Electric energy meter equipment state monitoring method and system based on Internet of Things

Abstract

The invention relates to the technical field of electric energy meter monitoring, in particular to an electric energy meter equipment state monitoring method and system based on the internet of things, and the method comprises the steps: obtaining basic parameters, total electric energy and appearance structure state parameters of all electric energy meter equipment in a target region in a current monitoring time period for monitoring; the method comprises the steps of obtaining a basic evaluation value, an electric energy evaluation value and an appearance evaluation value, determining an equipment operation evaluation value according to the evaluation values, dividing electric energy meter equipment into a normal operation group and an abnormal operation group, positioning abnormal electric energy meter equipment in the abnormal operation group at present, and performing remote diagnosis on the abnormal electric energy meter equipment through a monitoring terminal. Therefore, multi-dimensional sensing and intelligent evaluation of the running state of the electric energy meter are realized, and the timeliness and accuracy of equipment abnormity identification are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter monitoring, and in particular to an electric energy meter device status monitoring method and system based on the Internet of Things. Background Art

[0002] With the continuous development of Internet of Things (IoT) technology and smart grids, electricity meters, as key terminals for energy consumption measurement and power management, have been widely deployed in various electricity consumption scenarios. However, existing electricity meter status monitoring methods often rely on manual inspections or static threshold warning mechanisms based on single parameter settings. These methods suffer from problems such as limited monitoring dimensions, delayed anomaly identification, and insufficient assessment accuracy.

[0003] On the one hand, existing technologies often only monitor the electricity consumption or power parameters of the electricity meter, which makes it difficult to fully reflect the multi-dimensional conditions of the equipment during actual operation, such as temperature and voltage fluctuations, and easily leads to missed detection of potential abnormalities in the equipment. On the other hand, there are obvious differences in electricity consumption behavior in different time periods. Traditional energy consumption assessment methods fail to consider time factors and are prone to misjudgment. At the same time, as outdoor or semi-enclosed equipment, electricity meters often suffer from external damage such as breakage and cracks due to natural or human factors. Existing monitoring methods are also unable to timely identify the risk of physical damage to the equipment.

[0004] In order to solve the above-mentioned defects, a technical solution is now provided. Summary of the Invention

[0005] In response to the above-mentioned shortcomings of the existing technology, the present invention provides an Internet of Things-based electricity meter equipment status monitoring method and system, which can effectively solve the problems of the existing technology that rely on manual inspections or static threshold warning mechanisms based on single parameter settings, and have limited monitoring dimensions, delayed abnormality identification, and insufficient evaluation accuracy.

[0006] To achieve the above objectives, the present invention can be implemented through the following technical solutions:

[0007] The present invention provides a method for monitoring the status of an electric energy meter device based on the Internet of Things, comprising the following steps:

[0008] Monitor the basic parameters of each electric energy meter device in the target area corresponding to the current monitoring period, obtain the device temperature change value, active power change value and voltage change value, and determine the basic assessment value based on each change value;

[0009] Obtain the total amount of electric energy of each electric energy meter device in the target area corresponding to the current monitoring period, and combine it with the estimated total amount of electric energy to determine the electric energy assessment value;

[0010] Monitor the appearance and structural status parameters of each electric energy meter device in the target area corresponding to the current monitoring period to obtain the appearance and structural status parameters, which include the number of damaged parts, the number of cracks, the total damaged area, and the total length of the cracks, and then determine the appearance assessment value;

[0011] The equipment operation evaluation value is determined based on the basic evaluation value, electric energy evaluation value and appearance evaluation value, thereby dividing the electric energy meter equipment into normal operation group and abnormal operation group, and locating the abnormal electric energy meter equipment currently in the abnormal operation group. The abnormal electric energy meter equipment is remotely diagnosed through the monitoring terminal, and the diagnosis and disposal plan is automatically matched.

[0012] Furthermore, the specific process of solving the device temperature change value is as follows:

[0013] Real-time acquisition of the device temperature data of each electric energy meter device in the target area corresponding to the current monitoring period. The current monitoring period is used as the horizontal coordinate and the device temperature is used as the vertical coordinate. Based on this, a dynamic device temperature coordinate system is established. The device temperature data is used as data points on the dynamic device temperature coordinate system and the discrete data points are sequentially connected with line segments to obtain a device temperature fluctuation graph.

[0014] The device temperatures corresponding to adjacent data points in the device temperature fluctuation graph are calculated to obtain the device temperature difference. The line segments with device temperature difference greater than zero are marked as rising segments. The angle between each rising segment and the horizontal axis is obtained by calculating its slope. The slope value is converted into an angle value through the inverse tangent function, thereby obtaining the rising angle value of each rising segment and marking it as st s b , s represents the number of each rising line segment, and s=1, 2, 3…s * , s * Indicates the total number of ascending line segments, b indicates the number of each electric energy meter device, and b=1, 2, 3…m, m indicates the total number of each electric energy meter device number, according to the formula: , calculate the device temperature change value δ1 b , where st s-1 b It represents the rising angle value of the s-1th rising line segment, and a1 represents the set correction factor.

[0015] Furthermore, the specific process of solving the active power change value is as follows:

[0016] Real-time acquisition of the active power g1 of each electric energy meter device in the target area at each monitoring time point in the current monitoring period i b , where i is the number of each monitoring time point in the current monitoring period, and i=1, 2, 3…n, and n represents the total number of monitoring time point numbers in the current monitoring period;

[0017] Extract the active power data of each electric energy meter device in the target area during the historical period from the cloud database, and calculate its historical average active power as the reference power benchmark GL b ;

[0018] According to the formula , calculate the active power change value δ2 of each electric energy meter device in the target area corresponding to the current monitoring period b , among which Gpg i b It is expressed as the active power deviation value, e and p are both natural constants, a2 and a3 are respectively the influence factors of the set active power increase and active power decrease.

[0019] Furthermore, the specific process of solving the voltage change value is as follows:

[0020] Real-time acquisition of voltage waveforms of each electric energy meter device in the target area at each monitoring time point in the current monitoring period;

[0021] Extract the voltage waveform of each electric energy meter device in the target area under normal operating conditions from the cloud database as a reference voltage waveform;

[0022] The voltage waveforms of each electric energy meter device in the target area corresponding to each monitoring time point in the current monitoring period are overlapped and compared with the reference voltage waveform to obtain the voltage waveform overlap diagram of each electric energy meter device in the target area corresponding to each monitoring time point in the current monitoring period, and the deviation area, peak deviation value and trough deviation value are extracted from them and marked as mj respectively. i b 、bf i b and bg i b , and take its value at the same time, according to the formula: , calculate the voltage change value δ3 of each electric energy meter device in the target area corresponding to the current monitoring period b , among which MJ b , BF b and BG b They are respectively represented as the set reference deviation area, reference peak deviation value and reference trough deviation value, and a4, a5 and a6 are all represented as the set influencing factors.

[0023] Furthermore, the specific process of solving the estimated total amount of electric energy is as follows:

[0024] Extract the estimated total electric energy of each electric energy meter device in the target area on the corresponding monitoring day from the cloud database, obtain the actual time period of the current monitoring period, and match the actual time period of the current monitoring period with the estimated total electric energy impact value corresponding to each set actual time period to obtain the estimated total electric energy impact value of the current monitoring period;

[0025] Divide the estimated total amount of electric energy of each electric energy meter device in the target area corresponding to the monitoring day by the number of actual time periods in the actual monitoring day to obtain the estimated total amount of electric energy of each electric energy meter device in the target area corresponding to the actual time period in the monitoring day;

[0026] The estimated total electric energy of each electric energy meter device in the target area corresponding to each actual time period of the monitoring day is subtracted from the estimated total electric energy impact value of the current monitoring period to obtain the estimated total electric energy of each electric energy meter device in the target area corresponding to the current monitoring period.

[0027] Furthermore, the specific process of solving the appearance evaluation value is as follows:

[0028] Collect the appearance and structural images of each electric energy meter device in the target area corresponding to the current monitoring period, and extract the number of damaged parts and the number of cracks from them. At the same time, extract the damaged area and crack length of each damaged part. The damaged areas of each damaged part are integrated to obtain the total damaged area, and the crack lengths of each crack are integrated to obtain the total crack length.

[0029] According to the formula Calculate the appearance evaluation value ZLP of each electric energy meter device in the target area corresponding to the current monitoring period b , where ps * 、lh * 、pm * and lc * represent the reference number of damaged locations, the reference number of cracks, the reference total damaged area, and the reference total crack length, respectively. μ1, μ2, μ3, and μ4 represent the weight coefficients of the number of damaged locations, the number of cracks, the total damaged area, and the total crack length, respectively. μ1+μ2+μ3+μ4=1, ps b 、lh b 、pm b and lc b They represent the number of damaged areas, the number of cracks, the total damaged area and the total length of cracks respectively.

[0030] Furthermore, the specific process of dividing into normal operation group and abnormal operation group is as follows:

[0031] Compare and analyze the equipment operation evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period with the preset equipment operation evaluation threshold;

[0032] If the device operation evaluation value of a certain electric energy meter device in the target area corresponding to the current monitoring period is greater than or equal to the preset device operation evaluation threshold, the electric energy meter device in the target area is recorded as an abnormal electric energy meter device, and an abnormal operation group is constructed based on this;

[0033] If the device operation evaluation value of a certain electric energy meter device in the target area corresponding to the current monitoring period is less than the preset device operation evaluation threshold, the electric energy meter device in the target area is recorded as a normal electric energy meter device, and a normal operation group is constructed accordingly.

[0034] Furthermore, the specific process of remotely diagnosing abnormal energy meter equipment through the monitoring terminal is as follows:

[0035] A diagnostic model is preset inside the monitoring terminal, where the diagnostic model is constructed based on a machine learning algorithm. Specifically, the multi-dimensional data of the electric energy meter during its historical operation process can be input into the machine learning model for training. The model is supervised by learning a sample set formed by historical operation data and fault status, so that the diagnostic model can quickly predict the fault status of the electric energy meter equipment in subsequent actual operation based solely on the real-time collected operation data, and output the corresponding diagnostic data. After receiving the diagnostic data of an electric energy meter equipment in the abnormal operation group, the monitoring terminal can automatically match the diagnostic disposal plan.

[0036] Furthermore, an electric energy meter device status monitoring system based on the Internet of Things includes:

[0037] The basic operation monitoring and analysis module is used to monitor the basic parameters of each electric energy meter device in the target area corresponding to the current monitoring period, obtain the device temperature change value, active power change value and voltage change value, and determine the basic evaluation value based on each change value;

[0038] The consumption operation monitoring and analysis module is used to obtain the total amount of electric energy of each electric energy meter device in the target area corresponding to the current monitoring period, and combine it with the estimated total amount of electric energy to determine the electric energy evaluation value;

[0039] The quality monitoring and analysis module is used to monitor the appearance and structural status parameters of each electric energy meter device in the target area corresponding to the current monitoring period, obtain the appearance and structural status parameters, which include the number of damaged parts, the number of cracks, the total damaged area, and the total length of the cracks, and then determine the appearance assessment value;

[0040] The management and analysis module is used to determine the equipment operation evaluation value based on the basic evaluation value, electric energy evaluation value and appearance evaluation value, thereby dividing the electric energy meter equipment into normal operation group and abnormal operation group, and locating the abnormal electric energy meter equipment currently in the abnormal operation group, remotely diagnosing the abnormal electric energy meter equipment through the monitoring terminal, and automatically matching the diagnosis and disposal plan.

[0041] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0042] 1. The present invention constructs a set of intelligent monitoring methods for the status of electricity meter equipment based on the Internet of Things by integrating multi-dimensional perception parameters, electricity consumption behavior analysis and image recognition technology, realizing comprehensive perception and diagnostic management of the operating status of electricity meters, and significantly improving the refinement and automation level of equipment management. Specifically, in terms of basic status monitoring, a basic evaluation model is constructed by comprehensively considering multiple indicators such as temperature, active power and voltage. Compared with the traditional static early warning mechanism that relies on a single parameter, the accuracy and sensitivity of monitoring are greatly improved. In terms of energy consumption analysis, the actual time period behavior characteristics and the total electricity amount evaluation model are introduced to enhance the response capability to the differences in electricity consumption in different time periods and effectively avoid energy consumption misjudgment. In terms of equipment quality monitoring, image processing technology is combined to quantitatively analyze the damage and cracks of electricity meter equipment, making up for the lack of existing ability to identify external damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 It is a flowchart of the overall process of the present invention.

[0045] Figure 2 It is the overall module block diagram of the present invention. DETAILED DESCRIPTION

[0046] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings 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 making creative efforts are within the scope of protection of the present invention.

[0047] like Figure 1 As shown, a method for monitoring the status of an electric energy meter device based on the Internet of Things includes the following steps:

[0048] Step 1: Monitor the basic parameters of each electric energy meter device in the target area corresponding to the current monitoring period, and analyze the basic status of each electric energy meter device in the target area corresponding to the current monitoring period. The specific analysis steps are as follows:

[0049] Real-time acquisition of the device temperature data of each electric energy meter device in the target area corresponding to the current monitoring period. The current monitoring period is used as the horizontal coordinate and the device temperature is used as the vertical coordinate. Based on this, a dynamic device temperature coordinate system is established. The device temperature data is used as data points on the dynamic device temperature coordinate system and the discrete data points are sequentially connected with line segments to obtain a device temperature fluctuation graph.

[0050] The device temperatures corresponding to adjacent data points in the device temperature fluctuation graph are subtracted to obtain the device temperature difference. If the device temperature difference is greater than zero, it means that the device temperature is rising. The line segment with the device temperature difference greater than zero is marked as a rising line segment. Then, by calculating its slope (that is, a quantitative representation of the inclination of the line segment), the angle between each rising line segment and the horizontal axis is obtained. That is, the slope value is converted into an angle value through the inverse tangent function, thereby obtaining the rising angle value of each rising line segment and marking it as st s b , s represents the number of each rising line segment, and s=1, 2, 3…s * , s * Indicates the total number of ascending line segments, b indicates the number of each electric energy meter device, and b=1, 2, 3…m, m indicates the total number of each electric energy meter device number, according to the formula: , calculate the device temperature change value δ1 b , where st s-1 b represents the rising angle value of the s-1th rising line segment, a1 represents the set correction factor, which is used to improve the accuracy of the calculation results. The specific setting of the correction factor is reasonably set by those skilled in the art according to actual conditions.

[0051] Real-time acquisition of the active power g1 of each electric energy meter device in the target area at each monitoring time point in the current monitoring period i b , which is used to reflect the actual power consumption intensity of the electric energy meter equipment in the current monitoring period, where i is the number of each monitoring time point in the current monitoring period, and i=1, 2, 3...n, and n is the total number of monitoring time point numbers in the current monitoring period;

[0052] Extract the active power data of each electric energy meter device in the target area during the historical period from the cloud database, and calculate its historical average active power as the reference power benchmark GL b ,The historical average active power is used to characterize the power level of the ,electrical energy meter equipment under a typical operating state, ,providing a comparative reference for the current state;

[0053] According to the formula , calculate the active power change value δ2 of each electric energy meter device in the target area corresponding to the current monitoring period b, among which Gpg i b It is expressed as the active power deviation value, e and p are both natural constants, a2 and a3 are respectively the influence factors of the set active power increase and active power decrease.

[0054] Real-time acquisition of voltage waveforms of each electric energy meter device in the target area at each monitoring time point in the current monitoring period;

[0055] Extract the voltage waveform of each electric energy meter device in the target area under normal operating conditions from the cloud database as a reference voltage waveform;

[0056] The voltage waveforms of each electric energy meter device in the target area corresponding to each monitoring time point in the current monitoring period are overlapped and compared with the reference voltage waveform to obtain the voltage waveform overlap diagram of each electric energy meter device in the target area corresponding to each monitoring time point in the current monitoring period, and the deviation area, peak deviation value and trough deviation value are extracted from them and marked as mj respectively. i b 、bf i b and bg i b , and take its value at the same time, according to the formula: , calculate the voltage change value δ3 of each electric energy meter device in the target area corresponding to the current monitoring period b , among which MJ b , BF b and BG b They are respectively represented as the set reference deviation area, reference peak deviation value and reference trough deviation value, and a4, a5 and a6 are all represented as the set influencing factors;

[0057] According to the formula: Calculate the basic evaluation value JCP of each electric energy meter device in the target area corresponding to the current monitoring period b , where γ1, γ2, and γ3 represent the weight coefficients corresponding to the set device temperature change value, active power change value, and voltage change value, respectively, and γ1<γ2<γ3;

[0058] In a specific embodiment, the present invention comprehensively analyzes the basic parameters of each electric energy meter device in the target area corresponding to the current monitoring period, and constructs the device temperature change value, active power change value and voltage change value based on this, and then calculates the basic evaluation value, thereby achieving an accurate assessment of the basic status of the electric energy meter device, and providing data support for the subsequent early identification and management of potential operational abnormalities of the electric energy meter device.

[0059] Step 2: Obtain the total amount of electric energy of each electric energy meter device in the target area corresponding to the current monitoring period, and analyze the electric energy evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period to obtain the electric energy evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period. The specific analysis steps are as follows:

[0060] Extract the estimated total electric energy of each electric energy meter device in the target area on the corresponding monitoring day from the cloud database, obtain the actual time period of the current monitoring period, and match the actual time period of the current monitoring period with the estimated total electric energy impact value corresponding to each set actual time period to obtain the estimated total electric energy impact value of the current monitoring period;

[0061] It should be noted that the actual time periods, for example, 08:00-09:00 and 19:00-20:00, are different. Different actual time periods correspond to different electricity usage behavior patterns, and therefore have different impacts on the total amount of electricity of the electricity meter device, because each actual time period has a corresponding estimated total amount of electricity impact value;

[0062] Divide the estimated total amount of electric energy of each electric energy meter device in the target area corresponding to the monitoring day by the number of actual time periods in the actual monitoring day to obtain the estimated total amount of electric energy of each electric energy meter device in the target area corresponding to the actual time period in the monitoring day;

[0063] The estimated total electric energy of each electric energy meter device in the target area corresponding to each actual time period of the monitoring day is subtracted from the estimated total electric energy impact value of the current monitoring period to obtain the estimated total electric energy of each electric energy meter device in the target area corresponding to the current monitoring period;

[0064] Extract the estimated total energy value of each electric energy meter device in the target area corresponding to the current monitoring period, denoted as yn b , extract the total amount of electric energy of each electric energy meter device in the target area corresponding to the current monitoring period, recorded as dn b .

[0065] According to the formula: Calculate the electric energy evaluation value DNP of each electric energy meter device in the target area corresponding to the current monitoring period b .

[0066] Step 3: Monitor the appearance and structural status parameters of each electric energy meter device in the target area corresponding to the current monitoring period, and analyze the appearance evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period to obtain the appearance evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period. The specific analysis steps are as follows:

[0067] The appearance and structure images of each electric energy meter device in the target area corresponding to the current monitoring period are collected by a high-definition camera to obtain the appearance and structure images of each electric energy meter device in the target area corresponding to the current monitoring period;

[0068] Extract the number of damaged areas and cracks from the appearance structural image of each electric energy meter device in the target area corresponding to the current monitoring period, and then extract the damaged area and crack length of each damaged area. Integrate the damaged areas of each damaged area to obtain the total damaged area, and integrate the crack lengths of each crack to obtain the total crack length.

[0069] The number of damaged parts, the number of cracks, the total damaged area and the total length of cracks of each electric energy meter device in the target area corresponding to the current monitoring period are thus obtained, which constitute the appearance structural state parameters of each electric energy meter device in the target area corresponding to the current monitoring period;

[0070] The values ​​of the number of damaged parts, the number of cracks, the total damaged area and the total length of cracks are extracted from the appearance and structural state parameters of each electric energy meter device in the target area corresponding to the current monitoring period, and are recorded as ps b 、lh b 、pm b and lc b ;

[0071] According to the formula Calculate the appearance evaluation value ZLP of each electric energy meter device in the target area corresponding to the current monitoring period b , where ps * 、lh * 、pm * and lc * They represent the number of reference damaged locations, the number of reference cracks, the reference total damaged area and the reference total crack length, respectively. μ1, μ2, μ3 and μ4 represent the weight coefficients of the number of damaged locations, the number of cracks, the total damaged area and the total crack length, respectively, and μ1+μ2+μ3+μ4=1.

[0072] Step 3: Based on the basic evaluation value JCP of each electric energy meter device in the target area corresponding to the current monitoring period b , Electric energy evaluation value DNP b and appearance evaluation value ZLP b The equipment operation evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period is analyzed to obtain the equipment operation evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period. The calculation formula of the equipment operation evaluation value is set as follows: , where η1, η2 and η3 represent the weight coefficients of basic evaluation value, electric energy evaluation value and appearance evaluation value respectively, and satisfy η1+η2+η3=1.

[0073] Step 4: Based on the equipment operation evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period, the electric energy meter devices are divided into normal operation group and abnormal operation group, and the abnormal electric energy meter devices currently in the abnormal operation group are located. Then, remote diagnosis of the abnormal electric energy meter devices is performed through the monitoring terminal. The specific operation process is as follows:

[0074] Compare and analyze the equipment operation evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period with the preset equipment operation evaluation threshold;

[0075] If the device operation evaluation value of a certain electric energy meter device in the target area corresponding to the current monitoring period is greater than or equal to the preset device operation evaluation threshold, the electric energy meter device in the target area is recorded as an abnormal electric energy meter device, and an abnormal operation group is constructed based on this;

[0076] If the device operation evaluation value of a certain electric energy meter device in the target area corresponding to the current monitoring period is less than the preset device operation evaluation threshold, the electric energy meter device in the target area is recorded as a normal electric energy meter device, and a normal operation group is constructed based on this;

[0077] This clarifies the need for refined group management and targeted remote diagnostics to improve the overall operational health of electricity meters, reduce fault response time, and reduce maintenance costs.

[0078] Optionally, in an embodiment of the present invention, the abnormal electric energy meter device currently in the abnormal operation group is located, and the abnormal electric energy meter device is remotely diagnosed through the monitoring terminal. It should be pointed out that the monitoring terminal refers to a control center for unified integrated management of the electric energy meter device, and a diagnostic model is preset inside the monitoring terminal. The diagnostic model is usually constructed based on a machine learning algorithm. Specifically, the multi-dimensional data of the electric energy meter during the historical operation process - including but not limited to: operating parameters (instantaneous voltage, current, active power, reactive power, power factor), environmental parameters (installation environment temperature, humidity, magnetic field interference intensity, vibration amplitude), sensor readings (internal temperature rise, terminal contact resistance, signal communication quality, metering error) and fault The status (fault occurrence time, fault type, maintenance record, and disposal result) is input into a machine learning model (including but not limited to support vector machine (SVM), random forest, gradient boosting decision tree (GBDT), deep neural network, etc.) for training. The model is supervised by learning a sample set formed by historical operating data and fault status, so that the diagnosis model can quickly predict the fault status of the electric energy meter equipment in subsequent actual operation based solely on the real-time collected operating data, and output the corresponding diagnostic data. After receiving the diagnostic data of an electric energy meter equipment in the abnormal operation group, the monitoring terminal can automatically match the diagnosis and disposal plan (such as remote reset, parameter recalibration, dispatch on-site maintenance, etc.), thereby realizing timely prediction and emergency disposal of electric energy meter faults.

[0079] like Figure 2 As shown, an electric energy meter equipment status monitoring system based on the Internet of Things includes: a basic operation monitoring and analysis module, a consumption operation monitoring and analysis module, a quality monitoring and analysis module, a management analysis module, a cloud database and a monitoring terminal;

[0080] The basic operation monitoring and analysis module is used to monitor the basic parameters of each electric energy meter device in the target area corresponding to the current monitoring period, thereby analyzing the basic status of each electric energy meter device in the target area corresponding to the current monitoring period. The specific analysis steps are as follows:

[0081] Real-time acquisition of the device temperature data of each electric energy meter device in the target area corresponding to the current monitoring period. The current monitoring period is used as the horizontal coordinate and the device temperature is used as the vertical coordinate. Based on this, a dynamic device temperature coordinate system is established. The device temperature data is used as data points on the dynamic device temperature coordinate system and the discrete data points are sequentially connected with line segments to obtain a device temperature fluctuation graph.

[0082] The device temperatures corresponding to adjacent data points in the device temperature fluctuation graph are subtracted to obtain the device temperature difference. If the device temperature difference is greater than zero, it means that the device temperature is rising. The line segment with the device temperature difference greater than zero is marked as a rising line segment. Then, by calculating its slope (that is, a quantitative representation of the inclination of the line segment), the angle between each rising line segment and the horizontal axis is obtained. That is, the slope value is converted into an angle value through the inverse tangent function, thereby obtaining the rising angle value of each rising line segment and marking it as st s b , s represents the number of each rising line segment, and s=1, 2, 3…s * , s * Indicates the total number of ascending line segments, b indicates the number of each electric energy meter device, and b=1, 2, 3…m, m indicates the total number of each electric energy meter device number, according to the formula: , calculate the device temperature change value δ1 b , where st s-1 b represents the rising angle value of the s-1th rising line segment, a1 represents the set correction factor, which is used to improve the accuracy of the calculation results. The specific setting of the correction factor is reasonably set by those skilled in the art according to actual conditions.

[0083] Real-time acquisition of the active power g1 of each electric energy meter device in the target area at each monitoring time point in the current monitoring period i b , which is used to reflect the actual power consumption intensity of the electric energy meter equipment in the current monitoring period, where i is the number of each monitoring time point in the current monitoring period, and i=1, 2, 3...n, and n is the total number of monitoring time point numbers in the current monitoring period;

[0084] Extract the active power data of each electric energy meter device in the target area during the historical period from the cloud database, and calculate its historical average active power as the reference power benchmark GL b ,The historical average active power is used to characterize the power level of the ,electrical energy meter equipment under a typical operating state, ,providing a comparative reference for the current state;

[0085] According to the formula , calculate the active power change value δ2 of each electric energy meter device in the target area corresponding to the current monitoring period b , among which Gpg i b It is expressed as the active power deviation value, e and p are both natural constants, a2 and a3 are respectively the influence factors of the set active power increase and active power decrease.

[0086] Real-time acquisition of voltage waveforms of each electric energy meter device in the target area at each monitoring time point in the current monitoring period;

[0087] Extract the voltage waveform of each electric energy meter device in the target area under normal operating conditions from the cloud database as a reference voltage waveform;

[0088] The voltage waveforms of each electric energy meter device in the target area corresponding to each monitoring time point in the current monitoring period are overlapped and compared with the reference voltage waveform to obtain the voltage waveform overlap diagram of each electric energy meter device in the target area corresponding to each monitoring time point in the current monitoring period, and the deviation area, peak deviation value and trough deviation value are extracted from them and marked as mj respectively. i b 、bf i b and bg i b , and take its value at the same time, according to the formula: , calculate the voltage change value δ3 of each electric energy meter device in the target area corresponding to the current monitoring period b , among which MJ b , BF b and BG b They are respectively represented as the set reference deviation area, reference peak deviation value and reference trough deviation value, and a4, a5 and a6 are all represented as the set influencing factors;

[0089] According to the formula: Calculate the basic evaluation value JCP of each electric energy meter device in the target area corresponding to the current monitoring period b , where γ1, γ2, and γ3 represent the weight coefficients corresponding to the set device temperature change value, active power change value, and voltage change value, respectively, and γ1<γ2<γ3.

[0090] The consumption operation monitoring and analysis module is used to obtain the total amount of electric energy of each electric energy meter device in the target area corresponding to the current monitoring period, and combine it with the estimated total amount of electric energy to determine the electric energy evaluation value. The specific analysis process is as follows:

[0091] Extract the estimated total electric energy of each electric energy meter device in the target area on the corresponding monitoring day from the cloud database, obtain the actual time period of the current monitoring period, and match the actual time period of the current monitoring period with the estimated total electric energy impact value corresponding to each set actual time period to obtain the estimated total electric energy impact value of the current monitoring period;

[0092] Divide the estimated total amount of electric energy of each electric energy meter device in the target area corresponding to the monitoring day by the number of actual time periods in the actual monitoring day to obtain the estimated total amount of electric energy of each electric energy meter device in the target area corresponding to the actual time period in the monitoring day;

[0093] The estimated total electric energy of each electric energy meter device in the target area corresponding to each actual time period of the monitoring day is subtracted from the estimated total electric energy impact value of the current monitoring period to obtain the estimated total electric energy of each electric energy meter device in the target area corresponding to the current monitoring period;

[0094] Extract the estimated total energy value of each electric energy meter device in the target area corresponding to the current monitoring period, denoted as yn b , extract the total amount of electric energy of each electric energy meter device in the target area corresponding to the current monitoring period, recorded as dn b .

[0095] According to the formula: Calculate the electric energy evaluation value DNP of each electric energy meter device in the target area corresponding to the current monitoring period b .

[0096] The quality monitoring and analysis module is used to monitor the appearance and structural status parameters of each electric energy meter device in the target area corresponding to the current monitoring period, thereby analyzing the appearance evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period, and obtaining the appearance evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period. The specific analysis process is as follows:

[0097] The appearance and structure images of each electric energy meter device in the target area corresponding to the current monitoring period are collected by a high-definition camera to obtain the appearance and structure images of each electric energy meter device in the target area corresponding to the current monitoring period;

[0098] Extract the number of damaged areas and cracks from the appearance structural image of each electric energy meter device in the target area corresponding to the current monitoring period, and then extract the damaged area and crack length of each damaged area. Integrate the damaged areas of each damaged area to obtain the total damaged area, and integrate the crack lengths of each crack to obtain the total crack length.

[0099] The number of damaged parts, the number of cracks, the total damaged area and the total length of cracks of each electric energy meter device in the target area corresponding to the current monitoring period are thus obtained, which constitute the appearance structural state parameters of each electric energy meter device in the target area corresponding to the current monitoring period;

[0100] The values ​​of the number of damaged parts, the number of cracks, the total damaged area and the total length of cracks are extracted from the appearance and structural state parameters of each electric energy meter device in the target area corresponding to the current monitoring period, and are recorded as ps b 、lh b 、pm b and lc b ;

[0101] According to the formula Calculate the appearance evaluation value ZLP of each electric energy meter device in the target area corresponding to the current monitoring period b , where ps * 、lh * 、pm * and lc * They represent the number of reference damaged locations, the number of reference cracks, the reference total damaged area and the reference total crack length, respectively. μ1, μ2, μ3 and μ4 represent the weight coefficients of the number of damaged locations, the number of cracks, the total damaged area and the total crack length, respectively, and μ1+μ2+μ3+μ4=1.

[0102] The management and analysis module is used to analyze the equipment operation evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period based on the basic evaluation value, electric energy evaluation value and appearance evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period, and obtain the equipment operation evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period;

[0103] Based on the equipment operation evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period, the electric energy meter devices are divided into normal operation group and abnormal operation group, and the abnormal electric energy meter devices currently in the abnormal operation group are located, thereby remotely diagnosing the abnormal electric energy meter devices through the monitoring terminal;

[0104] Optionally, in an embodiment of the present invention, a diagnostic model is preset inside the monitoring terminal, wherein the diagnostic model is generally constructed based on a machine learning algorithm. Specifically, the multi-dimensional data of the electric energy meter during its historical operation process can be input into the machine learning model for training. The model is supervised by learning a sample set formed by historical operation data and fault status, so that the diagnostic model can quickly predict the fault status of the electric energy meter equipment in subsequent actual operation based only on the operation data collected in real time, and output the corresponding diagnostic data. After receiving the diagnostic data of an electric energy meter equipment in the abnormal operation group, the monitoring terminal can automatically match the diagnostic disposal plan.

[0105] The cloud database is used to store active power data within the historical period, voltage waveforms under normal operating conditions, and estimated total electric energy on the monitoring day.

[0106] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for monitoring the status of an electric energy meter device based on the Internet of Things, characterized in that: The following steps are involved: Monitor the basic parameters of each electric energy meter device in the target area corresponding to the current monitoring period, obtain the device temperature change value, active power change value and voltage change value, and determine the basic assessment value based on each change value; Obtain the total amount of electric energy of each electric energy meter device in the target area corresponding to the current monitoring period, and combine it with the estimated total amount of electric energy to determine the electric energy assessment value; Monitor the appearance and structural status parameters of each electric energy meter device in the target area corresponding to the current monitoring period to obtain the appearance and structural status parameters, which include the number of damaged parts, the number of cracks, the total damaged area, and the total length of the cracks, and then determine the appearance assessment value; The equipment operation evaluation value is determined based on the basic evaluation value, electric energy evaluation value and appearance evaluation value, thereby dividing the electric energy meter equipment into normal operation group and abnormal operation group, and locating the abnormal electric energy meter equipment currently in the abnormal operation group. The abnormal electric energy meter equipment is remotely diagnosed through the monitoring terminal, and the diagnosis and disposal plan is automatically matched.

2. The method for monitoring the state of an electric energy meter device based on the Internet of Things according to claim 1, characterized in that: The specific process of solving the device temperature change value is as follows: Real-time acquisition of the device temperature data of each electric energy meter device in the target area corresponding to the current monitoring period. The current monitoring period is used as the horizontal coordinate and the device temperature is used as the vertical coordinate. Based on this, a dynamic device temperature coordinate system is established. The device temperature data is used as data points on the dynamic device temperature coordinate system and the discrete data points are sequentially connected with line segments to obtain a device temperature fluctuation graph. The device temperatures corresponding to adjacent data points in the device temperature fluctuation graph are calculated to obtain the device temperature difference. The line segments with device temperature difference greater than zero are marked as rising segments. The angle between each rising segment and the horizontal axis is obtained by calculating its slope. The slope value is converted into an angle value through the inverse tangent function, thereby obtaining the rising angle value of each rising segment and marking it as st s b , s represents the number of each rising line segment, and s=1, 2, 3…s * , s * Indicates the total number of ascending line segments, b indicates the number of each electric energy meter device, and b=1, 2, 3…m, m indicates the total number of each electric energy meter device number, according to the formula: , calculate the device temperature change value δ1 b , where st s-1 b It represents the rising angle value of the s-1th rising line segment, and a1 represents the set correction factor.

3. The method for monitoring the state of an electric energy meter device based on the Internet of Things according to claim 1, characterized in that: The specific process of solving the active power change value is as follows: Real-time acquisition of the active power g1 of each electric energy meter device in the target area at each monitoring time point in the current monitoring period i b , where i is the number of each monitoring time point in the current monitoring period, and i=1, 2, 3…n, and n represents the total number of monitoring time point numbers in the current monitoring period; Extract the active power data of each electric energy meter device in the target area during the historical period from the cloud database, and calculate its historical average active power as the reference power benchmark GL b According to the formula , calculate the active power change value δ2 of each electric energy meter device in the target area corresponding to the current monitoring period b , among which Gpg i b It is expressed as the active power deviation value, e and p are both expressed as natural constants, a2 and a3 are respectively expressed as the influence factor of the set active power increase and the influence factor of the active power decrease.

4. The method for monitoring the state of an electric energy meter device based on the Internet of Things according to claim 1, characterized in that: The specific process of solving the voltage change value is as follows: Real-time acquisition of voltage waveforms of each electric energy meter device in the target area at each monitoring time point in the current monitoring period; Extract the voltage waveform of each electric energy meter device in the target area under normal operating conditions from the cloud database as a reference voltage waveform; The voltage waveforms of each electric energy meter device in the target area corresponding to each monitoring time point in the current monitoring period are overlapped and compared with the reference voltage waveform to obtain the voltage waveform overlap diagram of each electric energy meter device in the target area corresponding to each monitoring time point in the current monitoring period, and the deviation area, peak deviation value and trough deviation value are extracted from them and marked as mj respectively. i b 、bf i b and bg i b , and take its value at the same time, according to the formula: , calculate the voltage change value δ3 of each electric energy meter device in the target area corresponding to the current monitoring period b , among which MJ b , BF b and BG b They are respectively represented as the set reference deviation area, reference peak deviation value and reference trough deviation value, and a4, a5 and a6 are all represented as the set influencing factors.

5. The method for monitoring the state of an electric energy meter device based on the Internet of Things according to claim 1, characterized in that: The specific process of solving the estimated total amount of electric energy is as follows: Extract the estimated total electric energy of each electric energy meter device in the target area on the corresponding monitoring day from the cloud database, obtain the actual time period of the current monitoring period, and match the actual time period of the current monitoring period with the estimated total electric energy impact value corresponding to each set actual time period to obtain the estimated total electric energy impact value of the current monitoring period; Divide the estimated total amount of electric energy of each electric energy meter device in the target area corresponding to the monitoring day by the number of actual time periods in the actual monitoring day to obtain the estimated total amount of electric energy of each electric energy meter device in the target area corresponding to the actual time period in the monitoring day; The estimated total electric energy of each electric energy meter device in the target area corresponding to each actual time period of the monitoring day is subtracted from the estimated total electric energy impact value of the current monitoring period to obtain the estimated total electric energy of each electric energy meter device in the target area corresponding to the current monitoring period.

6. The method for monitoring the state of an electric energy meter device based on the Internet of Things according to claim 1, characterized in that: The specific process of solving the appearance evaluation value is as follows: Collect the appearance and structural images of each electric energy meter device in the target area corresponding to the current monitoring period, and extract the number of damaged parts and the number of cracks from them. At the same time, extract the damaged area and crack length of each damaged part. The damaged areas of each damaged part are integrated to obtain the total damaged area, and the crack lengths of each crack are integrated to obtain the total crack length. According to the formula Calculate the appearance evaluation value ZLP of each electric energy meter device in the target area corresponding to the current monitoring period b , where ps * 、lh * 、pm * and lc * represent the reference number of damaged locations, the reference number of cracks, the reference total damaged area, and the reference total crack length, respectively. μ1, μ2, μ3, and μ4 represent the weight coefficients of the number of damaged locations, the number of cracks, the total damaged area, and the total crack length, respectively. μ1+μ2+μ3+μ4=1, ps b 、lh b 、pm b and lc b They represent the number of damaged areas, the number of cracks, the total damaged area and the total length of cracks respectively.

7. The method for monitoring the state of an electric energy meter device based on the Internet of Things according to claim 1, characterized in that: The specific process of dividing into normal operation group and abnormal operation group is as follows: Compare and analyze the equipment operation evaluation value of each electric energy meter device in the target area corresponding to the current monitoring period with the preset equipment operation evaluation threshold; If the device operation evaluation value of a certain electric energy meter device in the target area corresponding to the current monitoring period is greater than or equal to the preset device operation evaluation threshold, the electric energy meter device in the target area is recorded as an abnormal electric energy meter device, and an abnormal operation group is constructed based on this; If the device operation evaluation value of a certain electric energy meter device in the target area corresponding to the current monitoring period is less than the preset device operation evaluation threshold, the electric energy meter device in the target area is recorded as a normal electric energy meter device, and a normal operation group is constructed accordingly.

8. The method for monitoring the state of an electric energy meter device based on the Internet of Things according to claim 1, characterized in that: The specific process of remote diagnosis of abnormal electric energy meter equipment through the monitoring terminal is as follows: A diagnostic model is preset inside the monitoring terminal, where the diagnostic model is constructed based on a machine learning algorithm. Specifically, the multi-dimensional data of the electric energy meter during its historical operation process can be input into the machine learning model for training. The model is supervised by learning a sample set formed by historical operation data and fault status, so that the diagnostic model can quickly predict the fault status of the electric energy meter equipment in subsequent actual operation based solely on the real-time collected operation data, and output the corresponding diagnostic data. After receiving the diagnostic data of an electric energy meter equipment in the abnormal operation group, the monitoring terminal can automatically match the diagnostic disposal plan.

9. An electric energy meter device status monitoring system based on the Internet of Things, applied to the electric energy meter device status monitoring method based on the Internet of Things according to claim 1, characterized in that: include: The basic operation monitoring and analysis module is used to monitor the basic parameters of each electric energy meter device in the target area corresponding to the current monitoring period, obtain the device temperature change value, active power change value and voltage change value, and determine the basic evaluation value based on each change value; The consumption operation monitoring and analysis module is used to obtain the total amount of electric energy of each electric energy meter device in the target area corresponding to the current monitoring period, and combine it with the estimated total amount of electric energy to determine the electric energy evaluation value; The quality monitoring and analysis module is used to monitor the appearance and structural status parameters of each electric energy meter device in the target area corresponding to the current monitoring period, obtain the appearance and structural status parameters, which include the number of damaged parts, the number of cracks, the total damaged area, and the total length of the cracks, and then determine the appearance assessment value; The management and analysis module is used to determine the equipment operation evaluation value based on the basic evaluation value, electric energy evaluation value and appearance evaluation value, thereby dividing the electric energy meter equipment into normal operation group and abnormal operation group, and locating the abnormal electric energy meter equipment currently in the abnormal operation group, remotely diagnosing the abnormal electric energy meter equipment through the monitoring terminal, and automatically matching the diagnosis and disposal plan.

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