A fault monitoring system and method for smart electric meter

Abstract

The present invention relates to the technical field of smart meter monitoring, and discloses a fault monitoring system and method for smart meters, comprising: acquiring a collection time set, target temperature data and target device data, forming temperature determination data, forming abnormality analysis data, forming device analysis data, and determining the cause of abnormal temperature of the target meter. The fault monitoring system and method for the smart meter judge the cause of abnormal temperature of the meter according to the temperature in the meter, automatically judge whether the meter has a problem of poor wiring contact, automatically judge whether the load connected to the meter is overloaded, and judge whether the abnormal temperature of the meter is affected by surrounding equipment according to the wind speed and wind direction around the meter, thereby reducing misjudgment, increasing the service life of the meter as much as possible, facilitating user use, improving the work efficiency of maintenance personnel, and reducing the cost of repairing and replacing meter equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of smart meter monitoring, and in particular to a fault monitoring system and method for a smart meter. Background Art

[0002] Smart meters are modern meters that integrate electronic technology, computer technology, and communication technology. They can measure electricity consumption in real time, store and process data, and transmit data to power companies and users through communication networks. Compared with traditional mechanical meters, smart meters have the advantages of accurate data collection, real-time monitoring, and remote management. With the improvement of sensor technology and data processing capabilities, smart meters can collect and process power data more accurately. Wireless communication technologies (such as GPRS, LoRa, NB-IoT, etc.) and wired communication technologies (such as PLC) enable smart meters to achieve real-time data transmission. The application of big data analysis and machine learning algorithms can more accurately detect abnormal behaviors and faults of meters. Advanced fault diagnosis technologies (such as pattern recognition and fault tree analysis) can quickly identify and locate meter faults. However, the amount of data generated by smart meters is huge, requiring efficient data processing and storage solutions. The complexity of smart meter systems increases the difficulty of fault monitoring and diagnosis. Fault detection and response need to be completed in real time or near real time to ensure the stability of the system.

[0003] Existing fault monitoring systems and methods for smart meters cannot determine the cause of abnormal temperature of the meter based on the temperature inside the meter, cannot automatically determine whether the meter has a problem with poor wiring contact, cannot automatically determine whether the load connected to the meter is overloaded, and cannot determine whether the abnormal temperature of the meter is affected by surrounding equipment based on the wind speed and wind direction around the meter. This can easily lead to misjudgment, shortening the service life of the meter, thereby increasing the cost of repairing and replacing the meter equipment, and its practicality has certain limitations. Summary of the invention

[0004] The present invention provides a fault monitoring system and method for a smart electric meter, which has the advantages of judging the cause of abnormal temperature of the electric meter according to the temperature in the electric meter, automatically judging whether the electric meter has a problem of poor wiring contact, automatically judging whether the load connected to the electric meter is overloaded, judging whether the abnormal temperature of the electric meter is affected by surrounding equipment according to the wind speed and wind direction around the electric meter, reducing misjudgment, increasing the service life of the electric meter as much as possible, facilitating user use, improving the work efficiency of maintenance personnel, and reducing the cost of repairing and replacing the electric meter equipment. The system and method solve the problems mentioned in the above background technology that it is impossible to judge the cause of abnormal temperature of the electric meter according to the temperature in the electric meter, it is impossible to automatically judge whether the electric meter has a problem of poor wiring contact, it is impossible to automatically judge whether the load connected to the electric meter is overloaded, it is impossible to judge whether the abnormal temperature of the electric meter is affected by surrounding equipment according to the wind speed and wind direction around the electric meter, which is easy to cause misjudgment, shortening the service life of the electric meter, thereby increasing the cost of repairing and replacing the electric meter equipment, and its practicality has certain limitations.

[0005] The present invention provides the following technical solution: a fault monitoring method for a smart meter, comprising:

[0006] Get target temperature data;

[0007] According to the target temperature data, the temperature determination data is formed through the temperature determination strategy;

[0008] If the target temperature data is the first temperature determination data, continue to obtain the target temperature data;

[0009] If the target temperature data is the second temperature determination data, the collection time set is obtained, and the abnormal analysis strategy is executed to form abnormal analysis data;

[0010] Get target device data;

[0011] According to the target temperature data, abnormal analysis data and target device data, the device analysis data is formed through the device analysis strategy;

[0012] Determine the cause of abnormal temperature of the target meter based on abnormal analysis data and equipment analysis data;

[0013] The temperature determination strategy is specifically as follows:

[0014] Set temperature thresholds;

[0015] If the target temperature data is less than the temperature threshold, it is determined that the temperature in the target electric meter is normal, and the target temperature data is identified as the first temperature determination data;

[0016] If the target temperature data is greater than or equal to the temperature threshold, it is determined that the temperature inside the target electric meter is abnormal, and the target temperature data is identified as the second temperature determination data.

[0017] As a fault monitoring method for a smart meter according to the present invention, the acquisition of the collection time set is specifically:

[0018] Get the current time point;

[0019] Obtaining the power data collected at the current time point and identifying it as the current power data;

[0020] Set the collection duration;

[0021] Calculate the collection time point, collection time point = current time point - collection duration;

[0022] The collection time point is taken as the starting time point and the current time point is taken as the ending time point to form a collection period;

[0023] Set the unit duration;

[0024] Taking the collection time point as the starting time point, within the collection period, each interval of unit time length forms a data collection time point;

[0025] All data collection time points within the collection period are extracted to form a collection time set.

[0026] As a fault monitoring method for a smart meter described in the present invention, the abnormality analysis strategy is specifically:

[0027] Get the collection time set;

[0028] Obtain the collected power data corresponding to each element in the collection time set and identify it as the analyzed power data;

[0029] Each analyzed power data is matched one by one with each element of the collection time set to form an analyzed data set;

[0030] Setting analysis thresholds;

[0031] If the analyzed power data is less than or equal to the analysis threshold value × (1 + 10%), the analyzed power data is considered normal power data;

[0032] If the analyzed power data>analyzed threshold×(1+10%), the analyzed power data is identified as abnormal power data;

[0033] If there is abnormal power data in the analyzed data set, it is determined that the target meter detection end is abnormal;

[0034] If there is no abnormal power data in the analysis data set, it is determined that the target meter detection terminal is normal and the device analysis strategy is executed.

[0035] As a fault monitoring method for a smart meter described in the present invention, the device analysis strategy is specifically:

[0036] Get target device data;

[0037] Get the collection time set;

[0038] Obtain the target device data corresponding to each element in the collection time set and identify it as the target load data;

[0039] Each target load data is matched one by one with each element of the collection time set to form a load data set;

[0040] Set load thresholds;

[0041] If the target load data is less than the load threshold × (1 + 5%), the target load data is considered as normal load data;

[0042] If the target load data ≥ load threshold × (1 + 5%), the target load data is identified as abnormal load data;

[0043] If there is abnormal load data in the load data set, it is determined that the load of the target meter is abnormal;

[0044] If there is no abnormal load data in the load data set, it is determined that the load of the target meter is normal, and the fault analysis strategy is executed.

[0045] As a fault monitoring method for a smart meter described in the present invention, the fault analysis strategy is specifically:

[0046] Set the judgment distance;

[0047] The target electric meter is taken as the center of the circle and the determination distance is taken as the radius to form the determination range;

[0048] Obtain all equipment within the determination range and define them as analytical equipment;

[0049] Obtain the number of analytical devices within the determination range, which is defined as the number of devices;

[0050] If the number of devices = 0, the target meter is determined to be faulty;

[0051] If the number of devices = 1, execute the first device determination strategy;

[0052] If the number of devices is greater than 1, the second device determination strategy is executed.

[0053] As a fault monitoring method for a smart meter described in the present invention, wherein: the first device determination strategy is specifically:

[0054] Implement wind determination strategies;

[0055] Acquire a wind speed data set, first wind speed data, and second wind speed data;

[0056] If the first wind speed data exists in the wind speed data set, the wind direction within the determination range is obtained and determined as the target wind direction;

[0057] Take the analysis equipment as the starting point and the target wind direction as the reference direction to make the determination direction;

[0058] Set the judgment angle;

[0059] Taking the analysis device as the starting point, the determination direction is rotated to both sides thereof by determination angles to form a first direction line and a second direction line respectively;

[0060] The range between the first direction line and the second direction line is identified as the target range;

[0061] If the target electric meter is within the target range, the temperature of the analysis device is obtained and set as the device temperature;

[0062] Set device temperature thresholds;

[0063] If the device temperature is ≥ the device temperature threshold, the device is judged to be abnormal;

[0064] If the device temperature is less than the device temperature threshold, the target electric meter is judged to be faulty;

[0065] If the target meter is not within the target range, the target meter is determined to be faulty;

[0066] If all the wind speed data in the wind speed data set are the second wind speed data, it is determined that the target electric meter is faulty.

[0067] As a fault monitoring method for a smart meter described in the present invention, the second device determination strategy is specifically:

[0068] Implement wind determination strategies;

[0069] Acquire a wind speed data set, first wind speed data, and second wind speed data;

[0070] If the first wind speed data exists in the wind speed data set, the wind direction within the determination range is obtained and determined as the target wind direction;

[0071] Take the target electric meter as the starting point and the opposite direction of the target wind direction as the reference direction to determine the direction;

[0072] Set the judgment angle;

[0073] Taking the target electric meter as the starting point, the determination direction is rotated to both sides thereof by determination angles to form a first direction line and a second direction line respectively;

[0074] The range between the first direction line and the second direction line is identified as the target range;

[0075] If there is an analytical device within the target range, the analytical device is identified as a determination device;

[0076] Obtain the temperature of each determination device and set it as the device temperature;

[0077] Set device temperature thresholds;

[0078] If the device temperature is ≥ the device temperature threshold, the device temperature is determined to be abnormal;

[0079] Extract the device that determines the abnormal temperature of the device and identify it as the target device;

[0080] The target device is judged to be abnormal;

[0081] If the device temperature is less than the device temperature threshold, the target electric meter is judged to be faulty;

[0082] If there is no analysis device within the target range, the target meter is determined to be faulty;

[0083] If all the wind speed data in the wind speed data set are the second wind speed data, it is determined that the target electric meter is faulty.

[0084] As a fault monitoring method for a smart meter described in the present invention, the wind force determination strategy is specifically as follows:

[0085] Obtain wind speed data within the determination range;

[0086] Get the collection time set;

[0087] Obtain the collected wind speed data corresponding to each element in the collection time set and identify it as the target wind speed data;

[0088] Each target wind speed data is matched one by one with each element of the collection time set to form a wind speed data set;

[0089] Set the wind speed value for determination;

[0090] If the target wind speed data is ≥ the determined wind speed value, it is determined as the first wind speed data;

[0091] If the target wind speed data is less than the determined wind speed value, it is determined to be the second wind speed data.

[0092] The present invention also discloses a system for executing a fault monitoring method for a smart meter, which includes:

[0093] Data acquisition module: used to obtain acquisition time set, target temperature data and target device data;

[0094] Data analysis module: used to form temperature determination data according to target temperature data and temperature determination strategy;

[0095] Interface analysis module: used to generate abnormal analysis data through abnormal analysis strategies;

[0096] Equipment analysis module: used for target temperature data, abnormal analysis data and target equipment data, and forms equipment analysis data through equipment analysis strategy;

[0097] Abnormal determination module: used to determine the cause of abnormal temperature of the target meter based on abnormal analysis data and equipment analysis data.

[0098] The present invention has the following beneficial effects:

[0099] 1. The fault monitoring system and method for smart meters obtains the temperature of the target meter to determine whether the temperature of the target meter is normal, and obtains the power data of the meter within a certain period, that is, the voltage and current, for the meter with abnormal temperature. According to the power data collected within the period, it is determined whether the power data of the meter fluctuates, thereby determining whether the meter has a problem of poor wiring contact, reducing misjudgment, and increasing the service life of the meter as much as possible, making it easier for users to use, improving the work efficiency of maintenance personnel, and reducing the cost of repairing and replacing meter equipment.

[0100] 2. The fault monitoring system and method for smart electric meters obtains the connected load of the electric meter with normal connection and the load data of the electric meter within a certain period. According to the load data collected within the period, it is judged whether the load of the electric meter is too large, thereby determining whether the electric meter has an overload problem, reducing misjudgment, increasing the service life of the electric meter as much as possible, facilitating user use, improving the work efficiency of maintenance personnel, and reducing the cost of repairing and replacing electric meter equipment.

[0101] 3. The fault monitoring system and method for smart meters determine whether the abnormal temperature of the meter is affected by the surrounding equipment by obtaining the number of devices around the meter and the wind speed and direction around the meter. If there is no equipment around the meter, it means that the meter itself has a fault. If there is equipment around the meter, but the wind around it is not strong enough to transfer the heat in the air to the vicinity of the meter or there is no equipment with abnormal temperature around the meter, it means that the meter itself has a fault. If there is equipment around the meter and the wind around it is strong enough to transfer the heat in the air to the vicinity of the meter, and there is equipment with abnormal temperature, it means that the abnormal temperature of the meter is affected by the temperature of the surrounding equipment, and there is no problem with the meter itself, thereby reducing the possibility of misjudgment, increasing the service life of the meter as much as possible, facilitating user use, improving the work efficiency of maintenance personnel, and reducing the cost of repairing and replacing meter equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 This is a flow chart of a fault monitoring method for a smart meter according to the present invention;

[0103] Figure 2 The present invention is a system block diagram for executing the fault monitoring method for a smart meter. DETAILED DESCRIPTION

[0104] 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.

[0105] Embodiment 1, a fault monitoring method for a smart meter, see Figure 1 ,include:

[0106] Acquire target temperature data, where the target temperature data is a current temperature value in a target electric meter;

[0107] According to the target temperature data, the temperature determination data is formed through the temperature determination strategy;

[0108] If the target temperature data is the first temperature determination data, then the target temperature data is continued to be acquired, indicating that the target electric meter temperature is normal;

[0109] If the target temperature data is the second temperature determination data, the collection time set is obtained, and the abnormal analysis strategy is executed to form abnormal analysis data to determine whether the abnormal temperature of the target electric meter is caused by poor contact of the wiring port for a long time;

[0110] Get target device data;

[0111] According to the target temperature data, abnormal analysis data and target device data, the device analysis data is formed through the device analysis strategy to determine whether the abnormal temperature of the target meter is caused by excessive load, resulting in overload of the target meter, or the existence of high-temperature devices around the target meter, resulting in temperature abnormality;

[0112] Determine the cause of abnormal temperature of the target meter based on abnormal analysis data and equipment analysis data;

[0113] The temperature determination strategy is specifically as follows:

[0114] Setting a temperature threshold, wherein the temperature threshold is the maximum temperature value inside the electric meter when the electric meter operates normally in the environment in which the electric meter is located;

[0115] If the target temperature data is less than the temperature threshold, it is determined that the temperature in the target electric meter is normal, and the target temperature data is identified as the first temperature determination data;

[0116] If the target temperature data is greater than or equal to the temperature threshold, it is determined that the temperature inside the target electric meter is abnormal, and the target temperature data is identified as the second temperature determination data.

[0117] Through the above method, the cause of abnormal temperature of the electric meter is judged according to the temperature inside the electric meter, whether the electric meter has a problem of poor wiring contact, whether the load connected to the electric meter is overloaded, and whether the abnormal temperature of the electric meter is affected by surrounding equipment according to the wind speed and wind direction around the electric meter. The misjudgment is reduced, the service life of the electric meter is increased as much as possible, the user is more convenient to use, the work efficiency of maintenance personnel is improved, and the cost of repairing and replacing the electric meter equipment is reduced.

[0118] Embodiment 2: This embodiment is an improvement made on the basis of embodiment 1. In the fault monitoring method for smart meters, the acquisition time set is specifically:

[0119] Get the current time point;

[0120] Acquire power data collected at the current time point and identify it as current power data, where the power data is current and voltage values;

[0121] Set the collection time, which is 7 days;

[0122] Calculate the collection time point, collection time point = current time point - collection duration;

[0123] The collection time point is taken as the starting time point and the current time point is taken as the ending time point to form a collection period;

[0124] Set the unit duration, which is 1s;

[0125] Taking the collection time point as the starting time point, within the collection period, each interval of unit time length forms a data collection time point;

[0126] All data collection time points within the collection period are extracted to form a collection time set.

[0127] This embodiment also provides that the abnormality analysis strategy is specifically:

[0128] Get the collection time set;

[0129] Obtain the collected power data corresponding to each element in the collection time set and identify it as the analyzed power data;

[0130] Each analyzed power data is matched one by one with each element of the collection time set to form an analyzed data set;

[0131] Setting an analysis threshold, where the analysis threshold is the current and voltage values ​​collected by the target meter when all devices connected to the target meter are operating normally;

[0132] If the analyzed power data is less than or equal to the analysis threshold value × (1 + 10%), the analyzed power data is considered normal power data;

[0133] If the analyzed power data>analyzed threshold×(1+10%), the analyzed power data is identified as abnormal power data;

[0134] If there is abnormal power data in the analyzed data set, it is determined that the target meter detection end is abnormal;

[0135] If there is no abnormal power data in the analysis data set, it is determined that the target meter detection terminal is normal and the device analysis strategy is executed.

[0136] Embodiment 3: This embodiment is an improvement made on the basis of Embodiment 2. In this embodiment, the device analysis strategy is specifically as follows:

[0137] Acquire target device data, where the target device data is load data connected to a target electric meter for real-time monitoring;

[0138] Get the collection time set;

[0139] Obtain the target device data corresponding to each element in the collection time set and identify it as the target load data;

[0140] Each target load data is matched one by one with each element of the collection time set to form a load data set;

[0141] Setting a load threshold, where the load threshold is the load data monitored and collected by the target electric meter when all devices are operating normally;

[0142] If the target load data is less than the load threshold × (1 + 5%), the target load data is considered as normal load data;

[0143] If the target load data ≥ load threshold × (1 + 5%), the target load data is identified as abnormal load data;

[0144] If there is abnormal load data in the load data set, it is determined that the load of the target meter is abnormal;

[0145] If there is no abnormal load data in the load data set, it is determined that the load of the target meter is normal, and the fault analysis strategy is executed.

[0146] The fault analysis strategy is specifically as follows:

[0147] Set the judgment distance, the judgment distance is 2m;

[0148] The target electric meter is taken as the center of the circle and the determination distance is taken as the radius to form the determination range;

[0149] Obtain all equipment within the determination range and define them as analytical equipment;

[0150] Obtain the number of analytical devices within the determination range, which is defined as the number of devices;

[0151] If the number of devices = 0, the target meter is determined to be faulty;

[0152] If the number of devices = 1, execute the first device determination strategy;

[0153] If the number of devices is greater than 1, the second device determination strategy is executed.

[0154] The first device determination strategy is specifically:

[0155] Implement wind determination strategies;

[0156] Acquire a wind speed data set, first wind speed data, and second wind speed data;

[0157] If the first wind speed data exists in the wind speed data set, the wind direction within the determination range is obtained and determined as the target wind direction;

[0158] Take the analysis equipment as the starting point and the target wind direction as the reference direction to make the determination direction;

[0159] Setting a judgment angle, wherein the judgment angle is 5°;

[0160] Taking the analysis device as the starting point, the determination direction is rotated to both sides thereof by determination angles to form a first direction line and a second direction line respectively;

[0161] The range between the first direction line and the second direction line is identified as the target range;

[0162] If the target electric meter is within the target range, the temperature of the analysis device is obtained and set as the device temperature;

[0163] Setting a device temperature threshold, wherein the device temperature threshold is the maximum internal temperature value of the device when the device operates normally in its environment;

[0164] If the device temperature is ≥ the device temperature threshold, the device is judged to be abnormal;

[0165] If the device temperature is less than the device temperature threshold, the target electric meter is judged to be faulty;

[0166] If the target meter is not within the target range, the target meter is determined to be faulty;

[0167] If all the wind speed data in the wind speed data set are the second wind speed data, it is determined that the target electric meter is faulty.

[0168] The second device determination strategy is specifically:

[0169] Implement wind determination strategies;

[0170] Acquire a wind speed data set, first wind speed data, and second wind speed data;

[0171] If the first wind speed data exists in the wind speed data set, the wind direction within the determination range is obtained and determined as the target wind direction;

[0172] Take the target electric meter as the starting point and the opposite direction of the target wind direction as the reference direction to determine the direction;

[0173] Setting a judgment angle, wherein the judgment angle is 5°;

[0174] Taking the target electric meter as the starting point, the determination direction is rotated to both sides thereof by determination angles to form a first direction line and a second direction line respectively;

[0175] The range between the first direction line and the second direction line is identified as the target range;

[0176] If there is an analytical device within the target range, the analytical device is identified as a determination device;

[0177] Obtain the temperature of each determination device and set it as the device temperature;

[0178] Setting a device temperature threshold, wherein the device temperature threshold is the maximum temperature value inside the device when the device operates normally in its environment;

[0179] If the device temperature is ≥ the device temperature threshold, the device temperature is determined to be abnormal;

[0180] Extract the device that determines the abnormal temperature of the device and identify it as the target device;

[0181] The target device is judged to be abnormal;

[0182] If the device temperature is less than the device temperature threshold, the target electric meter is judged to be faulty;

[0183] If there is no analysis device within the target range, the target meter is determined to be faulty;

[0184] If all the wind speed data in the wind speed data set are the second wind speed data, it is determined that the target electric meter is faulty.

[0185] This embodiment also provides that the wind force determination strategy is specifically:

[0186] Obtain wind speed data within the determination range;

[0187] Get the collection time set;

[0188] Obtain the collected wind speed data corresponding to each element in the collection time set and identify it as the target wind speed data;

[0189] Each target wind speed data is matched one by one with each element of the collection time set to form a wind speed data set;

[0190] Setting a determination wind speed value, wherein the determination wind speed value is a wind speed value that allows air to flow and temperature to diffuse;

[0191] If the target wind speed data is ≥ the determined wind speed value, it is determined as the first wind speed data;

[0192] If the target wind speed data is less than the determined wind speed value, it is determined to be the second wind speed data.

[0193] Embodiment 4: This embodiment also discloses a system for executing a fault monitoring method for a smart meter, see Figure 2 ,include:

[0194] Data acquisition module: used to obtain acquisition time set, target temperature data and target device data;

[0195] Data analysis module: used to form temperature determination data according to target temperature data and temperature determination strategy;

[0196] Interface analysis module: used to generate abnormal analysis data through abnormal analysis strategies;

[0197] Equipment analysis module: used for target temperature data, abnormal analysis data and target equipment data, and forms equipment analysis data through equipment analysis strategy;

[0198] Abnormal determination module: used to determine the cause of abnormal temperature of the target meter based on abnormal analysis data and equipment analysis data.

[0199] In this embodiment, the cause of abnormal temperature of the electric meter is determined based on the temperature inside the electric meter, and whether the electric meter has a problem of poor wiring contact and whether the load connected to the electric meter is overloaded is automatically determined. Based on the wind speed and wind direction around the electric meter, it is determined whether the abnormal temperature of the electric meter is affected by surrounding equipment, thereby reducing the possibility of misjudgment, increasing the service life of the electric meter as much as possible, facilitating user use, improving the work efficiency of maintenance personnel, and reducing the cost of repairing and replacing electric meter equipment.

[0200] The embodiments of the present disclosure also provide a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the fault monitoring system and method for smart meters in the aforementioned method embodiments.

[0201] It should be noted that more specific examples of computer-readable storage media may include portable computer disks, hard disks, erasable programmable read-only memories (EPROMs or flash memories), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present disclosure, computer-readable storage media may be any tangible medium that contains or stores a program, which may be used by or in combination with an instruction execution system, apparatus, or device, and the computer-readable medium may be contained in the electronic device; or it may exist independently without being assembled into the electronic device.

[0202] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can implement the solution provided by the method embodiment.

[0203] Computer program code for performing operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, and may be executed entirely on a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0204] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0205] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A fault monitoring method for a smart meter, characterized in that: include: Get target temperature data; According to the target temperature data, the temperature determination data is formed through the temperature determination strategy; If the target temperature data is the first temperature determination data, continue to obtain the target temperature data; If the target temperature data is the second temperature determination data, a collection time set is obtained, and an abnormal analysis strategy is executed to form abnormal analysis data; Get target device data; According to the target temperature data, abnormal analysis data and target device data, the device analysis data is formed through the device analysis strategy; Determine the cause of the abnormal temperature of the target meter based on the abnormal analysis data and the equipment analysis data; The temperature determination strategy is specifically as follows: Set temperature thresholds; If the target temperature data is less than the temperature threshold, it is determined that the temperature in the target electric meter is normal, and the target temperature data is identified as the first temperature determination data; If the target temperature data is greater than or equal to the temperature threshold, it is determined that the temperature in the target electric meter is abnormal, and the target temperature data is identified as the second temperature determination data; The acquisition time set is specifically: Get the current time point; Obtaining the power data collected at the current time point and identifying it as the current power data; Set the collection duration; Calculate the collection time point, collection time point = current time point - collection duration; The collection time point is taken as the starting time point and the current time point is taken as the ending time point to form a collection period; Set the unit duration; Taking the collection time point as the starting time point, within the collection period, each interval of unit time length forms a data collection time point; Extract all data collection time points within the collection period to form a collection time set; The abnormal analysis strategy is specifically: Get the collection time set; Obtain the collected power data corresponding to each element in the collection time set and identify it as the analyzed power data; Each analyzed power data is matched one by one with each element of the collection time set to form an analyzed data set; Setting analysis thresholds; If the analyzed power data is less than or equal to the analysis threshold value × (1 + 10%), the analyzed power data is considered normal power data; If the analyzed power data>analyzed threshold×(1+10%), the analyzed power data is identified as abnormal power data; If there is abnormal power data in the analyzed data set, it is determined that the target meter detection end is abnormal; If there is no abnormal power data in the analysis data set, it is determined that the target meter detection terminal is normal, and the device analysis strategy is executed; The device analysis strategy is specifically: Get target device data; Get the collection time set; Obtain the target device data corresponding to each element in the collection time set and identify it as the target load data; Each target load data is matched one by one with each element of the collection time set to form a load data set; Set load thresholds; If the target load data is less than the load threshold × (1 + 5%), the target load data is considered as normal load data; If the target load data ≥ load threshold × (1 + 5%), the target load data is identified as abnormal load data; If there is abnormal load data in the load data set, it is determined that the load of the target meter is abnormal; If there is no abnormal load data in the load data set, it is determined that the load of the target meter is normal, and the fault analysis strategy is executed; The fault analysis strategy is specifically: Set the judgment distance; The target electric meter is taken as the center of the circle and the determination distance is taken as the radius to form the determination range; Obtain all equipment within the determination range and define them as analytical equipment; Obtain the number of analytical devices within the determination range, which is defined as the number of devices; If the number of devices = 0, the target meter is determined to be faulty; If the number of devices = 1, execute the first device determination strategy; If the number of devices is greater than 1, the second device determination strategy is executed; The first device determination strategy is specifically: Implement wind determination strategies; Acquire a wind speed data set, first wind speed data, and second wind speed data; If the first wind speed data exists in the wind speed data set, the wind direction within the determination range is obtained and determined as the target wind direction; Take the analysis equipment as the starting point and the target wind direction as the reference direction to make the determination direction; Set the judgment angle; Taking the analysis device as the starting point, the determination direction is rotated to both sides thereof by determination angles to form a first direction line and a second direction line respectively; The range between the first direction line and the second direction line is identified as the target range; If the target electric meter is within the target range, the temperature of the analysis device is obtained and set as the device temperature; Set device temperature thresholds; If the device temperature is ≥ the device temperature threshold, the device is judged to be abnormal; If the device temperature is less than the device temperature threshold, the target electric meter is judged to be faulty; If the target meter is not within the target range, the target meter is determined to be faulty; If all the wind speed data in the wind speed data set are the second wind speed data, it is determined that the target electric meter is faulty; The second device determination strategy is specifically: Implement wind determination strategies; Acquire a wind speed data set, first wind speed data, and second wind speed data; If the first wind speed data exists in the wind speed data set, the wind direction within the determination range is obtained and determined as the target wind direction; Take the target electric meter as the starting point and the opposite direction of the target wind direction as the reference direction to determine the direction; Set the judgment angle; Taking the target electric meter as the starting point, the determination direction is rotated to both sides thereof by determination angles to form a first direction line and a second direction line respectively; The range between the first direction line and the second direction line is identified as the target range; If there is an analytical device within the target range, the analytical device is identified as a determination device; Obtain the temperature of each determination device and set it as the device temperature; Set device temperature thresholds; If the device temperature is ≥ the device temperature threshold, the device temperature is determined to be abnormal; Extract the device that determines the abnormal temperature of the device and identify it as the target device; The target device is judged to be abnormal; If the device temperature is less than the device temperature threshold, the target electric meter is judged to be faulty; If there is no analysis device within the target range, the target meter is determined to be faulty; If all the wind speed data in the wind speed data set are the second wind speed data, it is determined that the target electric meter is faulty; The wind force determination strategy is specifically as follows: Obtain wind speed data within the determination range; Get the collection time set; Obtain the collected wind speed data corresponding to each element in the collection time set and identify it as the target wind speed data; Each target wind speed data is matched one by one with each element of the collection time set to form a wind speed data set; Set the wind speed value for determination; If the target wind speed data is ≥ the determined wind speed value, it is determined as the first wind speed data; If the target wind speed data is less than the determined wind speed value, it is determined to be the second wind speed data.

2. A system for executing the fault monitoring method for a smart meter according to claim 1, characterized in that: include: Data acquisition module: used to obtain acquisition time set, target temperature data and target device data; Data analysis module: used to form temperature determination data according to target temperature data and temperature determination strategy; Interface analysis module: used to generate abnormal analysis data through abnormal analysis strategies; Equipment analysis module: used for target temperature data, abnormal analysis data and target equipment data, and forms equipment analysis data through equipment analysis strategy; Abnormal determination module: used to determine the cause of abnormal temperature of the target meter based on abnormal analysis data and equipment analysis data.

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