A remote intelligent monitoring system for power energy consumption

Abstract

The present invention belongs to the technical field of electric energy consumption monitoring, and specifically discloses a remote intelligent monitoring system for electric energy consumption. The system analyzes the response timeliness of the monitoring system based on historical monitoring logs, and at the same time obtains the usage proportion of each device based on the historical usage records of the production line, and then determines the energy consumption monitoring frequency of each device by combining the two, thereby realizing targeted adjustment of the energy consumption monitoring frequency of the production line. The system can not only adapt to the response capability of the current monitoring system, but also reasonably invest monitoring resources for different devices. In addition, the system collects the operating environment data of the device while collecting the energy consumption data of the device, thereby forming a corresponding relationship between the energy consumption data and the operating environment data based on the collection time, and analyzing the adaptability of the operating environment based on the operating environment data, thereby retaining the energy consumption data adapted to the operating environment for energy consumption anomaly analysis, and can avoid misjudgment of energy consumption anomaly analysis due to the environment to the greatest extent.

Description

Technical Field

[0001] The present invention belongs to the technical field of power energy consumption monitoring, and in particular relates to power energy consumption monitoring technology for equipment on a production line, and specifically is a remote intelligent monitoring system for power energy consumption. Background Art

[0002] Power equipment is usually one of the devices with higher energy consumption. Through energy consumption monitoring, the energy consumption of power equipment can be accurately evaluated, which helps enterprises to effectively control energy costs. This energy consumption monitoring is more obvious on the production line. The equipment on the production line is usually one of the main energy consumers in the production process. By monitoring the energy consumption of equipment on the production line, enterprises can accurately evaluate the energy consumption of each device and identify abnormal energy consumption equipment. This can help enterprises formulate more accurate cost control strategies and optimize energy use.

[0003] In order to improve the efficiency of energy consumption monitoring, remote monitoring is now mostly used, which can reduce the frequency of on-site personnel dispatching, eliminate the need for regular inspections or manual data collection, save labor costs, and enable the operation and maintenance team to focus on more important work and emergencies.

[0004] However, the current remote monitoring of equipment energy consumption on the production line has the following shortcomings: 1. The energy consumption monitoring frequency of different equipment is set too fixed. First, it does not take into account the differences in the operation continuity of different equipment on the production line. If the monitoring frequency is fixed at a higher or lower level, it may lead to excessive investment in monitoring resources for some equipment, while insufficient monitoring of other equipment. This will cause an unreasonable allocation of monitoring resources, which will waste the processing capacity and data storage space of the monitoring system. Second, it ignores the timeliness of the response of the remote monitoring system. The energy consumption data of the equipment needs to be uploaded and processed after collection before energy consumption anomaly analysis can be performed. The smaller the time difference between collection and upload, the more timely the monitoring system responds. However, with the continuous use of the remote monitoring system, the response speed shows a decaying trend, and there will be more and more energy consumption data that has not been uploaded after collection. If the previous monitoring frequency is maintained, it will cause the continuous accumulation of energy consumption data, which may exceed the processing capacity of the system storage capacity, thereby reducing the efficiency of the monitoring system to a certain extent, which will not only delay the analysis time of energy consumption data, but also increase the maintenance cost of the monitoring system.

[0005] 2. The energy consumption of equipment on the production line is related to the environment in which it is located. When the environment is not suitable, it will also cause abnormal energy consumption. However, the current abnormal analysis based on the energy consumption data of the equipment ignores the impact of the environment on energy consumption. Energy consumption fluctuations caused by environmental changes may be mistakenly judged as problems or failures of the equipment itself, which may easily lead to unnecessary repairs or adjustments, increasing the company's operation and maintenance costs. Summary of the invention

[0006] In view of this, the present invention aims to propose a remote intelligent monitoring system for electric energy consumption to effectively solve the problems raised in the above-mentioned background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions: a remote intelligent monitoring system for electric energy consumption, comprising the following modules: a historical monitoring record extraction module, used to retrieve the historical monitoring log of the remote monitoring system for electric energy consumption from the background, and extract the collection time and upload time of energy consumption data therefrom.

[0008] The remote monitoring response evaluation module is used to compare the collection time and upload time of energy consumption data in historical monitoring records to evaluate the timeliness of remote monitoring response.

[0009] The production line equipment statistics module is used to count the number of devices used on the production line, retrieve the historical usage records of the production line, and then extract the operating time of each device and the production line usage time from the historical usage records.

[0010] The equipment usage proportion analysis module is used to analyze the usage proportion of each device based on the operating time of each device and the production line usage time in the historical usage records.

[0011] The energy consumption monitoring frequency determination module is used to determine the energy consumption monitoring frequency of each device based on the response timeliness of remote monitoring and the corresponding usage proportion of each device.

[0012] The energy consumption data collection module is used to collect the energy consumption data of each device according to the energy consumption monitoring frequency of each device during the startup of the current production line.

[0013] The operating environment data collection module is used to collect the operating environment data of each device, including temperature and humidity, according to the energy consumption monitoring frequency of each device during the startup of the current production line.

[0014] The invalid energy consumption data identification and elimination module is used to analyze the adaptability based on the operating environment data of each device. If a piece of operating environment data of a device is not compatible, the device and the operating environment data will be marked. At the same time, the energy consumption data corresponding to the marked operating environment data will be obtained, recorded as invalid energy consumption data, and eliminated.

[0015] The energy consumption analysis module is used to perform energy consumption trend analysis and energy consumption anomaly identification on the energy consumption data corresponding to each device.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention analyzes the response timeliness of the monitoring system based on historical monitoring logs, and at the same time obtains the usage proportion of each device based on the historical usage records of the production line, and then combines the two to determine the energy consumption monitoring frequency of each device, thereby realizing targeted adjustment of the energy consumption monitoring frequency of the production line. It can not only adapt to the response capability of the current monitoring system and avoid the continuous accumulation of energy consumption data, which is beneficial to improving the efficiency of the monitoring system and ensuring the timeliness and accuracy of energy consumption data analysis, but also can reasonably invest monitoring resources in different equipment, greatly improving the effective utilization rate of monitoring resources.

[0017] 2. The present invention collects the operating environment data of the equipment while collecting the energy consumption data of the equipment, thereby forming a corresponding relationship between the energy consumption data and the operating environment data based on the collection time, and at the same time analyzes the adaptability of the operating environment based on the operating environment data, and then eliminates the energy consumption data corresponding to the operating environment data that is not compatible with the operating environment, so as to perform energy consumption anomaly analysis based on the retained energy consumption data, which can minimize the misjudgment of energy consumption anomaly analysis due to the environment, is conducive to improving the accuracy and practicality of energy consumption analysis, and can also avoid unnecessary maintenance or adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1As shown, the present invention proposes a remote intelligent monitoring system for electric energy consumption, including a historical monitoring record extraction module, a remote monitoring response evaluation module, a production line equipment statistics module, an equipment usage ratio analysis module, an energy consumption monitoring frequency determination module, an energy consumption data acquisition module, an operating environment data acquisition module, an invalid energy consumption data identification and elimination module and an energy consumption analysis module, wherein the historical monitoring record extraction module is connected to the remote monitoring response evaluation module, the production line equipment statistics module is connected to the equipment usage ratio analysis module, the remote monitoring response evaluation module and the equipment usage ratio analysis module are both connected to the energy consumption monitoring frequency determination module, the energy consumption monitoring frequency determination module is respectively connected to the energy consumption data acquisition module and the operating environment data acquisition module, the energy consumption data acquisition module and the operating environment data acquisition module are both connected to the invalid energy consumption data identification and elimination module, and the invalid energy consumption data identification and elimination module is connected to the energy consumption analysis module.

[0022] The historical monitoring record extraction module is used to retrieve the historical monitoring log of the electric power consumption remote monitoring system from the background, and extract the collection time and upload time of the energy consumption data therefrom.

[0023] It should be added that the energy consumption data mentioned above specifically refers to the amount of electrical energy consumed by the equipment during operation.

[0024] It should be explained that the collection time refers to the specific time point when the data is actually collected or recorded, which records the time when the data is generated at the source. The upload time refers to the time point when the data is sent or uploaded to the remote monitoring center, which indicates the time when the data enters the remote monitoring system. This time point reflects the moment when the data begins to be processed and analyzed in the monitoring system.

[0025] The remote monitoring response evaluation module is used to compare the collection time and upload time of the energy consumption data in the historical monitoring records to evaluate the timeliness of the remote monitoring response. The specific evaluation process is as follows: compare the collection time and upload time of the energy consumption data in each historical monitoring log to obtain the energy consumption data upload processing time corresponding to each historical monitoring log.

[0026] The energy consumption data upload processing time corresponding to each historical monitoring log is compared with the ideal upload processing time of the remote monitoring system, and the evaluation function is used to , get the response timeliness of remote monitoring , where Indicates The energy consumption data upload processing time corresponding to the historical monitoring log, Indicates the number of the historical monitoring log. , Indicates the number of historical monitoring logs retrieved. Indicates the ideal upload processing time for the remote monitoring system.

[0027] It should be added that the ideal upload processing time of the remote monitoring system is set when the system is initially designed based on the data transmission bandwidth and data processing capabilities. For example, the ideal upload processing time is 2s, but as the monitoring system continues to be put into use, the system's data processing capabilities decrease, causing the energy consumption data upload processing time to show a trend of being less than the ideal upload processing time.

[0028] The closer the energy consumption data upload processing time is to the ideal upload processing time, the more timely the remote monitoring response is, which means that the data transmission delay has less impact on the monitoring system, and the monitoring system can receive updated data more timely, maintain real-time perception of the monitored object, and the data received by the monitoring center can better represent the real on-site situation. This helps to accurately analyze and predict, and take necessary control measures in a timely manner.

[0029] The production line equipment statistics module is used to count the number of devices used on the production line, and retrieve the historical usage records of the production line, and then extract the operating time of each device and the production line usage time from the historical usage records.

[0030] It should be noted that the production line mentioned in the present invention includes but is not limited to an assembly line, an assembly line, etc.

[0031] The equipment usage proportion analysis module is used to analyze the usage proportion corresponding to each device based on the operating time of each device in the historical usage record and the usage time of the production line. The specific analysis process is: compare the operating time of each device in the historical usage record of the production line with the usage time to obtain the ratio of the operating time of each device in the historical usage record.

[0032] The operating time of the same device in each historical usage record is compared and averaged to obtain the corresponding usage proportion of each device.

[0033] It is important to know that not all equipment will start running immediately when the production line is started. This is because the design of the production line is usually scheduled and arranged according to the production rhythm and production capacity of the product. Some equipment may need to wait for the previous process to be completed before it can start or proceed to the next step. This can ensure that the entire production process is carried out in an orderly manner and avoid production congestion or quality problems caused by premature equipment operation. In addition, some of the equipment on the production line are main equipment and some are auxiliary equipment. The main equipment is the core equipment on the production line, responsible for the main production or processing procedures. These equipment need to run continuously to ensure that the product is produced on time and maintain production capacity. The operation of auxiliary equipment is usually based on production needs. It may be started when a specific process requires it and does not need to run continuously. For example, cleaning equipment may only run when changing lines or cleaning regularly; testing equipment may be used after each batch is completed. Based on these situations, there will be differences in the operating time of different equipment on the production line. By comparing the operating time of the equipment on the production line with the use time of the production line, the proportion of equipment use is obtained to reflect the continuity of the equipment on the production line. The closer the operating time of the equipment is to the use time of the production line, the more continuous the equipment is.

[0034] The energy consumption monitoring frequency determination module is used to determine the energy consumption monitoring frequency of each device based on the response timeliness of remote monitoring and the corresponding usage proportion of each device. The specific operation is as follows: multiply the response timeliness of remote monitoring by the initially set energy consumption monitoring frequency to obtain the demand-adjusted energy consumption monitoring frequency of the monitoring system.

[0035] It should be pointed out that the energy consumption monitoring frequency mentioned in the present invention is the frequency of collecting energy consumption data per unit time. In a specific example, the energy consumption monitoring frequency is 5 / unit time, which means that the energy consumption data is collected 5 times per unit time, where the unit time can be divided into units.

[0036] It should be understood that the greater the remote monitoring response timeliness, the better the data processing capability of the remote monitoring system. At this time, the energy consumption monitoring frequency does not need to be reduced, and the closer it is to the initially set energy consumption monitoring frequency, the better. In the example where the initially set energy consumption monitoring frequency is 5 / unit time, the remote monitoring response timeliness is 0.8. At this time, the monitoring system needs to adjust the energy consumption monitoring frequency to / unit time.

[0037] The usage proportion corresponding to each device is averaged to obtain the average usage proportion, and then the usage proportion corresponding to each device is combined with the average usage proportion to calculate the variance to obtain the usage proportion variance value corresponding to each device , Indicates the equipment number used on the production line, .

[0038] The energy consumption monitoring frequency is adjusted according to the demand of the monitoring system and combined with the corresponding usage weight variance value of each device to utilize the analysis model Get the energy consumption monitoring frequency of each device , where represents the limited variance set by the system, Indicates The usage ratio of the equipment, Indicates the average usage ratio, Indicates the need to adjust the energy consumption monitoring frequency of the monitoring system.

[0039] From the above formula, we can see that the greater the proportion of equipment usage, the greater the corresponding energy consumption monitoring frequency, and the more frequent the energy consumption monitoring. This is because equipment that runs continuously usually consumes a lot of energy, and energy consumption accounts for an important proportion of production costs. Frequent energy consumption monitoring can help companies understand the energy consumption of equipment in real time, discover energy waste or anomalies in time, and take adjustment measures to control and optimize energy costs.

[0040] The energy consumption data collection module is used to collect energy consumption data of each device according to the energy consumption monitoring frequency of each device during the startup of the current production line, wherein the energy consumption data can be collected using a smart meter.

[0041] The operating environment data collection module is used to collect operating environment data of each device, including temperature and humidity, according to the energy consumption monitoring frequency of each device during the startup of the current production line.

[0042] It should be noted that the device operating environment mentioned in the present invention refers to the operating environment on the surface of the device.

[0043] It should be noted that the temperature and humidity are used as operating environment parameters in the equipment operation energy consumption monitoring because temperature and humidity have a great impact on the energy consumption of the equipment, which is specifically reflected in 1. Temperature: In a high temperature environment, the equipment usually requires more energy to maintain normal operating temperature. For example, electronic equipment and mechanical equipment are prone to overheating under high temperature conditions, so more energy is required to drive the cooling system, such as fans, coolants or cooling towers, which increases the total energy consumption of the equipment. In addition, some equipment may show a decrease in working efficiency under high temperature conditions. In order to maintain normal operating efficiency, more energy input may be required. In a low temperature environment, the equipment may require additional heating to maintain a suitable operating temperature. In this case, additional energy will be consumed, increasing the overall energy consumption. Whether it is a high or low temperature environment, it may have an adverse effect on the energy consumption of the equipment.

[0044] 2. Humidity: In a high humidity environment, the equipment may require more energy to control the temperature. Since increased humidity reduces the efficiency of heat transfer in the air, the cooling system may require more energy to remove the heat generated by the equipment, thereby increasing energy consumption; at the same time, high humidity may affect the stability and reliability of electronic equipment, and additional energy may be required to maintain the normal operation of the equipment to avoid the impact of moisture on electronic components. Low humidity may cause mechanical parts to become dry and susceptible to wear, so more lubrication and maintenance may be required. These additional maintenance work may require more energy. In addition, the generation and accumulation of static electricity may increase in a low humidity environment, which poses a threat to the stability and safety of electronic equipment. In order to reduce the impact of static electricity, additional energy may be required to manage and control static electricity. It can be seen that both high humidity and low humidity conditions may have an impact on the energy consumption of equipment.

[0045] The invalid energy consumption data identification and elimination module is used to analyze the adaptability based on the operating environment data of each device. If a certain operating environment data of a device is not compatible, the device and the operating environment data will be marked. At the same time, the energy consumption data corresponding to the marked operating environment data will be obtained, recorded as invalid energy consumption data, and eliminated.

[0046] Applied to the above embodiment, the specific operation of analyzing the adaptability based on the operating environment data of each device is as follows: obtaining the specification model of each device, thereby obtaining the suitable operating environment range of each device.

[0047] Compare the collected operating environment data of each device with the appropriate operating environment range of the corresponding device, and analyze Get the adaptability of each device to each operating environment data , Indicates The device corresponds to Running environment data, , Respectively represent The equipment corresponds to the lower and upper limits of the suitable operating environment range.

[0048] The adaptability of each device to each operating environment data is compared with the standard adaptability of the operating environment corresponding to each device. If the adaptability of a device to a certain operating environment data is less than the standard adaptability, it is determined that the operating environment data corresponding to the device is not compatible.

[0049] It should be added that, since the operating environment of the equipment includes temperature and humidity, the adaptability of the operating environment data obtained includes temperature adaptability and humidity adaptability. When analyzing the adaptability of the operating environment, it is necessary to compare the temperature adaptability and humidity adaptability with the corresponding standard adaptability respectively. If the temperature adaptability and the humidity adaptability are both greater than or equal to the corresponding standard adaptability, then the operating environment data is analyzed to be adapted; otherwise, the operating environment data is analyzed to be incompatible.

[0050] Further applied to the above embodiment, the process of obtaining the compliance adaptability of the operating environment corresponding to each device is as follows: set the reference interval length. Exemplarily, the reference interval length can be 5, where the reference interval length is used to assist in calculating the sensitivity of the operating environment, and obtain the length of the suitable operating environment interval corresponding to each device, and then compare the length of the suitable operating environment interval corresponding to each device with the length of the reference interval, and then use three quarters as the base and the ratio of the length of the suitable operating environment interval corresponding to each device to the length of the reference interval as an exponent to calculate the sensitivity of the operating environment corresponding to each device.

[0051] The operating environment sensitivity corresponding to each device is multiplied by the adaptability threshold, where the adaptability threshold is 1, to obtain the standard adaptability of the operating environment corresponding to each device.

[0052] It should be understood that the present invention does not make a unified setting for the standard adaptability, but makes targeted settings based on the sensitivity of the device to the environment. Since different devices adopt different working principles and materials, these factors determine the different sensitivities of the devices to environmental conditions. This sensitivity directly affects the different settings of the standard adaptability. The greater the sensitivity, the larger the standard adaptability needs to be set. In addition, when analyzing the sensitivity of the device to the environment, the length of the suitable operating environment interval corresponding to the device is based on the length of the suitable operating environment interval for the device. The length of the environment interval suitable for the device reflects the range of environmental changes that it can tolerate. The smaller the length of the suitable operating environment interval, the more limited the suitable operating environment corresponding to the device, indicating that the smaller the environmental changes that the device can tolerate, the greater the sensitivity of the device to the environment.

[0053] Furthermore, the energy consumption data corresponding to the marked operating environment data is obtained by referring to the following process; the collection time corresponding to the marked operating environment data is extracted and recorded as the abnormal collection time.

[0054] The energy consumption data collected by the marking device is retrieved, and the collection time corresponding to each piece of energy consumption data is synchronously compared with the abnormal collection time, and the synchronized energy consumption data is selected as the energy consumption data corresponding to the marking operating environment data.

[0055] It should be understood that since the energy consumption data and the operating environment collection data are collected according to the energy consumption monitoring frequency, this indicates that the energy consumption data and the operating environment collection data are collected synchronously, and the energy consumption data corresponding to the abnormal collection time can be extracted based on the abnormal collection time of the operating environment data.

[0056] The present invention collects the operating environment data of the equipment while collecting the energy consumption data of the equipment, thereby forming a corresponding relationship between the energy consumption data and the operating environment data based on the collection time, and at the same time analyzes the adaptability of the operating environment based on the operating environment data, and then eliminates the energy consumption data corresponding to the operating environment data that is not compatible with the operating environment, so as to perform energy consumption anomaly analysis based on the retained energy consumption data, which can avoid misjudgment of energy consumption anomaly analysis due to the environment to the greatest extent, is conducive to improving the accuracy and practicality of energy consumption analysis, and can also avoid unnecessary maintenance or adjustment.

[0057] The energy consumption analysis module is used to perform energy consumption trend analysis and energy consumption anomaly identification on the retained energy consumption data corresponding to each device.

[0058] Preferably, the energy consumption data corresponding to each device is retained for energy consumption trend analysis as follows: the collection time of each piece of energy consumption data corresponding to each device is extracted respectively, and compared with the startup time of each device to obtain the running time of each piece of energy consumption data corresponding to each device.

[0059] A two-dimensional coordinate system is constructed with the operating time as the horizontal axis and the energy consumption data as the vertical axis. Several points are marked in the constructed two-dimensional coordinate system according to the operating time of each device corresponding to each energy consumption data, forming an energy consumption trend curve corresponding to each device.

[0060] Further preferably, energy consumption anomalies are identified as follows: the energy consumption trend curve corresponding to each device is overlapped and compared with the expected energy consumption trend curve corresponding to the device to obtain the length of the overlapped curve, and compared with the length of the energy consumption trend curve to obtain the overlap degree of the current energy consumption trend corresponding to each device.

[0061] It should be understood that the expected energy consumption trend curve corresponding to the equipment is a curve formed by the expected energy consumption of the equipment under different operating time periods determined during the design of the equipment based on the functional requirements, performance requirements and technical standards of the equipment, which can be obtained from the equipment's instructions for use.

[0062] Obtain the time when each device is put into the production line and compare it with the current time to obtain the service life of each device, and then obtain the overlap of energy consumption trends that can be achieved by each device in the corresponding service life.

[0063] What needs to be known is that as the equipment is used for a longer period of time, it will wear out and age, and its energy efficiency will decrease, which will cause the energy consumption of the equipment to increase slightly with its age. In this way, the energy consumption trend curve under different service years will have a downward trend in overlap with the expected energy consumption trend curve. This energy consumption trend overlap can be obtained from the equipment's instructions for use.

[0064] The current energy consumption trend overlap corresponding to each device is compared with the energy consumption trend overlap that the device can achieve in the corresponding service life. If the current energy consumption trend overlap corresponding to a device is less than the energy consumption trend overlap that the device can achieve in the corresponding service life, it is identified that the device has energy consumption abnormality.

[0065] In a further innovation of the above scheme, after energy consumption anomalies are identified, the equipment with energy consumption anomalies will be recorded as abnormal equipment, and then the cause of the energy consumption anomaly of the abnormal equipment will be predicted. The specific implementation process is as follows: from the collected energy consumption data corresponding to the abnormal equipment, the voltage waveform and current waveform of the corresponding collection time are retrieved based on the collection time, and the voltage peak-to-peak value and current peak-to-peak value are extracted therefrom, wherein the voltage peak-to-peak value refers to the difference between the maximum positive value and the minimum negative value of the voltage waveform, which represents the maximum amplitude difference of the voltage waveform within one cycle, and the current peak-to-peak value refers to the difference between the maximum positive value and the minimum negative value of the current waveform, which represents the maximum amplitude difference of the current waveform within one cycle, and the voltage peak-to-peak value and current peak-to-peak value are compared with the corresponding effective voltage and effective current respectively, and then the comparison results are added and divided by 2 to obtain the power supply fluctuation corresponding to each energy consumption data.

[0066] The effective voltage and effective current mentioned above refer to the equivalent DC voltage and current values ​​generated by the AC voltage and current waveforms in one complete cycle. In the case of a sinusoidal waveform, the effective voltage and effective current are equal to their peak voltage and peak current divided by the square root of 2.

[0067] It is important to understand that when the production line is running, the load changes of the power grid itself or when the equipment on the production line switches working states will cause fluctuations in the power supply system.

[0068] The power supply fluctuation corresponding to each energy consumption data is compared with the allowable power supply fluctuation set by the system. For example, the allowable power supply fluctuation is 0.3. The proportion of energy consumption data greater than the allowable power supply fluctuation is counted and compared with the proportion threshold configured by the system. For example, the proportion threshold is 0.8. If the proportion of energy consumption data greater than the allowable power supply fluctuation is greater than the proportion threshold, it is predicted that the cause of abnormal energy consumption is power supply fluctuation. This is because unstable power supply may cause abnormal energy consumption of the equipment when the voltage fluctuates or the power outage occurs. The equipment may run unstably or inefficiently when the voltage fluctuates, thereby increasing energy consumption. Otherwise, it is predicted that the cause of abnormal energy consumption is a problem with the equipment itself, such as increased equipment aging.

[0069] It should be understood that when predicting the cause of abnormal energy consumption of equipment after identifying abnormal energy consumption of equipment, it is necessary to take into account that the factors causing abnormal energy consumption of equipment mainly depend on the equipment itself, the operating environment, energy supply, and high-load operation. Since the high-load operation of equipment will be largely reflected in the increase in the surface temperature of the equipment, the operating environment and high-load operation can be excluded by eliminating the corresponding energy consumption data that is not suitable for the operating environment. In this way, only the problems of the equipment itself and the energy supply need to be considered.

[0070] What needs to be further understood is that for abnormal equipment energy consumption caused by energy supply, voltage stabilizing equipment can be used to stabilize it. In this way, the abnormal equipment energy consumption is very likely caused by the equipment itself, which can provide a reliable timing requirement for equipment replacement.

[0071] The present invention can make more informed decisions on repair and maintenance by reliably predicting the cause of the abnormality after identifying the abnormal energy consumption equipment, avoiding blind replacement of equipment or unnecessary repairs, helping to reduce operating costs and improve the overall reliability of the equipment. At the same time, by reliably predicting the cause of the abnormality and solving the problem in a timely manner, it can reduce the downtime and production losses of the production line and improve the overall production efficiency.

[0072] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A remote intelligent monitoring system for power consumption, It is characterized in that Includes the following modules: The historical monitoring record extraction module is used to retrieve the historical monitoring logs of the power energy consumption remote monitoring system from the background, and extract the collection time and upload time of the energy consumption data; The remote monitoring response evaluation module is used to compare the collection time and upload time of energy consumption data in historical monitoring records to evaluate the timeliness of remote monitoring response; The production line equipment statistics module is used to count the number of devices used on the production line, retrieve the historical usage records of the production line, and then extract the operating time of each device and the production line usage time from the historical usage records; The equipment usage ratio analysis module is used to analyze the usage ratio of each device based on the operation time of each device and the production line usage time in the historical usage records; The energy consumption monitoring frequency determination module is used to determine the energy consumption monitoring frequency of each device according to the response timeliness of remote monitoring and the corresponding usage proportion of each device; The energy consumption data collection module is used to collect the energy consumption data of each device according to the energy consumption monitoring frequency of each device during the startup of the current production line; The operating environment data collection module is used to collect the operating environment data of each device, including temperature and humidity, according to the energy consumption monitoring frequency of each device during the startup of the current production line; The invalid energy consumption data identification and elimination module is used to analyze the adaptability based on the operating environment data of each device. If a piece of operating environment data of a device is not compatible, the device and the piece of operating environment data are marked, and at the same time, the energy consumption data corresponding to the marked operating environment data is obtained, recorded as invalid energy consumption data, and eliminated; Energy consumption analysis module, used to analyze energy consumption trends and identify energy consumption anomalies based on the energy consumption data corresponding to each device; The specific operations of analyzing the adaptability based on the operating environment data of each device are as follows: Obtain the specifications and models of each device, thereby obtaining the suitable operating environment range of each device; Compare the collected operating environment data of each device with the appropriate operating environment range of the corresponding device, and use the analytical formula Get the adaptability of each device to each operating environment data , Indicates The device corresponds to Running environment data, , Respectively represent The lower and upper limits of the equipment’s suitable operating environment range; The adaptability of each device to each operating environment data is compared with the standard adaptability of the operating environment corresponding to each device. If the adaptability of a device to a certain operating environment data is less than the standard adaptability, it is determined that the operating environment data corresponding to the device is not compatible.

2. A remote intelligent monitoring system for power consumption as claimed in claim 1, Features: The process of evaluating the timeliness of remote monitoring response is as follows: Compare the collection time and upload time of the energy consumption data in each historical monitoring log to obtain the upload processing time of the energy consumption data corresponding to each historical monitoring log; The energy consumption data upload processing time corresponding to each historical monitoring log is compared with the ideal upload processing time of the remote monitoring system, and the evaluation function is used to , get the timeliness of remote monitoring response , where Indicates The energy consumption data upload processing time corresponding to the historical monitoring log, Indicates the number of the historical monitoring log. , Indicates the number of historical monitoring logs retrieved. Indicates the ideal upload processing time for the remote monitoring system.

3. A remote intelligent monitoring system for power consumption as claimed in claim 1, Features: The usage proportion of each device is analyzed as follows: Compare the operating time of each device in the historical usage record of the production line with the usage time to obtain a ratio of the operating time of each device in the historical usage record; The operating time of the same device in each historical usage record is compared and averaged to obtain the corresponding usage proportion of each device.

4. A remote intelligent monitoring system for power consumption as claimed in claim 2, Features: The energy consumption monitoring frequency of each device is determined as follows: The energy consumption monitoring frequency of the monitoring system is adjusted by multiplying the response timeliness of the remote monitoring by the initially set energy consumption monitoring frequency; The usage proportion corresponding to each device is averaged to obtain the average usage proportion, and then the usage proportion corresponding to each device is combined with the average usage proportion to calculate the variance to obtain the usage proportion variance value corresponding to each device , Indicates the equipment number used on the production line, ; The energy consumption monitoring frequency is adjusted according to the demand of the monitoring system and combined with the corresponding usage weight variance value of each device to utilize the analysis model Get the energy consumption monitoring frequency of each device , where represents the limited variance set by the system, Indicates The usage ratio of the equipment, Indicates the average usage ratio, Indicates the need to adjust the energy consumption monitoring frequency of the monitoring system.

5. A remote intelligent monitoring system for power consumption as claimed in claim 1, Features: The process of obtaining the compliance degree of each device to the operating environment is as follows: The length of the reference interval is set, and the length of the suitable operating environment interval corresponding to each device is obtained, and then the length of the suitable operating environment interval corresponding to each device is compared with the length of the reference interval, and then three quarters is used as the base, and the ratio of the length of the suitable operating environment interval corresponding to each device to the length of the reference interval is used as the index to calculate the operating environment sensitivity corresponding to each device; The operating environment sensitivity corresponding to each device is multiplied by the adaptability threshold to obtain the standard adaptability of the operating environment corresponding to each device.

6. A remote intelligent monitoring system for power consumption as claimed in claim 1, Features: The process of obtaining the energy consumption data corresponding to the marked operating environment data is as follows; Extract the collection time corresponding to the marked operating environment data and record it as the abnormal collection time; The energy consumption data collected by the marking device is retrieved, and the collection time corresponding to each piece of energy consumption data is synchronously compared with the abnormal collection time, and the synchronized energy consumption data is selected as the energy consumption data corresponding to the marking operating environment data.

7. A remote intelligent monitoring system for electric energy consumption as claimed in claim 1, Features: The energy consumption data corresponding to each device will be retained for energy consumption trend analysis as follows: The collection time of each piece of energy consumption data corresponding to each device is extracted respectively, and compared with the startup time of each device to obtain the running time of each piece of energy consumption data corresponding to each device; A two-dimensional coordinate system is constructed with the operating time as the horizontal axis and the energy consumption data as the vertical axis. Several points are marked in the constructed two-dimensional coordinate system according to the operating time of each device corresponding to each energy consumption data, forming an energy consumption trend curve corresponding to each device.

8. A remote intelligent monitoring system for electric energy consumption as claimed in claim 7, Features: The energy consumption anomaly is identified as follows: The energy consumption trend curve corresponding to each device is overlapped and compared with the expected energy consumption trend curve corresponding to the device to obtain the overlap degree of the current energy consumption trend corresponding to each device; Obtain the time when each device is put into the production line, and compare it with the current time to obtain the service life of each device, and then obtain the energy consumption trend overlap that can be achieved by each device under the corresponding service life; The current energy consumption trend overlap corresponding to each device is compared with the energy consumption trend overlap that the device can achieve in the corresponding service life. If the current energy consumption trend overlap corresponding to a device is less than the energy consumption trend overlap that the device can achieve in the corresponding service life, it is identified that the device has energy consumption abnormality.

9. A remote intelligent monitoring system for electric energy consumption as claimed in claim 8, Features: After the energy consumption anomaly is identified, the device with energy consumption anomaly is recorded as an abnormal device, and then the cause of the energy consumption anomaly of the abnormal device is predicted. The specific implementation process is as follows: The voltage waveform and current waveform of the corresponding collection time are retrieved from the collected energy consumption data corresponding to the abnormal equipment based on the collection time, and the peak-to-peak value of the voltage and the peak-to-peak value of the current are extracted therefrom, and the peak-to-peak value of the voltage and the peak-to-peak value of the current are respectively compared with the corresponding effective voltage and effective current, and then the comparison results are added and divided by 2 to obtain the power supply fluctuation corresponding to each energy consumption data; The power supply fluctuation corresponding to each energy consumption data is compared with the allowable power supply fluctuation set by the system, and the proportion of energy consumption data greater than the allowable power supply fluctuation is counted and compared with the proportion threshold configured by the system. If the proportion of energy consumption data greater than the allowable power supply fluctuation is greater than the proportion threshold, it is predicted that the cause of the abnormal energy consumption is power supply fluctuation; otherwise, it is predicted that the cause of the abnormal energy consumption is a problem with the equipment itself.

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