Artificial intelligence-based electrical automation equipment state prediction method and device

Abstract

The present invention relates to the field of data processing technology, and specifically discloses an artificial intelligence-based electrical automation equipment state prediction method and device. The method can evaluate the heat dissipation efficiency in real time and effectively monitor the thermal management state of the electrical automation equipment by accurately collecting and analyzing the operating environment parameters of the equipment. By utilizing advanced artificial intelligence algorithms, thermal management anomalies can be quickly identified, and potential faults can be warned in a timely manner, thereby reducing the risk of production interruption of electrical automation equipment due to sudden faults. In addition, through intelligent analysis of the operating state parameters of the electrical automation equipment, the control strategy can be dynamically optimized to ensure that the electrical automation equipment is in the best operating state. At the same time, the operating accuracy level of the electrical automation equipment can be accurately predicted, providing a scientific basis for precise control and further improving the operating accuracy of the electrical automation equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an artificial intelligence-based electrical automation equipment state prediction method and device. Background Art

[0002] With the rapid development of intelligent manufacturing, automated equipment plays an increasingly important role in production and manufacturing, energy distribution, transportation and other fields. However, during long-term operation, these devices often experience performance degradation or failure due to wear, aging, environmental factors or improper operation. This not only affects production efficiency but may also cause safety hazards. Therefore, accurate prediction of the status of automated equipment and timely maintenance have become one of the key technologies to improve equipment operation reliability and reduce maintenance costs.

[0003] For example, the invention patent with publication number CN119025841A discloses a method and system for monitoring the status of electrical equipment, which involves the field of electrical equipment monitoring technology, including collecting data based on the collection items of the monitored electrical equipment and eliminating outliers; setting the initial collection frequency and performing linear interpolation and spline interpolation based on the collection items to fill in the collection item data; and constructing a long short-term memory network LSTM model for time series prediction.

[0004] For example, the invention patent with publication number CN113760992A discloses a method, apparatus, device and storage medium for predicting the operating status of electrical equipment, which relates to the field of computer technology, and in particular to the field of artificial intelligence technology such as deep learning and big data. The specific implementation scheme is: obtaining historical operating status parameters of the target electrical equipment; obtaining a reference data set according to the type of the target electrical equipment, wherein the reference data set includes operating status parameters of multiple reference electrical equipment at various periods; determining the operating status of the target electrical equipment based on the historical operating status parameters of the target electrical equipment and the operating status parameters of multiple reference electrical equipment at various periods.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: when predicting the performance status of automation equipment, the existing technology generally only analyzes the operating parameters of the automation equipment itself, so that the performance prediction model cannot fully capture all key factors affecting the performance of the equipment. This limitation limits the depth and breadth of the prediction results, making the prediction results not accurate and comprehensive enough. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the present invention provides an artificial intelligence-based electrical automation equipment state prediction method and device, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an electrical automation equipment state prediction method based on artificial intelligence, including: step one, a data collection device collects the operating environment parameters of the electrical automation equipment, and a data processing device uses the first artificial intelligence method to evaluate the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment; step two, the data collection device obtains the thermal management state parameters of the electrical automation equipment, and the data processing device uses the second artificial intelligence method to analyze the thermal management anomaly index of the electrical automation equipment, and determines whether to optimize the preset electrical automation equipment operating state parameter control set based on the thermal management anomaly index of the electrical automation equipment; step three, the control device controls the electrical automation equipment through the operating state parameter control set of the electrical automation equipment, the data collection device collects the operating state parameters of the electrical automation equipment, the data processing device uses the third artificial intelligence method to determine the operating precision factor of the electrical automation equipment, and predicts the operating accuracy level of the electrical automation equipment based on the operating accuracy factor of the electrical automation equipment, and the feedback device controls and feeds back the electrical automation equipment state based on the operating accuracy level of the electrical automation equipment.

[0008] As a further method, the electrical automation equipment is controlled by the operating status parameter control set of the electrical automation equipment. Specifically, if it is determined that the preset operating status parameter control set of the electrical automation equipment is not to be optimized, the electrical automation equipment is directly controlled by the operating status parameter control set of the preset electrical automation equipment; if it is determined that the preset operating status parameter control set of the electrical automation equipment is to be optimized, the optimized operating status parameter control set of the electrical automation equipment is recorded as the operating status parameter control set of the electrical automation equipment that is updated once, and the electrical automation equipment is controlled by updating the operating status parameter control set of the electrical automation equipment once.

[0009] As a further method, the operation accuracy level of the electrical automation equipment is predicted, and the specific prediction process is: comparing the operation accuracy factor of the electrical automation equipment with the first operation accuracy factor interval, the second operation accuracy factor interval and the third operation accuracy factor interval stored in the electrical database; if the operation accuracy factor of the electrical automation equipment belongs to the first operation accuracy factor interval, then the operation accuracy level of the electrical automation equipment is predicted to be the first level; if the operation accuracy factor of the electrical automation equipment belongs to the second operation accuracy factor interval, then the operation accuracy level of the electrical automation equipment is predicted to be the second level; if the operation accuracy factor of the electrical automation equipment belongs to the third operation accuracy factor interval, then the operation accuracy level of the electrical automation equipment is predicted to be the third level.

[0010] As a further method, the control feedback of the state of the electrical automation equipment is performed, and the specific control feedback process is: if the operating accuracy level of the electrical automation equipment is the first level, the thermal management state parameters of the electrical automation equipment are continuously obtained; if the operating accuracy level of the electrical automation equipment is the second level, a second optimization control set is matched from the electrical database according to the operating accuracy factor of the electrical automation equipment, and the operating state parameter control set of the once-updated electrical automation equipment is optimized to obtain the operating state parameter control set of the second-updated electrical automation equipment, and the electrical automation equipment is controlled by the operating state parameter control set of the second-updated electrical automation equipment; if the operating accuracy level of the electrical automation equipment is the third level, an early warning log is generated for feedback.

[0011] The second aspect of the present invention provides an electrical automation equipment state prediction device based on artificial intelligence, including: a data collection device, a data processing device, a control device and a feedback device; the data collection device is used to collect the operating environment parameters of the electrical automation equipment, obtain the thermal management state parameters of the electrical automation equipment and collect the operating state parameters of the electrical automation equipment; the data processing device is used to use a first artificial intelligence method to evaluate the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment, use a second artificial intelligence method to analyze the thermal management anomaly index of the electrical automation equipment and use a third artificial intelligence method to determine the operating precision factor of the electrical automation equipment; the control device is used to control the electrical automation equipment through the operating state parameter control set of the electrical automation equipment; the feedback device is used to control and feedback the state of the electrical automation equipment based on the operating precision level of the electrical automation equipment.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0013] (1) The present invention provides an artificial intelligence-based electrical automation equipment state prediction method and device. The method can evaluate the heat dissipation efficiency in real time and effectively monitor the thermal management state of the electrical automation equipment by accurately collecting and analyzing the operating environment parameters of the equipment. By using advanced artificial intelligence algorithms, it can quickly identify thermal management anomalies and timely warn of potential faults, thereby greatly shortening the time window for equipment maintenance and reducing the risk of production interruption of electrical automation equipment due to sudden faults. In addition, through intelligent analysis of the operating state parameters of the electrical automation equipment, it can dynamically optimize the control strategy to ensure that the electrical automation equipment is in the best operating state. At the same time, it can also accurately predict the operating accuracy level of the electrical automation equipment, provide a scientific basis for precise control, and further improve the operating accuracy of the electrical automation equipment. This method not only significantly enhances the reliability and stability of the electrical automation equipment, but also realizes efficient automated management of the electrical automation equipment through an intelligent prediction and control feedback mechanism.

[0014] (2) By collecting and analyzing the operating environment parameters of electrical automation equipment, this method can provide a comprehensive understanding of the various key factors that affect the performance of electrical automation equipment, thereby overcoming the limitations of traditional performance prediction models and effectively solving the problem that the prediction results of previous prediction methods were not accurate and comprehensive due to the inability to fully capture all key factors. By deeply exploring the complex relationship between environmental parameters and the performance of electrical automation equipment, it not only improves the accuracy of the prediction results, but also greatly expands the depth and breadth of the prediction, providing a more reliable scientific basis for the status monitoring and maintenance of electrical automation equipment.

[0015] (3) This method achieves personalized control feedback on the status of electrical automation equipment by accurately determining the operating accuracy factor of electrical automation equipment, effectively avoiding over-maintenance or under-maintenance, and significantly improving the operating accuracy of electrical automation equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0017] Figure 1 Schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1 As shown, the first aspect of the present invention provides an electrical automation equipment state prediction method based on artificial intelligence, including: step one, a data collection device collects operating environment parameters of the electrical automation equipment, and a data processing device uses the first artificial intelligence method to evaluate the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment.

[0020] The above-mentioned first artificial intelligence method represents a specific evaluation method for the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment.

[0021] In a specific embodiment, this method can comprehensively understand the various key factors affecting the performance of electrical automation equipment by collecting and analyzing the operating environment parameters of electrical automation equipment, thereby overcoming the limitations of traditional performance prediction models and effectively solving the problem that the prediction results of previous prediction methods were not accurate and comprehensive due to the inability to fully capture all key factors. By deeply exploring the complex relationship between environmental parameters and the performance of electrical automation equipment, it not only improves the accuracy of the prediction results, but also greatly expands the depth and breadth of the prediction, providing a more reliable scientific basis for the status monitoring and maintenance of electrical automation equipment.

[0022] Specifically, the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment is evaluated, and the specific evaluation process is as follows: the operating environment parameters of the electrical automation equipment include the average temperature of the operating environment of the electrical automation equipment during the environmental monitoring period, the average air flow speed of the operating environment of the electrical automation equipment during the environmental monitoring period, and the average thermal radiation intensity of the operating environment of the electrical automation equipment during the environmental monitoring period; the above-mentioned environmental monitoring period represents the time point for monitoring and analyzing the operating environment of the electrical automation equipment, and the specific duration is determined by the equipment manager; the average temperature of the operating environment of the electrical automation equipment during the environmental monitoring period represents the average temperature level of the operating environment of the electrical automation equipment during the environmental monitoring period, which can be achieved by installing The temperature sensors in the operating environment perform real-time measurements, and all the measured values ​​are averaged. The above-mentioned average air flow velocity represents the average value of the air flow velocity in the operating environment of the electrical automation equipment during the environmental monitoring period, and can be obtained by real-time measurement by the anemometers installed in the operating environment, and all the measured values ​​are averaged. The above-mentioned average thermal radiation intensity represents the average value of the thermal radiation intensity received by the operating environment of the electrical automation equipment during the environmental monitoring period. The thermal radiation intensity refers to the energy passing through a unit area per unit time, which reflects the strength of the thermal radiation in the environment. It can be measured in real time by the thermal radiation meters installed in the operating environment, and all the measured values ​​are averaged.

[0023] The average temperature of the surface detection portion of the electrical automation equipment during the environmental monitoring period is obtained, and the difference between the difference and the average temperature of the operating environment of the electrical automation equipment during the environmental monitoring period is performed. The result of the difference is then compared with the average temperature of the operating environment of the electrical automation equipment during the environmental monitoring period to ultimately obtain the heat dissipation efficiency value of the operating environment of the electrical automation equipment during the environmental monitoring period. The average temperature of the surface detection portion of the electrical automation equipment during the environmental monitoring period represents the average temperature of the surface detection portion of the electrical automation equipment during the environmental monitoring period and can be obtained by monitoring and analyzing using infrared imaging technology (such as an infrared thermal imager). During the environmental monitoring period, the infrared thermal imager scans the surface detection portion of the electrical automation equipment multiple times at predetermined time intervals, records the temperature data obtained from each scan, and calculates the average temperature of the surface detection portion during the monitoring period by statistically analyzing and averaging the temperature data. The surface detection portion refers to the surface area of ​​the electrical automation equipment that can be monitored by the infrared imaging technology. The heat dissipation efficiency value is used to quantify the efficiency of the electrical automation equipment in dissipating heat to the operating environment through its surface during the environmental monitoring period.

[0024] Comprehensively evaluate the heat dissipation efficiency value of the operating environment of the electrical automation equipment during the environmental monitoring period, the average air flow speed of the operating environment of the electrical automation equipment during the environmental monitoring period, and the average thermal radiation intensity of the operating environment of the electrical automation equipment during the environmental monitoring period to obtain the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment. The heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment is used to comprehensively quantify the heat dissipation capacity of the operating environment of the electrical automation equipment.

[0025] The specific evaluation method for the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment is as follows:

[0026] ;

[0027] Where, is the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment. It is the heat dissipation efficiency value of the operating environment of the electrical automation equipment during the environmental monitoring period. It is the average air flow velocity in the operating environment of the electrical automation equipment during the environmental monitoring period. It is the average heat radiation intensity of the operating environment of the electrical automation equipment during the environmental monitoring period. It is the defined heat dissipation efficiency value preset in the electrical database. The average air velocity is defined in the electrical database. The average heat radiation intensity is defined in the electrical database. The influence weight of the heat dissipation efficiency value preset in the electrical database, The average air flow velocity influence weight preset in the electrical database is is the average thermal radiation intensity influence weight preset in the electrical database, and e is a natural constant.

[0028] The above-defined heat dissipation efficiency value indicates the minimum allowable heat dissipation efficiency value of the operating environment of the electrical automation equipment during the environmental monitoring period; the above-defined average air flow velocity indicates the minimum allowable average air flow velocity of the operating environment of the electrical automation equipment during the environmental monitoring period; the above-defined average thermal radiation intensity indicates the maximum allowable average thermal radiation intensity of the operating environment of the electrical automation equipment during the environmental monitoring period.

[0029] The above-mentioned heat dissipation efficiency value influence weight represents the numerical value of the influence degree of the unit value of the heat dissipation efficiency value on the heat dissipation efficiency coefficient; the above-mentioned air flow average speed influence weight represents the numerical value of the influence degree of the unit value of the air flow average speed on the heat dissipation efficiency coefficient; the above-mentioned average thermal radiation intensity influence weight represents the numerical value of the influence degree of the unit value of the average thermal radiation intensity on the heat dissipation efficiency coefficient. The electrical database stores the correspondence between the heat dissipation efficiency value and its corresponding influence weight, the correspondence between the average air flow speed and its corresponding influence weight, and the correspondence between the average thermal radiation intensity and its corresponding influence weight. For example, if the heat dissipation efficiency value, the average air flow speed and the average thermal radiation intensity are input into the electrical database, the electrical database can match the heat dissipation efficiency value influence weight, the average air flow speed influence weight and the average thermal radiation intensity influence weight, and the value range is between 0 and 1.

[0030] It needs to be explained that the increase in the average air flow speed has a significant enhancing effect on the convective heat dissipation effect of heat in the operating environment. This increase allows the ambient temperature to be lower than the temperature of the electrical automation equipment, thereby directly improving the heat dissipation efficiency value. The reason is that air flow accelerates the heat transfer rate from the surface of the electrical automation equipment to the surrounding environment, effectively avoiding the accumulation of the equipment surface temperature in the environment. At the same time, air flow also promotes the widespread diffusion of heat, further reducing the thermal radiation intensity in the operating environment, so that the average thermal radiation intensity can be weakened accordingly, thereby improving the heat dissipation capacity of the operating environment to which the electrical automation equipment belongs. However, when the heat dissipation efficiency value increases abnormally and significantly, this strongly indicates that within a given environmental monitoring period, the average temperature recorded at the surface detection part of the electrical automation equipment shows an abnormally high value compared to the average temperature of the operating environment to which it belongs. This abnormal phenomenon profoundly reveals that the operating environment temperature is too low. This low temperature environment not only directly leads to a significant reduction in the thermal radiation intensity, thereby weakening the effect of heat conduction and convection heat dissipation, but also in this situation, the average air flow speed also The excessive increase of this coefficient also has an adverse effect on the operating accuracy of the electrical automation equipment. The electronic components and mechanical parts inside the electrical automation equipment are designed with a certain temperature operating range. When the ambient temperature is too low, the performance of these components and parts may change. For example, the thermal expansion and contraction of the material will cause dimensional changes, which in turn affects the fitting clearance of precision parts; the viscosity of the lubricating oil will increase, reducing the operating efficiency of the mechanical parts; and the conductive performance of the electronic components may be affected by the low temperature, resulting in signal transmission delay or distortion. It is difficult for the electrical automation equipment to maintain the best working state and performance. The low thermal efficiency coefficient of the operating environment of the electrical automation equipment clearly indicates poor heat dissipation, which directly leads to the difficulty of effectively dissipating the heat accumulated inside the electrical automation equipment, accelerating the aging process of the electronic components, and may even directly cause component failure, resulting in a sharp drop in performance or complete failure of the electrical automation equipment. Therefore, the thermal efficiency coefficient of the operating environment of the electrical automation equipment needs to be controlled within a reasonable range.

[0031] The electrical database is a database used to store parameters involved in the artificial intelligence-based electrical automation equipment state prediction method and device.

[0032] Step 2: The data collection device obtains the thermal management status parameters of the electrical automation equipment, and the data processing device uses the second artificial intelligence method to analyze the thermal management anomaly index of the electrical automation equipment, and determines whether to optimize the preset operating status parameter control set of the electrical automation equipment based on the thermal management anomaly index of the electrical automation equipment.

[0033] The above-mentioned second artificial intelligence method represents a specific analysis method for the thermal management anomaly index of electrical automation equipment.

[0034] Specifically, the determination of whether to optimize the preset operating status parameter control set of the electrical automation equipment is as follows: extracting the thermal management anomaly threshold of the electrical automation equipment from the electrical database and comparing it with the thermal management anomaly index of the electrical automation equipment; if the thermal management anomaly index of the electrical automation equipment is less than or equal to the thermal management anomaly threshold of the electrical automation equipment, it is determined that the preset operating status parameter control set of the electrical automation equipment is not optimized; the thermal management anomaly threshold of the electrical automation equipment represents the maximum value of the reasonable range of the thermal management anomaly index of the electrical automation equipment.

[0035] If the thermal management anomaly index of the electrical automation equipment is greater than the thermal management anomaly threshold of the electrical automation equipment, it is determined that the preset operating state parameter control set of the electrical automation equipment is optimized. The specific optimization process is: the temperature coefficient of the electrical automation equipment is compared with the reference temperature coefficient to obtain a comparison result. Based on the comparison result, a first optimized control set is matched from the electrical database, and the operating state parameter control set of the preset electrical automation equipment is updated once to obtain an updated operating state parameter control set of the electrical automation equipment; the above comparison results include comparison result one (that is, the temperature coefficient of the electrical automation equipment is greater than the reference temperature coefficient), comparison result two (that is, the temperature coefficient of the electrical automation equipment is equal to the reference temperature coefficient) and comparison result three (that is, the temperature coefficient of the electrical automation equipment is less than the reference temperature coefficient); The first optimization control set, the specific matching process is: the first optimization control set corresponding to each comparison result is stored in the electrical database, the comparison result of the temperature coefficient of the electrical automation equipment and the reference temperature coefficient is queried, the first optimization control set corresponding to the comparison result is the first optimization control set matched by the comparison result of the temperature coefficient of the electrical automation equipment and the reference temperature coefficient, the specific value in the first optimization control set is formulated by the thermal management anomaly index of the electrical automation equipment, in an example embodiment, assuming that the thermal management anomaly index of the electrical automation equipment is G, the thermal management anomaly threshold of the electrical automation equipment is GY, and the comparison result of the temperature coefficient of the electrical automation equipment and the reference temperature coefficient is comparison result 1, then the first optimization control set includes: reducing the operating power in the operating state parameter control set of the electrical automation equipment to the original ( ) times, reducing the operating speed of the centralized control of the operating status parameters of the electrical automation equipment to the original ( ) times, the remaining parameters in the operating status parameter control set of the electrical automation equipment remain unchanged.

[0036] Specifically, the thermal management abnormality index of the electrical automation equipment is analyzed by the following method:

[0037] ;

[0038] ;

[0039] ;

[0040] Where, is the thermal management abnormality index of electrical automation equipment, is the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment. It is the reference heat dissipation efficiency coefficient preset in the electrical database. is the temperature coefficient of electrical automation equipment, is the reference temperature coefficient preset in the electrical database, is the temperature growth deviation coefficient of the electrical automation equipment, is the average heat flux density of the surface detection part of the electrical automation equipment during the equipment monitoring cycle, is the reference average heat flux density preset in the electrical database, is the power density of the electrical automation equipment during the equipment monitoring cycle, is the reference power density preset in the electrical database, is the heat dissipation efficiency coefficient weight preset in the electrical database, The temperature coefficient weights preset in the electrical database are: The temperature growth deviation coefficient weight preset in the electrical database, is the average heat flux weight preset in the electrical database, is the power density weight preset in the electrical database, It is the average temperature of the surface detection part of the electrical automation equipment during the equipment monitoring period. It is the average temperature of the internal detection parts of the electrical automation equipment during the equipment monitoring cycle. It is the first average temperature impact value preset in the electrical database. It is the second average temperature impact value preset in the electrical database. It is the temperature growth deviation rate of the surface detection part of the electrical automation equipment during the equipment monitoring cycle. It is the temperature growth deviation rate of the internal detection parts of the electrical automation equipment during the equipment monitoring cycle. It is the first temperature growth deviation rate impact value preset in the electrical database. It is the second temperature growth deviation rate impact value preset in the electrical database.

[0041] The above-mentioned heat dissipation efficiency coefficient weight represents the numerical value of the influence of the unit value of the heat dissipation efficiency coefficient on the thermal management anomaly index; the above-mentioned temperature coefficient weight represents the numerical value of the influence of the unit value of the temperature coefficient on the thermal management anomaly index; the above-mentioned temperature growth deviation coefficient weight represents the numerical value of the influence of the unit value of the temperature growth deviation coefficient on the thermal management anomaly index; the above-mentioned average heat flux density weight represents the numerical value of the influence of the unit value of the average heat flux density on the thermal management anomaly index; and the above-mentioned power density weight represents the numerical value of the influence of the unit value of the power density on the thermal management anomaly index. The electrical database stores the corresponding relationship between the heat dissipation efficiency coefficient and its corresponding weight, the corresponding relationship between the temperature coefficient and its corresponding weight, the corresponding relationship between the temperature growth deviation coefficient and its corresponding weight, the corresponding relationship between the average heat flux density and its corresponding weight, and the corresponding relationship between the power density and its corresponding weight. For example, if the heat dissipation efficiency coefficient, temperature coefficient, temperature growth deviation coefficient, average heat flux density, and power density are input into the electrical database, the electrical database can match the heat dissipation efficiency coefficient weight, temperature coefficient weight, temperature growth deviation coefficient weight, average heat flux density weight, and power density weight, and the value range is between 0 and 1.

[0042] The above-mentioned first average temperature influence value represents the influence degree of the average temperature unit value of the surface detection part of the electrical automation equipment during the equipment monitoring period on the temperature coefficient; the above-mentioned second average temperature influence value represents the influence degree of the average temperature unit value of the internal detection part of the electrical automation equipment during the equipment monitoring period on the temperature coefficient; the above-mentioned first temperature growth deviation rate influence value represents the influence degree of the temperature growth deviation rate unit value of the surface detection part of the electrical automation equipment during the equipment monitoring period on the temperature growth deviation coefficient; the above-mentioned second temperature growth deviation rate influence value represents the influence degree of the temperature growth deviation rate unit value of the internal detection part of the electrical automation equipment during the equipment monitoring period on the temperature growth deviation coefficient. The electrical database stores the correspondence between the average temperature of the surface detection part of the electrical automation equipment during the equipment monitoring period and its corresponding influence value, the internal detection part of the electrical automation equipment The correspondence between the average temperature of the detection part during the equipment monitoring period and its corresponding influence value, the correspondence between the temperature growth deviation rate of the surface detection part of the electrical automation equipment during the equipment monitoring period and its corresponding influence value, and the correspondence between the temperature growth deviation rate of the internal detection part of the electrical automation equipment during the equipment monitoring period and its corresponding influence value. For example, the average temperature of the surface detection part of the electrical automation equipment during the equipment monitoring period, the average temperature of the internal detection part of the electrical automation equipment during the equipment monitoring period, the temperature growth deviation rate of the surface detection part of the electrical automation equipment during the equipment monitoring period, and the temperature growth deviation rate of the internal detection part of the electrical automation equipment during the equipment monitoring period are input into the electrical database, and the electrical database can match the first average temperature influence value, the second average temperature influence value, the first temperature growth deviation rate influence value, and the second temperature growth deviation rate influence value, and the value range is between 0 and 1.

[0043] The temperature coefficient of the above-mentioned electrical automation equipment is used to comprehensively quantify the temperature level of the electrical automation equipment during the equipment monitoring period; the temperature growth deviation coefficient of the above-mentioned electrical automation equipment is used to comprehensively quantify the degree of temperature growth deviation of the electrical automation equipment during the equipment monitoring period; the above-mentioned reference heat dissipation efficiency coefficient represents the reference value of the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment; the above-mentioned reference temperature coefficient represents the reference value of the temperature coefficient of the electrical automation equipment; the above-mentioned reference average heat flux density represents the reference value of the average heat flux density of the surface detection part of the electrical automation equipment during the equipment monitoring period; the above-mentioned reference power density represents the reference value of the power density of the electrical automation equipment during the equipment monitoring period.

[0044] It should be explained that the heat dissipation efficiency coefficient is a key indicator to measure the heat dissipation capacity of the operating environment of the electrical automation equipment. It is compared with the reference heat dissipation efficiency coefficient preset in the electrical database to reflect whether the heat dissipation conditions of the current environment meet the standards. When the heat dissipation efficiency coefficient deviates significantly from the corresponding reference value, it may cause the temperature of the electrical automation equipment to be unable to diffuse, resulting in an abnormal increase, or it may cause the electrical automation equipment to be unable to operate normally, causing the temperature of the electrical automation equipment to be at an abnormally low level. At this time, the temperature coefficient will change and will deviate significantly from the corresponding reference value. The temperature growth deviation coefficient further refines the abnormal situation of temperature change. It calculates the temperature growth of the electrical automation equipment. The degree of deviation is closely related to the stable operation state of the electrical automation equipment. When the temperature growth deviation coefficient increases, it indicates that the temperature growth of the electrical automation equipment exceeds the normal range. This may be due to the combined effect of multiple factors such as decreased heat dissipation efficiency and internal heat accumulation. The average heat flux density is an important parameter for evaluating the heat dissipation performance of the equipment surface. Compared with the reference average heat flux density preset in the electrical database, it can reflect whether the heat dissipation of the equipment surface is uniform and effective. When the actual average heat flux density deviates significantly from the corresponding reference value, it means that the electrical automation equipment may have a problem of poor heat dissipation. The change in the average heat flux density will directly affect the temperature distribution of the electrical automation equipment. And the heat dissipation efficiency coefficient, which in turn affects the temperature coefficient and the temperature growth deviation coefficient. The power density reflects the energy consumption of the electrical automation equipment during operation. It is closely related to the heat generation of the electrical automation equipment, because the heat generation of the electrical automation equipment is usually proportional to its power. When the power density increases, the heat generation of the electrical automation equipment will also increase accordingly, which will put higher requirements on the heat dissipation system. If the heat dissipation system cannot dissipate the heat in a timely and effective manner, it will cause the temperature of the electrical automation equipment to rise. Therefore, when the power density deviates significantly from the corresponding reference value, it will cause the heat generation of the electrical automation equipment to be abnormal, thereby aggravating the deviation of the temperature coefficient from the corresponding reference value. The degree of value, which in turn leads to a series of thermal management problems. In summary, the heat dissipation efficiency coefficient, temperature coefficient, temperature growth deviation coefficient, average heat flux density and power density are interrelated. Abnormal heat dissipation efficiency will directly lead to abnormal temperature coefficient and temperature growth deviation coefficient; and changes in average heat flux density will affect the heat dissipation efficiency coefficient and temperature coefficient of the equipment; an increase in power density will increase the heat generation of the equipment, posing a greater challenge to the thermal management of electrical automation equipment. Changes in these parameters will eventually affect the calculation results of the thermal management anomaly index through a complex interaction mechanism, thereby reflecting the performance status of the electrical automation equipment in thermal management.

[0045] Furthermore, the thermal management anomaly index of the electrical automation equipment is analyzed, and the specific analysis process is: the thermal management status parameters of the electrical automation equipment include the average temperature of the surface detection parts of the electrical automation equipment during the equipment monitoring period, the average temperature of the internal detection parts of the electrical automation equipment during the equipment monitoring period, the temperature growth rate of the surface detection parts of the electrical automation equipment during the equipment monitoring period, the temperature growth rate of each internal detection part of the electrical automation equipment during the equipment monitoring period, the average heat flux density of the surface detection parts of the electrical automation equipment during the equipment monitoring period, and the power density of the electrical automation equipment during the equipment monitoring period.

[0046] The above-mentioned equipment monitoring cycle indicates the time period for testing the thermal management performance of the electrical automation equipment, and the specific duration is determined by the equipment administrator; the average temperature of the surface detection parts of the above-mentioned electrical automation equipment during the equipment monitoring cycle indicates the average temperature level of the surface detection parts of the electrical automation equipment during the equipment monitoring cycle, and the acquisition method is consistent with the acquisition method of the average temperature of the surface detection parts of the electrical automation equipment during the environmental monitoring cycle; the average temperature of the internal detection parts of the above-mentioned electrical automation equipment during the equipment monitoring cycle indicates the average temperature level of the internal detection parts of the electrical automation equipment during the equipment monitoring cycle. The real-time temperature of each internal detection part during the equipment monitoring cycle is monitored by the temperature sensors installed at each internal detection part, and all data are integrated and averaged to obtain the average temperature of the internal detection parts of the electrical automation equipment during the equipment monitoring cycle. The internal detection parts include but are not limited to the center position of the reducer and the detection position points on the motor surface set by the equipment management personnel. The specific location of each internal detection part can be determined by the equipment administrator.

[0047] The temperature growth rate of the surface detection part of the above-mentioned electrical automation equipment during the equipment monitoring period represents the temperature growth rate of the surface detection part of the electrical automation equipment during the equipment monitoring period. The temperature of the surface detection part of the electrical automation equipment at the end time point of the equipment monitoring period and the temperature of the surface detection part of the electrical automation equipment at the start time point of the equipment monitoring period are difference processed, and the processing result and the duration corresponding to the equipment monitoring period are averaged to finally obtain the temperature growth rate of the surface detection part of the electrical automation equipment during the equipment monitoring period. The temperature of the surface detection part of the electrical automation equipment at the end time point of the equipment monitoring period and the temperature of the surface detection part of the electrical automation equipment at the start time point of the equipment monitoring period can both be monitored and analyzed by infrared imaging technology (such as an infrared thermal imager).

[0048] The temperature growth rate of each internal detection part of the above-mentioned electrical automation equipment during the equipment monitoring cycle represents the temperature growth rate of each internal detection part of the electrical automation equipment during the equipment monitoring cycle. The temperature of each internal detection part of the electrical automation equipment at the end time point of the equipment monitoring cycle and the temperature of each internal detection part of the corresponding electrical automation equipment at the start time point of the equipment monitoring cycle are difference processed, and the processing result and the duration corresponding to the equipment monitoring cycle are averaged to finally obtain the temperature growth rate of each internal detection part of the electrical automation equipment during the equipment monitoring cycle. The temperature of each internal detection part of the electrical automation equipment at the end time point of the equipment monitoring cycle and the temperature of each internal detection part of the corresponding electrical automation equipment at the start time point of the equipment monitoring cycle can both be measured by temperature sensors.

[0049] The average heat flux density of the surface detection parts of the above-mentioned electrical automation equipment during the equipment monitoring period represents the average value of the heat flux density of the surface detection parts of the equipment during the monitoring period. Heat flux density refers to the amount of heat passing through a unit area per unit time and can be measured by a heat flux density sensor. The power density of the above-mentioned electrical automation equipment during the equipment monitoring period represents the average output power per unit volume of the electrical automation equipment during the equipment monitoring period. The volume of the electrical automation equipment can be extracted from the specification manual. The average output power of the electrical automation equipment during the equipment monitoring period can be measured by a power meter. The average output power of the electrical automation equipment during the equipment monitoring period is divided by the volume of the electrical automation equipment to obtain the power density of the electrical automation equipment during the equipment monitoring period.

[0050] The temperature growth rates of the internal detection parts of the electrical automation equipment during the equipment monitoring period are averaged to obtain the average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period, which represents the average value of the temperature growth rates of the internal detection parts of the electrical automation equipment during the equipment monitoring period.

[0051] According to the power density of the electrical automation equipment during the equipment monitoring period, the reference average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period is matched from the electrical database, and the reference average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period and the average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period are processed in sequence by difference and absolute value. The processing result is processed with the reference average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period, and finally the temperature growth deviation rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period is obtained; the above-mentioned internal detection parts of the electrical automation equipment The reference average temperature growth rate within the equipment monitoring period is specifically matched as follows: the reference average temperature of the internal detection parts corresponding to each power density interval within the equipment monitoring period is stored in the electrical database, and the power density interval stored in the electrical database to which the power density of the electrical automation equipment within the equipment monitoring period belongs is queried. The reference average temperature of the internal detection parts corresponding to the power density interval within the equipment monitoring period is the reference average temperature growth rate of the internal detection parts of the electrical automation equipment within the equipment monitoring period; the temperature growth deviation rate of the internal detection parts of the above-mentioned electrical automation equipment within the equipment monitoring period is used to quantify the degree of abnormal temperature growth of the internal detection parts of the electrical automation equipment within the equipment monitoring period.

[0052] According to the average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period, the reference temperature growth rate of the surface detection parts of the electrical automation equipment during the equipment monitoring period is matched from the electrical database. The temperature growth rate of the surface detection parts of the electrical automation equipment during the equipment monitoring period and the reference temperature growth rate of the surface detection parts of the electrical automation equipment during the equipment monitoring period are processed in sequence by difference and absolute value. The processing result is processed by ratio with the reference temperature growth rate of the surface detection parts of the electrical automation equipment during the equipment monitoring period, and finally the temperature growth deviation rate of the surface detection parts of the electrical automation equipment during the equipment monitoring period is obtained; the reference temperature growth rate of the surface detection parts of the electrical automation equipment during the equipment monitoring period, the specific matching process is: electrical data The library stores the reference temperature growth rate of the surface detection part during the equipment monitoring period corresponding to the average temperature growth rate interval of each internal detection part during the equipment monitoring period, and queries the average temperature growth rate interval of the internal detection part during the equipment monitoring period stored in the electrical database to which the average temperature growth rate of the internal detection part of the electrical automation equipment belongs. The reference temperature growth rate of the surface detection part during the equipment monitoring period corresponding to the average temperature growth rate interval of the internal detection part during the equipment monitoring period is the reference temperature growth rate of the surface detection part of the electrical automation equipment during the equipment monitoring period; the temperature growth deviation rate of the surface detection part of the electrical automation equipment during the equipment monitoring period is used to quantify the degree of abnormal temperature growth of the surface detection part of the electrical automation equipment during the equipment monitoring period.

[0053] A comprehensive analysis is performed on the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment, the average temperature of the surface detection parts of the electrical automation equipment during the equipment monitoring period, the average temperature of the internal detection parts of the electrical automation equipment during the equipment monitoring period, the temperature growth deviation rate of the surface detection parts of the electrical automation equipment during the equipment monitoring period, the temperature growth deviation rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period, the average heat flux density of the surface detection parts of the electrical automation equipment during the equipment monitoring period, and the power density of the electrical automation equipment during the equipment monitoring period to obtain the thermal management anomaly index of the electrical automation equipment. The thermal management anomaly index of the electrical automation equipment is used to comprehensively quantify the degree of thermal management anomaly of the electrical automation equipment.

[0054] Step 3: The control device controls the electrical automation equipment through the operating status parameter control set of the electrical automation equipment, the data collection device collects the operating status parameters of the electrical automation equipment, the data processing device determines the operating accuracy factor of the electrical automation equipment using the third artificial intelligence method, and predicts the operating accuracy level of the electrical automation equipment based on the operating accuracy factor of the electrical automation equipment. The feedback device controls and feeds back the status of the electrical automation equipment based on the operating accuracy level of the electrical automation equipment.

[0055] The above-mentioned third method of artificial intelligence refers to a specific method for determining the operating precision factor of electrical automation equipment; the above-mentioned control of electrical automation equipment through the operating status parameter control set of electrical automation equipment refers to the control device packaging the operating status parameter control set of the electrical automation equipment into instructions that the electrical automation equipment can recognize and receive, marking it as the operating status parameter control set instruction of the electrical automation equipment, and sending the operating status parameter control set instruction of the electrical automation equipment to the electrical automation equipment through network transmission. After receiving the operating status parameter control set instruction of the electrical automation equipment, the electrical automation equipment will immediately parse and execute it.

[0056] In a specific embodiment, this method achieves personalized control feedback on the status of electrical automation equipment by accurately determining the operating accuracy factor of electrical automation equipment, effectively avoiding excessive or insufficient maintenance, and significantly improving the operating accuracy of electrical automation equipment.

[0057] Specifically, the electrical automation equipment is controlled by the operating status parameter control set of the electrical automation equipment. The specific control is: if it is determined not to optimize the operating status parameter control set of the preset electrical automation equipment, the electrical automation equipment is directly controlled by the operating status parameter control set of the preset electrical automation equipment; if it is determined to optimize the operating status parameter control set of the preset electrical automation equipment, the optimized operating status parameter control set of the electrical automation equipment is recorded as the operating status parameter control set of the electrical automation equipment that is updated once, and the electrical automation equipment is controlled by updating the operating status parameter control set of the electrical automation equipment once.

[0058] Specifically, the operation accuracy level of the electrical automation equipment is predicted, and the specific prediction process is: comparing the operation accuracy factor of the electrical automation equipment with the first operation accuracy factor interval, the second operation accuracy factor interval and the third operation accuracy factor interval stored in the electrical database; if the operation accuracy factor of the electrical automation equipment belongs to the first operation accuracy factor interval, then the operation accuracy level of the electrical automation equipment is predicted to be the first level; if the operation accuracy factor of the electrical automation equipment belongs to the second operation accuracy factor interval, then the operation accuracy level of the electrical automation equipment is predicted to be the second level; if the operation accuracy factor of the electrical automation equipment belongs to the third operation accuracy factor interval, then the operation accuracy level of the electrical automation equipment is predicted to be the third level. It should be explained that all operation accuracy factors corresponding to the first operation accuracy factor interval are greater than all operation accuracy factors corresponding to the second operation accuracy factor interval, and all operation accuracy factors corresponding to the second operation accuracy factor interval are greater than all operation accuracy factors corresponding to the third operation accuracy factor interval. In an example embodiment, the first level represents excellent, the second level represents good, and the third level represents poor.

[0059] Specifically, the control feedback of the state of the electrical automation equipment is performed, and the specific control feedback process is: if the operating accuracy level of the electrical automation equipment is the first level, the thermal management state parameters of the electrical automation equipment are continuously obtained; if the operating accuracy level of the electrical automation equipment is the second level, a second optimization control set is matched from the electrical database according to the operating accuracy factor of the electrical automation equipment, and the operating status parameter control set of the once-updated electrical automation equipment is optimized to obtain the operating status parameter control set of the second-updated electrical automation equipment, and the electrical automation equipment is controlled by the operating status parameter control set of the second-updated electrical automation equipment; if the operating accuracy level of the electrical automation equipment is the third level, an early warning log is generated for feedback; the above-mentioned second optimization control set, the specific matching process is: the second optimization control set corresponding to each operating accuracy factor interval is stored in the electrical database, and the operating accuracy factor interval in the electrical database to which the operating accuracy factor of the electrical automation equipment belongs is queried, and the second optimization control set corresponding to the operating accuracy factor interval in the electrical database to which the operating accuracy factor of the electrical automation equipment belongs is the second optimization control set matched according to the operating accuracy factor of the electrical automation equipment.

[0060] Furthermore, the operation accuracy factor of the electrical automation equipment is determined, and the specific determination process is as follows: the operation status parameters of the electrical automation equipment include the motion trajectory curves of the electrical automation equipment during the operation monitoring cycle, the average vibration amplitude of the electrical automation equipment during the operation monitoring cycle, and the real-time acceleration of the electrical automation equipment during the operation monitoring cycle; the above-mentioned operation monitoring cycle represents the time period for analyzing the operation accuracy of the electrical automation equipment, and the specific duration is determined by the equipment administrator; the above-mentioned motion trajectory curve describes the motion path of the first position point preset on the electrical automation equipment in space during the operation monitoring cycle, usually presented in the form of a curve graph, which can intuitively reflect the motion law, speed change and possible errors of the electrical automation equipment, install a position sensor (such as a laser rangefinder or an optical sensor, etc.) at the first position point preset on the electrical automation equipment, and record the motion position of the electrical automation equipment in real time, import the position data collected by the position sensor into the data analysis software (such as Matrix Laboratory), and obtain the motion trajectory curve of the robotic arm through curve processing, which records the electrical automation equipment completing a movement from the start The complete movement process from the starting position point to the ending position point can be expressed as a motion trajectory curve of the electrical automation equipment within the operation monitoring cycle, recording the complete movement process of the electrical automation equipment from the ending position point to the starting position point, or it can be expressed as a motion trajectory curve of the electrical automation equipment within the operation monitoring cycle. If the motion trajectory curve is incomplete, the motion trajectory curve may not be included in the subsequent analysis. The starting position point represents the static position point of the first position point preset before the electrical automation equipment starts to perform a certain action or task. The ending position point represents the target position point that the first position point preset after the electrical automation equipment completes a specific action or task. The starting position point and the ending position point are both marked by the user of the electrical automation equipment; the above-mentioned average vibration amplitude represents the average level of the vibration amplitude of the electrical automation equipment within the operation monitoring cycle, which can be monitored by the vibration sensor installed on the electrical automation equipment by the equipment administrator, and the unit is millimeter; the above-mentioned real-time acceleration represents the acceleration of the second position point preset on the electrical automation equipment at any time within the operation monitoring cycle, which can be measured by the acceleration sensor.

[0061] The above-mentioned preset first position point and preset second position point are both set by the equipment administrator. The preset first position point needs to be able to reflect the movement law of the electrical automation equipment. The first position point should be located on the moving part of the electrical automation equipment, and its movement state can represent or reflect the typical movement characteristics of the electrical automation equipment. The preset second position point needs to be able to reflect the acceleration law of the electrical automation equipment. The second position point may be located at the joint of the electrical automation equipment, the end of the moving part or other key positions, so as to gain an in-depth understanding of the dynamic performance of the electrical automation equipment by measuring the acceleration of the second position point.

[0062] The reference action trajectory curve of the electrical automation equipment is extracted from the electrical database, and each action trajectory curve of the electrical automation equipment in the operation monitoring cycle is overlapped and compared with the reference action trajectory curve of the electrical automation equipment. The action trajectory curve deviation value of the electrical automation equipment in the operation monitoring cycle is comprehensively obtained, which is used to quantify the degree of deviation of the action trajectory curve of the electrical automation equipment in the operation monitoring cycle relative to the reference action trajectory curve. The specific acquisition method is: output each action trajectory curve of the electrical automation equipment in the operation monitoring cycle and the reference action trajectory curve of the electrical automation equipment to the data processing software (such as the matrix laboratory), and the data processing software is used to compare the electrical automation equipment in the operation monitoring cycle with the reference action trajectory curve of the electrical automation equipment. Each position point on the action trajectory curve of each time in the operation monitoring cycle is compared with the corresponding position point on the reference action trajectory curve of the electrical automation equipment in turn, and the shortest straight-line distance between each position point on the action trajectory curve of the electrical automation equipment in the operation monitoring cycle and the corresponding position point on the reference action trajectory curve of the electrical automation equipment is calculated to obtain the deviation distance of each position point of each action trajectory curve of the electrical automation equipment in the operation monitoring cycle, and integrate all the deviation distances of each position point of the deviation distance of each action trajectory curve of the electrical automation equipment in the operation monitoring cycle and accumulate them to obtain the deviation value of the action trajectory curve of the electrical automation equipment in the operation monitoring cycle.

[0063] The real-time operating acceleration of the electrical automation equipment during the operation monitoring cycle is processed with standard deviation to obtain the operating acceleration fluctuation value of the electrical automation equipment during the operation monitoring cycle, which is used to quantify the degree of operating acceleration fluctuation of the electrical automation equipment during the operation monitoring cycle.

[0064] Comprehensively determine the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment, the thermal management anomaly index of the electrical automation equipment, the movement trajectory curve deviation value of the electrical automation equipment during the operation monitoring period, the average vibration amplitude of the electrical automation equipment during the operation monitoring period, and the operation acceleration fluctuation value of the electrical automation equipment during the operation monitoring period to obtain the operation precision factor of the electrical automation equipment. The operation precision factor of the electrical automation equipment is used to comprehensively quantify the operation accuracy of the electrical automation equipment.

[0065] The specific determination method of the operating accuracy factor of the electrical automation equipment is as follows:

[0066] ;

[0067] ;

[0068] Where, is the operating precision factor of the electrical automation equipment, is the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment. It is the reference heat dissipation efficiency coefficient preset in the electrical database. is the thermal management abnormality index of electrical automation equipment, is the operating accuracy index of electrical automation equipment, is the heat dissipation efficiency coefficient weight preset in the electrical database, is the thermal management anomaly index weight preset in the electrical database, It is the operating accuracy index weight preset in the electrical database. It is the deviation value of the trajectory curve of the electrical automation equipment during the operation monitoring cycle. is the average vibration amplitude of the electrical automation equipment during the operation monitoring period, It is the operating acceleration fluctuation value of the electrical automation equipment during the operation monitoring cycle. It is the deviation value of the defined action trajectory curve preset in the electrical database. The average vibration amplitude is defined in the electrical database. It is the defined operating acceleration fluctuation value preset in the electrical database. It is the influence coefficient of the deviation value of the action trajectory curve preset in the electrical database. It is the average vibration amplitude influence coefficient preset in the electrical database. It is the operating acceleration fluctuation value influence coefficient preset in the electrical database.

[0069] The above-mentioned operating accuracy index of the electrical automation equipment is used to quantify the operating accuracy of the electrical automation equipment itself, and is part of the operating accuracy factor of the electrical automation equipment.

[0070] The above-mentioned heat dissipation efficiency coefficient weight represents the proportion of the heat dissipation efficiency coefficient to the operating precision factor; the above-mentioned thermal management anomaly index weight represents the proportion of the thermal management anomaly index to the operating precision factor; the above-mentioned operating precision index weight represents the proportion of the operating precision index to the operating precision factor. The electrical database stores the correspondence between the heat dissipation efficiency coefficient and its corresponding weight, the correspondence between the thermal management anomaly index and its corresponding weight, and the correspondence between the operating precision index and its corresponding weight. For example, by inputting the heat dissipation efficiency coefficient, the thermal management anomaly index and the operating precision index into the electrical database, the electrical database can match the heat dissipation efficiency coefficient weight, the thermal management anomaly index weight and the operating precision index weight, and the value range is between 0 and 1.

[0071] The above-mentioned action trajectory curve deviation value influence coefficient represents the numerical value of the influence degree of the action trajectory curve deviation value unit on the operation accuracy index; the above-mentioned average vibration amplitude influence coefficient represents the numerical value of the influence degree of the average vibration amplitude unit on the operation accuracy index; the above-mentioned operation acceleration fluctuation value influence coefficient represents the numerical value of the influence degree of the operation acceleration fluctuation value unit on the operation accuracy index. The electrical database stores the correspondence between the action trajectory curve deviation value and its corresponding influence coefficient, the correspondence between the average vibration amplitude and its corresponding influence coefficient, and the correspondence between the operation acceleration fluctuation value and its corresponding influence coefficient. For example, the action trajectory curve deviation value, the average vibration amplitude and the operation acceleration fluctuation value are input into the electrical database, and the electrical database can match the action trajectory curve deviation value influence coefficient, the average vibration amplitude influence coefficient and the operation acceleration fluctuation value influence coefficient, and the value range is between 0 and 1.

[0072] The above-defined motion trajectory curve deviation value indicates the maximum allowable value of the motion trajectory curve deviation value of the electrical automation equipment during the operation monitoring cycle; the above-defined average vibration amplitude indicates the maximum allowable value of the average vibration amplitude of the electrical automation equipment during the operation monitoring cycle; the above-defined operation acceleration fluctuation value indicates the maximum allowable value of the operation acceleration fluctuation value of the electrical automation equipment during the operation monitoring cycle.

[0073] It needs to be explained that when the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment deviates significantly from its preset reference value, the thermal management system of the electrical automation equipment will suffer significant environmental impacts and thus present an abnormal state. This abnormal state is specifically manifested as a significant increase in the abnormal level of thermal management of the electrical automation equipment. The abnormality of thermal management will directly affect the normal working conditions of the internal components of the electrical automation equipment. Under normal circumstances, these components can maintain stable performance and expected functions within a specific temperature range. However, once the thermal management is abnormal, the working environment temperature of the internal components of the electrical automation equipment will deviate from the optimal range, resulting in a decline in component performance and may even cause failures. Therefore, a comprehensive analysis of the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment, the thermal management abnormality index of the electrical automation equipment and the operating accuracy index of the electrical automation equipment can accurately identify the operating accuracy of the electrical automation equipment and the key factors affecting the operating accuracy of the electrical automation equipment.

[0074] It should also be explained that if the average vibration amplitude of electrical automation equipment shows an upward trend, this usually means that the vibration state of the electrical automation equipment is beyond the normal range, manifested as excessive vibration amplitude. This abnormal vibration state will directly affect the motion stability and accuracy of the electrical automation equipment. Specifically, excessive vibration will cause the deviation value of the motion trajectory curve of the electrical automation equipment to increase when performing tasks. This is because vibration will interfere with the motion control of the electrical automation equipment, causing the actual motion trajectory of the electrical automation equipment to deviate from the expected trajectory, thereby causing the problem of discontinuous motion. At the same time, the increase in the average vibration amplitude will also cause an increase in the operating acceleration fluctuation value. Under the influence of vibration, the acceleration sensor of the electrical automation equipment will detect frequent acceleration changes, indicating that the dynamic performance of the electrical automation equipment has been affected. Acceleration fluctuations not only affect the motion smoothness of the electrical automation equipment, but may also cause additional stress on the mechanical structure and electronic components of the electrical automation equipment, accelerating their aging and wear. Therefore, a comprehensive analysis of the three parameters of average vibration amplitude, motion trajectory curve deviation value and operating acceleration fluctuation value can be used to evaluate the operating accuracy of the electrical automation equipment itself.

[0075] In a specific embodiment, the present invention provides an artificial intelligence-based electrical automation equipment status prediction method. This method can evaluate the heat dissipation efficiency in real time and effectively monitor the thermal management status of the electrical automation equipment by accurately collecting and analyzing the operating environment parameters of the equipment. It uses advanced artificial intelligence algorithms to quickly identify thermal management anomalies and timely warn of potential faults, thereby greatly shortening the time window for equipment maintenance and reducing the risk of production interruption of electrical automation equipment due to sudden failures. In addition, through intelligent analysis of the operating status parameters of the electrical automation equipment, the control strategy can be dynamically optimized to ensure that the electrical automation equipment is in the best operating state. At the same time, it can also accurately predict the operating accuracy level of the electrical automation equipment, providing a scientific basis for precise control and further improving the operating accuracy of the electrical automation equipment. This method not only significantly enhances the reliability and stability of the electrical automation equipment, but also realizes efficient automated management of the electrical automation equipment through an intelligent prediction and control feedback mechanism.

[0076] The second aspect of the present invention provides an electrical automation equipment state prediction device based on artificial intelligence, including: a data collection device, a data processing device, a control device and a feedback device; the data collection device is used to collect the operating environment parameters of the electrical automation equipment, obtain the thermal management state parameters of the electrical automation equipment and collect the operating state parameters of the electrical automation equipment; the data processing device is used to use a first artificial intelligence method to evaluate the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment, use a second artificial intelligence method to analyze the thermal management anomaly index of the electrical automation equipment and use a third artificial intelligence method to determine the operating precision factor of the electrical automation equipment; the control device is used to control the electrical automation equipment through the operating state parameter control set of the electrical automation equipment; the feedback device is used to control and feedback the state of the electrical automation equipment based on the operating precision level of the electrical automation equipment.

[0077] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. An artificial intelligence-based electrical automation equipment state prediction method, characterized in that: include: Step 1: The data collection device collects operating environment parameters of the electrical automation equipment, and the data processing device uses the first artificial intelligence method to evaluate the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment; The first artificial intelligence method represents a specific evaluation method for the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment; The specific evaluation process for evaluating the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment is as follows: The operating environment parameters of the electrical automation equipment include the average temperature of the operating environment of the electrical automation equipment during the environmental monitoring period, the average air flow speed of the operating environment of the electrical automation equipment during the environmental monitoring period, and the average thermal radiation intensity of the operating environment of the electrical automation equipment during the environmental monitoring period; Obtain the average temperature of the surface detection part of the electrical automation equipment during the environmental monitoring period, perform subtraction processing on the average temperature of the operating environment of the electrical automation equipment during the environmental monitoring period, perform ratio processing on the processing result with the average temperature of the operating environment of the electrical automation equipment during the environmental monitoring period, and finally obtain the heat dissipation efficiency value of the operating environment of the electrical automation equipment during the environmental monitoring period; Comprehensively evaluate the heat dissipation efficiency value of the operating environment of the electrical automation equipment during the environmental monitoring period, the average air flow velocity of the operating environment of the electrical automation equipment during the environmental monitoring period, and the average thermal radiation intensity of the operating environment of the electrical automation equipment during the environmental monitoring period to obtain the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment. The heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment is used to comprehensively quantify the heat dissipation capacity of the operating environment of the electrical automation equipment; Step 2: The data collection device obtains thermal management status parameters of the electrical automation equipment, and the data processing device uses the second artificial intelligence method to analyze the thermal management anomaly index of the electrical automation equipment, and determines whether to optimize the preset operating status parameter control set of the electrical automation equipment based on the thermal management anomaly index of the electrical automation equipment; The second artificial intelligence method represents a specific analysis method for the thermal management anomaly index of electrical automation equipment; The specific analysis process of analyzing the thermal management abnormality index of the electrical automation equipment is as follows: The thermal management state parameters of the electrical automation equipment include the average temperature of the surface detection parts of the electrical automation equipment during the equipment monitoring period, the average temperature of the internal detection parts of the electrical automation equipment during the equipment monitoring period, the temperature growth rate of the surface detection parts of the electrical automation equipment during the equipment monitoring period, the temperature growth rate of each internal detection part of the electrical automation equipment during the equipment monitoring period, the average heat flux density of the surface detection parts of the electrical automation equipment during the equipment monitoring period, and the power density of the electrical automation equipment during the equipment monitoring period; Performing mean processing on the temperature growth rates of the internal detection parts of the electrical automation equipment during the equipment monitoring period to obtain the average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period; According to the power density of the electrical automation equipment during the equipment monitoring period, the reference average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period is matched from the electrical database. The reference average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period and the average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period are subjected to difference and absolute value processing in sequence. The processing result is subjected to ratio processing with the reference average temperature growth rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period, and finally the temperature growth deviation rate of the internal detection parts of the electrical automation equipment during the equipment monitoring period is obtained; According to the average temperature growth rate of the internal detection part of the electrical automation equipment during the equipment monitoring period, the reference temperature growth rate of the surface detection part of the electrical automation equipment during the equipment monitoring period is matched from the electrical database, the temperature growth rate of the surface detection part of the electrical automation equipment during the equipment monitoring period and the reference temperature growth rate of the surface detection part of the electrical automation equipment during the equipment monitoring period are subjected to difference and absolute value processing in sequence, and the processing result is subjected to ratio processing with the reference temperature growth rate of the surface detection part of the electrical automation equipment during the equipment monitoring period, and finally the temperature growth deviation rate of the surface detection part of the electrical automation equipment during the equipment monitoring period is obtained; Comprehensively analyze the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment, the average temperature of the surface detection part of the electrical automation equipment during the equipment monitoring period, the average temperature of the internal detection part of the electrical automation equipment during the equipment monitoring period, the temperature growth deviation rate of the surface detection part of the electrical automation equipment during the equipment monitoring period, the temperature growth deviation rate of the internal detection part of the electrical automation equipment during the equipment monitoring period, the average heat flux density of the surface detection part of the electrical automation equipment during the equipment monitoring period, and the power density of the electrical automation equipment during the equipment monitoring period to obtain a thermal management anomaly index of the electrical automation equipment, which is used to comprehensively quantify the degree of thermal management anomaly of the electrical automation equipment; Step 3: The control device controls the electrical automation equipment through the operating status parameter control set of the electrical automation equipment, the data collection device collects the operating status parameters of the electrical automation equipment, the data processing device determines the operating accuracy factor of the electrical automation equipment using the third artificial intelligence method, and predicts the operating accuracy level of the electrical automation equipment based on the operating accuracy factor of the electrical automation equipment. The feedback device provides control feedback on the state of the electrical automation equipment based on the operating accuracy level of the electrical automation equipment. The third artificial intelligence method represents a specific method for determining the operating precision factor of electrical automation equipment; The specific determination process of determining the operating accuracy factor of the electrical automation equipment is as follows: The operating status parameters of the electrical automation equipment include each movement trajectory curve of the electrical automation equipment during the operation monitoring period, the average vibration amplitude of the electrical automation equipment during the operation monitoring period, and the real-time acceleration of the electrical automation equipment during the operation monitoring period; Extract the reference motion trajectory curve of the electrical automation equipment from the electrical database, overlap and compare each motion trajectory curve of the electrical automation equipment within the operation monitoring cycle with the reference motion trajectory curve of the electrical automation equipment, and comprehensively obtain the deviation value of the motion trajectory curve of the electrical automation equipment within the operation monitoring cycle; Performing standard deviation processing on the real-time running acceleration of the electrical automation equipment during the running monitoring period to obtain the running acceleration fluctuation value of the electrical automation equipment during the running monitoring period; Comprehensively determine the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment, the thermal management anomaly index of the electrical automation equipment, the movement trajectory curve deviation value of the electrical automation equipment during the operation monitoring period, the average vibration amplitude of the electrical automation equipment during the operation monitoring period, and the operation acceleration fluctuation value of the electrical automation equipment during the operation monitoring period to obtain the operation precision factor of the electrical automation equipment. The operation precision factor of the electrical automation equipment is used to comprehensively quantify the operation accuracy of the electrical automation equipment.

2. The method for predicting electrical automation equipment status based on artificial intelligence according to claim 1, characterized in that: The specific determination process of whether to optimize the preset operating state parameter control set of the electrical automation equipment is as follows: Extracting a thermal management anomaly threshold of the electrical automation equipment from the electrical database and comparing it with the thermal management anomaly index of the electrical automation equipment; if the thermal management anomaly index of the electrical automation equipment is less than or equal to the thermal management anomaly threshold of the electrical automation equipment, determining not to optimize the preset operating state parameter control set of the electrical automation equipment; If the thermal management anomaly index of the electrical automation equipment is greater than the thermal management anomaly threshold of the electrical automation equipment, it is determined that the preset operating status parameter control set of the electrical automation equipment is optimized. The specific optimization process is: comparing the temperature coefficient of the electrical automation equipment with the reference temperature coefficient to obtain a comparison result, and based on the comparison result, matching the first optimized control set from the electrical database, updating the preset operating status parameter control set of the electrical automation equipment once, and obtaining an updated operating status parameter control set of the electrical automation equipment.

3. The method for predicting electrical automation equipment status based on artificial intelligence according to claim 1, characterized in that: The electrical automation equipment is controlled by the operating state parameter control set of the electrical automation equipment, specifically: if it is determined that the preset operating state parameter control set of the electrical automation equipment is not to be optimized, the electrical automation equipment is directly controlled by the preset operating state parameter control set of the electrical automation equipment; If it is determined that the preset operating status parameter control set of the electrical automation equipment is optimized, the optimized operating status parameter control set of the electrical automation equipment is recorded as the operating status parameter control set of the electrical automation equipment that is updated once, and the electrical automation equipment is controlled by updating the operating status parameter control set of the electrical automation equipment once.

4. The method for predicting electrical automation equipment status based on artificial intelligence according to claim 1, characterized in that: The specific prediction process of predicting the operating accuracy level of the electrical automation equipment is as follows: Comparing the operating dilution of precision of the electrical automation equipment with a first operating dilution of precision interval, a second operating dilution of precision interval, and a third operating dilution of precision interval stored in an electrical database; If the operating precision factor of the electrical automation equipment belongs to the first operating precision factor interval, then the operating precision level of the electrical automation equipment is predicted to be the first level; If the operating precision factor of the electrical automation equipment belongs to the second operating precision factor interval, then the operating precision level of the electrical automation equipment is predicted to be the second level; If the operating precision factor of the electrical automation equipment belongs to the third operating precision factor interval, it is predicted that the operating precision level of the electrical automation equipment is the third level.

5. The method for predicting the state of electrical automation equipment based on artificial intelligence according to claim 2, characterized in that: The control feedback of the electrical automation equipment status is specifically carried out as follows: If the operating accuracy level of the electrical automation equipment is the first level, the thermal management status parameters of the electrical automation equipment are continuously obtained; If the operation accuracy level of the electrical automation equipment is the second level, matching a second optimized control set from the electrical database according to the operation accuracy factor of the electrical automation equipment, optimizing the operation status parameter control set of the once-updated electrical automation equipment to obtain the operation status parameter control set of the second-updated electrical automation equipment, and controlling the electrical automation equipment by using the operation status parameter control set of the second-updated electrical automation equipment; If the operating accuracy level of the electrical automation equipment is the third level, an early warning log will be generated for feedback.

6. A device using the artificial intelligence-based electrical automation equipment state prediction method according to any one of claims 1 to 5, characterized in that: include: Data collection device, data processing device, control device and feedback device; The data collection device is used to collect operating environment parameters of the electrical automation equipment, obtain thermal management status parameters of the electrical automation equipment, and collect operating status parameters of the electrical automation equipment; The data processing device is used to evaluate the heat dissipation efficiency coefficient of the operating environment of the electrical automation equipment using a first artificial intelligence method, analyze the thermal management anomaly index of the electrical automation equipment using a second artificial intelligence method, and determine the operating accuracy factor of the electrical automation equipment using a third artificial intelligence method; The control device is used to control the electrical automation equipment through the operating status parameter control set of the electrical automation equipment; The feedback device is used to control and feedback the state of the electrical automation equipment based on the operation accuracy level of the electrical automation equipment.

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