A data anomaly monitoring method and system

Abstract

The present invention discloses a data anomaly monitoring method and system, and relates to the technical field of power consumption data anomaly monitoring. The data anomaly monitoring method obtains historical power consumption data, historical temperature data, and historical usage data of each power consumption device to be analyzed; the historical power consumption data, historical temperature data, and historical usage data of each power consumption device to be analyzed are comprehensively analyzed respectively, and the historical daily initial power consumption average, temperature influence factor, and duration influence factor of each power consumption device to be analyzed are obtained, and a comprehensive analysis and abnormal evaluation are performed. The present invention analyzes the daily initial power consumption average and its influence factor, thereby allowing the operation team to predict and identify potential problems before the equipment shows a significant deviation from the normal operating mode. This early warning mechanism reduces the cost caused by unexpected downtime due to equipment failure, improves the pertinence and timeliness of maintenance work, and thus reduces long-term operation and maintenance costs.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormal power consumption data monitoring, and in particular to a data abnormality monitoring method and system. Background Art

[0002] In today's industrial and commercial environment, the reliability and efficiency of electrical equipment are crucial to ensuring production stability and reducing operating costs. With the development of technology, more and more companies rely on complex electrical equipment to perform critical tasks. The normal operation of these equipment directly affects the production efficiency and energy consumption of enterprises. Therefore, effectively monitoring and managing the power consumption of these devices has become the key to improving production efficiency and reducing operating costs.

[0003] Traditional equipment monitoring methods often rely on regular physical inspections and manual recording of electricity consumption data. These methods are not only time-consuming and labor-intensive, but also prone to human error, which can affect the accuracy and timeliness of data. In addition, traditional methods make it difficult to achieve real-time monitoring and analysis of equipment status, which limits the company's ability to prevent and respond quickly to equipment failures early. With the rapid development of information technology and big data analysis technology, a new data anomaly monitoring method has emerged.

[0004] Based on the above scheme, it is found that the limitations of the existing technology include at least the following problems. First, in many power equipment monitoring systems, they usually only rely on simple real-time or periodic performance consumption monitoring, without considering the deep impact of the equipment operation status on energy consumption, which easily leads to a response only when the energy consumption exceeds the standard, and it is difficult to predict and prevent potential energy consumption increases. Secondly, the existing power equipment monitoring system usually does not fully consider the impact of ambient temperature and equipment temperature on power efficiency. In some cases, temperature changes may be the key factor leading to a sudden increase in energy consumption, but this is often easily overlooked in data analysis. Finally, traditional equipment monitoring systems usually fail to consider the impact of the cumulative use time of the equipment on its performance. As the use time increases, the wear of the equipment may lead to an increase in energy consumption, but this gradual change is usually ignored in daily monitoring. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a data anomaly monitoring method and system, which solves the problem that in many power equipment monitoring systems, usually only simple real-time or periodic performance consumption monitoring is relied on, and the deep-seated impact of the equipment operating status on energy consumption is not considered, which easily leads to a response only when the energy consumption exceeds the standard, and it is difficult to predict and prevent potential energy consumption increases. Secondly, the existing power equipment monitoring systems usually do not fully consider the impact of ambient temperature and equipment temperature on power efficiency. In some cases, temperature changes may be the key factor leading to a sudden increase in energy consumption, but this is often easily overlooked in data analysis. Finally, traditional equipment monitoring systems usually fail to consider the impact of the cumulative use time of the equipment on its performance. As the use time increases, the wear of the equipment may lead to increased energy consumption, but this gradual change is usually ignored in daily monitoring.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data anomaly monitoring method, comprising the following steps: obtaining historical power consumption data, historical temperature data, and historical usage data of each electrical device to be analyzed, wherein the historical power consumption data is specifically the historical power consumption value of the electrical device in each time period of each historical day, the historical temperature data includes historical device temperature data, historical ambient temperature data, and optimal operating temperature value, and the historical usage data is specifically the cumulative usage days corresponding to each historical day of the electrical device; performing comprehensive analysis on the historical power consumption data, historical temperature data, and historical usage data of each electrical device to be analyzed, respectively, to obtain the historical daily initial power consumption average, temperature influence factor, and duration influence factor of each electrical device to be analyzed; performing comprehensive analysis on the historical daily initial power consumption average, temperature influence factor, and duration influence factor of each electrical device to be analyzed, to obtain the power consumption evaluation value of each electrical device to be analyzed; performing abnormal analysis on the historical power consumption data of each electrical device to be analyzed based on the power consumption evaluation value, and if there is an electrical device to be analyzed whose abnormal analysis does not meet the expected standards, it is marked as power consumption abnormality, and a power consumption abnormality warning is sent.

[0007] Furthermore, the historical device temperature data specifically refers to the device temperature value of the electrical device at each time period of each day in history, and the historical environment temperature data specifically refers to the environment temperature value of the environment where the electrical device is located at each time period of each day in history.

[0008] Furthermore, the specific steps for obtaining the electricity consumption evaluation value of each electrical equipment to be analyzed are as follows: read the historical daily average initial electricity consumption, temperature influence factor, and duration influence factor of each electrical equipment to be analyzed, and perform comprehensive analysis to obtain the historical daily adjusted electricity consumption average of each electrical equipment to be analyzed; combine the moving exponential average method to perform mean analysis on the historical daily adjusted electricity consumption average of each electrical equipment to be analyzed, and obtain the electricity consumption evaluation value of each electrical equipment to be analyzed.

[0009] Furthermore, the specific formula for calculating the power consumption evaluation value of each power-consuming device to be analyzed is as follows: ;in, To be analyzed The power consumption evaluation value of each electrical equipment, To be analyzed The history of electrical equipment The average adjusted electricity consumption for the day, To be analyzed The history of electrical equipment The weighting coefficient of the adjusted average electricity consumption for the day, To be analyzed The history of electrical equipment The average initial electricity consumption per day, To be analyzed The history of electrical equipment The temperature of the day affects the To be analyzed The history of electrical equipment The length of the day affects the factor, To be analyzed The history of electrical equipment The average adjusted electricity consumption for the day, To be analyzed The history of electrical equipment The weighting coefficient of the adjusted average electricity consumption for the day, , , is the number of electrical equipment to be analyzed, , The number of historical days to obtain.

[0010] Furthermore, the specific steps for obtaining the historical daily average initial power consumption of each electrical device to be analyzed are as follows: read the historical power consumption value of each time period of each day for each electrical device to be analyzed, and perform preprocessing; perform mean analysis on the preprocessed historical power consumption values ​​of each time period of each day for each electrical device to be analyzed, and obtain the historical daily average initial power consumption of each electrical device to be analyzed.

[0011] Furthermore, the specific steps for obtaining the historical daily temperature influencing factor of each electrical equipment to be analyzed are as follows: read the optimal operating temperature value of each electrical equipment to be analyzed, the equipment temperature value of each historical time period of each day, and the ambient temperature value of each historical time period of the environment; perform difference-mean analysis on the optimal operating temperature value of each electrical equipment to be analyzed and the equipment temperature value of each historical time period of each day, and obtain the historical first temperature difference mean of each electrical equipment to be analyzed; perform difference-mean analysis on the optimal operating temperature value of each electrical equipment to be analyzed and the ambient temperature value of each historical time period of the environment, and obtain the historical second temperature difference mean of each electrical equipment to be analyzed; perform comprehensive analysis on the historical first temperature difference mean and the second temperature difference mean of each electrical equipment to be analyzed, and obtain the historical daily temperature influencing factor of each electrical equipment to be analyzed.

[0012] Furthermore, the specific formula for calculating the historical daily temperature impact factor of each electrical device to be analyzed is as follows: ;in, To be analyzed The history of electrical equipment The temperature of the day affects the To be analyzed The history of electrical equipment The first temperature difference of the day is the average value, To be analyzed The history of electrical equipment The proportionality coefficient of the first temperature difference of the day, To be analyzed The history of electrical equipment Day's The device temperature value for each period of time, To be analyzed The optimal operating temperature value of each electrical equipment, To be analyzed The history of electrical equipment The second mean temperature difference of the day, To be analyzed The history of electrical equipment The proportionality coefficient of the second temperature difference mean of the day, To be analyzed The history of the environment in which electrical equipment is located Day's The ambient temperature value for each period of time, To be analyzed The history of electrical equipment The adjustment factor for the day, , is the number of electrical equipment to be analyzed, , To obtain the historical days, , The total number of time periods.

[0013] Furthermore, the specific steps for obtaining the daily duration impact factor of each electrical device to be analyzed are as follows: reading the cumulative usage days corresponding to each electrical device to be analyzed on a daily basis, and performing a comprehensive analysis to obtain the daily duration impact factor of each electrical device to be analyzed, the calculation formula of which is as follows: ;in, To be analyzed The history of electrical equipment The length of the day affects the factor, The first The maximum energy consumption change index of each electrical equipment, The first The change rate index of each electrical equipment, To be analyzed The history of electrical equipment The cumulative number of days of use corresponding to the day, is the duration adjustment coefficient, , is the number of electrical equipment to be analyzed, , The number of historical days to obtain.

[0014] Furthermore, based on the electricity consumption evaluation value, an abnormality analysis is performed on the historical electricity consumption data of each electrical equipment to be analyzed. If there is an electrical equipment to be analyzed whose abnormal analysis does not meet the expected standards, the specific steps of marking it as abnormal electricity consumption are as follows: comparing and analyzing the historical electricity consumption values ​​of each electrical equipment in each time period of each day with the electricity consumption evaluation value corresponding to the electrical equipment, and obtaining the total number of time periods in which the historical electricity consumption value of each electrical equipment is higher than the electricity consumption evaluation value; comparing and analyzing the total number of time periods in which the historical electricity consumption value of each electrical equipment is higher than the electricity consumption evaluation value with the set maximum number of time periods, and marking the electrical equipment corresponding to the total number of time periods higher than the set maximum number of time periods as abnormal electricity consumption. At the same time, corresponding markings are made for each time period in which the historical electricity consumption value of the electrical equipment is higher than the electricity consumption evaluation value, and the marks are sent to relevant staff.

[0015] A data anomaly monitoring system comprises: a data acquisition module, a data analysis module, an evaluation analysis module, and a judgment analysis module; the data acquisition module is used to acquire historical power consumption data, historical temperature data, and historical usage data of each power-consuming device to be analyzed, wherein the historical power consumption data is specifically the historical power consumption value of the power-consuming device in each period of each day, the historical temperature data includes historical device temperature data, historical ambient temperature data, and optimal operating temperature value, and the historical usage data is specifically the cumulative usage days corresponding to each day of the power-consuming device; the data analysis module is used to acquire the historical power consumption data, historical temperature data, and historical usage data of each power-consuming device to be analyzed. The temperature and time influence factors of each electrical device to be analyzed are comprehensively analyzed to obtain the historical daily average initial power consumption, temperature influence factor and time influence factor of each electrical device to be analyzed; the evaluation and analysis module is used to comprehensively analyze the historical daily average initial power consumption, temperature influence factor and time influence factor of each electrical device to be analyzed to obtain the power consumption evaluation value of each electrical device to be analyzed; the judgment and analysis module is used to perform an abnormality analysis on the historical power consumption data of each electrical device to be analyzed based on the power consumption evaluation value; if there is an electrical device to be analyzed whose abnormal analysis does not meet the expected standards, it is marked as abnormal power consumption and an abnormal power consumption warning is sent.

[0016] The present invention has the following beneficial effects:

[0017] (1) This data anomaly monitoring method analyzes the daily initial power consumption average and its influencing factors, allowing the operation team to predict and identify potential problems before the equipment shows significant deviations from the normal operating mode. This early warning mechanism reduces the cost of unexpected downtime due to equipment failure, improves the pertinence and timeliness of maintenance work, and thus reduces long-term operation and maintenance costs.

[0018] (2) This data anomaly monitoring method can not only ensure that the equipment operates in the best condition by real-time monitoring of the equipment's power consumption status and environmental adaptability, but also adjust the equipment configuration or schedule maintenance plans by analyzing the equipment's temperature adaptability and usage time, which helps to extend the equipment's service life and ensure maximum equipment efficiency while reducing wear and tear caused by excessive use or environmental mismatch.

[0019] (3) This data anomaly monitoring method enables managers to better understand and control energy consumption through accurate electricity consumption assessment. After identifying high-energy-consuming equipment and time periods, they can take corresponding measures to optimize energy, such as adjusting equipment working hours or replacing inefficient equipment. This optimization is directly reflected in the reduction of energy consumption and the saving of operating costs, and also helps the company's sustainable development goals.

[0020] (4) The data anomaly monitoring system greatly improves the response speed and effectiveness to power consumption anomalies through the real-time monitoring and automatic alarm functions of the judgment and analysis module. When the system detects any power consumption behavior that exceeds the preset threshold, it can immediately trigger an alarm and notify relevant personnel, allowing corrective measures to be taken quickly. This real-time response capability not only reduces potential equipment damage and reduces maintenance costs, but also improves the safety of the entire power system. Through timely anomaly detection and processing, enterprises can avoid serious equipment failures and possible safety accidents, ensuring the stability and continuity of the production process.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is a flow chart of a method for monitoring data anomalies.

[0023] Figure 2 The present invention is a flowchart of the specific steps of obtaining the power consumption evaluation value of each power-consuming device to be analyzed in a data anomaly monitoring method.

[0024] Figure 3 The present invention is a flowchart of the specific steps of obtaining the historical daily average initial power consumption of each power-consuming device to be analyzed in a data anomaly monitoring method.

[0025] Figure 4 The present invention is a block diagram of a data anomaly monitoring system. DETAILED DESCRIPTION

[0026] The embodiment of the present application solves the problem that many power equipment monitoring systems usually rely only on simple real-time or periodic energy consumption monitoring, without considering the deep impact of equipment operating status on energy consumption, which easily leads to responding only when energy consumption exceeds the standard, and it is difficult to predict and prevent potential energy consumption increases. Secondly, existing power equipment monitoring systems usually do not fully consider the impact of ambient temperature and equipment temperature on power efficiency. In some cases, temperature changes may be the key factor leading to a sudden increase in energy consumption, but this is often easily overlooked in data analysis. Finally, traditional equipment monitoring systems usually fail to consider the impact of the accumulated usage time of the equipment on its performance. As the usage time increases, the wear of the equipment may lead to increased energy consumption, but this gradual change is usually overlooked in daily monitoring.

[0027] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0028] First, collect detailed historical data of each power-consuming device, including the power consumption value in each time period of each day, the temperature value of the equipment and environment, and the cumulative number of days the equipment is used. Preprocess this data to ensure the accuracy and completeness of the data, laying a good foundation for subsequent analysis. Perform a comprehensive analysis of the preprocessed data to calculate the initial average power consumption of each day, as well as the influencing factors caused by temperature and usage time. Using these influencing factors, combined with the initial average power consumption of the equipment, further calculate the daily adjusted average power consumption and the power consumption evaluation value of the equipment. Determine whether the equipment has power consumption anomalies based on the calculated power consumption evaluation value. If the power consumption of the equipment in a certain period exceeds the evaluation value, the system marks the period as abnormal, and records and reports the abnormal situation. This step includes sending power consumption anomaly warnings to relevant staff so that necessary maintenance or adjustment measures can be taken.

[0029] See also Figure 1 The embodiment of the present invention provides a technical solution: a data anomaly monitoring method, comprising the following steps: obtaining historical power consumption data, historical temperature data, and historical usage data of each power-consuming device to be analyzed, wherein the historical power consumption data is specifically the historical power consumption value of the power-consuming device in each period of each historical day, the historical temperature data includes historical device temperature data, historical ambient temperature data, and optimal operating temperature value, and the historical usage data is specifically the cumulative usage days corresponding to each historical day of the power-consuming device; performing comprehensive analysis on the historical power consumption data, historical temperature data, and historical usage data of each power-consuming device to be analyzed, respectively, to obtain the historical daily initial power consumption average, temperature influence factor, and duration influence factor of each power-consuming device to be analyzed; performing comprehensive analysis on the historical daily initial power consumption average, temperature influence factor, and duration influence factor of each power-consuming device to be analyzed, to obtain the power consumption evaluation value of each power-consuming device to be analyzed; performing abnormal analysis on the historical power consumption data of each power-consuming device to be analyzed based on the power consumption evaluation value, and if there is a power-consuming device to be analyzed whose abnormal analysis does not meet the expected standard, it is marked as power consumption abnormality, and a power consumption abnormality warning is sent, and relevant staff are notified at the same time.

[0030] The historical equipment temperature data specifically refers to the equipment temperature value of the electrical equipment in each time period of each day in history. The equipment temperature value is measured and collected at several time points in the time period, and then the mean analysis is performed. The historical ambient temperature data specifically refers to the ambient temperature value of the environment in which the electrical equipment is located in each time period of each day in history. The ambient temperature value is measured and collected at several time points in the time period, and then the mean analysis is performed.

[0031] Among them, the historical electricity consumption value is obtained through the smart meter, the optimal operating temperature value is obtained through the equipment manual, the equipment temperature value and the ambient temperature value are obtained through the temperature sensor, and the cumulative usage days are obtained through the operation and maintenance management software or the equipment log file.

[0032] Specifically, Figure 2 As shown, the specific steps for obtaining the power consumption evaluation value of each power-consuming device to be analyzed are as follows: read the historical daily average initial power consumption, temperature influence factor, and duration influence factor of each power-consuming device to be analyzed, and perform comprehensive analysis to obtain the historical daily adjusted power consumption average of each power-consuming device to be analyzed; combine the moving exponential average method to perform mean analysis on the historical daily adjusted power consumption average of each power-consuming device to be analyzed, and obtain the power consumption evaluation value of each power-consuming device to be analyzed.

[0033] The specific formula for calculating the power consumption evaluation value of each power-consuming device to be analyzed is as follows: ;in, To be analyzed The power consumption evaluation value of each electrical equipment, To be analyzed The history of electrical equipment The average adjusted electricity consumption for the day, To be analyzed The history of electrical equipment The weighting coefficient of the adjusted average electricity consumption for the day, To be analyzed The history of electrical equipment The average initial electricity consumption per day, To be analyzed The history of electrical equipment The temperature of the day affects the To be analyzed The history of electrical equipment The length of the day affects the factor, To be analyzed The history of electrical equipment The average adjusted electricity consumption for the day, To be analyzed The history of electrical equipment The weighting coefficient of the adjusted average electricity consumption for the day, , , is the number of electrical equipment to be analyzed, , The number of historical days to obtain.

[0034] It needs to be explained that , The specific calculation process is as follows: The history of electrical equipment , The average adjusted power consumption of the day is summed to obtain the adjusted power consumption value. The history of electrical equipment , The average adjusted electricity consumption of the day is compared with the adjusted electricity consumption and the value, and the ratio analysis result is used as the corresponding weighting coefficient.

[0035] In this implementation scheme, the use of historical data and influencing factors for comprehensive analysis enables the system to more accurately evaluate the power consumption behavior of each device. By combining the historical daily initial power consumption average, temperature influencing factors and duration influencing factors, the method can take into account various variables, such as the impact of environmental conditions and equipment aging on power consumption. This method not only provides a comprehensive perspective on power consumption behavior, but also helps to identify devices that may deviate from normal power consumption patterns due to external conditions or internal performance degradation. By combining the moving exponential average method, the method can dynamically update the power consumption evaluation value to reflect recent changes and trends. This method is particularly suitable for processing time series data and helps to smooth short-term fluctuations in historical data, thereby providing a more stable and reliable power consumption evaluation. Dynamic evaluation allows the system to quickly adapt to new operating conditions and environmental changes, thereby improving the monitoring system. Adaptability and responsiveness. This method calculates and adjusts the average power consumption and uses it as a basis for power consumption assessment, helping enterprises or facility managers identify high-energy-consuming equipment or time periods to optimize energy use. This method can significantly improve the efficiency of energy management and reduce energy waste and costs by predicting and adjusting power consumption strategies. In addition, accurate power consumption assessment supports the formulation of more effective energy-saving measures and maintenance strategies to further reduce operating costs. Using the set power consumption assessment value as a standard, this method can identify abnormal power consumption behavior that exceeds normal consumption patterns. This monitoring and early warning mechanism is crucial for early detection of potential equipment failures or inefficiencies, so timely maintenance or optimization measures can be taken to prevent problems from expanding. Timely fault diagnosis and response not only helps avoid expensive repair and equipment replacement costs, but also ensures the continuity of production and operational activities.

[0036] Specifically, Figure 3 As shown, the specific steps for obtaining the historical daily average initial power consumption of each electrical device to be analyzed are as follows: read the historical power consumption value of each time period of each day for each electrical device to be analyzed, and perform preprocessing; perform mean analysis on the preprocessed historical power consumption values ​​of each time period of each day for each electrical device to be analyzed, and obtain the historical daily average initial power consumption of each electrical device to be analyzed.

[0037] The specific formula for calculating the historical daily average initial power consumption of each power-consuming device to be analyzed is as follows: ;in, To be analyzed The history of electrical equipment The average initial electricity consumption per day, To be analyzed The history of electrical equipment Day's The historical electricity consumption value of each period, , is the number of electrical equipment to be analyzed, , To obtain the historical days, , The total number of time periods.

[0038] In this implementation scheme, by accurately calculating the average of the power consumption values ​​in each time period of each day, the initial power consumption average value obtained can more accurately reflect the actual power consumption behavior of the equipment. This method improves the accuracy and reliability of the data by eliminating the impact of accidental fluctuations and abnormal data. More accurate data supports more effective decision-making and helps equipment managers understand the energy efficiency level and operating status of the equipment. By systematically recording and analyzing historical power consumption data, the changing trend of equipment power consumption can be identified. For example, if the power consumption average of a certain equipment continues to rise, this may indicate that the efficiency of the equipment is declining or there is a potential failure. Early identification of these trends can intervene in advance to prevent greater maintenance costs or more serious problems. The calculated historical daily initial power consumption average can be used to optimize energy management strategies. For example, by comparing the power consumption averages of different devices or systems, managers can identify high-energy-consuming devices and take measures to improve their energy efficiency, or adjust the equipment usage patterns to reduce energy consumption. This not only helps to reduce energy costs, but also improves the energy efficiency of the entire facility. The calculation of the power consumption average sets a baseline value for the equipment's energy consumption. These baseline values ​​can be used as a standard for evaluating the effectiveness of equipment upgrades or improvement measures. In addition, continuous monitoring and comparison with baseline values ​​can promptly detect deviations from the normal range, allowing the maintenance team to respond in a timely manner to ensure that the equipment continues to operate in the best condition.

[0039] Specifically, the specific steps for obtaining the historical daily temperature influencing factor of each electrical equipment to be analyzed are as follows: read the optimal operating temperature value of each electrical equipment to be analyzed, the equipment temperature value of each historical time period of each day, and the ambient temperature value of each historical time period of the environment; perform difference-mean analysis on the optimal operating temperature value of each electrical equipment to be analyzed and the equipment temperature value of each historical time period of each day, and obtain the historical first temperature difference mean of each electrical equipment to be analyzed; perform difference-mean analysis on the optimal operating temperature value of each electrical equipment to be analyzed and the ambient temperature value of each historical time period of the environment, and obtain the historical second temperature difference mean of each electrical equipment to be analyzed; perform comprehensive analysis on the historical first temperature difference mean and the second temperature difference mean of each electrical equipment to be analyzed, and obtain the historical daily temperature influencing factor of each electrical equipment to be analyzed.

[0040] The specific formula for calculating the historical daily temperature impact factor of each electrical equipment to be analyzed is as follows: ;in, To be analyzed The history of electrical equipment The temperature of the day affects the To be analyzed The history of electrical equipment The first temperature difference of the day is the average value, To be analyzed The history of electrical equipment The proportionality coefficient of the first temperature difference of the day, To be analyzed The history of electrical equipment Day's The device temperature value for each period of time, To be analyzed The optimal operating temperature value of each electrical equipment, To be analyzed The history of electrical equipment The second mean temperature difference of the day, To be analyzed The history of electrical equipment The proportionality coefficient of the second temperature difference mean of the day, To be analyzed The history of the environment in which electrical equipment is located Day's The ambient temperature value for each period of time, To be analyzed The history of electrical equipment The adjustment factor of the day, and the value is dynamically adjusted. , is the number of electrical equipment to be analyzed, , To obtain the historical days, , The total number of time periods.

[0041] It needs to be explained that and The specific calculation process is as follows: The history of electrical equipment The absolute values ​​of the first and second temperature difference averages of the day are summed to obtain the temperature difference sum. The history of electrical equipment The absolute values ​​of the first and second temperature difference averages of the day are respectively analyzed by ratio with the temperature difference sum, and the ratio analysis results are used as the corresponding proportional coefficients.

[0042] In this embodiment, by analyzing the difference between the equipment temperature and the optimal operating temperature, as well as the difference between the equipment and the ambient temperature, and combining the dynamically adjusted adjustment coefficient for comprehensive analysis, the operating efficiency of the equipment under different environmental conditions can be more accurately understood. The calculation of the temperature impact factor allows managers to adjust the equipment settings or environment to approach the optimal operating conditions, thereby reducing energy waste and improving overall operating efficiency. Temperature is one of the key factors affecting the performance and life of electrical equipment. By monitoring the temperature performance of the equipment and comparing it with the ideal state, the system can help predict possible equipment failures. For example, if the actual operating temperature of a certain equipment is continuously higher or lower than the optimal operating temperature, it may indicate that the equipment needs maintenance or has a potential failure risk. This early diagnosis can reduce sudden failures and related maintenance costs. Early maintenance can avoid expensive emergency repairs. By systematically analyzing the temperature performance of the equipment, The temperature data and calculation of temperature impact factors can provide strong data support for equipment management. These data can be used to optimize energy management strategies, improve equipment configuration, or adjust operating hours to adapt to temperature changes, ultimately helping companies make more informed business decisions. Systematic temperature monitoring and influencing factor analysis provide an effective means to track the long-term performance of equipment, which not only helps to adjust operations immediately to cope with short-term temperature fluctuations, but also identifies long-term trends and patterns, providing a basis for future equipment upgrades or replacements. Long-term data accumulation can also help study the durability and environmental adaptability of equipment, and provide important information for designing more efficient and reliable equipment. Combining the analysis of equipment temperature with ambient temperature can comprehensively evaluate the impact of environmental conditions on equipment performance. This analysis enables equipment operations to better adapt to environmental changes and ensure that equipment can maintain good performance even under adverse conditions.

[0043] Specifically, the specific steps for obtaining the daily duration impact factor of each electrical device to be analyzed are as follows: read the cumulative usage days corresponding to each electrical device to be analyzed, and perform a comprehensive analysis to obtain the daily duration impact factor of each electrical device to be analyzed, and the calculation formula is as follows: ;in, To be analyzed The history of electrical equipment The length of the day affects the factor, The first The maximum energy consumption change index of an electrical device describes the maximum percentage by which energy consumption is expected to increase from the initial state of the device to the end of its service life. The first The change speed index of each electrical device. This parameter controls the speed at which the energy consumption changes to the maximum value, that is, the decay speed. To be analyzed The history of electrical equipment The cumulative number of days of use corresponding to the day, is the duration adjustment coefficient, , is the number of electrical equipment to be analyzed, , The number of historical days to obtain.

[0044] It should be explained that the specific calculation example of the maximum energy consumption change index is as follows:

[0045] For example, for a certain power-consuming device, the energy consumption data for the past seven days is obtained, which is divided into: 320kWh, 318kWh, 322kWh, 319kWh, 317kWh, 316kWh, 318kWh;

[0046] Based on this, the average energy consumption is calculated to obtain the baseline energy consumption:

[0047] (320+318+322+319+317+316+318) / 7≈318.6kWh / day;

[0048] Based on data provided by the manufacturer and historical energy consumption data for similar equipment, the energy consumption of the equipment is expected to increase by approximately 25% in 10 years;

[0049] Based on this, the final energy consumption is estimated as follows: 318.6kWh / day*1.25=398.25kWh / day;

[0050] Then the maximum energy consumption change index of the electrical equipment is:

[0051] ((398.25-318.6) / 318.6)*100≈25%, which is 0.25.

[0052] The specific calculation example of the change speed index is as follows:

[0053] For example, for a certain electrical equipment, its expected life is 10 years, and the maximum energy consumption change reaches 50% in 5 years;

[0054] Then the change rate index of the electrical equipment is: -(ln(0.5)) / 5≈0.139.

[0055] In this implementation scheme, by considering the cumulative number of days the equipment is in use and the corresponding energy consumption change forecast, this method can very accurately predict the decline in equipment performance. Using the maximum energy consumption change index and the change speed index, managers can predict the future energy consumption change trend of the equipment, thereby formulating more effective maintenance strategies or predicting the replacement time of the equipment. This prediction not only helps to reduce sudden equipment failures, but also optimizes resource allocation and capital investment. By analyzing the equipment usage time and the expected energy consumption changes, managers can more accurately determine when to perform maintenance or replacement to maintain the efficient operation of the equipment. For example, if a device is close to its expected energy consumption increase threshold, it may need to be maintained or replaced in advance. , to avoid inefficiency and potential operational risks. This approach can significantly reduce operational interruptions and related costs caused by equipment aging. Calculating the duration impact factor enables enterprises to monitor changes in the energy efficiency of equipment and take measures before the equipment efficiency decreases, such as adjusting the equipment workload or updating the equipment configuration to optimize energy use. This not only helps to reduce energy costs, but also improves the energy efficiency of the overall equipment and supports the company's sustainable development goals. By monitoring the equipment's usage time and energy consumption changes in real time, the system can promptly identify conditions that may cause equipment failures or safety issues. Such monitoring ensures the reliability and safety of equipment operation and provides an extra layer of protection to prevent accidents and failures.

[0056] Specifically, based on the electricity consumption evaluation value, an abnormality analysis is performed on the historical electricity consumption data of each electrical equipment to be analyzed. If there is an electrical equipment to be analyzed whose abnormal analysis does not meet the expected standards, the specific steps of marking it as abnormal electricity consumption are as follows: compare and analyze the historical electricity consumption values ​​of each electrical equipment in each time period of each day with the electricity consumption evaluation value corresponding to the electrical equipment, and obtain the total number of time periods when the historical electricity consumption value of each electrical equipment is higher than the electricity consumption evaluation value; compare and analyze the total number of time periods when the historical electricity consumption value of each electrical equipment is higher than the electricity consumption evaluation value with the set maximum number of time periods, and mark the electrical equipment corresponding to the total number of time periods higher than the set maximum number of time periods as abnormal electricity consumption. At the same time, each time period when the historical electricity consumption value of the electrical equipment is higher than the electricity consumption evaluation value is marked accordingly and sent to relevant staff.

[0057] In this implementation scheme, this method enables enterprises to promptly detect abnormal power consumption behavior of equipment, such as abnormally high power consumption, which is often an early sign of equipment failure, configuration error or maintenance needs. By promptly identifying these anomalies, enterprises can intervene before the problem causes more serious equipment damage or affects production efficiency. This early diagnosis reduces the need for emergency repairs, reduces maintenance costs, and ensures the continuous operation of equipment. By analyzing the power consumption value of the equipment and comparing it with the preset evaluation value, energy consumption can be effectively monitored and managed. For equipment that continuously exhibits high energy consumption, managers can consider energy efficiency optimization measures, such as adjusting operating hours, replacing more efficient equipment, or reconfiguring equipment settings, which not only helps to reduce energy costs , while also supporting the company's environmental protection and sustainable development goals. This method provides a data-driven basis for equipment maintenance, making maintenance plans more accurate and effective. For example, based on the analysis of historical electricity consumption data, the maintenance team can give priority to equipment that often has abnormal electricity consumption, thereby improving the efficiency of maintenance work and the reliability of equipment. In addition, through systematic analysis, companies can better understand the operating status and performance trends of equipment, and provide decision support for future equipment investment and technology upgrades. The automation of abnormal electricity consumption alarms ensures that problems can be quickly communicated to relevant personnel, greatly improving the response speed of handling abnormalities. This rapid response mechanism not only improves customer satisfaction, but also protects the company's asset security and production continuity.

[0058] See also Figure 4The embodiment of the present invention provides a technical solution: a data anomaly monitoring system, comprising: a data acquisition module, a data analysis module, an evaluation analysis module, and a judgment analysis module; the data acquisition module is used to acquire the historical power consumption data, historical temperature data, and historical usage data of each power-consuming device to be analyzed, wherein the historical power consumption data is specifically the historical power consumption value of each period of each day of the power-consuming device, the historical temperature data includes the historical device temperature data, the historical environment temperature data, and the optimal operating temperature value, and the historical usage data is specifically the cumulative usage days corresponding to each day of the power-consuming device; the data analysis module is used to obtain the historical power consumption data of each power-consuming device to be analyzed. The system uses a memory module to analyze the historical data, historical temperature data, and historical usage data, respectively, to obtain the historical daily average initial power consumption, temperature influence factor, and duration influence factor of each power-consuming device to be analyzed; an evaluation and analysis module is used to conduct a comprehensive analysis of the historical daily average initial power consumption, temperature influence factor, and duration influence factor of each power-consuming device to be analyzed, to obtain the power consumption evaluation value of each power-consuming device to be analyzed; a judgment and analysis module is used to conduct an abnormality analysis on the historical power consumption data of each power-consuming device to be analyzed based on the power consumption evaluation value; if there is a power-consuming device to be analyzed whose abnormal analysis does not meet the expected standards, it is marked as power consumption abnormality, and a power consumption abnormality warning is sent.

[0059] A specific embodiment of a data anomaly monitoring method is as follows:

[0060] For example, for a certain electrical equipment, historical power consumption data, historical temperature data, and historical usage data for the past three days are obtained respectively. In this embodiment, the number of time periods divided each day is 12, the optimal operating temperature value is 25.13°C, the maximum energy consumption change index and the change speed index are 0.01 and 0.05 respectively, the duration adjustment coefficient is 0.3, the maximum total number of time periods is set to 15, and the specific data are as follows:

[0061] The specific data examples of historical power consumption values, equipment temperature values, ambient temperature values, optimal operating temperature values, and cumulative usage days for 12 periods on the first day of history are shown in Table 1:

[0062] Table 1 Specific data examples of historical power consumption values, equipment temperature values, ambient temperature values, optimal operating temperature values, and cumulative usage days for 12 periods on the first day of history

[0063] The initial average power consumption is: 273.50;

[0064] The first temperature difference mean is: 0.517;

[0065] The second temperature difference mean is: 0.258;

[0066] The proportionality coefficient of the first temperature difference mean is approximately: 0.6667;

[0067] The proportionality coefficient of the first temperature difference mean is approximately: 0.3333;

[0068] And the adjustment coefficient in this embodiment is: 0.0167;

[0069] Based on this, combined with the calculation formula of temperature influence factor, a comprehensive analysis was conducted and the temperature influence factor was obtained to be approximately: 1.01;

[0070] Combining the calculation formula of duration impact factor for comprehensive analysis, we get the duration impact factor to be approximately: 1.00581.

[0071] The specific examples of historical power consumption values, equipment temperature values, ambient temperature values, optimal operating temperature values, and cumulative usage days for 12 periods on the second day are shown in Table 2:

[0072] Table 2 Specific data examples of historical power consumption values, equipment temperature values, ambient temperature values, optimal operating temperature values, and cumulative usage days for 12 periods on the second day of history

[0073] The initial average power consumption is: 259.86;

[0074] The first temperature difference mean is: -0.2763;

[0075] The second temperature difference mean is: -4.4319;

[0076] The proportionality coefficient of the first temperature difference mean is approximately: 0.05893;

[0077] The proportionality coefficient of the first temperature difference mean is approximately: 0.94107;

[0078] And the adjustment coefficient in this embodiment is: 0.01008;

[0079] Based on this, combined with the calculation formula of temperature influence factor, a comprehensive analysis was conducted and the temperature influence factor was obtained to be approximately: 1.017;

[0080] Combining the calculation formula of duration impact factor for comprehensive analysis, we get the duration impact factor to be approximately: 1.00582.

[0081] The specific examples of historical power consumption values, equipment temperature values, ambient temperature values, optimal operating temperature values, and cumulative usage days for 12 periods on the third day of history are shown in Table 3:

[0082] Table 3 Specific data examples of historical power consumption values, equipment temperature values, ambient temperature values, optimal operating temperature values, and cumulative usage days for 12 periods on the third day of history

[0083] The initial average power consumption is: 277.40;

[0084] The first temperature difference mean is: −0.0660;

[0085] The second temperature difference mean is: −5.0370;

[0086] The proportionality coefficient of the first temperature difference mean is approximately: 0.01294;

[0087] The proportionality coefficient of the first temperature difference mean is approximately: 0.98706;

[0088] And the adjustment coefficient in this embodiment is: 0.01431;

[0089] Based on this, combined with the calculation formula of temperature influence factor, a comprehensive analysis was conducted and the temperature influence factor was obtained to be approximately: 1.0143;

[0090] Combined with the calculation formula of duration impact factor, a comprehensive analysis was conducted and the duration impact factor was approximately: 1.00582;

[0091] In summary, combined with the calculation formula for adjusting the average power consumption, a comprehensive analysis shows that the average power consumption for the three historical days is approximately: 277.84kWh, 265.82kWh, 283.01kWh;

[0092] Combined with the specific formula of the electricity consumption evaluation value, a comprehensive analysis was conducted on the adjusted electricity consumption average of the historical three days, and the electricity consumption evaluation value of the electrical equipment was obtained to be approximately: 273.32kWh.

[0093] Therefore, the total number of time periods when the historical power consumption value of the electrical equipment is higher than the power consumption assessment value is 18 time periods, which is greater than the set maximum number of time periods. Therefore, the electrical equipment is marked as abnormal power consumption, and an abnormal power consumption warning is sent. At the same time, each time period when the historical power consumption value of the electrical equipment is higher than the power consumption assessment value is marked accordingly and sent to relevant staff.

[0094] In summary, this application has at least the following effects:

[0095] By analyzing the daily average initial power consumption and its influencing factors, the operations team can predict and identify potential problems before the equipment shows significant deviations from normal operating modes. This early warning mechanism reduces the costs of unexpected downtime due to equipment failures, improves the pertinence and timeliness of maintenance work, and thus reduces long-term operation and maintenance costs.

[0096] By real-time monitoring of the power usage status and environmental adaptability of the equipment, it is not only possible to ensure that the equipment is operating in the best condition, but also to adjust the equipment configuration or schedule maintenance plans by analyzing the temperature adaptation and usage time of the equipment, which helps to extend the service life of the equipment, ensure maximum equipment efficiency, and reduce wear caused by excessive use or environmental mismatch.

[0097] Through accurate electricity consumption assessment, managers can better understand and control energy consumption. After identifying high-energy-consuming equipment and time periods, they can take corresponding measures to optimize energy, such as adjusting equipment working hours or replacing inefficient equipment. This optimization is directly reflected in the reduction of energy consumption and savings in operating costs, and also contributes to the company's sustainable development goals.

[0098] The real-time monitoring and automatic alarm functions of the judgment and analysis module have greatly improved the response speed and effectiveness to power consumption anomalies. When the system detects any power consumption behavior that exceeds the preset threshold, it can immediately trigger an alarm and notify relevant personnel, allowing corrective measures to be taken quickly. This real-time response capability not only reduces potential equipment damage and reduces maintenance costs, but also improves the safety of the entire power system. Through timely anomaly detection and processing, enterprises can avoid serious equipment failures and possible safety accidents, and ensure the stability and continuity of the production process.

[0099] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0100] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A data anomaly monitoring method, characterized in that: The following steps are involved: Obtaining historical power consumption data, historical temperature data, and historical usage data of each electrical device to be analyzed, wherein the historical power consumption data is specifically the historical power consumption value of the electrical device in each period of each day, the historical temperature data includes historical device temperature data, historical ambient temperature data, and optimal operating temperature value, and the historical usage data is specifically the cumulative usage days corresponding to each day of the electrical device; Comprehensively analyze the historical power consumption data, historical temperature data, and historical usage data of each power-consuming device to be analyzed, and obtain the historical daily average initial power consumption, temperature impact factor, and duration impact factor of each power-consuming device to be analyzed; Comprehensively analyze the historical daily average initial power consumption, temperature influence factor, and duration influence factor of each power-consuming device to be analyzed to obtain the power consumption evaluation value of each power-consuming device to be analyzed; Based on the power consumption evaluation value, the historical power consumption data of each power consumption device to be analyzed is analyzed for abnormalities. If there is a power consumption device to be analyzed whose abnormal analysis does not meet the expected standards, it is marked as power consumption abnormality and a power consumption abnormality warning is sent; The specific steps to obtain the power consumption evaluation value of each power-consuming device to be analyzed are as follows: Read the historical daily average initial power consumption, temperature influence factor, and duration influence factor of each power-consuming device to be analyzed, and perform comprehensive analysis to obtain the historical daily adjusted power consumption average of each power-consuming device to be analyzed; Combined with the moving exponential average method, the historical daily adjusted power consumption average of each power-consuming device to be analyzed is analyzed to obtain the power consumption evaluation value of each power-consuming device to be analyzed; The specific formula for calculating the power consumption evaluation value of each power-consuming device to be analyzed is as follows: ； in, To be analyzed The power consumption evaluation value of each electrical equipment, To be analyzed The history of electrical equipment The average adjusted electricity consumption for the day, To be analyzed The history of electrical equipment The weighting coefficient of the adjusted average electricity consumption for the day, To be analyzed The history of electrical equipment The average initial electricity consumption per day, To be analyzed The history of electrical equipment The temperature of the day affects the To be analyzed The history of electrical equipment The length of the day affects the factor, To be analyzed The history of electrical equipment The average adjusted electricity consumption for the day, To be analyzed The history of electrical equipment The weighting coefficient of the adjusted average electricity consumption for the day, , , is the number of electrical equipment to be analyzed, , The number of historical days to obtain; The specific steps for obtaining the historical daily average initial power consumption of each power-consuming device to be analyzed are as follows: Read the historical power consumption value of each time period of each day for each power-consuming device to be analyzed, and perform preprocessing; Performing mean analysis on the pre-processed historical power consumption values ​​of each time period of each day for each power-consuming device to be analyzed, to obtain the historical initial power consumption mean value of each power-consuming device to be analyzed; The specific steps to obtain the historical daily temperature impact factor of each electrical equipment to be analyzed are as follows: Read the optimal operating temperature value of each electrical device to be analyzed, the device temperature value at each time period of each day in history, and the ambient temperature value at each time period of each day in history of the environment in which it is located; Perform difference mean analysis on the optimal operating temperature value of each electrical equipment to be analyzed and the equipment temperature value of each period of each day in history, and obtain the first temperature difference mean of each electrical equipment to be analyzed in history; Perform difference mean analysis on the optimal operating temperature value of each electrical device to be analyzed and the ambient temperature value of each period of each day in the environment to be analyzed, and obtain the second temperature difference mean value of each electrical device to be analyzed; Comprehensively analyze the historical daily average first temperature difference and the historical daily average second temperature difference of each electrical equipment to be analyzed, and obtain the historical daily temperature impact factor of each electrical equipment to be analyzed; The specific formula for calculating the historical daily temperature impact factor of each electrical equipment to be analyzed is as follows: ； in, To be analyzed The history of electrical equipment The temperature of the day affects the To be analyzed The history of electrical equipment The first temperature difference of the day, To be analyzed The history of electrical equipment The proportionality coefficient of the first temperature difference of the day, To be analyzed The history of electrical equipment Day's The device temperature value for each period of time, To be analyzed The optimal operating temperature value of each electrical equipment, To be analyzed The history of electrical equipment The second mean temperature difference of the day, To be analyzed The history of electrical equipment The proportionality coefficient of the second temperature difference mean of the day, To be analyzed The history of the environment in which electrical equipment is located Day's The ambient temperature value for each period of time, To be analyzed The history of electrical equipment The adjustment factor for the day, , is the number of electrical equipment to be analyzed, , To obtain the historical days, , is the total number of time periods; The specific steps for obtaining the historical daily duration influencing factors of each electrical equipment to be analyzed are as follows: The cumulative number of days of use corresponding to each electrical device to be analyzed is read, and a comprehensive analysis is performed to obtain the daily duration impact factor of each electrical device to be analyzed. The calculation formula is as follows: ； in, To be analyzed The history of electrical equipment The length of the day affects the factor, The first The maximum energy consumption change index of each electrical equipment, The first The change rate index of each electrical equipment, To be analyzed The history of electrical equipment The cumulative number of days of use corresponding to the day, is the duration adjustment coefficient, , is the number of electrical equipment to be analyzed, , The number of historical days to obtain.

2. The data anomaly monitoring method according to claim 1, characterized in that: The historical device temperature data specifically refers to the device temperature value of the electrical device at each time period of each day in history, and the historical environment temperature data specifically refers to the environment temperature value of the environment where the electrical device is located at each time period of each day in history.

3. The data anomaly monitoring method according to claim 1, characterized in that: Based on the power consumption evaluation value, an abnormal analysis is performed on the historical power consumption data of each power consumption device to be analyzed. If there is a power consumption device to be analyzed that does not meet the expected standards, the specific steps for marking it as abnormal power consumption are as follows: Compare and analyze the historical power consumption value of each power-consuming device in each time period of each day with the power consumption evaluation value corresponding to the power-consuming device, and obtain the total number of time periods when the historical power consumption value of each power-consuming device is higher than the power consumption evaluation value; The total number of time periods in which the historical power consumption value of each power-consuming device is higher than the power consumption assessment value is compared and analyzed with the set maximum time period total number, and the power-consuming devices corresponding to the time period total number higher than the set maximum time period total number are marked as power consumption abnormalities. At the same time, each time period in which the historical power consumption value of the power-consuming device is higher than the power consumption assessment value is marked accordingly and sent to relevant staff.

4. A data anomaly monitoring system, applying the data anomaly monitoring method according to any one of claims 1 to 3, characterized in that: include: Data acquisition module, data analysis module, evaluation analysis module, judgment analysis module; The data acquisition module is used to acquire the historical power consumption data, historical temperature data, and historical usage data of each electrical device to be analyzed. The historical power consumption data is specifically the historical power consumption value of the electrical device in each period of each day. The historical temperature data includes historical device temperature data, historical ambient temperature data, and optimal operating temperature value. The historical usage data is specifically the cumulative usage days corresponding to each day of the electrical device. The data analysis module is used to comprehensively analyze the historical power consumption data, historical temperature data, and historical usage data of each power-consuming device to be analyzed, and obtain the historical daily initial power consumption average, temperature influence factor, and duration influence factor of each power-consuming device to be analyzed; The evaluation and analysis module is used to comprehensively analyze the historical daily average initial power consumption, temperature influence factor, and duration influence factor of each power-consuming device to be analyzed, and obtain the power consumption evaluation value of each power-consuming device to be analyzed; The judgment and analysis module is used to perform anomaly analysis on the historical power consumption data of each power-consuming device to be analyzed based on the power consumption evaluation value. If there is a power-consuming device to be analyzed whose abnormal analysis does not meet the expected standards, it is marked as power consumption abnormality and a power consumption abnormality warning is sent.

Images