An Internet of Things-based electromechanical equipment monitoring system

Abstract

The present invention discloses an electromechanical equipment monitoring system based on the Internet of Things, which relates to the technical field of electromechanical equipment monitoring. The system includes a data acquisition module, a data processing module, a comprehensive evaluation and prediction module, and an optimization and adjustment module. Based on the Internet of Things and through the data acquisition module, the operation data of the electromechanical equipment is collected in real time and remotely. The operation data includes the power, load, and temperature of the electromechanical equipment during operation. The collected operation data is preprocessed by the data processing module, and the operation state evaluation value JDY and the warning and maintenance value WJ of the electromechanical equipment are output by the comprehensive evaluation and prediction module. The operation state evaluation value JDY and the warning and maintenance value WJ are input into the optimization and adjustment module to output the maintenance adjustment factor TY. Based on the maintenance adjustment factor TY, the parameter values of the load and power in the current operation state of the electromechanical equipment are adjusted, avoiding production interruption and safety accidents caused by electromechanical equipment failures, and thus improving the reliability and safety of the operation and use of the electromechanical equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical equipment monitoring, and particularly to an electromechanical equipment monitoring system based on the Internet of Things. Background Art

[0002] Electromechanical equipment is a kind of equipment generally installed in an electrical cabinet, which is usually connected to a power supply system by power transmission lines to ensure the operation and rotation of the electromechanical equipment. Therefore, it plays a crucial role in production. If the electromechanical equipment fails, safety accidents may occur. In order to reduce the occurrence of safety accidents or greatly reduce losses or risks when safety accidents occur, a monitoring system based on the Internet of Things is generally installed on the electromechanical equipment to facilitate the monitoring of the operation status of the electromechanical equipment, which is conducive to reducing the possibility of safety accidents or being able to respond in a timely manner when safety accidents occur and reducing losses.

[0003] At present, it is difficult to achieve real-time monitoring of electromechanical equipment through manual inspection of electrical equipment, and it is difficult to timely discover potential problems of electromechanical equipment, resulting in insufficient real-time performance.

[0004] Moreover, traditional monitoring means can often only obtain limited data parameters, and it is difficult to comprehensively reflect the operation status of electromechanical equipment, resulting in incomplete data collection. In addition, the strategy based on regular maintenance often leads to inaccurate maintenance timing, which may waste resources due to over-maintenance or accelerate the aging of electromechanical equipment due to insufficient maintenance, thus resulting in a lag in maintenance decisions. Traditional systems lack self-learning and adaptive capabilities and are difficult to automatically adjust maintenance strategies according to the actual operation of electromechanical equipment, lacking intelligent adjustment of maintenance effects. Summary of the Invention

[0005] The purpose of the present invention is to provide an electromechanical equipment monitoring system based on the Internet of Things, which solves the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides an electromechanical equipment monitoring system based on the Internet of Things, including a data acquisition module, a data processing module, a comprehensive evaluation and prediction module, and an optimization and adjustment module;

[0007] The steps of monitoring electromechanical equipment are as follows:

[0008] Data acquisition: Based on the Internet of Things and through the data acquisition module, the operation data of electromechanical equipment is collected in real time and remotely. The operation data includes the power, load, and temperature of the electromechanical equipment during operation;

[0009] Data processing: The collected operation data is preprocessed by the data processing module;

[0010] Optimization strategy and parameter value adjustment: By comprehensively evaluating the operation status evaluation value JDY and the early warning maintenance value WJ of the electromechanical equipment output by the prediction module, the operation status evaluation value JDY and the early warning maintenance value WJ are input into the optimization adjustment module to output the maintenance adjustment factor TY, and the parameter values of the load and power in the current operation status of the electromechanical equipment are adjusted based on the maintenance adjustment factor TY.

[0011] Optionally, the comprehensive evaluation prediction module includes: a current operation status evaluation unit and a demand maintenance prediction unit;

[0012] The optimization adjustment module includes: a parameter value adjustment and optimization unit.

[0013] Optionally, the evaluation process of the current operation status evaluation unit is as follows:

[0014]

[0015]

[0016] Where:

[0017] JDY is the operation status evaluation value;

[0018] GL is the current power value of the equipment, FZ is the current load value of the equipment, W is the current temperature value of the equipment, and JW is the standard constant temperature value of the equipment;

[0019] a is the weight factor of PY;

[0020] PY is the temperature offset value, and PY reflects the offset degree of W relative to the temperature of JW;

[0021] The current power value GL, the current load value FZ, and the current temperature value W of the equipment processed by the data processing module are input into the current operation status evaluation unit to obtain the operation status evaluation value JDY for evaluating the current comprehensive operation status of the electromechanical equipment.

[0022] Optionally, the prediction process of the demand maintenance prediction unit is as follows:

[0023]

[0024] Where:

[0025] WJ is the early warning maintenance value;

[0026] YS is the equipment operation time value, JDY is the operation status evaluation value, and JDZ is the preset operation status evaluation benchmark value;

[0027] b is the scaling factor, and b is used to adjust the numerical range of WJ;

[0028] GL is the current power value of the device, and BGL is the reference power value of the device;

[0029] Input the operation status evaluation value JDY obtained by the current operation status evaluation unit into the demand maintenance prediction unit to obtain the warning maintenance value WJ for predicting the maintenance demand of the electromechanical equipment.

[0030] Optionally, the adjustment and optimization process of the parameter value adjustment and optimization unit is as follows:

[0031]

[0032]

[0033] Where:

[0034] TY is the maintenance adjustment factor;

[0035] WJ is the warning maintenance value;

[0036] SSJDY is the penultimate operation status evaluation value, and SJDY is the previous operation status evaluation value;

[0037] YG is the improvement value of the operation status evaluation value after the previous maintenance;

[0038] is the evaluation value of the effectiveness of the current maintenance strategy;

[0039] Input the warning maintenance value WJ obtained from the demand maintenance prediction unit and the difference between the operation status evaluation values JDY obtained by the current operation status evaluation unit for two adjacent times into the parameter value adjustment and optimization unit to obtain the maintenance adjustment factor TY;

[0040] Based on the maintenance adjustment factor TY:

[0041] 1. If the maintenance adjustment factor TY < 0 and the current temperature value W of the device > the standard constant temperature value JW of the device, it indicates that the operating temperature of the electromechanical equipment is too high, and the current load value FZ of the device is reduced;

[0042] 2. If TY > 0 and the current power value GL of the device < the reference power value BGL of the device, it indicates that the utilization rate of the operating power of the electromechanical equipment is low, and the current power value GL of the device is increased;

[0043] 3. If TY = 0, the current temperature value W of the device = the standard constant temperature value JW of the device, and the current power value GL of the device = the reference power value BGL of the device, it indicates that the temperature and efficiency of the operation of the electromechanical equipment are in a good operating state, and real-time detection and output can be performed.

[0044] Optionally, the data acquisition module includes a data acquisition unit, and the data processing module includes a data cleaning unit, a data processing unit, and a data monitoring unit.

[0045] Optionally, the data acquisition unit cleans, denoises, and normalizes the collected operation data through the data cleaning unit and the data processing unit, and then uses the data monitoring unit to monitor and give early warnings about the operation status evaluation value JDY of the electrical equipment.

[0046] Optionally, the data acquisition module includes a sensor, a time recorder, and a data acquisition system, and the data processing module includes a computing and processing device and an alarm system.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] First, through the Internet of Things technology, the present invention can collect various operation data of electromechanical equipment in real time, such as power, load, and temperature, and immediately calculate the real-time operation status evaluation value of the equipment by using the current operation status evaluation unit, realizing the immediate grasp of the status of electromechanical equipment and enhancing the real-time nature of monitoring.

[0049] Second, the present invention not only collects traditional operation data but also obtains historical data of electromechanical equipment through the Internet of Things technology, such as the evaluation of preset operation status, and combined with the calculation of the current operation status evaluation unit, can more comprehensively reflect the health status of electromechanical equipment;

[0050] In addition, the electromechanical equipment monitoring system calculates the maintenance early warning result by using the demand-based maintenance prediction unit, and predicts the maintenance demand based on the operation time and status evaluation of the electromechanical equipment, making the maintenance decision more accurate and scientific. This not only reduces unnecessary downtime but also improves the utilization rate of electromechanical equipment, thereby not only achieving comprehensive data improvement but also ensuring the precision of maintenance decisions.

[0051] Third, through the calculation of the parameter value adjustment and optimization unit, the electromechanical equipment monitoring system can evaluate the effectiveness of the current maintenance strategy and automatically adjust and optimize according to the evaluation result. This closed-loop feedback mechanism enables the electromechanical equipment monitoring system to have self-learning and self-adaptive capabilities, continuously improving the maintenance efficiency and equipment performance, and ensuring the intelligent optimization effect.

[0052] Fourth, through predictive maintenance and precise maintenance decisions, the electromechanical equipment monitoring system can effectively reduce the number of emergency repairs and downtime, thereby reducing maintenance costs. At the same time, by optimizing the operation parameter values and maintenance strategies of electromechanical equipment, it can further reduce energy consumption and operating costs.

[0053] V. By using the real-time monitoring and early warning functions, the electromechanical equipment monitoring system of the present invention can timely detect and handle potential equipment faults, avoiding production interruptions and safety accidents caused by electromechanical equipment failures, and thus improving the reliability and safety of the operation and use of electromechanical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is the method flowchart of the electromechanical equipment monitoring system based on the Internet of Things;

[0055] Figure 2 is the overall structural schematic diagram of the electromechanical equipment monitoring system based on the Internet of Things;

[0056] Figure 3 is the structural schematic diagram of the data processing module of the present invention. DETAILED IMPLEMENTATION MANNER

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] Regarding the electromechanical equipment monitoring system based on the Internet of Things, different from the existing electromechanical equipment monitoring systems, the existing electromechanical equipment monitoring systems are difficult to achieve real-time monitoring of electromechanical equipment through manual inspection of electrical equipment, and it is impossible to timely detect potential problems of electromechanical equipment, resulting in insufficient real-time performance, and it is difficult to comprehensively reflect the operating status of electromechanical equipment, with incomplete data collection. In addition, only adopting the strategy of regular maintenance will lead to inaccurate maintenance timing. Finally, the traditional system lacks self-learning and adaptive capabilities. However, the algorithm unit of the present invention comprehensively considers the power, load, temperature, and historical data of electromechanical equipment, not only realizes the immediate grasp of the status of electromechanical equipment, enhances the real-time performance of monitoring, but also can more comprehensively reflect the health status of electromechanical equipment, ensuring the accuracy of maintenance decisions. The closed-loop feedback mechanism for automatic adjustment and optimization according to the evaluation results can continuously improve the maintenance efficiency and equipment performance. At the same time, by optimizing the operating parameter values and maintenance strategies of electromechanical equipment, the energy consumption and operating costs can be further reduced. Finally, by using the real-time monitoring and early warning functions, the reliability and safety of the operation and use of electromechanical equipment are improved.

[0059] Example 1, please refer to Figures 1 to 3 , this embodiment provides an electromechanical equipment monitoring system based on the Internet of Things, which is implemented by using a data acquisition module, a data processing module, a comprehensive evaluation and prediction module, and an optimization and adjustment module;

[0060] The specific implementation process is as follows:

[0061] Based on the Internet of Things and through a data acquisition module, the operating data of electromechanical equipment is collected remotely in real time. The operating data includes the power, load, and temperature of the electromechanical equipment during operation;

[0062] The collected operating data is uploaded to the terminal of the electromechanical equipment monitoring system;

[0063] The collected operating data is preprocessed by a data processing module. The preprocessing of the data includes cleaning, sorting, and analysis;

[0064] Through a comprehensive evaluation and prediction module, the operating status evaluation value JDY and the warning and maintenance value WJ of the electromechanical equipment are output. The operating status evaluation value JDY and the warning and maintenance value WJ are input into an optimization and adjustment module to output a maintenance adjustment factor TY. Based on the maintenance adjustment factor TY, the parameter values of the load and power in the current operating state of the electromechanical equipment are adjusted.

[0065] The comprehensive evaluation and prediction module includes a current operating status evaluation unit and a demand maintenance prediction unit. The specific implementation process of the comprehensive evaluation and prediction module is as follows:

[0066] Step S1: The current power value GL, the current load value FZ, and the current temperature value W of the equipment processed by the data processing module are input into the current operating status evaluation unit to obtain an operating status evaluation value JDY for evaluating the current comprehensive operating status of the electromechanical equipment;

[0067] Step S2: The operating status evaluation value JDY obtained by the current operating status evaluation unit is input into the demand maintenance prediction unit to obtain a warning and maintenance value WJ for predicting the maintenance requirements of the electromechanical equipment;

[0068] The optimization and adjustment module includes a parameter value adjustment and optimization unit. The specific implementation process of the parameter value adjustment and optimization unit is as follows:

[0069] The warning and maintenance value WJ obtained from the demand maintenance prediction unit and the difference between the operating status evaluation values JDY obtained by the current operating status evaluation unit in two adjacent times are input into the parameter value adjustment and optimization unit to obtain a maintenance adjustment factor TY. Based on the maintenance adjustment factor TY, the parameter values of the load and power in the current operating state of the electromechanical equipment are adjusted to optimize the maintenance strategy.

[0070] In this embodiment, through the mutual combination of the current operating status evaluation unit, the demand maintenance prediction unit, and the parameter value adjustment and optimization unit, the system solves the problems of insufficient real-time performance, incomplete data, lagging maintenance decision-making, and lack of intelligence existing in traditional monitoring methods.

[0071] It has achieved a comprehensive, real-time, and intelligent monitoring and optimization management effect on the status of electromechanical equipment, bringing significant economic and social benefits to the enterprise.

[0072] By combining the three calculation results of the operation status evaluation value JDY, the early warning maintenance value WJ, and the maintenance adjustment factor TY, a monitoring system for electromechanical equipment with comprehensive, real-time, and intelligent monitoring and optimization management can be formed.

[0073] Through continuous monitoring, evaluation, and adjustment, this system can gradually bring the electromechanical equipment closer to the optimal operation state, thereby improving the reliability and efficiency of the electromechanical equipment.

[0074] JDY is the operation status evaluation value. By evaluating the current operation status of the electromechanical equipment, it provides basic operation data for subsequent monitoring, analysis, and maintenance, helps identify potential problems of the electromechanical equipment, including overload or overheating problems, and uses the evaluation results as the basis for maintenance decisions.

[0075] WJ is the early warning maintenance value. It is used to predict the maintenance requirements of the electromechanical equipment and, based on the operation time and status evaluation of the equipment, provides a quantitative basis for formulating maintenance plans to ensure that the electromechanical equipment is maintained in a timely manner when needed.

[0076] TY is the maintenance adjustment factor. It not only evaluates the effectiveness of the current maintenance strategy but also guides the adjustment of future parameter values or the optimization of the maintenance strategy. Through a closed-loop feedback mechanism, it continuously optimizes the operation state and maintenance strategy of the electromechanical equipment to improve the overall performance and reliability of the electromechanical equipment. Moreover, the early warning maintenance value WJ can also affect the calculation fed back to the current operation status evaluation unit, thus jointly constituting the core algorithm framework of an electromechanical equipment monitoring system based on the Internet of Things. Through mutual association and feedback mechanisms, it realizes the real-time monitoring, evaluation, and optimization of the status of electromechanical equipment, making the three algorithms of this system have high relevance and entanglement, enabling the overall algorithm system to perform automated feedback and optimization according to the actual situation to be closer to reality.

[0077] Please refer to Figures 1 to 3 , the evaluation process of the current operation status evaluation unit is as follows:

[0078]

[0079]

[0080] Among them:

[0081] JDY is the operation status evaluation value;

[0082] GL is the current power value of the equipment;

[0083] FZ is the current load value of the equipment;

[0084] W is the current temperature value of the device;

[0085] JW is the standard constant temperature value of the device, which can be specifically set to 25 degrees Celsius;

[0086] a is the weight factor of PY;

[0087] PY is the temperature offset value, and PY reflects the degree of offset of W relative to the temperature of JW.

[0088] In this embodiment, first of all, this unit is mainly used to evaluate the operation status evaluation value JDY of the electromechanical device. It is a comprehensive index that combines multiple parameter values of the current power value GL, the current load value FZ, and the current temperature value W of the electromechanical device. This evaluation is crucial for monitoring the health status of the electromechanical device, timely discovering potential problems, and formulating maintenance plans. Moreover, all the data input for monitoring and calculation are real-time operation data of the current operation state of the electromechanical device, avoiding the occurrence of incorrect operation data and inaccurate calculation caused by insufficient real-time performance, thereby ensuring the rigor of the monitoring and calculation of the electromechanical device.

[0089] It is worth noting that the current power value GL of the device reflects the working intensity of the device. The larger the current load value FZ of the device, the more tasks the electromechanical device is processing. The higher the current temperature value W of the device, the electromechanical device will be in a high-temperature state, and high temperature will cause the electromechanical device to be overloaded or there are signs of low efficiency in the cooling system.

[0090] In addition, in the current operation state evaluation unit, JW is the standard constant temperature value of the device. The standard constant temperature value JW of the device represents the room temperature and serves as the reference point for the temperature offset amount, that is, the reference for the offset situation of the current temperature value W of the device;

[0091] This algorithm unit enables the electromechanical device monitoring system to continuously monitor the operation state of the electromechanical device by calculating the operation state evaluation value JDY in real time and timely discover any abnormalities or situations deviating from the normal operation range, achieving the purpose of real-time monitoring.

[0092] When the operation state evaluation value JDY is lower or higher than the safety value of the electromechanical device monitoring system, the electromechanical device monitoring system can issue a warning signal to prompt the operator or maintenance team to pay attention, avoiding the occurrence of faults or damages to the electromechanical device and ensuring the function of problem warning.

[0093] The operation state evaluation value JDY will serve as an important basis for formulating maintenance plans and making decisions, helping to determine the priority and timing of maintenance and providing maintenance decision support.

[0094] Please refer to Figures 1 to 3 , the prediction process of the demand maintenance prediction unit is as follows:

[0095]

[0096] Wherein:

[0097] WJ is the warning maintenance value;

[0098] YS is the equipment operation time value;

[0099] JDY is the operation status evaluation value;

[0100] JDZ is the preset operation status evaluation reference value;

[0101] b is the scaling factor, and b is used to adjust the numerical range of WJ;

[0102] GL is the current power value of the equipment;

[0103] BGL is the power reference value of the equipment.

[0104] In this embodiment, first, by combining the operation status evaluation value JDY, the current power value GL of the equipment, the equipment operation time value YS, the preset operation status evaluation reference value JDZ, and the power reference value BGL of the equipment, the warning maintenance value WJ is calculated, which is used to predict the maintenance requirements of the electromechanical equipment. This result value helps to plan maintenance activities in advance and avoid equipment downtime due to sudden failures.

[0105] Among them, both the operation status evaluation value JDY and the current power value GL of the equipment have reference safety values set by the electromechanical equipment monitoring system. They can not only further evaluate the cumulative wear or potential risks of the electromechanical equipment but also add additional considerations to the power of the electromechanical equipment, enabling high-power electromechanical equipment to reach the state requiring maintenance faster.

[0106] This unit calculates the warning maintenance value WJ, enabling the electromechanical equipment monitoring system to predict the maintenance requirements of the electromechanical equipment, arrange maintenance activities in advance, reduce the downtime caused by equipment failures, and has the effect of predictive maintenance.

[0107] According to the warning maintenance value WJ, a more reasonable and efficient maintenance plan can be formulated to ensure that the electromechanical equipment is maintained in a timely manner when needed, thereby optimizing the maintenance plan of the electromechanical equipment, reducing the number of emergency repairs, extending the service life of the electromechanical equipment, and reducing the overall maintenance cost.

[0108] Please refer to Figures 1 to 3 , and the adjustment and optimization process of the parameter value adjustment and optimization unit is as follows:

[0109]

[0110]

[0111] Wherein:

[0112] TY is the maintenance adjustment factor;

[0113] WJ is the warning maintenance value;

[0114] SSJDY is the evaluation value of the operation state of the penultimate run;

[0115] SJDY is the evaluation value of the operation state of the last run;

[0116] YG is the improvement value of the evaluation value of the operation state after the last maintenance;

[0117] is the evaluation value of the effectiveness of the current maintenance strategy.

[0118] In this embodiment, this unit calculates the maintenance adjustment factor TY based on the warning maintenance value WJ and the improvement value YG of the evaluation value of the operation state after the last maintenance. The maintenance adjustment factor TY will be used to guide the adjustment of the parameter values of subsequent electromechanical equipment or the optimization of the maintenance strategy. By calculating the maintenance adjustment factor TY, the effectiveness of the current maintenance strategy is evaluated, and the maintenance adjustment factor TY is an important part of the closed-loop feedback mechanism, which helps the system to continuously learn and optimize.

[0119] In this algorithm unit, by calculating the maintenance adjustment factor TY, the electromechanical equipment monitoring system can evaluate the effect of the current maintenance strategy and make adjustments and optimizations as needed to ensure the optimization and adjustment of the maintenance strategy; by optimizing the maintenance strategy, it can ensure that the electromechanical equipment operates in the best state, improve its performance and reliability, so as to improve the performance of the electromechanical equipment. The calculation of the maintenance adjustment factor TY enables the electromechanical equipment monitoring system to automatically adjust the maintenance strategy according to the actual situation, enhancing its adaptability and flexibility.

[0120] Please refer to Figures 1 to 3 , based on the maintenance adjustment factor TY, the steps for adjusting the maintenance strategy of the current operation state evaluation unit are as follows:

[0121] S1. For the maintenance adjustment factor TY, the current power value GL of the equipment, and the current temperature value W of the equipment, reference values for safe operation are set in the electromechanical equipment monitoring system. For example, the safe value of the current power value GL of the equipment set in the electromechanical equipment monitoring system is the equipment power reference value BGL input for calculation in the demand maintenance prediction unit.

[0122] According to the safe operation range set in the electromechanical equipment monitoring system, the adjustment of the electromechanical equipment maintenance strategy is as follows:

[0123] S2. If TY < 0 and W > JW, it reflects that the operating temperature of the electromechanical equipment is too high, and the current load value FZ of the equipment needs to be reduced to lower the operating temperature of the electromechanical equipment and reduce heat generation;

[0124] S3. If TY > 0 and GL < BGL, it indicates that the utilization rate of the operating power of the electromechanical equipment is relatively low, and the current power value GL of the equipment needs to be increased to improve the operating efficiency of the electromechanical equipment;

[0125] S4. If TY = 0, W = JW, and GL = BGL, it indicates that the temperature and efficiency of the electromechanical equipment operation are in a good operating state, and real-time detection and feedback can be carried out.

[0126] In this embodiment, this algorithm unit is based on the maintenance adjustment factor TY, the current power value GL of the equipment, the current temperature value W of the equipment, as well as the equipment power reference value BGL and the equipment standard constant temperature value JW as the reference benchmark for the monitoring of the electromechanical equipment. Further, when the maintenance adjustment factor TY is relatively low or high, it indicates that the current operating state or maintenance strategy of the electromechanical equipment is not optimal. At this time, according to the maintenance adjustment factor TY and the specific situation of the electromechanical equipment, the parameters of the current power value GL of the equipment and the current load value FZ in the current operating state evaluation unit can be adjusted;

[0127] For example, if the maintenance adjustment factor TY is low and the equipment temperature is high, an attempt can be made to reduce the current load value FZ of the equipment to reduce the heat generation during the operation of the electromechanical equipment;

[0128] If the maintenance adjustment factor TY is low and the equipment power utilization rate is not high, it can be considered to increase the current power value GL of the equipment to improve the efficiency. According to the change of the monitoring value of the maintenance adjustment factor TY and the change of different operating conditions of the electromechanical equipment, it will be able to more comprehensively and accurately reflect the operating condition problems of the electromechanical equipment. Moreover, adjusting the current power value GL of the equipment and the current load value FZ will directly affect the operating conditions and performance of the electromechanical equipment, and then be reflected in the calculation of the next operating state evaluation value JDY. If the adjustment is effective, the operating state evaluation value JDY will increase, indicating that the operating state of the electromechanical equipment has been improved;

[0129] This embodiment can reduce the occurrence of faults of the electromechanical equipment, extend the service life of the equipment, and improve the reliability of the electromechanical equipment through timely maintenance and adjustment. Secondly, by adjusting the load and power, it can ensure that the electromechanical equipment operates at the best working point, thereby improving the energy utilization efficiency and production efficiency; In addition, through predictive maintenance, intervention can be carried out before the electromechanical equipment fails, thus avoiding expensive emergency repairs and downtime and reducing the maintenance cost.

[0130] In summary, in the specific implementation process, this adjustment is part of a closed-loop feedback process that allows the electromechanical equipment monitoring system to optimize future operating conditions based on the current operating status and maintenance effects. Through continuous monitoring, evaluation, and adjustment, the electromechanical equipment monitoring system can gradually approach the optimal operating state of the equipment, thereby improving the reliability and efficiency of the electromechanical equipment.

[0131] Example 2, please refer to Figures 1 to 3 , the data acquisition module includes a data acquisition unit, and the data processing module includes a data cleaning unit, a data processing unit, and a data monitoring unit.

[0132] The data acquisition unit cleans, denoises, and normalizes the collected operation data through the data cleaning unit and the data processing unit, and then uses the data monitoring unit to monitor and give early warnings for the evaluation value JDY of the operating state of the electromechanical equipment.

[0133] The devices used in the data acquisition module include sensors, time recorders, and data acquisition systems, and the devices used in the data processing module include computing and processing devices and alarm systems.

[0134] In this embodiment, can the use of Internet of Things technology, sensors, and time recorders achieve real-time monitoring and data acquisition of the operating state of electromechanical equipment? The computing and processing devices perform preprocessing operations such as cleaning, denoising, and normalizing the collected operation data. In addition, the alarm system can provide an early warning function for the evaluation value JDY of the operating state.

[0135] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An electromechanical equipment monitoring system based on the Internet of Things, characterized in that include: Data collection module, data processing module, comprehensive evaluation and prediction module, and optimization and adjustment module; The steps for monitoring electromechanical equipment are as follows: Data collection: Based on the Internet of Things and through the data collection module, the operation data of electromechanical equipment is collected remotely in real time. The operation data includes the power, load and temperature of the electromechanical equipment during operation; Data processing: pre-process the collected operation data through the data processing module; Optimization strategy and parameter value adjustment: The operation status evaluation value JDY and the early warning maintenance value WJ of the electromechanical equipment are output through the comprehensive evaluation and prediction module, the operation status evaluation value JDY and the early warning maintenance value WJ are input into the optimization adjustment module, and the maintenance adjustment factor TY is output. Based on the maintenance adjustment factor TY, the load and power parameter values ​​in the current operation state of the electromechanical equipment are adjusted; The comprehensive evaluation and prediction module includes: a current operation status evaluation unit and a maintenance demand prediction unit; The optimization and adjustment module includes: a parameter value adjustment and optimization unit; The evaluation process of the current operation status evaluation unit is as follows: in: JDY is the operating status evaluation value; GL is the current power value of the device, FZ is the current load value of the device, W is the current temperature value of the device, and JW is the standard constant temperature value of the device; a is the weight factor of PY; PY is the temperature offset value, which reflects the degree of temperature offset of W relative to JW; The current power value GL, the current load value FZ and the current temperature value W of the equipment processed by the data processing module are input into the current operation status evaluation unit to obtain the operation status evaluation value JDY for evaluating the current comprehensive operation status of the electromechanical equipment; The prediction process of the demand maintenance prediction unit is as follows: in: WJ is the early warning maintenance value; YS is the equipment operation time value, JDY is the operation status evaluation value, and JDZ is the preset operation status evaluation benchmark value; b is the scaling factor, which is used to adjust the numerical range of WJ; GL is the current power value of the device, and BGL is the power baseline value of the device; The operation status evaluation value JDY obtained by the current operation status evaluation unit is input into the demand maintenance prediction unit to obtain the early warning maintenance value WJ for predicting the maintenance demand of the electromechanical equipment; The adjustment and optimization process of the parameter value adjustment and optimization unit is as follows: in: TY is the maintenance adjustment factor; WJ is the early warning maintenance value; SSJDY is the last operation status evaluation value, and SJDY is the last operation status evaluation value; YG is the improvement value of the operating status evaluation value after the last maintenance; Provide an assessment of the effectiveness of the current maintenance strategy; The early warning maintenance value WJ obtained by the demand maintenance prediction unit and the difference between two adjacent operation status evaluation values ​​JDY obtained by the current operation status evaluation unit are input into the parameter value adjustment optimization unit to obtain the maintenance adjustment factor TY.

2. The electromechanical equipment monitoring system based on the Internet of Things according to claim 1 is characterized in that: Based on the maintenance adjustment factor TY: If the maintenance adjustment factor TY < 0, and the current temperature value W of the equipment > the standard constant temperature value JW of the equipment, it means that the operating temperature of the electromechanical equipment is too high, and the current load value FZ of the equipment should be reduced; If TY>0, and the current power value GL of the equipment is less than the power reference value BGL of the equipment, it means that the utilization rate of the operating power of the electromechanical equipment is low, and the current power value GL of the equipment is increased; If TY=0, and the current temperature value W of the equipment = the standard constant temperature value JW of the equipment, and the current power value GL of the equipment = the power reference value BGL of the equipment, then the temperature and efficiency of the electromechanical equipment are in a good operating state, and real-time detection and output can be performed.

3. The electromechanical equipment monitoring system based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module includes a data acquisition unit, and the data processing module includes a data cleaning unit, a data processing unit and a data monitoring unit.

4. The electromechanical equipment monitoring system based on the Internet of Things according to claim 3 is characterized in that: The data collection unit performs cleaning, denoising and normalization preprocessing operations on the collected operation data through the data cleaning unit and the data processing unit, and then uses the data monitoring unit to monitor and warn the electrical equipment operation status evaluation value JDY.

5. The electromechanical equipment monitoring system based on the Internet of Things according to claim 4 is characterized in that: The data acquisition module includes a sensor, a time recorder and a data acquisition system, and the data processing module includes a computing and processing device and an alarm system.

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