Intelligent electric meter positioning method and device

Abstract

The invention provides an intelligent electric meter positioning method and device, and relates to the technical field of intelligent power grids. The method comprises the steps that N target sampling sequences of a to-be-detected intelligent electric meter are acquired, the to-be-detected intelligent electric meter is any intelligent electric meter in a to-be-detected area, each target sampling sequence is multiple pieces of sampling data of a single date in a target time period, each piece of sampling data comprises a light intensity value and corresponding acquisition time, N is a positive integer greater than or equal to 2, and N is a positive integer greater than or equal to 2; the target time period comprises a sunrise time period and a sunset time period; calculating a corresponding sampling slope by using each piece of sampling data; for each target sampling sequence, selecting a target sampling slope from a plurality of sampling slopes of the target sampling sequence, and determining a geographic coordinate curve of a date corresponding to the target sampling sequence based on the acquisition time corresponding to the target sampling slope; and determining the position information of the to-be-tested intelligent electric meter according to the geographic coordinate curve of the N dates. The accuracy of the position information of the intelligent electric meter can be improved.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and in particular to a smart meter positioning method and device. Background Technology

[0002] In power grids, it is often necessary to locate electricity meters. For example, when a meter malfunctions, the location information of the meter can help maintenance personnel quickly locate the faulty meter and carry out emergency repairs, effectively improving work efficiency. Furthermore, when power companies conduct fixed asset statistics, they can easily compile statistics on the electricity meters based on their location information.

[0003] Currently, positioning solutions in smart grids mainly include satellite positioning and Received Signal Strength Indication (RSSI) ranging.

[0004] The satellite positioning method is as follows: The Global Navigation Satellite System (GNSS) writes satellite clock and orbit data into the navigation message and sends it to the ground receiver. The receiver processes the satellite signals to obtain its own position and precise time information. The receiver processing includes: the satellite signal enters the radio frequency front end, where it is amplified, mixed, and converted from digital to digital, resulting in a digital signal. This digital signal is then input to the baseband section, where a coarse spreading code and carrier frequency are obtained through the acquisition process. The tracking process then yields the precise spreading code and carrier information, and the navigation message, including satellite position and time information, is deciphered. To accurately determine the receiver's three-dimensional position and clock deviation, data from at least four satellites is required to establish a system of equations, thereby obtaining accurate receiver position and clock information.

[0005] The inventors discovered that while satellite positioning offers extremely high accuracy, it requires specialized positioning chips. High-performance positioning chips are complex to design and more expensive, which would significantly increase deployment costs if used on a large scale in smart grids. Furthermore, since smart meters are not necessarily installed outdoors, satellite positioning accuracy can be affected to some extent.

[0006] RSSI ranging and positioning involves a base station sending a ranging signal, which the meter terminal receives and calculates the RSSI to obtain the transmission distance. This distance, combined with the known location of the base station, leads to the terminal's location. Specifically, at least four gateways send data signals. The meter terminal receives these signals, extracts the identification number of each gateway, and then looks up the location of each gateway in a table. Simultaneously, the meter terminal calculates the RSSI of the received signal and uses a corresponding algorithm to obtain the distance to each gateway. The relationship between received power and transmission distance is: P r =A-10*n*lg(R), where Pr Let A be the received power, n be the propagation factor, and R be the transmission distance. The meter terminal location is obtained by combining the transmission distances obtained from the four sets of gateway ranging measurements with the respective gateway locations.

[0007] The inventors discovered that the ranging accuracy of RSSI ranging and positioning is greatly affected by the transmission channel, and the usage environment of smart meter terminals often has less than 4 gateways, making it difficult to meet the positioning requirements. Summary of the Invention

[0008] This application provides a smart meter positioning method and apparatus to solve the problems of high cost and complex operation of existing meter positioning methods.

[0009] Firstly, this application provides a method for locating a smart meter, including:

[0010] N target sampling sequences of the smart meter to be tested are obtained. The smart meter to be tested is any smart meter in the test area. Each target sampling sequence is multiple sampling data of a single date within a target time period. Each sampling data includes light intensity value and corresponding collection time. N is a positive integer greater than or equal to 2. The target time period includes sunrise time period and sunset time period.

[0011] Calculate the corresponding sampling slope using each sampled data; and for each target sampling sequence, select the target sampling slope from multiple sampling slopes of the target sampling sequence, and determine the geographic coordinate curve of the date corresponding to the target sampling sequence based on the collection time corresponding to the target sampling slope.

[0012] The location information of the smart meter under test is determined based on the geographic coordinate curves of N dates.

[0013] Secondly, this application provides a smart meter positioning device, comprising:

[0014] The sequence acquisition module is used to acquire N target sampling sequences of the smart meter under test. The smart meter under test is any smart meter in the test area. Each target sampling sequence is multiple sampling data of a single date within a target time period. Each sampling data includes a light intensity value and the corresponding acquisition time. N is a positive integer greater than or equal to 2. The target time period includes sunrise time period and sunset time period.

[0015] The curve determination module is used to calculate the corresponding sampling slope using each sampled data; and for each target sampling sequence, it selects the target sampling slope from multiple sampling slopes of the target sampling sequence, and determines the geographic coordinate curve of the date corresponding to the target sampling sequence based on the collection time corresponding to the target sampling slope.

[0016] The location determination module is used to determine the location information of the smart meter under test based on the geographical coordinate curves of N dates.

[0017] This application provides a method and apparatus for locating smart meters. The method involves acquiring N target sampling sequences of a smart meter under test. The smart meter under test is any smart meter within the test area. Each target sampling sequence consists of multiple sampling data points for a single date within a target time period. Each sampling data point includes a light intensity value and the corresponding acquisition time. N is a positive integer greater than or equal to 2, and the target time period includes sunrise and sunset periods. The method calculates the corresponding sampling slope using each sampling data point. For each target sampling sequence, a target sampling slope is selected from the multiple sampling slopes of that target sampling sequence. Based on the acquisition time corresponding to the target sampling slope, the geographic coordinate curve of the date corresponding to the target sampling sequence is determined. The location information of the smart meter under test is determined based on the geographic coordinate curves of the N dates. This application calculates the sampling slope of each sampled data point and selects a target sampling slope from multiple sampling slopes to determine the geographic coordinate curve. Selecting the target sampling slope can filter out the most representative illumination change information and exclude some potentially interfered or irrelevant sampling data, thereby more accurately depicting the geographic coordinate curve related to the geographic location and improving positioning accuracy. Furthermore, this application mainly uses the light intensity value collected by the smart meter itself and the corresponding collection time for positioning, without the need to install additional dedicated positioning equipment or signal transmitting devices in the area to be measured. For large-scale smart meter positioning scenarios, such as residential communities and industrial parks in cities, it can save a lot of hardware procurement, installation and maintenance costs, and reduce the overall implementation difficulty and cost of the positioning system. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the structure of the operating system provided in the embodiments of this application;

[0020] Figure 2 This is a flowchart illustrating the smart meter positioning method provided in an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of the installation location of the ambient light sensing module for an electricity meter provided in an embodiment of this application;

[0022] Figure 4 This is a schematic diagram of the reverse calculation of latitude and longitude provided in the embodiments of this application;

[0023] Figure 5 This is a schematic diagram of the structure of the smart meter positioning device provided in the embodiments of this application. Detailed Implementation

[0024] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0026] To address the problems of high cost and complex operation in existing meter positioning methods, this application provides a smart meter positioning method, which is applied to... Figure 1 The operating system shown includes a concentrator, an information system, and an operator terminal. The concentrator aggregates the sampling sequences from each smart meter, calculates the sunrise / sunset times, and sends them to the information system. The information system uses the sunrise / sunset times to calculate the location information of each smart meter. The operator terminal displays the location information of each smart meter.

[0027] The specific process includes: using the ambient light sensing module (such as an infrared sensor, light sensor, or ultraviolet sensor) in the concentrator to sample the time period before and after sunrise and sunset multiple times, forming a time-sampling sequence; calculating the slope of each data set in the time-sampling sequence; and identifying the slope point closest to the preset sampling slope, which is defined as the sunrise and sunset time for that day. The sunrise / sunset times for multiple days (two days or more) are transmitted to the information system, and the daily meter geographic coordinate sequence is derived using a particle swarm optimization algorithm. Points with the same coordinate sequence over multiple days are recorded as the meter's location information.

[0028] Figure 2 The implementation flowchart of the smart meter positioning method provided in the embodiments of this application is described in detail below:

[0029] In step 101, N target sampling sequences of the smart meter to be tested are obtained. The smart meter to be tested is any smart meter in the testing area. Each target sampling sequence is multiple sampling data of a single date within a target time period. Each sampling data includes light intensity value and corresponding collection time. N is a positive integer greater than or equal to 2. The target time period includes sunrise time period and sunset time period.

[0030] In this embodiment, the ambient light sensor module of the electricity meter collects sampling data corresponding to multiple times during the sunrise period for N dates, and constructs a target sampling sequence for each date by combining the sampling data corresponding to the sunrise period. Similarly, the ambient light sensor module of the electricity meter collects sampling data corresponding to multiple times during the sunset period for N dates, and constructs a target sampling sequence for each date by combining the sampling data corresponding to the sunset period. The target sampling sequence includes the light intensity value and the corresponding collection time.

[0031] in, Figure 3 This diagram illustrates the installation location of the ambient light sensor module in an electricity meter. The ambient light sensor module can be an infrared sensor, a light sensor, or an ultraviolet sensor, etc. Taking a light sensor as an example, a photoresistor and its measuring device are required to record the current value under the same voltage as the light intensity measurement value. If an infrared sensor module is used, the infrared transceiver module needs to be modified to measure infrared intensity.

[0032] This application's embodiments sample a target time period including sunrise and sunset. Natural light exhibits a clear variation pattern during this period, with sunrise and sunset times differing across geographical locations, and the slope of light intensity change over time also correlated with geographical location. Utilizing these characteristics for positioning allows for full utilization of stable and distinguishable geographical information provided by natural phenomena, improving positioning accuracy compared to some positioning methods that rely on artificial signal sources or are susceptible to interference.

[0033] Furthermore, this application embodiment acquires sampling data from the smart meter under test over target time periods (covering sunrise and sunset times) on multiple dates, including light intensity values ​​and collection times. Utilizing these rich data sources for location calculations provides a more comprehensive reflection of the light variation characteristics of the meter's environment compared to location methods that rely solely on single data points or single time periods, thereby effectively improving location accuracy.

[0034] In one possible implementation, the target sampling sequence includes a first sampling sequence during the sunrise period and a second sampling sequence during the sunset period; before acquiring N target sampling sequences from the smart meter under test, the method may further include:

[0035] Obtain the time zone, longitude, and latitude of the region to be measured for each date;

[0036] The sunrise time of the area under test is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area under test using the radian system, and the sunset time of the area under test is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area under test using the radian system.

[0037] The preset time periods before and after the sunrise time of the area to be tested are taken as the sunrise time period, and the preset time periods before and after the sunset time of the area to be tested are taken as the sunset time period;

[0038] Accordingly, acquiring N target sampling sequences from the smart meter under test can include:

[0039] Obtain the first sampling sequence of the smart meter under test during the sunrise time period on N dates;

[0040] Obtain the second sampling sequence of the smart meter under test during the sunset time period on N dates.

[0041] Optionally, embodiments of this application may use radians to calculate the sunrise and sunset times of the area to be measured, then use a preset time period before and after sunrise as the sunrise time period, and a preset time period before and after sunset as the sunset time period. The specific formulas for calculating sunrise and sunset times are as follows:

[0042] Sunrise time = (180 + time zone * 15 - longitude - arccos(tan((10547π / 81000) * cos(2π * (date + 9) / 365) * tan(latitude * π / 180)) * 180 / π) / 15

[0043] Sunset time = (180 + time zone * 15 - longitude + arccos(tan((10547π / 81000) * cos(2π * (date + 9) / 365) * tan(latitude * π / 180)) * 180 / π) / 15

[0044] In this context, longitude and latitude are both measured in degrees, with east longitude and north latitude being positive and west longitude and south latitude being negative. For example, 100°13'30" east longitude and 35°20'15" north latitude represent longitude = 100.225 and latitude = 35.3375; another example is 25°15'54" west longitude and 50°45'18" south latitude, which represent longitude = -25.265 and latitude = -50.755.

[0045] Eastern time zones are positive, and western time zones are negative. For example, China's time zone is East 8, so time zone = 8.

[0046] The dates are Gregorian calendar dates. For example, January 1st means date = 1, February 15th means date = 46, and December 31st means date = 365.

[0047] For example, if the value calculated according to the above sunrise time calculation formula is 6.69, it means that the sunrise time is 6:41:24; if the value calculated according to the above sunset time calculation formula is 19.26, it means that the sunset time is 19:15:36.

[0048] In one possible implementation, the target sampling sequence includes a first sampling sequence during the sunrise period and a second sampling sequence during the sunset period; before acquiring N target sampling sequences from the smart meter under test, the method may further include:

[0049] Obtain the time zone, longitude, and latitude of the region to be measured for each date;

[0050] The sunrise time of the area under test is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area under test using the angle system, and the sunset time of the area under test is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area under test using the angle system.

[0051] The preset time periods before and after the sunrise time of the area to be tested are taken as the sunrise time period, and the preset time periods before and after the sunset time of the area to be tested are taken as the sunset time period;

[0052] Accordingly, acquiring N target sampling sequences from the smart meter under test can include:

[0053] Obtain the first sampling sequence of the smart meter under test during the sunrise time period on N dates;

[0054] Obtain the second sampling sequence of the smart meter under test during the sunset time period on N dates.

[0055] Optionally, embodiments of this application may use an angle system to calculate the sunrise and sunset times of the area to be measured, then use a preset time period before and after sunrise as the sunrise time period, and a preset time period before and after sunset as the sunset time period. The specific formulas for calculating sunrise and sunset times are as follows:

[0056] Sunrise time = (180 + time zone * 15 - longitude - arccos(tan((10547 / 450) * cos(360 * (date + 9) / 365)) * tan(latitude))) / 15

[0057] Sunset time = (180 + time zone * 15 - longitude + arccos(tan((10547 / 450) * cos(360 * (date + 9) / 365)) * tan(latitude))) / 15

[0058] In one possible implementation, the pre-set time periods before and after can be determined as follows:

[0059] Determine geographical distances using the longitude of the area to be measured;

[0060] Geographical distance is used to determine the pre- and post-test time periods of the area to be tested.

[0061] Optionally, in this embodiment of the application, the corresponding preset time period before and after is calculated by the method of calculating the relationship between the preset time period before and after and the actual distance. Specifically, taking the longest geographical distance of 1 degree longitude at the equator as 111 kilometers, it was found in the calculation process that for every 1 degree change in longitude, the longest sunrise time changes by 240 seconds, that is, the preset time period before and after each kilometer is 2.16 seconds.

[0062] In step 102, the corresponding sampling slope is calculated using each sampled data; and for each target sampling sequence, the target sampling slope is selected from multiple sampling slopes of the target sampling sequence, and the geographic coordinate curve of the date corresponding to the target sampling sequence is determined based on the collection time corresponding to the target sampling slope.

[0063] In this embodiment, a corresponding sampling slope is calculated based on each sampled data point. Then, one sampling slope is selected from multiple sampling slopes in each target sampling sequence as the target sampling slope. Finally, using the acquisition time corresponding to the target sampling slope, the geographic coordinate curve for the corresponding date of the target sampling sequence is calculated. The geographic coordinate curve is a curve composed of geographic coordinates representing the same sunrise or sunset times on the same date.

[0064] This application's embodiments calculate the sampling slope of each sampled data point and select a target sampling slope from multiple sampling slopes to determine the geographic coordinate curve. The sampling slope reflects the rate of change of light intensity over time; the light intensity changes drastically at sunrise and sunset, and the corresponding sampling slopes have distinct characteristics. Selecting a target sampling slope can filter out the most representative light change information, eliminating some potentially interfered or irrelevant sampling data, thereby more accurately depicting the geographic coordinate curve related to the geographical location and improving positioning accuracy. Furthermore, although this application's embodiments involve multiple sampling sequences and slope calculations, these calculation processes are relatively simple, mainly based on basic mathematical operations, and have low computational resource requirements. Therefore, data processing and positioning calculations can be completed in a short time, achieving efficient smart meter positioning and meeting the real-time requirements of practical applications.

[0065] In one possible implementation, calculating the corresponding sampling slope using each sampled data point can include:

[0066] A Cartesian coordinate system is constructed using each sampled data point, with the acquisition time as the X-axis and the light intensity value as the Y-axis.

[0067] Based on a Cartesian coordinate system, calculate the sampling slope corresponding to each light intensity value.

[0068] Optionally, the sampled data is mapped to a Cartesian coordinate system, with the acquisition time as the X-axis and the light intensity value as the Y-axis. Then, for each smart meter, the light intensity value and sampling time of each group in the target sampling sequence of that smart meter constitute a coordinate system of (x, y). When calculating the slope of the target light intensity value, the difference between the light intensity value at the first moment and the target light intensity value is calculated as the first difference, and the time difference between the acquisition time corresponding to the first moment and the target light intensity value is calculated as the second difference. Finally, the ratio of the first difference to the second difference is used as the sampling slope at the target moment. Here, the first moment is the moment adjacent to but later than the acquisition time corresponding to the target light intensity value.

[0069] The sampling slope is calculated for all groups in the target sampling sequence of the smart meter, forming the slope sequence of the smart meter.

[0070] The formula for calculating the sampling slope is as follows:

[0071]

[0072] Among them, K i Let x be the sampling slope at time i. i Let x be the light intensity value at time i. i+1 Let y be the light intensity value at time i+1. i For time i, y i+1 Let t be the (i+1)th time, t be the preset time interval before and after, and n be the data collection time interval.

[0073] In one possible implementation, for each target sampling sequence, a target sampling slope is selected from multiple sampling slopes of the target sampling sequence, and based on the acquisition time corresponding to the target sampling slope, the geographic coordinate curve of the date corresponding to the target sampling sequence is determined, which may include:

[0074] Calculate the absolute value of the difference between each sampling slope of the target sampling sequence and the preset sampling slope, and take the sampling slope with an absolute value of zero as the target sampling slope;

[0075] The sampling time corresponding to the target sampling slope is taken as the sampling time, and the location information of the target sampling sequence is calculated using the sampling time. The sampling time includes the sampling sunrise time and the sampling sunset time.

[0076] The particle swarm optimization algorithm is used to obtain a series of location information that are consistent with the location information of the target sampling sequence, which constitutes the geographic coordinate curve of the target sampling sequence for the corresponding date.

[0077] Optionally, in this embodiment, the absolute value of the difference between the sampling slope at each moment in each target sampling sequence and a preset sampling slope (including the sunrise slope and sunset slope) is calculated. Then, for each target sampling sequence, one of the sampling slopes with an absolute value of zero is selected as the target sampling slope of that target sampling sequence. The acquisition time corresponding to the target sampling slope of that target sampling sequence is then used as the sampling time of the corresponding date of that target sampling sequence, and the geographical location information corresponding to that date is calculated using this sampling time. Finally, a series of location information consistent with the aforementioned geographical location information is found using a particle swarm optimization algorithm, forming the geographical coordinate curve of the date corresponding to the target sampling sequence.

[0078] The calculation method for the geographical location information of the date corresponding to each target sampling sequence can be obtained by reverse calculation using the formula for sunrise or sunset time in radians or angles, based on the sampling time (i.e. sunrise or sunset time), the time zone, latitude and longitude of the smart meter under test, and the collection date.

[0079] For example, after calculating the sunrise and sunset times of smart meter A in each target sampling sequence, and using radians or angles to inversely deduce the longitude and latitude of smart meter A corresponding to each target sampling sequence, the particle swarm algorithm can be used to achieve the specified accuracy. The longitude or latitude is the coordinate with 6 decimal places and will not change.

[0080] In one possible implementation, before calculating the absolute value of the difference between each sampling slope of the target sampling sequence and a preset sampling slope, the method further includes:

[0081] Obtain the location information of the center point of the area to be tested;

[0082] The preset sampling slope is calculated using the location information of the center point of the area to be measured.

[0083] Optionally, the preset sampling slope in this application embodiment can be a fixed value. The specific calculation process is as follows: the sunrise and sunset times of the area to be measured are calculated using the center point of the area to be measured in radians or angles. Then, the light intensity values ​​and collection times of all smart meters in the area to be measured are collected within a preset time period before or after the sunrise or sunset time. The preset sampling slope is then calculated using the light intensity values ​​and collection times.

[0084] In one possible implementation, before calculating the absolute value of the difference between each sampling slope of the target sampling sequence and a preset sampling slope, the method further includes:

[0085] Obtain the location information and light intensity value of a reference smart meter. The reference smart meter is any smart meter in the test area other than the smart meter under test.

[0086] The target time is calculated using the location information of a reference smart meter. The target time includes sunrise and sunset times.

[0087] The preset sampling slope is calculated using the target time and light intensity value of the reference smart meter.

[0088] Optionally, the preset sampling slope in this embodiment may not be fixed. When it is not fixed, the calculation process of the preset sampling slope is as follows:

[0089] First, select all smart meters at the locations to be tested within the testing area, and choose one smart meter as a reference smart meter. The reference smart meter should be installed outdoors, away from direct sunlight, and the light it receives should be time-dependent; generally, it can be installed on a shady wall of the building. The measurement date should be a sunny day, avoiding rainy, foggy, or dusty weather as much as possible.

[0090] Then, all smart meters to be measured are remotely time-calibrated to ensure that the time is consistent with the standard time.

[0091] The location of the reference smart meter is measured on-site, and the sunrise and sunset times of the reference smart meter are calculated. A task is sent to the reference smart meter to collect light intensity values ​​within a preset time period t before or after the sunrise or sunset time, requiring data to be collected once every n seconds. This forms the first sampling sequence within the sunrise time period and the second sampling sequence within the sunset time period of the reference smart meter for that day. The sunrise slope of the reference smart meter is calculated based on the first sampling sequence, and the sunset slope of the reference smart meter is calculated based on the second sampling sequence.

[0092] The selection of the preset time period t before and after the meter is related to the geographical range of the smart meter. According to calculations, the minimum value of the preset time period t for each kilometer range is 2.16 seconds.

[0093] For example, if the sunrise time in Shijiazhuang on a certain day is calculated to be 5:31:31, it is necessary to measure the area within a radius of 20 kilometers in Shijiazhuang city. The preset time period t is set to 1 minute and n is set to 5 seconds. That is, the light intensity value is collected every 5 seconds from 5:30:31 to 5:32:31, for a total of 25 light intensity values ​​and corresponding collection times.

[0094] In step 103, the location information of the smart meter under test is determined based on the geographic coordinate curves of N dates.

[0095] In this embodiment of the application, the actual location information of the smart meter under test is determined based on the geographical coordinate curves of the dates corresponding to the N target sampling sequences determined in step 102.

[0096] This application embodiment determines the location of the electricity meter by acquiring N (N≥2) target sampling sequences, that is, by using data from multiple dates. Weather conditions and lighting conditions may vary on different dates, but by analyzing data from multiple dates, the positioning error caused by special circumstances on a single day (such as localized brief cloudy days, abnormal lighting interference, etc.) can be reduced. Through statistical analysis of data from multiple days, the geographical location of the electricity meter can be determined more stably and accurately, enhancing the reliability of the positioning results.

[0097] In one possible implementation, the location information of the smart meter under test is determined based on geographic coordinate curves for N dates, including:

[0098] The latitude and longitude of the intersection points of the geographic coordinate curves for N dates are used as the location information of the smart meter under test.

[0099] Optionally, the geographic coordinate curves of N dates can be placed in a coordinate system, with longitude and latitude as the XY axis and sunrise / sunset time as the Z axis, and the longitude and latitude corresponding to the intersection of multiple geographic coordinate curves can be used as the location information of the smart meter under test.

[0100] For example, refer to Figure 4 The geographic coordinates of smart meter A corresponding to the sunrise time on two dates are represented by a solid line and a dashed line. The latitude and longitude of the intersection of the two curves are unique, meaning that the latitude and longitude of that point are the latitude and longitude coordinates of smart meter A.

[0101] This application provides a method for locating smart meters. The method involves acquiring N target sampling sequences of a smart meter under test. The smart meter under test can be any smart meter within the test area. Each target sampling sequence consists of multiple sampling data points for a single date within a target time period. Each sampling data point includes a light intensity value and the corresponding acquisition time. N is a positive integer greater than or equal to 2, and the target time period includes sunrise and sunset periods. The method calculates the corresponding sampling slope using each sampling data point. For each target sampling sequence, a target sampling slope is selected from the multiple sampling slopes of that target sampling sequence. Based on the acquisition time corresponding to the target sampling slope, the geographical coordinate curve of the date corresponding to that target sampling sequence is determined. The location information of the smart meter under test is determined based on the geographical coordinate curves of the N dates. This application calculates the sampling slope of each sampled data point and selects a target sampling slope from multiple sampling slopes to determine the geographic coordinate curve. Selecting the target sampling slope can filter out the most representative illumination change information and exclude some potentially interfered or irrelevant sampling data, thereby more accurately depicting the geographic coordinate curve related to the geographic location and improving positioning accuracy. Furthermore, this application mainly uses the light intensity value collected by the smart meter itself and the corresponding collection time for positioning, without the need to install additional dedicated positioning equipment or signal transmitting devices in the area to be measured. For large-scale smart meter positioning scenarios, such as residential communities and industrial parks in cities, it can save a lot of hardware procurement, installation and maintenance costs, and reduce the overall implementation difficulty and cost of the positioning system.

[0102] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0103] The following are device embodiments of this application. For details not described in detail, please refer to the corresponding method embodiments described above.

[0104] Figure 5 A schematic diagram of the smart meter positioning device provided in an embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown, and are described in detail below:

[0105] like Figure 5 As shown, the smart meter positioning device 5 includes:

[0106] The sequence acquisition module 51 is used to acquire N target sampling sequences of the smart meter under test. The smart meter under test is any smart meter in the test area. Each target sampling sequence is multiple sampling data of a single date within a target time period. Each sampling data includes light intensity value and corresponding acquisition time. N is a positive integer greater than or equal to 2. The target time period includes sunrise time period and sunset time period.

[0107] The slope determination module 52 is used to calculate the corresponding sampling slope using each sampled data; and for each target sampling sequence, to select the target sampling slope from multiple sampling slopes of the target sampling sequence, and to determine the geographic coordinate curve of the date corresponding to the target sampling sequence based on the collection time corresponding to the target sampling slope.

[0108] The location determination module 53 is used to determine the location information of the smart meter under test based on the geographical coordinate curves of N dates.

[0109] This application provides a smart meter positioning device. It acquires N target sampling sequences of a smart meter under test, where the smart meter under test is any smart meter within the test area. Each target sampling sequence consists of multiple sampling data points for a single date within a target time period. Each sampling data point includes a light intensity value and the corresponding acquisition time. N is a positive integer greater than or equal to 2, and the target time period includes sunrise and sunset periods. The device calculates the corresponding sampling slope using each sampling data point. For each target sampling sequence, it selects a target sampling slope from the multiple sampling slopes of that target sampling sequence and determines the geographic coordinate curve of the date corresponding to the target sampling sequence based on the acquisition time corresponding to the target sampling slope. Finally, it determines the location information of the smart meter under test based on the geographic coordinate curves of the N dates. This application calculates the sampling slope of each sampled data point and selects a target sampling slope from multiple sampling slopes to determine the geographic coordinate curve. Selecting the target sampling slope can filter out the most representative illumination change information and exclude some potentially interfered or irrelevant sampling data, thereby more accurately depicting the geographic coordinate curve related to the geographic location and improving positioning accuracy. Furthermore, this application mainly uses the light intensity value collected by the smart meter itself and the corresponding collection time for positioning, without the need to install additional dedicated positioning equipment or signal transmitting devices in the area to be measured. For large-scale smart meter positioning scenarios, such as residential communities and industrial parks in cities, it can save a lot of hardware procurement, installation and maintenance costs, and reduce the overall implementation difficulty and cost of the positioning system.

[0110] In one possible implementation, the slope determination module can be used to:

[0111] A Cartesian coordinate system is constructed using each sampled data point, with the acquisition time as the X-axis and the light intensity value as the Y-axis.

[0112] Based on a Cartesian coordinate system, calculate the sampling slope corresponding to each light intensity value.

[0113] In one possible implementation, the slope determination module can also be used for:

[0114] Calculate the absolute value of the difference between each sampling slope of the target sampling sequence and the preset sampling slope, and take the sampling slope with an absolute value of zero as the target sampling slope;

[0115] The sampling time corresponding to the target sampling slope is taken as the sampling time, and the location information of the target sampling sequence is calculated using the sampling time. The sampling time includes the sampling sunrise time and the sampling sunset time.

[0116] The particle swarm optimization algorithm is used to obtain a series of location information that are consistent with the location information of the target sampling sequence, which constitutes the geographic coordinate curve of the target sampling sequence for the corresponding date.

[0117] In one possible implementation, the device may further include a first preset slope calculation module, which can be used to:

[0118] Obtain the location information of the center point of the area to be tested;

[0119] The preset sampling slope is calculated using the location information of the center point of the area to be measured.

[0120] In one possible implementation, the device may further include a second preset slope calculation module, which can be used to:

[0121] Obtain the location information and light intensity value of a reference smart meter. The reference smart meter is any smart meter in the test area other than the smart meter under test.

[0122] The target time is calculated using the location information of a reference smart meter. The target time includes sunrise and sunset times.

[0123] The preset sampling slope is calculated using the target time and light intensity value of the reference smart meter.

[0124] In one possible implementation, the target sampling sequence includes a first sampling sequence during the sunrise period and a second sampling sequence during the sunset period; the device may further include a first computing module, which can be used for:

[0125] Obtain the time zone, longitude, and latitude of the region to be measured for each date;

[0126] The sunrise time of the area under test is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area under test using the radian system, and the sunset time of the area under test is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area under test using the radian system.

[0127] The preset time periods before and after the sunrise time of the area to be tested are taken as the sunrise time period, and the preset time periods before and after the sunset time of the area to be tested are taken as the sunset time period;

[0128] Accordingly, the sequence acquisition module can be used for:

[0129] Obtain the first sampling sequence of the smart meter under test during the sunrise time period on N dates;

[0130] Obtain the second sampling sequence of the smart meter under test during the sunset time period on N dates.

[0131] In one possible implementation, the target sampling sequence includes a first sampling sequence during the sunrise period and a second sampling sequence during the sunset period; the device may further include a second computing module, which can be used for:

[0132] Obtain the time zone, longitude, and latitude of the region to be measured for each date;

[0133] The sunrise time of the area under test is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area under test using the angle system, and the sunset time of the area under test is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area under test using the angle system.

[0134] The preset time periods before and after the sunrise time of the area to be measured are taken as the sunrise time period, and the preset time periods before and after the sunset time of the area to be measured are taken as the sunset time period;

[0135] Accordingly, the sequence acquisition module can be used for:

[0136] Obtain the first sampling sequence of the smart meter under test during the sunrise time period on N dates;

[0137] Obtain the second sampling sequence of the smart meter under test during the sunset time period on N dates.

[0138] In one possible implementation, the pre-set time periods before and after are determined as follows:

[0139] Determine geographical distances using the longitude of the area to be measured;

[0140] Geographical distance is used to determine the pre- and post-test time periods of the area to be tested.

[0141] In one possible implementation, the location determination module can be used to:

[0142] The latitude and longitude of the intersection points of the geographic coordinate curves for N dates are used as the location information of the smart meter under test.

[0143] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0144] Those skilled in the art will recognize that the templates, units, and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0145] If the module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various smart meter positioning method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0146] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for locating a smart meter, characterized in that, include: N target sampling sequences of the smart meter to be tested are obtained. The smart meter to be tested is any smart meter in the test area. Each target sampling sequence is multiple sampling data of a single date within a target time period. Each sampling data includes light intensity value and corresponding collection time. N is a positive integer greater than or equal to 2. The target time period includes sunrise time period and sunset time period. Calculate the corresponding sampling slope using each sampled data; and for each target sampling sequence, select the target sampling slope from multiple sampling slopes of the target sampling sequence, and determine the geographic coordinate curve of the date corresponding to the target sampling sequence based on the collection time corresponding to the target sampling slope. The location information of the smart meter under test is determined based on the geographic coordinate curves of N dates.

2. The smart meter positioning method according to claim 1, characterized in that, The calculation of the corresponding sampling slope using each sampled data includes: A Cartesian coordinate system is constructed using each sampled data point, with the acquisition time as the X-axis and the light intensity value as the Y-axis. Based on the Cartesian coordinate system, the sampling slope corresponding to each light intensity value is calculated.

3. The smart meter positioning method according to claim 1, characterized in that, For each target sampling sequence, the process of selecting a target sampling slope from multiple sampling slopes of the target sampling sequence, and determining the geographic coordinate curve of the corresponding date of the target sampling sequence based on the collection time corresponding to the target sampling slope, includes: Calculate the absolute value of the difference between each sampling slope of the target sampling sequence and the preset sampling slope, and take the sampling slope with an absolute value of zero as the target sampling slope; The sampling time corresponding to the target sampling slope is taken as the sampling time, and the location information of the target sampling sequence is calculated using the sampling time. The sampling time includes the sampling sunrise time and the sampling sunset time. The particle swarm optimization algorithm is used to obtain a series of location information that are consistent with the location information of the target sampling sequence, which constitutes the geographic coordinate curve of the target sampling sequence for the corresponding date.

4. The smart meter positioning method according to claim 3, characterized in that, Before calculating the absolute value of the difference between each sampling slope of the target sampling sequence and a preset sampling slope, the method further includes: Obtain the location information of the center point of the area to be tested; The preset sampling slope is calculated using the location information of the center point of the area to be measured.

5. The smart meter positioning method according to claim 3, characterized in that, Before calculating the absolute value of the difference between each sampling slope of the target sampling sequence and a preset sampling slope, the method further includes: The location information and light intensity value of a reference smart meter are obtained, wherein the reference smart meter is any smart meter in the test area other than the smart meter under test; The target time is calculated using the location information of the reference smart meter, and the target time includes sunrise and sunset times. The preset sampling slope is calculated using the target time and the light intensity value of the reference smart meter.

6. The smart meter positioning method according to claim 1, characterized in that, The target sampling sequence includes a first sampling sequence within the sunrise time period and a second sampling sequence within the sunset time period; before acquiring N target sampling sequences of the smart meter under test, the method further includes: Obtain the time zone, longitude, and latitude of the region to be measured for each date; The sunrise time of the area to be measured is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area to be measured using radians, and the sunset time of the area to be measured is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area to be measured using radians. The preset time period before and after the sunrise time of the area to be tested is taken as the sunrise time period, and the preset time period before and after the sunset time of the area to be tested is taken as the sunset time period; Accordingly, acquiring N target sampling sequences of the smart meter under test includes: Obtain the first sampling sequence of the smart meter under test during the sunrise time period on N dates; Obtain the second sampling sequence of the smart meter under test during the sunset time period on N dates.

7. The smart meter positioning method according to claim 1, characterized in that, The target sampling sequence includes a first sampling sequence within the sunrise time period and a second sampling sequence within the sunset time period; before acquiring N target sampling sequences of the smart meter under test, the method further includes: Obtain the time zone, longitude, and latitude of the region to be measured for each date; The sunrise time of the area to be measured is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area to be measured using the angle system, and the sunset time of the area to be measured is obtained by calculating the time zone, longitude, latitude, and corresponding acquisition time of the area to be measured using the angle system. The preset time period before and after the sunrise time of the area to be tested is taken as the sunrise time period, and the preset time period before and after the sunset time of the area to be tested is taken as the sunset time period; Accordingly, acquiring N target sampling sequences of the smart meter under test includes: Obtain the first sampling sequence of the smart meter under test during the sunrise time period on N dates; Obtain the second sampling sequence of the smart meter under test during the sunset time period on N dates.

8. The smart meter positioning method according to any one of claims 6 or 7, characterized in that, The predetermined time periods before and after are determined as follows: The geographical distance is determined using the longitude of the area to be measured. The geographical distance is used to determine the preset time periods before and after the area to be tested.

9. The smart meter positioning method according to claim 1, characterized in that, The step of determining the location information of the smart meter under test based on the geographic coordinate curves of N dates includes: The latitude and longitude corresponding to the intersection of the geographic coordinate curves of N dates are used as the location information of the smart meter under test.

10. A smart meter positioning device, characterized in that, include: The sequence acquisition module is used to acquire N target sampling sequences of the smart meter under test. The smart meter under test is any smart meter in the test area. Each target sampling sequence is multiple sampling data of a single date within a target time period. Each sampling data includes a light intensity value and the corresponding acquisition time. N is a positive integer greater than or equal to 2. The target time period includes sunrise time period and sunset time period. The curve determination module is used to calculate the corresponding sampling slope using each sampled data; and for each target sampling sequence, it selects the target sampling slope from multiple sampling slopes of the target sampling sequence, and determines the geographic coordinate curve of the date corresponding to the target sampling sequence based on the collection time corresponding to the target sampling slope. The location determination module is used to determine the location information of the smart meter under test based on the geographical coordinate curves of N dates.

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