Electromechanical equipment monitoring system based on Internet of Things

Abstract

The invention discloses an electromechanical equipment monitoring system based on the Internet of Things, and relates to the technical field of electromechanical equipment monitoring, the electromechanical equipment monitoring system comprises a data acquisition module, a data processing module, a comprehensive evaluation prediction module and an optimization adjustment module, and operation data of electromechanical equipment are remotely acquired in real time through the data acquisition module based on the Internet of Things. The operation data comprises the power, the load and the temperature of the electromechanical equipment during operation, the data processing module is used for preprocessing the collected operation data, the comprehensive evaluation and prediction module is used for outputting an operation state evaluation value JDY and an early warning maintenance value WJ of the electromechanical equipment, the operation state evaluation value JDY and the early warning maintenance value WJ are input into the optimization and adjustment module, and the optimization and adjustment module is used for performing optimization and adjustment on the operation state evaluation value JDY and the early warning maintenance value WJ. According to the method, the maintenance adjustment factor TY is output, and the parameter values of the load and the power in the current operation state of the electromechanical equipment are adjusted based on the maintenance adjustment factor TY, so that production interruption and safety accidents caused by faults of the electromechanical equipment are avoided, and the reliability and the safety of operation and use of the electromechanical equipment are further improved.

Description

Technical Field

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

[0002] Electromechanical equipment is a type of equipment that is generally installed in an electrical cabinet. It is usually connected to a transmission line and a power supply system to ensure the operation and movement of the electromechanical equipment. Therefore, it plays a vital role in production. If an electromechanical equipment fails, a safety accident may occur. In order to reduce the occurrence of safety accidents or greatly reduce losses or dangers when a safety accident occurs, a monitoring system based on the Internet of Things is generally installed on the electromechanical equipment to monitor the operation of the electromechanical equipment, which is conducive to reducing the possibility of safety accidents or being able to respond in time to reduce losses when a safety accident occurs.

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

[0004] Moreover, traditional monitoring methods can often only obtain limited data parameters, which makes it difficult to fully reflect the operating status of electromechanical equipment, and there is incompleteness in the collected data. In addition, strategies based on regular maintenance often lead to inaccurate maintenance timing, which may waste resources due to excessive maintenance, and may also accelerate the aging of electromechanical equipment due to insufficient maintenance, thereby causing delays in maintenance decisions. Traditional systems lack self-learning and adaptive capabilities, and it is difficult to automatically adjust maintenance strategies according to the actual operating conditions of electromechanical equipment, and lack intelligent adjustment and 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-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following 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 for monitoring electromechanical equipment are as follows:

[0008] 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;

[0009] Data processing: pre-process the collected operation data through the data processing module;

[0010] Optimization strategy and parameter value adjustment: The comprehensive evaluation and prediction module outputs the operating status evaluation value JDY and the early warning maintenance value WJ of the electromechanical equipment, inputs the operating status evaluation value JDY and the early warning maintenance value WJ into the optimization adjustment module, outputs the maintenance adjustment factor TY, and adjusts the parameter values ​​of the load and power in the current operating state of the electromechanical equipment based on the maintenance adjustment factor TY.

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

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

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

[0014]

[0015]

[0016] in:

[0017] JDY is the operating status evaluation value;

[0018] 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;

[0019] a is the weight factor of PY;

[0020] PY is the temperature offset value, which reflects the degree of temperature offset of W relative to 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] in:

[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, which is used to adjust the numerical range of WJ;

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

[0029] The operating state evaluation value JDY obtained by the current operating state 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.

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

[0031]

[0032]

[0033] in:

[0034] TY is the maintenance adjustment factor;

[0035] WJ is the early warning maintenance value;

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

[0037] YG is the improvement value of the operating status evaluation value after the last maintenance;

[0038] Provide an assessment of the effectiveness of the current maintenance strategy;

[0039] The early warning maintenance value WJ obtained by the demand maintenance prediction unit and the difference between the two 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;

[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 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;

[0042] 2. 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;

[0043] 3. 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.

[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 collection unit performs cleaning, denoising and normalization preprocessing operations on the collected operating data by the data cleaning unit and the data processing unit, and then uses the data monitoring unit to monitor and warn the electrical equipment operating status evaluation value JDY.

[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 present invention has the following beneficial effects:

[0048] 1. The present invention uses the Internet of Things technology to collect various operating data of electromechanical equipment, such as power, load and temperature, in real time, and immediately uses the current operating status evaluation unit to calculate the real-time operating status evaluation value of the equipment, thereby realizing instant grasp of the status of the electromechanical equipment and enhancing the real-time nature of monitoring.

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

[0050] In addition, the electromechanical equipment monitoring system uses the demand maintenance prediction unit to calculate the maintenance warning results, and predicts maintenance needs based on the operating time and status evaluation of the electromechanical equipment, making maintenance decisions more accurate and scientific. It not only reduces unnecessary downtime, but also improves the utilization rate of electromechanical equipment. This not only achieves comprehensive data improvement, but also ensures the accuracy of maintenance decisions.

[0051] 3. The present invention adjusts the calculation of the optimization unit through parameter values, and the electromechanical equipment monitoring system can evaluate the effectiveness of the current maintenance strategy, and automatically adjust and optimize according to the evaluation results. This closed-loop feedback mechanism enables the electromechanical equipment monitoring system to have self-learning and self-adaptive capabilities, which can continuously improve maintenance efficiency and equipment performance, and ensure intelligent optimization effects.

[0052] 4. Through predictive maintenance and precise maintenance decision-making, the present invention enables the electromechanical equipment monitoring system to effectively reduce the number of emergency repairs and downtime, thereby reducing maintenance costs. At the same time, by optimizing the operating parameter values ​​and maintenance strategies of electromechanical equipment, energy consumption and operating costs can be further reduced.

[0053] 5. The present invention utilizes real-time monitoring and early warning functions to enable the electromechanical equipment monitoring system to promptly detect and handle hidden equipment failures, thereby avoiding production interruptions and safety accidents caused by electromechanical equipment failures, and thereby improving the reliability and safety of the operation of electromechanical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The method flow chart of the electromechanical equipment monitoring system based on the Internet of Things is as follows;

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

[0056] Figure 3 It is a structural schematic diagram of the data processing module of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Regarding this electromechanical equipment monitoring system based on the Internet of Things, it is different from the existing electromechanical equipment monitoring system. The existing electromechanical equipment monitoring system is difficult to realize real-time monitoring of electromechanical equipment through manual inspection of the added equipment, and it is impossible to timely discover potential problems of electromechanical equipment, resulting in insufficient real-time performance, and it is difficult to fully reflect the operating status of the electromechanical equipment. There is incomplete collection of data. In addition, only the strategy of regular maintenance will lead to inaccurate maintenance timing. Finally, the traditional system lacks self-learning and adaptive capabilities, while this algorithm unit fully considers the power, load, temperature and historical data of the electromechanical equipment, which not only realizes the instant grasp of the status of the electromechanical equipment and enhances the real-time monitoring, but also can more comprehensively reflect the health status of the electromechanical equipment, ensuring the accuracy of maintenance decisions, and the closed-loop feedback mechanism that automatically adjusts and optimizes 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, it can further reduce energy consumption and operating costs. Finally, the real-time monitoring and early warning functions are used to improve the reliability and safety of the operation and use of electromechanical equipment.

[0059] For example, see Figures 1 to 3 ,This implementation provides a mechatronic equipment monitoring system based on the Internet of Things, which is implemented by 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 the data acquisition 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;

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

[0063] The collected operation data is pre-processed through the data processing module, and the data pre-processing includes cleaning, sorting and analysis;

[0064] The comprehensive evaluation and prediction module outputs the operating status evaluation value JDY and the early warning maintenance value WJ of the electromechanical equipment, which are then input into the optimization and adjustment module, and the maintenance adjustment factor TY is output. 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 the current operation status evaluation unit and the demand maintenance prediction unit. The specific implementation process of the comprehensive evaluation and prediction module is as follows:

[0066] Step S1, inputting 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 into the current operation state evaluation unit to obtain the operation state evaluation value JDY for evaluating the current comprehensive operation state of the electromechanical equipment;

[0067] Step S2, inputting the operation status evaluation value JDY obtained by the current operation status evaluation unit into the demand maintenance prediction unit to obtain the early warning maintenance value WJ for predicting the maintenance demand of the electromechanical equipment;

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

[0069] The warning maintenance value WJ obtained by the demand maintenance prediction unit and the difference between the two operating status evaluation values ​​JDY obtained by the current operating status evaluation unit are input into the parameter value adjustment optimization unit to obtain the 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, the system solves the problems of insufficient real-time performance, incomplete data, delayed maintenance decision-making and lack of intelligence in traditional monitoring methods through the mutual combination of a current operating status evaluation unit, a demand maintenance prediction unit and a parameter value adjustment optimization unit.

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

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

[0073] The system can continuously monitor, evaluate and adjust, so that the electromechanical equipment gradually approaches the optimal operating state, thereby improving the reliability and efficiency of the electromechanical equipment.

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

[0075] WJ is the early warning maintenance value, which uses the predicted maintenance needs of electromechanical equipment and the equipment's operating time and status evaluation to provide a quantitative basis for formulating maintenance plans, ensuring that electromechanical equipment is maintained in a timely manner when needed.

[0076] TY is the maintenance adjustment factor, which not only evaluates the effectiveness of the current maintenance strategy, but also guides the adjustment of future parameter values ​​or the optimization of maintenance strategies. Through the closed-loop feedback mechanism, the operating status and maintenance strategy of electromechanical equipment are continuously optimized to improve the overall performance and reliability of electromechanical equipment. The early warning maintenance value WJ can also affect the calculation of the current operating status evaluation unit, thus forming a core algorithm framework of an electromechanical equipment monitoring system based on the Internet of Things. Through mutual correlation and feedback mechanism, real-time monitoring, evaluation and optimization of the status of electromechanical equipment are realized, so that the three algorithms of this system have high correlation and entanglement, so that the overall algorithm system can perform automatic feedback and optimization according to the actual situation to be closer to reality.

[0077] See also Figures 1 to 3 , the evaluation process of the current running status evaluation unit is as follows:

[0078]

[0079]

[0080] in:

[0081] JDY is the operating status evaluation value;

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

[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 equipment, which can be set to 25 degrees Celsius;

[0086] a is the weight factor of PY;

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

[0088] In this embodiment, firstly, this unit is mainly used to evaluate the operating status evaluation value JDY of the electromechanical equipment. It is a comprehensive indicator that combines multiple parameter values ​​of the electromechanical equipment's current power value GL, the equipment's current load value FZ, and the equipment's current temperature value W. This evaluation is crucial for monitoring the health status of the electromechanical equipment, timely discovering potential problems, and formulating maintenance plans. The input for monitoring and calculation are all real-time operating data of the electromechanical equipment's current operating status, avoiding the occurrence of confusion in operating data and inaccurate calculations due to insufficient real-time performance, thereby ensuring the rigor of the monitoring and calculation of the electromechanical equipment.

[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 higher the electromechanical device will be at a high temperature. High temperature will cause the electromechanical device to be overloaded or the cooling system efficiency will be low.

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

[0091] This algorithm unit calculates the operating status evaluation value JDY in real time, so that the electromechanical equipment monitoring system can continuously monitor the operating status of the electromechanical equipment and promptly detect any abnormalities or deviations from the normal operating range, thereby achieving the purpose of real-time monitoring.

[0092] When the operating status assessment value JDY is lower or higher than the safety value of the electromechanical equipment monitoring system, the electromechanical equipment monitoring system can send out an early warning signal to alert the operator or maintenance team to avoid failure or damage of the electromechanical equipment and ensure the problem early warning function.

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

[0094] See also Figures 1 to 3 ,The prediction process of the demand maintenance prediction unit is as follows:

[0095]

[0096] in:

[0097] WJ is the early warning maintenance value;

[0098] YS is the equipment running time value;

[0099] JDY is the operating status evaluation value;

[0100] JDZ is the preset operating status assessment benchmark value;

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

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

[0103] BGL is the equipment power baseline value.

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

[0105] Among them, the operating status assessment value JDY and the equipment current power value GL are both reference safety values ​​set by the electromechanical equipment monitoring system. They can not only further evaluate the accumulated wear or potential risks of the electromechanical equipment, but also add additional considerations to the power of the electromechanical equipment, allowing high-power electromechanical equipment to reach a state requiring maintenance more quickly.

[0106] This unit calculates the early warning maintenance value WJ, so that the electromechanical equipment monitoring system can predict the maintenance needs of electromechanical equipment, arrange maintenance activities in advance, reduce downtime caused by equipment failure, and has a predictive maintenance effect.

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

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

[0109]

[0110]

[0111] in:

[0112] TY is the maintenance adjustment factor;

[0113] WJ is the early warning maintenance value;

[0114] SSJDY is the last operation status evaluation value;

[0115] SJDY is the last operation status evaluation value;

[0116] YG is the improvement value of the operating status evaluation value after the last maintenance;

[0117] Evaluate the effectiveness of the current maintenance strategy.

[0118] In this embodiment, this unit calculates the maintenance adjustment factor TY based on the early warning maintenance value WJ and the improvement value YG of the operating status evaluation value after the last maintenance. The maintenance adjustment factor TY will be used to guide the subsequent parameter value adjustment or maintenance strategy optimization of the electromechanical equipment. By calculating the maintenance adjustment factor TY, the effectiveness of the current maintenance strategy is evaluated. 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, the maintenance adjustment factor TY is calculated to enable the electromechanical equipment monitoring system to evaluate the effectiveness of the current maintenance strategy, and to adjust and optimize it as needed to ensure the optimal adjustment of the maintenance strategy; by optimizing the maintenance strategy, it can ensure that the electromechanical equipment operates in the best condition, improve its performance and reliability, thereby improving 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 actual conditions, thereby enhancing its adaptability and flexibility.

[0120] See also Figures 1 to 3 , based on the maintenance adjustment factor TY, the maintenance strategy adjustment steps for the current operating status evaluation unit are as follows:

[0121] S1, maintenance adjustment factor TY, current equipment power value GL and current equipment temperature value W are all set with reference values ​​for safe operation in the electromechanical equipment monitoring system. For example, the safety value of the current equipment power value GL 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 operating range set in the electromechanical equipment monitoring system, the electromechanical equipment maintenance strategy is adjusted 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 reduce 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 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 operation of the electromechanical equipment 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, an increase in the current power value GL of the equipment can be considered to improve the efficiency. According to the change of the monitored value of the maintenance adjustment factor TY and the change of different operating conditions of the electromechanical equipment, it can 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] Through timely maintenance and adjustment in this embodiment, the occurrence of faults in the electromechanical equipment can be reduced, the service life of the equipment can be extended, and the reliability of the electromechanical equipment can be improved. Secondly, by adjusting the load and power, it is possible to ensure that the electromechanical equipment operates at the best operating 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] To sum up, in the specific implementation process, this adjustment is part of a closed-loop feedback process, which 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] For 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 collection unit cleans, denoises and normalizes the collected operating data through the data cleaning unit and the data processing unit, and then uses the data monitoring unit to monitor and warn the operating status evaluation value JDY of the electromechanical equipment.

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

[0134] In this embodiment, whether the Internet of Things technology, sensors and time recorders can be used to realize real-time monitoring and data collection of the operating status of electromechanical equipment, and the computing and processing equipment can pre-process the collected operating data by cleaning, denoising and normalizing. In addition, the alarm system can provide an early warning function for the operating status evaluation value JDY.

[0135] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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 comprehensive evaluation and prediction module outputs the operating status evaluation value JDY and the early warning maintenance value WJ of the electromechanical equipment, inputs the operating status evaluation value JDY and the early warning maintenance value WJ into the optimization adjustment module, outputs the maintenance adjustment factor TY, and adjusts the parameter values ​​of the load and power in the current operating state of the electromechanical equipment based on 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: 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.

3. The electromechanical equipment monitoring system based on the Internet of Things according to claim 2 is characterized in that: 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.

4. The electromechanical equipment monitoring system based on the Internet of Things according to claim 3 is characterized in that: 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 operating state evaluation value JDY obtained by the current operating state 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.

5. The electromechanical equipment monitoring system based on the Internet of Things according to claim 4 is characterized in that: 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 the two 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; 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.

6. 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.

7. The electromechanical equipment monitoring system based on the Internet of Things according to claim 7 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.

8. The electromechanical equipment monitoring system based on the Internet of Things according to claim 8 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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