain the heat dissipation energy consumption warning result.

[0063] Specifically, the heat dissipation energy consumption warning results include controllable heat dissipation energy consumption and abnormal heat dissipation energy consumption; controllable heat dissipation energy consumption indicates the heat dissipation energy consumption warning result when the heat dissipation energy consumption warning value is within the preset allowable range of heat dissipation energy consumption, and abnormal heat dissipation energy consumption indicates the heat dissipation energy consumption warning result when the heat dissipation energy consumption warning value is outside the preset allowable range of heat dissipation energy consumption; the preset allowable range of heat dissipation energy consumption is set by professionals according to the standards in the field.

[0064] The specific steps for obtaining the heat dissipation energy consumption warning value within the preset heat dissipation energy consumption monitoring window are as follows:

[0065] First, acquire the heat dissipation energy consumption monitoring data within the preset heat dissipation energy consumption monitoring window. The heat dissipation energy consumption monitoring data includes the maximum energy consumption of the equipment, the minimum energy consumption of the equipment, the average energy consumption of the equipment, the peak energy consumption value of the equipment, and the heat dissipation impact judgment index. Specifically, acquire the equipment energy consumption value through the energy metering instrument deployed on the power equipment integrated with IBMS; obtain the maximum energy consumption, minimum energy consumption, and average energy consumption of the equipment by statistically analyzing the equipment energy consumption value using built-in Excel functions (such as MAX, MIN, and AVERAGE functions); and obtain the peak energy consumption value of the equipment by using built-in Excel functions (such as KURT function).

[0066] Secondly, the results of equipment energy consumption fluctuation ratio and equipment energy consumption kurtosis value (i.e., the heat dissipation energy consumption warning value) will be used to determine the proportion of equipment energy consumption fluctuation. The energy consumption energy consumption warning compensation amount (partially) is weighted and coupled with the energy consumption fluctuation amplitude warning compensation amount and energy consumption fluctuation kurtosis warning compensation amount obtained from the preset database. Then, it is interactively processed with the heat dissipation impact judgment index to obtain the heat dissipation energy consumption warning value. The numerical expression of the heat dissipation energy consumption warning value is as follows:

[0067] ;

[0068] In the formula, This indicates the preset heat dissipation energy consumption warning value within the heat dissipation energy consumption monitoring window. This indicates the amount of compensation for early warning of energy consumption fluctuations. This indicates the amount of compensation for early warning of energy consumption fluctuation peaks. This indicates the maximum energy consumption of the device within the preset heat dissipation energy consumption monitoring window. This indicates the minimum energy consumption of the device within the preset heat dissipation energy consumption monitoring window. This indicates the average energy consumption of the device within the preset heat dissipation energy consumption monitoring window. This indicates the peak energy consumption value of the device within the preset heat dissipation energy consumption monitoring window.

[0069] Among them, the result of equipment energy consumption fluctuation ratio is the heat dissipation energy consumption warning value. In addition, the heat dissipation energy consumption interaction processing is used to describe the interaction between the weighted coupling results of the equipment energy consumption fluctuation ratio and the equipment energy consumption kurtosis value and the heat dissipation impact judgment index; furthermore, the sum of the energy consumption fluctuation amplitude warning compensation amount and the energy consumption fluctuation kurtosis warning compensation amount is 1, which is used to describe the degree of influence of the equipment energy consumption fluctuation ratio and the equipment energy consumption kurtosis value on the heat dissipation energy consumption warning value, respectively. By inputting the real-time equipment energy consumption fluctuation ratio and the equipment energy consumption kurtosis value into the database, the corresponding energy consumption fluctuation amplitude warning compensation amount and energy consumption fluctuation kurtosis warning compensation amount are obtained.

[0070] It's important to understand that the heat dissipation energy consumption early warning value is used to quantitatively assess energy consumption fluctuations during the heat dissipation process of power equipment. It represents the quantitative data from heat dissipation energy consumption monitoring data on the assessment of energy consumption fluctuations during the heat dissipation process of power equipment. The heat dissipation energy consumption early warning value includes multiple parameters. Through quantification, the correlation and mutual influence between these parameters are considered. For example, the heat dissipation impact judgment index quantitatively judges the degree to which the heat dissipation performance of power equipment is affected by fluctuations in operating load. As the heat dissipation impact judgment index increases, the proportion of equipment energy consumption fluctuations increases accordingly. This proportion directly indicates the degree of energy consumption fluctuation. An increase in the proportion of equipment energy consumption fluctuations indicates increased energy consumption fluctuations during the heat dissipation process of power equipment, and the heat dissipation energy consumption early warning value increases accordingly. Furthermore, as the heat dissipation impact judgment index increases, the kurtosis value of equipment energy consumption also increases, indicating a greater sharpness in the distribution of equipment energy consumption data. A higher kurtosis value means an increase in extreme values ​​in the equipment energy consumption data. Simultaneously, an increase in the proportion of equipment energy consumption fluctuations indicates larger energy consumption fluctuations, which usually means that the equipment energy consumption data may have more extreme fluctuations, i.e., the kurtosis value increases accordingly. By quantifying the interrelationships and interactions among various parameters, a pre-warning value for heat dissipation energy consumption is obtained, enabling a numerical assessment of energy consumption fluctuations during the heat dissipation process of power equipment.

[0071] In this embodiment, the degree to which the heat dissipation performance of power equipment is affected by fluctuations in operating load is quantitatively determined by the heat dissipation impact judgment index. Based on the heat dissipation impact judgment result, it is determined whether to trigger a level one warning for abnormal heat dissipation of the equipment. At the same time, after triggering a level one warning for abnormal heat dissipation of the equipment, a level one abnormal equipment management optimization instruction is sent to execute level one abnormal equipment management and prompt for a level one warning response. In addition, when the level one warning for abnormal heat dissipation of the equipment is not triggered, the heat dissipation energy consumption warning result is obtained. This realizes the early warning and response optimization of the heat dissipation performance and heat dissipation energy consumption of power equipment, thereby improving the accuracy of the early warning management of abnormal heat dissipation of power equipment in the smart park.

[0072] Furthermore, based on the obtained heat dissipation energy consumption warning results, it is determined whether to trigger a level two warning for abnormal equipment heat dissipation. The specific process is as follows:

[0073] If the heat dissipation energy consumption warning result corresponds to heat dissipation energy consumption being controllable, the second-level warning for abnormal heat dissipation of the equipment will not be triggered, and a heat dissipation energy consumption warning monitoring instruction will be sent. The heat dissipation energy consumption warning monitoring instruction is used to continuously obtain the heat dissipation energy consumption warning result in order to determine whether the heat dissipation energy consumption warning result is controllable.

[0074] In addition, if the heat dissipation energy consumption warning result corresponds to abnormal heat dissipation energy consumption, a secondary warning for abnormal equipment heat dissipation is triggered. The secondary warning for abnormal equipment heat dissipation indicates that a secondary abnormal equipment management optimization instruction is sent and a secondary warning response is prompted. The secondary abnormal equipment management optimization instruction is used to prompt the execution of secondary abnormal equipment management.

[0075] Specifically, the procedure for implementing Level 2 abnormal equipment management is as follows:

[0076] A1. Iterate the initial radiator fan speed to the secondary adjustable radiator fan speed. The secondary adjustable radiator fan speed is the sum of the initial radiator fan speed and the secondary adjustable radiator fan speed value. The secondary adjustable radiator fan speed value represents the result of the ratio calculation between the difference between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold and the reference heat dissipation energy consumption warning threshold. The reference heat dissipation energy consumption warning threshold is the minimum value of the historical heat dissipation energy consumption warning values ​​collected when the heat dissipation energy consumption warning result corresponds to an abnormal heat dissipation energy consumption. The heat dissipation energy consumption warning value is not less than the reference heat dissipation energy consumption warning threshold.

[0077] A2 iterates the initial coolant flow rate to the secondary regulated coolant flow rate. The secondary regulated coolant flow rate is the sum of the initial coolant flow rate and the secondary regulated coolant flow rate value. The secondary regulated coolant flow rate value represents the result of a ratio calculation between the difference between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold and the reference heat dissipation energy consumption warning threshold.

[0078] A3, the motor speed adjustment force is input to the frequency converter to correct the speed of the motor of the power equipment. The motor speed adjustment force represents the result of mapping the deviation between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold from the preset database. The preset database has established a mapping relationship between the deviation between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold and the motor speed adjustment force. The deviation between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold is the difference between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold.

[0079] A4 determines whether the re-acquired heat dissipation energy consumption warning result is that heat dissipation energy consumption is controllable. If so, a level 2 abnormal device management end prompt is sent; otherwise, it returns to A1 until the heat dissipation energy consumption warning result is that heat dissipation energy consumption is controllable.

[0080] Specifically, the procedure for implementing a Level II early warning response is as follows:

[0081] First, the length of the heat dissipation energy consumption monitoring window is iterated to the length of the secondary early warning monitoring window. The length of the secondary early warning monitoring window is the difference between the heat dissipation energy consumption monitoring window and the energy consumption monitoring window adjustment value. The energy consumption monitoring window adjustment value represents the result of the ratio calculation between the difference between the heat dissipation energy consumption early warning value and the reference heat dissipation energy consumption early warning threshold and the reference heat dissipation energy consumption early warning threshold.

[0082] Secondly, the initial monitoring and acquisition frequency of the heat dissipation energy consumption monitoring data is iterated to the secondary early warning monitoring and acquisition frequency. The secondary early warning monitoring and acquisition frequency is the sum of the initial monitoring and acquisition frequency and the monitoring and acquisition frequency adjustment value. The monitoring and acquisition frequency adjustment value represents the result of the ratio calculation between the difference between the heat dissipation energy consumption early warning value and the reference heat dissipation energy consumption early warning threshold and the reference heat dissipation energy consumption early warning threshold.

[0083] Finally, obtain the heat dissipation energy consumption warning results within the preset warning monitoring period. If the number of times the heat dissipation energy consumption is abnormal reaches the preset maximum number, send a level-two warning emergency response prompt. The preset warning monitoring period and the preset maximum number are set by professionals according to industry standards.

[0084] In this embodiment, the determination of whether to trigger a secondary warning for abnormal heat dissipation of equipment is made by combining the heat dissipation energy consumption warning results. At the same time, after triggering the secondary warning for abnormal heat dissipation of equipment, a secondary abnormal equipment management optimization instruction is sent to execute the secondary abnormal equipment management and prompt for a secondary warning response. This effectively reduces energy consumption fluctuations during the heat dissipation warning management of power equipment, thereby improving the reliability of energy consumption fluctuation management during the heat dissipation warning management of power equipment.

[0085] In summary, this application embodiment assesses the impact of operating load fluctuations on the heat dissipation performance of power equipment using acquired early warning monitoring data to determine whether a first-level early warning for abnormal heat dissipation is triggered. It then determines whether a second-level early warning for abnormal heat dissipation is triggered, and finally obtains and visualizes the periodic inspection early warning results of the power equipment. Simultaneously, it establishes a file information system for the power equipment. This achieves early warning and response optimization for the heat dissipation performance and energy consumption of power equipment, thereby enabling more accurate early warning management of abnormal heat dissipation in power equipment within smart parks. This effectively solves the problem in existing technologies where energy consumption fluctuations during the heat dissipation process are not fully considered in the early warning management of abnormal heat dissipation in power equipment within smart parks.

[0086] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0090] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0091] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A device anomaly early warning and multi-dimensional management system based on IBMS, characterized in that, include: The system includes a data acquisition module for equipment operation, a primary heat dissipation early warning judgment module, an early warning management energy consumption assessment module, a secondary heat dissipation early warning management module, and a visual management module for equipment information. The equipment operation data acquisition module is used to collect equipment operation data within a preset time interval and store the equipment operation data into a preset database; The heat dissipation level 1 early warning determination module is used to acquire early warning monitoring data of power equipment in the smart park within a preset area, and to evaluate the degree of influence of the heat dissipation performance of the power equipment on the fluctuation of operating load based on the acquired early warning monitoring data, and to determine whether to trigger the level 1 early warning of abnormal heat dissipation of the equipment. The early warning management energy consumption assessment module is used to prompt a first-level early warning response after sending a first-level abnormal equipment management optimization instruction if a first-level early warning of equipment heat dissipation is triggered; otherwise, it assesses the energy consumption fluctuation during the heat dissipation process of the power equipment. The heat dissipation secondary warning management module is used to determine whether a secondary warning for abnormal heat dissipation of the equipment is triggered based on the obtained heat dissipation energy consumption warning result. If triggered, it prompts for a secondary warning response after sending a secondary abnormal equipment management optimization instruction; otherwise, it sends a heat dissipation energy consumption warning monitoring instruction. The equipment information visualization management module is used to obtain the periodic inspection and early warning results of power equipment and display them visually, while also establishing archive information for the power equipment; The Level 1 Abnormal Equipment Management Optimization Command is used to prompt the execution of Level 1 Abnormal Equipment Management. The Level 2 Abnormal Equipment Management Optimization Command is used to prompt the execution of Level 2 Abnormal Equipment Management; The specific process for implementing Level 2 abnormal device management is as follows: A1, the initial radiator fan speed is iterated to the secondary adjustment speed of the radiator fan. The secondary adjustment speed of the radiator fan is the sum of the initial radiator fan speed and the secondary adjustment value of the radiator fan. The secondary adjustment value of the radiator fan represents the result of the ratio calculation between the difference between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold and the reference heat dissipation energy consumption warning threshold. A2, the initial coolant flow rate is iterated to the secondary regulated coolant flow rate, wherein the secondary regulated coolant flow rate is the sum of the initial coolant flow rate and the secondary regulated coolant flow rate value, and the secondary regulated coolant flow rate value represents the result of a ratio calculation between the difference between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold and the reference heat dissipation energy consumption warning threshold. A3, input the motor speed adjustment force to the frequency converter to correct the speed of the motor of the power equipment. The motor speed adjustment force represents the result of mapping the degree of deviation between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold from the preset database. The degree of deviation between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold is the difference between the heat dissipation energy consumption warning value and the reference heat dissipation energy consumption warning threshold. The specific procedure for implementing a Level II early warning response is as follows: The length of the heat dissipation energy consumption monitoring window is iterated to the length of the secondary early warning monitoring window. The length of the secondary early warning monitoring window is the difference between the heat dissipation energy consumption monitoring window and the energy consumption monitoring window adjustment value. The energy consumption monitoring window adjustment value represents the result of the ratio calculation between the difference between the heat dissipation energy consumption early warning value and the reference heat dissipation energy consumption early warning threshold and the reference heat dissipation energy consumption early warning threshold. The initial monitoring and acquisition frequency of heat dissipation energy consumption monitoring data is iterated to the secondary early warning monitoring and acquisition frequency. The secondary early warning monitoring and acquisition frequency is the sum of the initial monitoring and acquisition frequency and the monitoring and acquisition frequency adjustment value. The monitoring and acquisition frequency adjustment value represents the result of the ratio calculation between the difference between the heat dissipation energy consumption early warning value and the reference heat dissipation energy consumption early warning threshold and the reference heat dissipation energy consumption early warning threshold. Obtain the heat dissipation energy consumption warning results within the preset warning monitoring period. If the number of times the heat dissipation energy consumption warning results show abnormal heat dissipation energy consumption reaches the preset maximum number, send a level 2 warning emergency response prompt.

2. The IBMS-based device anomaly early warning and multi-dimensional management system as described in claim 1, characterized in that, The assessment of the impact of operating load fluctuations on the heat dissipation performance of power equipment based on the acquired early warning monitoring data involves the following specific steps: Acquire early warning monitoring data within a preset early warning monitoring window. The early warning monitoring data includes equipment load fluctuation rate, equipment maximum load value, equipment minimum load value, equipment heat dissipation efficiency, and equipment heat dissipation thermal resistance value. The load fluctuation impact factor is obtained by weighting the results of the equipment load fluctuation rate proportion and the equipment load fluctuation amplitude deviation proportion with the fluctuation rate weighting coefficient and fluctuation amplitude weighting coefficient obtained from the preset database. The results of equipment heat dissipation efficiency and equipment heat dissipation thermal resistance are coupled with the heat dissipation efficiency judgment compensation value and heat dissipation thermal resistance judgment compensation value obtained from the preset database after weighted calculation. Then, they are processed with the load fluctuation impact factor to obtain the heat dissipation impact judgment index. The heat dissipation impact judgment index is used to quantitatively evaluate the degree of impact of the heat dissipation performance of power equipment on the operating load fluctuation. It represents the quantitative data of the early warning monitoring data on the degree of impact of the heat dissipation performance of power equipment on the operating load fluctuation.

3. The IBMS-based device anomaly early warning and multi-dimensional management system as described in claim 2, characterized in that, The specific process for determining whether a Level 1 warning for abnormal device heat dissipation has been triggered is as follows: The heat dissipation impact judgment index is compared with the preset heat dissipation abnormality impact judgment range obtained from the preset database to obtain the heat dissipation impact judgment result. The heat dissipation impact judgment result includes heat dissipation impact judgment qualified and heat dissipation impact judgment abnormal. If the heat dissipation impact determination result corresponds to heat dissipation impact determination abnormality, then a first-level warning for device heat dissipation abnormality is triggered. The heat dissipation impact determination abnormality means the heat dissipation impact determination result corresponding to the heat dissipation impact determination index being within the preset heat dissipation abnormality impact determination range. The first-level warning for device heat dissipation abnormality means sending a first-level abnormal device management optimization instruction and prompting for a first-level warning response. If the heat dissipation impact judgment result corresponds to a qualified heat dissipation impact judgment, then the first-level warning of abnormal heat dissipation of the equipment will not be triggered, and the heat dissipation energy consumption warning result will be obtained. The qualified heat dissipation impact judgment means that the heat dissipation impact judgment result is not within the preset heat dissipation abnormality impact judgment range.

4. The IBMS-based device anomaly early warning and multi-dimensional management system as described in claim 3, characterized in that, The specific process for implementing Level 1 abnormal device management is as follows: If the heat dissipation impact judgment result obtained again after the equipment heat dissipation adjustment is qualified, a first-level abnormal equipment management end prompt will be sent; otherwise, the equipment heat dissipation adjustment will continue to be performed until the preset number of times is reached, and then a first-level abnormal equipment management warning prompt will be sent. The equipment heat dissipation adjustment includes equipment load smoothing adjustment, cooling fan speed adjustment and coolant flow rate adjustment. The equipment load smoothing adjustment refers to inputting the load smoothing adjustment force into the load smoothing algorithm to smooth the fluctuations of the operating load of the power equipment. The load smoothing adjustment force represents the result of mapping the degree of deviation between the heat dissipation impact judgment index and the heat dissipation impact judgment threshold from a preset database. The cooling fan speed adjustment refers to iterating the initial cooling fan speed to the adjusted cooling fan speed, and the coolant flow rate adjustment refers to iterating the initial coolant flow rate to the adjusted coolant flow rate, wherein the adjusted coolant flow rate is the sum of the initial coolant flow rate and the adjusted coolant flow rate value.

5. The IBMS-based equipment anomaly early warning and multi-dimensional management system as described in claim 3, characterized in that, The specific procedure for issuing a Level 1 early warning response is as follows: The length of the early warning monitoring window is iterated to the length of the first-level early warning response window, wherein the length of the first-level early warning response window is the difference between the length of the early warning monitoring window and the adjustment value of the early warning response window; The initial acquisition frequency of the early warning monitoring data is iterated to the first-level early warning response acquisition frequency, which is the sum of the initial acquisition frequency and the acquisition frequency adjustment value. Obtain the heat dissipation impact assessment results within the preset warning period. If the number of times the heat dissipation impact assessment results show abnormalities reaches the preset maximum number, send a Level 1 warning emergency response prompt.

6. The IBMS-based device anomaly early warning and multi-dimensional management system as described in claim 3, characterized in that, The specific process for obtaining the heat dissipation energy consumption early warning result is as follows: Obtain the heat dissipation energy consumption warning value within the preset heat dissipation energy consumption monitoring window, compare the heat dissipation energy consumption warning value with the preset heat dissipation energy consumption allowable range obtained from the preset database, and obtain the heat dissipation energy consumption warning result. The heat dissipation energy consumption early warning results include controllable heat dissipation energy consumption and abnormal heat dissipation energy consumption; The term "controllable heat dissipation energy consumption" refers to the heat dissipation energy consumption warning result when the heat dissipation energy consumption warning value is within the preset allowable range, while the term "abnormal heat dissipation energy consumption" refers to the heat dissipation energy consumption warning result when the heat dissipation energy consumption warning value is outside the preset allowable range.

7. The IBMS-based equipment anomaly early warning and multi-dimensional management system as described in claim 6, characterized in that, The specific steps for obtaining the heat dissipation energy consumption early warning value within the preset heat dissipation energy consumption monitoring window are as follows: Obtain heat dissipation energy consumption monitoring data within a preset heat dissipation energy consumption monitoring window. The heat dissipation energy consumption monitoring data includes the maximum energy consumption of the equipment, the minimum energy consumption of the equipment, the average energy consumption of the equipment, the energy consumption kurtosis value of the equipment, and the heat dissipation impact judgment index. The results of equipment energy consumption fluctuation ratio, equipment energy consumption kurtosis value, and energy consumption fluctuation amplitude warning compensation amount and energy consumption fluctuation kurtosis warning compensation amount obtained from the preset database are weighted and coupled, and then processed with heat dissipation impact judgment index for heat dissipation energy consumption interaction to obtain heat dissipation energy consumption warning value. The aforementioned heat dissipation energy consumption early warning value represents the quantitative data used by heat dissipation energy consumption monitoring data to assess energy consumption fluctuations during the heat dissipation process of power equipment.

8. The IBMS-based equipment anomaly early warning and multi-dimensional management system as described in claim 6, characterized in that, The specific process for determining whether to trigger a secondary warning for abnormal device heat dissipation based on the acquired heat dissipation energy consumption warning results is as follows: If the heat dissipation energy consumption warning result corresponds to heat dissipation energy consumption being controllable, the second-level warning for abnormal heat dissipation of the equipment will not be triggered, and a heat dissipation energy consumption warning monitoring command will be sent. If the heat dissipation energy consumption warning result corresponds to abnormal heat dissipation energy consumption, a secondary warning for abnormal heat dissipation of the equipment is triggered. The secondary warning for abnormal heat dissipation of the equipment indicates that a secondary abnormal equipment management optimization instruction is sent and a secondary warning response is prompted.

9. The IBMS-based equipment anomaly early warning and multi-dimensional management system as described in claim 8, characterized in that, The specific process for implementing Level 2 abnormal device management also includes: A4 determines whether the re-acquired heat dissipation energy consumption warning result is that heat dissipation energy consumption is controllable. If so, a level 2 abnormal device management end prompt is sent; otherwise, it returns to A1 until the heat dissipation energy consumption warning result is that heat dissipation energy consumption is controllable.

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