Positioning module, positioning method and smart electric energy meter of smart electric energy meter

Abstract

The present invention discloses a positioning module, positioning method and smart electric energy meter for a smart electric energy meter, which relates to the field of smart electric energy meters. The method starts a signal receiving module, collects and transmits signal data sent by a reference node to a data processing center in real time, and the data processing center pre-processes and extracts features of the signal data to obtain a signal feature set. By correcting the received signal strength RSSI, the corrected signal value XRSSI is calculated. i , and calculate the node distance di based on this, realizing the effective correction and compensation of environmental noise and multipath effect, using the signal feature set and the corrected signal value XRSSI i Calculate the three-dimensional coordinates (x, y, z) of the energy meter and introduce the BIM system to calculate the actual three-dimensional position (x s ,y s , z s ) comparison, which not only improves the positioning accuracy, but also achieves accurate matching between the actual position of the electricity meter and the planned position.

Description

Technical Field

[0001] The present invention relates to the technical field of smart electric energy meters, and in particular to a positioning module and a positioning method for a smart electric energy meter, and a smart electric energy meter. Background Art

[0002] In modern smart grid systems, precise positioning of electricity meters is a key technical challenge. As essential terminal devices in power systems, smart meters are widely used in homes, businesses, and industrial locations. These smart meters not only measure energy but also monitor usage data in real time, providing users with power consumption analysis and optimization recommendations. Specifically, in one area of ​​smart grids, accurate positioning of electricity meters is a crucial foundation for grid management, fault detection, and user behavior analysis.

[0003] In the Chinese invention application with application publication number CN113784305A, a positioning module, positioning method and smart electricity meter of a smart electricity meter are disclosed. The positioning module includes: a processor, a narrowband Internet of Things NB-IoT communication module and a serial peripheral interface SPI interface. The positioning module is connected to the metering core and management core in the smart electricity meter through the SPI interface. The processor is connected to the NB-IoT communication module and the SPI interface respectively. The NB-IoT communication module communicates with external devices through the NB-IoT network. The processor processes data received and / or sent by the NB-IoT communication module to obtain positioning information of the smart electricity meter. Alternatively, the processor processes data received and / or sent by the NB-IoT communication module and sends the processing results to the external device through the NB-IoT communication module, so that the external device obtains the positioning information of the smart electricity meter based on the processing results.

[0004] In combination with the existing technology, the above application still has the following deficiencies:

[0005] Energy meter positioning methods primarily rely on initial records of installation locations and limited geographic information. However, over time and due to environmental changes, the meter's position may shift, rendering the original positioning information ineffective. Furthermore, existing positioning technologies such as GPS and traditional signal strength indicator (RSSI) positioning methods perform poorly in complex indoor environments. These methods often struggle with multipath effects and signal interference, resulting in low positioning accuracy, especially in situations where signals are obscured or reflected. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the present invention provides a positioning module, a positioning method and a smart electric energy meter for a smart electric energy meter, which solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a positioning module, a positioning method and a smart electric energy meter, comprising the following steps:

[0008] Step S1: Start the signal receiving module of the smart electric energy meter to collect signal data sent by the reference node in real time and transmit the collected signal data to the data processing center;

[0009] Step S2: After pre-processing the collected signal data at the data processing center, a signal data group is obtained, and feature extraction is performed on the signal data group to obtain a signal feature set;

[0010] Step S3: Calculate the corrected received signal strength RSSI based on the extracted signal feature set to obtain the corrected received signal value XRSSI. i , and then correct the received signal value XRSSI based on the obtained i Calculate and obtain the node distance d i ;

[0011] Step S4: Based on the acquired signal feature set and in combination with the acquired node distance di, the horizontal axis coordinate x, vertical axis coordinate y, and vertical axis coordinate z of the electric energy meter are calculated respectively. Then, based on the acquired horizontal axis coordinate x, vertical axis coordinate y, and vertical axis coordinate z, the signal feature set is introduced to perform correlation calculation to obtain the three-dimensional position (x, y, z) of the electric energy meter for positioning optimization;

[0012] Step S5: Integrate with the building information model BIM to extract the actual three-dimensional position (x s ,y s , z s ), and then perform difference calculation with the obtained three-dimensional position (x, y, z) to obtain the position error value Error, and compare and evaluate the preset error threshold W with the obtained position error value Error to analyze the accuracy of the current three-dimensional position (x, y, z).

[0013] Preferably, the step S1 includes the following steps:

[0014] S11. Deploy multiple reference nodes at the installation site of the smart energy meter. The reference nodes are Wi-Fi access points that send wireless signals in real time. The smart energy meter receives signal data sent by the reference nodes in real time through a wireless communication module.

[0015] The signal data includes signal strength RSSI, signal sending time t arrival , signal receiving time t send , signal horizontal transmission angle , signal horizontal plane angle , path correction factor MFF and environmental noise coefficient ENC;

[0016] S12. Transmitting the acquired signal data to a data processing center by setting up a 4G cellular network in the wireless communication module.

[0017] Preferably, step S2 includes the following steps:

[0018] S21. Receive signal data in real time through a data processing center, identify missing values ​​in the received signal data, for example, if the RSSI value is null, fill the missing value using interpolation, standardize the continuous variables of the signal data using mean and standard deviation, convert the signal data into a distribution with zero mean and unit standard deviation, perform standardization, and then aggregate to obtain a signal data set;

[0019] S22, then calculate the signal strength RSSI and signal receiving time t in the signal data group. arrival and signal sending time t send , perform feature extraction to obtain signal strength change rate ROC-RSSI and signal arrival time TOA;

[0020] S221, the signal strength change rate ROC-RSSI is calculated by recording the continuous signal strength RSSI at a time interval Δt to obtain the signal strength change rate ROC-RSSI, and the signal strength change rate ROC-RSSI is calculated using the following algorithm formula;

[0021] ;

[0022] Where △t represents the time interval, ROC-RSSI i Indicates the signal strength change rate of the i-th signal, RSSI i (t) represents the signal strength of the i-th signal at time t, RSSI i (t-1) represents the signal strength of the i-th signal at time t-1;

[0023] S222: The signal arrival time TOA is sent via a reference node to a wireless signal, and the signal reception time t is recorded. arrival , the wireless communication module receives the signal in real time and records the signal sending time t send , and then perform difference calculation to obtain the signal arrival time TOA; the signal arrival time TOA is calculated by the following algorithm formula: TOA=t arrival -t send ;

[0024] S23 , combining the extracted signal strength change rate ROC-RSSI and signal arrival time TOA with the signal data to generate a signal feature set.

[0025] Preferably, the step S3 includes the following steps:

[0026] S31, based on the extracted signal feature set, use the signal strength change rate ROC-RSSI of the i-th signal i And the environmental noise coefficient ENC, the received signal strength RSSI is corrected to obtain the received signal correction value XRSSI i ;

[0027] The received signal correction value XRSSI i Calculated using the following algorithm formula:

[0028] ;

[0029] Where, RSSI i Indicates the signal strength of the i-th signal, ENC i Represents the environmental noise coefficient of the i-th signal, XRSSI i represents the signal correction value of the i-th signal;

[0030] S32, then correct the received signal value XRSSI based on the obtained value i Combined with the path correction factor MFF in the signal feature set, the node distance di is calculated;

[0031] The node distance di is calculated by the following algorithm formula:

[0032] ;

[0033] Where Pt represents the transmit power, which is the power of the reference node when transmitting the signal. Usually, this is a known constant that has been determined when configuring the reference node. n represents the path loss exponent, and the specific value is obtained by the user through field testing. MFF i represents the path correction factor of the i-th signal.

[0034] Preferably, step S4 includes the following steps:

[0035] S41, based on the signal horizontal transmission angle extracted from the signal feature set Angle with the signal horizontal plane , calculate and obtain the horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z of the smart electric energy meter;

[0036] The horizontal axis coordinate x, the vertical axis coordinate y and the vertical axis coordinate z are calculated by the following algorithm formula:

[0037] ;

[0038] ;

[0039] ;

[0040] Where x0 represents the initial horizontal axis coordinate, y0 represents the initial vertical axis coordinate, z0 represents the initial vertical axis coordinate, cos represents the cosine function, and sin represents the sine function;

[0041] S42. Set up an API application program interface to integrate with the building information model (BIM), determine the reference node position, and extract the actual three-dimensional position of the reference node (x s ,y s , z s ).

[0042] Preferably, S43, based on the obtained horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z, and then compared with the actual three-dimensional position (x s ,y s , z s ), the signal strength change rate ROC-RSSI and the environmental noise coefficient ENC in the signal feature set are combined to calculate the three-dimensional position (x, y, z) of the electric energy meter, and the positioning of the electric energy meter reference node is optimized;

[0043] The three-dimensional position (x, y, z) is calculated using the following algorithm:

[0044] ;

[0045] Where, The signal strength change rate adjustment parameter is used to control the impact of the strength change rate ROC-RSSI on the total error. It represents the adjustment parameter of the environmental noise coefficient, which is used to control the influence of the environmental noise coefficient ENC on the total error. represents the minimized coordinate value, and N represents the total number of signals sent by the reference nodes.

[0046] Preferably, the step S5 includes the following steps:

[0047] S51, based on the obtained three-dimensional position (x, y, z) and the actual three-dimensional position (x s ,y s , z s ), perform correlation calculation to obtain the position error value Error, and analyze the error between the obtained positioning and the actual planned smart energy meter positioning;

[0048] The position error value Error is calculated by the following algorithm formula;

[0049] .

[0050] Preferably, S52, preset an error threshold W based on the error index, and then compare and evaluate it with the obtained position error value Error, and analyze the error between the currently calculated positioning information and the actual planned positioning information;

[0051] The specific evaluation plan is as follows:

[0052] When the position error value Error>error threshold W, it means that there is an error between the positioning information and the actual planned positioning information. At this time, the adjustment parameter of the signal strength change rate is adjusted. and environmental noise coefficient adjustment parameters After increasing by 50%, step S43 is executed again to perform iterative calculation to optimize the positioning information accuracy;

[0053] When the position error value Error ≤ error threshold W, it indicates that there is no error between the positioning information and the actual planned positioning information.

[0054] A positioning module for a smart electric energy meter, comprising a reference node module, a smart electric energy meter communication module, a data processing module and a positioning signal adjustment module;

[0055] The reference node module sends positioning signals to the surrounding smart energy meters through the WIFI signal transmitter, adjusts the transmission power to adjust the coverage range, and performs time synchronization;

[0056] The smart energy meter communication module processes the received signals in real time by setting a multi-mode receiver and setting a data transmission interface to transmit the collected signal data to the data processing center;

[0057] The data processing module includes a data preprocessing unit and a positioning algorithm unit;

[0058] The data preprocessing unit is used to preprocess the collected signal data and then extract features to obtain a signal feature set;

[0059] The positioning algorithm unit is used to implement multiple positioning algorithms based on the acquired signal feature set and integrate multiple parameters for optimization calculation;

[0060] The positioning signal adjustment module is used to correct and adjust the positioning signal to optimize the influence of environmental noise and multipath effect.

[0061] An intelligent electric energy meter comprises a metering core, a regulating core and the positioning module.

[0062] The present invention provides a positioning module, positioning method and smart electric energy meter for a smart electric energy meter. The invention has the following beneficial effects:

[0063] (1) First, this method introduces the path correction factor MFF, environmental noise coefficient ENC and signal strength change rate ROC-RSSI correction calculation in the positioning process of smart electric energy meters. This method effectively improves the positioning accuracy of electric energy meters in complex environments. Specifically, the corrected received signal strength XRSSI is used. i By using the metric to calculate the node distance di and optimizing the calculation by combining multiple signal characteristics, errors caused by signal interference and reflections can be significantly reduced. This increased accuracy helps grid management systems more accurately track and monitor the location of electricity meters, improving the stability and efficiency of grid operations.

[0064] (2) This method can be integrated with the building information model (BIM) to obtain the actual three-dimensional position of the energy meter (x s ,y s , z s ) is compared with the calculated three-dimensional position (x, y, z), the position error value Error is calculated, and adjustments are made based on the error. The specific steps include: extracting the actual three-dimensional position (x s ,y s , z s ), then calculates the difference between the obtained 3D position (x, y, z) and the obtained position error value Error. A preset error threshold W is used for comparison and evaluation. When the position error value Error exceeds the error threshold W, the adjustment parameters for the signal strength change rate and the ambient noise coefficient are increased to iteratively optimize positioning accuracy. When the position error value Error is less than or equal to the error threshold W, the positioning information is accurate. This feature enables the system to automatically adjust the parameters of the positioning algorithm to adapt to changes in different environments, improving environmental adaptability and flexibility.

[0065] (3) This method not only helps the power grid management system to accurately monitor and bill electricity consumption through smart electricity meter positioning, but also can promptly detect and correct positioning deviations, thereby reducing energy loss and management costs. By reducing positioning errors, the system can more accurately perform load management and power distribution, optimizing the overall energy efficiency of the power grid. In addition, accurate positioning information helps with the maintenance and troubleshooting of electricity meters, shortening response time and reducing maintenance costs and time. Furthermore, the use of 4G cellular networks to transmit signal data to the data processing center in real time improves the timeliness and reliability of data transmission, further enhancing the overall efficiency and benefits of the system. In summary, this positioning method provides comprehensive support for smart grid systems by improving positioning accuracy, enhancing system adaptability, and optimizing energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of the steps of a positioning method for a smart electric energy meter according to the present invention;

[0067] Figure 2 This is a schematic diagram of the steps for positioning a module of a smart electric energy meter according to the present invention. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0069] Example 1:

[0070] The present invention provides a positioning module, positioning method and smart electric energy meter for smart electric energy meter. Figure 1 , including the following steps:

[0071] Step S1: Start the signal receiving module of the smart electric energy meter to collect signal data sent by the reference node in real time and transmit the collected signal data to the data processing center;

[0072] Step S2: After pre-processing the collected signal data at the data processing center, a signal data group is obtained, and feature extraction is performed on the signal data group to obtain a signal feature set;

[0073] Step S3: Calculate the corrected received signal strength RSSI based on the extracted signal feature set to obtain the corrected received signal value XRSSI. i , and then correct the received signal value XRSSI based on the obtained i Calculate and obtain the node distance d i ;

[0074] Step S4: Based on the acquired signal feature set and in combination with the acquired node distance di, the horizontal axis coordinate x, vertical axis coordinate y, and vertical axis coordinate z of the electric energy meter are calculated respectively. Then, based on the acquired horizontal axis coordinate x, vertical axis coordinate y, and vertical axis coordinate z, the signal feature set is introduced to perform correlation calculation to obtain the three-dimensional position (x, y, z) of the electric energy meter for positioning optimization;

[0075] Step S5: Integrate with the building information model BIM to extract the actual three-dimensional position (x s ,y s , z s), and then perform difference calculation with the obtained three-dimensional position (x, y, z) to obtain the position error value Error, and compare and evaluate the preset error threshold W with the obtained position error value Error to analyze the accuracy of the current three-dimensional position (x, y, z).

[0076] In this embodiment, the method starts the signal receiving module, collects and transmits the signal data sent by the reference node to the data processing center in real time, effectively realizing the efficient collection and transmission of signal data. The data processing center pre-processes and extracts features of the signal data, obtains the signal feature set, and stores the data, laying the foundation for subsequent accurate calculations. By correcting the received signal strength RSSI, the corrected signal value XRSSI is calculated. i , and calculate the node distance di based on this, achieving effective correction and compensation for environmental noise and multipath effects. These steps ensure the high accuracy and reliability of the electric energy meter positioning process, using the signal feature set and the corrected signal value XRSSI i Calculate the three-dimensional coordinates (x, y, z) of the energy meter and introduce the BIM system to calculate the actual three-dimensional position (x s ,y s , z s ) comparison, which not only improves positioning accuracy but also ensures a precise match between the actual and planned locations of the electricity meters. Furthermore, by calculating the position error value Error in real time and comparing it with the preset error threshold W, positioning deviations can be promptly detected and corrected. Multiple optimizations and adjustments during this process significantly enhance the system's adaptability and positioning accuracy in complex environments.

[0077] Example 2:

[0078] This embodiment is explained in Example 1, please refer to Figure 1 Specifically, step S1 includes the following steps:

[0079] S11. At the installation site of the smart energy meter, multiple reference nodes are deployed. The reference nodes are WIFI access points that send wireless signals in real time. The smart energy meter receives the signal data sent by the reference nodes in real time through the wireless communication module.

[0080] Signal data includes signal strength RSSI, signal sending time t arrival , signal receiving time t send , signal horizontal transmission angle , signal horizontal plane angle , path correction factor MFF and environmental noise coefficient ENC;

[0081] S12. Transmitting the acquired signal data to a data processing center by setting up a 4G cellular network in the wireless communication module.

[0082] S21. Receive signal data in real time through a data processing center, identify missing values ​​in the received signal data, for example, if the RSSI value is null, fill the missing value using interpolation, standardize the continuous variables of the signal data using mean and standard deviation, convert the signal data into a distribution with zero mean and unit standard deviation, perform standardization, and then aggregate to obtain a signal data set;

[0083] S22, then calculate the signal strength RSSI and signal receiving time t in the signal data group. arrival and signal sending time t send , perform feature extraction to obtain signal strength change rate ROC-RSSI and signal arrival time TOA;

[0084] S221, the signal strength change rate ROC-RSSI is calculated by recording the continuous signal strength RSSI at time interval Δt to obtain the signal strength change rate ROC-RSSI. The signal strength change rate ROC-RSSI is calculated using the following algorithm formula;

[0085] ;

[0086] Where △t represents the time interval, ROC-RSSI i Indicates the signal strength change rate of the i-th signal, RSSI i (t) represents the signal strength of the i-th signal at time t, RSSI i (t-1) represents the signal strength of the i-th signal at time t-1;

[0087] S222, signal arrival time TOA sends a wireless signal through the reference node and records the signal reception time t arrival , the wireless communication module receives the signal in real time and records the signal sending time t send , and then perform difference calculation to obtain the signal arrival time TOA;

[0088] The signal arrival time TOA is calculated using the following algorithm formula: TOA=t arrival -t send ;

[0089] S23 , combining the extracted signal strength change rate ROC-RSSI and signal arrival time TOA with the signal data to generate a signal feature set.

[0090] In this embodiment, the method realizes efficient collection and transmission of signal data by deploying multiple WIFI access points on site as reference nodes, collecting and transmitting signal data to the data processing center in real time. By preprocessing the signal data, including missing value filling, data standardization and feature extraction, a signal feature set including the signal strength change rate ROC-RSSI and the signal arrival time TOA is generated. These steps ensure the integrity and consistency of the signal data, effectively reduce the interference of environmental noise and multipath effects on the signal, and significantly improve the reliability and accuracy of the data. Compared with traditional positioning technology, this method provides faster and more accurate positioning services by calculating signal features in real time and combining 4G cellular networks for data transmission, significantly improving the accuracy and response speed of smart electricity meter positioning.

[0091] Example 3:

[0092] This embodiment is explained in Example 2, please refer to Figure 1 , specifically: step S3 includes the following steps;

[0093] S31, based on the extracted signal feature set, use the signal strength change rate ROC-RSSI and the environmental noise coefficient ENC to correct the received signal strength RSSI to obtain the received signal correction value XRSSI i ;

[0094] Received signal correction value XRSSI i Calculated using the following algorithm formula:

[0095] ;

[0096] Where, RSSI i Indicates the signal strength of the i-th signal, ENC i Represents the environmental noise coefficient of the i-th signal, XRSSI i represents the signal correction value of the i-th signal;

[0097] S32, then correct the received signal value XRSSI based on the obtained value i Combined with the path correction factor MFF in the signal feature set, the node distance di is calculated;

[0098] The node distance di is calculated using the following algorithm formula:

[0099] ;

[0100] Where Pt represents the transmit power, which is the power of the reference node when transmitting the signal. Usually, this is a known constant that has been determined when configuring the reference node. n represents the path loss exponent, and the specific value is obtained by the user through field testing. MFF i represents the path correction factor of the i-th signal.

[0101] In this embodiment, the method corrects the received signal strength RSSI by introducing the signal strength change rate ROC-RSSI and the environmental noise coefficient ENC to obtain a more accurate received signal correction value XRSSI. i . This correction process effectively reduces the impact of environmental noise and multipath effects on signal accuracy and improves the reliability of the positioning signal. Subsequently, combined with the path correction factor MFF, the node distance di is calculated to further improve the positioning accuracy. This method significantly enhances the robustness and accuracy of the positioning system by comprehensively considering multiple parameters in the signal feature set, especially the quantitative correction of environmental factors, and can provide stable positioning performance, especially in complex environments. The application of this technology not only effectively improves the precise positioning capability of electricity meters in smart grids, but also provides data support for more refined energy management and control, and promotes the improvement of the intelligence level of power systems.

[0102] Example 4:

[0103] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: step S4 includes the following steps;

[0104] S41, based on the signal horizontal transmission angle extracted from the signal feature set Angle with the signal horizontal plane , calculate and obtain the horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z of the smart electric energy meter;

[0105] The horizontal axis coordinate x, vertical axis coordinate y, and vertical axis coordinate z are calculated using the following algorithm formula:

[0106] ;

[0107] ;

[0108] ;

[0109] Where x0 represents the initial horizontal axis coordinate, y0 represents the initial vertical axis coordinate, z0 represents the initial vertical axis coordinate, cos represents the cosine function, and sin represents the sine function;

[0110] S42. Set up an API application program interface to integrate with the building information model (BIM), determine the reference node position, and extract the actual three-dimensional position of the reference node (x s ,y s , z s ).

[0111] S43, based on the obtained horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z, and then compared with the actual three-dimensional position (x s ,y s , z s ), the signal strength change rate ROC-RSSI and the environmental noise coefficient ENC in the signal feature set are combined to calculate the three-dimensional position (x, y, z) of the electric energy meter, and the positioning of the electric energy meter reference node is optimized;

[0112] The three-dimensional position (x, y, z) is calculated using the following algorithm:

[0113] ;

[0114] Where, The signal strength change rate adjustment parameter is used to control the impact of the strength change rate ROC-RSSI on the total error. It represents the adjustment parameter of the environmental noise coefficient, which is used to control the influence of the environmental noise coefficient ENC on the total error. represents the minimized coordinate value, and N represents the total number of signals sent by the reference nodes.

[0115] In this embodiment, the three-dimensional positioning process of a smart electricity meter in this method comprehensively considers the horizontal signal transmission angle and the horizontal plane angle in the signal feature set to accurately calculate the meter's horizontal coordinates x, y, and z. By integrating with the Building Information Model (BIM), the actual three-dimensional position (xs, ys, zs) of the reference node is extracted and compared and optimized with the calculated coordinates, effectively improving positioning accuracy. This step not only ensures more accurate three-dimensional position (x, y, z) of the electricity meter, but also further optimizes the positioning result by introducing the signal strength change rate (ROC-RSSI) and the environmental noise coefficient (ENC), significantly reducing positioning error. This comprehensive consideration of multiple signal characteristics can provide more stable and accurate positioning results in complex environments. In particular, integration with the BIM system has significantly improved the accuracy of the positioning process in practical applications. The application of this technology effectively improves the spatial positioning capabilities of smart electricity meters, providing higher reliability and accuracy for device management and data acquisition in smart grids, and further promoting the intelligent and automated level of power systems.

[0116] Example 5:

[0117] This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: step S5 includes the following steps;

[0118] S51, based on the obtained three-dimensional position (x, y, z) and the actual three-dimensional position (x s ,y s , z s ), perform correlation calculation to obtain the position error value Error, and analyze the error between the obtained positioning and the actual planned smart energy meter positioning;

[0119] The position error value Error is calculated using the following algorithm formula;

[0120] .

[0121] S52: Preset an error threshold W based on the error index, and then compare and evaluate it with the obtained position error value Error to analyze the error between the currently calculated positioning information and the actual planned positioning information;

[0122] The specific evaluation plan is as follows:

[0123] When the position error value Error>error threshold W, it means that there is an error between the positioning information and the actual planned positioning information. At this time, the adjustment parameter of the signal strength change rate is adjusted. and environmental noise coefficient adjustment parameters After increasing by 50%, step S43 is executed again to perform iterative calculation to optimize the positioning information accuracy;

[0124] When the position error value Error ≤ error threshold W, it indicates that there is no error between the positioning information and the actual planned positioning information.

[0125] In this embodiment, the method evaluates the accuracy of the current positioning information by calculating the three-dimensional position error Error. If the error exceeds the preset error threshold W, the adjustment parameters for adjusting the signal strength change rate and the environmental noise coefficient are optimized and the calculation is iterated again to further improve the positioning accuracy. This step not only ensures that the system can detect and correct positioning errors in real time, but also responds to environmental changes by dynamically adjusting parameters, thereby improving the system's adaptability and accuracy. This method significantly improves the positioning accuracy of smart electricity meters. By introducing an error feedback mechanism into the system, self-correction of positioning results is achieved. This dynamic adjustment and optimization strategy enables the ability to cope with complex environmental changes in practical applications and reduces the impact of errors. Ultimately, this method not only improves the positioning accuracy of electricity meters, but also enhances the robustness and stability of the system, providing solid support for the efficient operation of smart grids.

[0126] A positioning module for smart electric energy meters, please refer to Figure 2 , including reference node module, smart energy meter communication module, data processing module and positioning signal adjustment module;

[0127] The reference node module sends positioning signals to the surrounding smart energy meters through the WiFi signal transmitter, adjusts the transmission power to adjust the coverage range, and performs time synchronization;

[0128] The smart energy meter communication module processes the received signals in real time by setting up a multi-mode receiver and setting up a data transmission interface to transmit the collected signal data to the data processing center;

[0129] The data processing module includes a data pre-processing unit and a positioning algorithm unit;

[0130] The data preprocessing unit is used to preprocess the collected signal data and then extract features to obtain a signal feature set;

[0131] The positioning algorithm unit is used to implement multiple positioning algorithms based on the acquired signal feature set and integrate multiple parameters for optimization calculation;

[0132] The positioning signal adjustment module is used to correct and adjust the positioning signal to optimize the impact of environmental noise and multipath effects.

[0133] An intelligent electric energy meter comprises a metering core, a regulating core and a positioning module.

[0134] While embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for locating a smart electric energy meter, characterized by: The following steps are involved: Step S1: Start the signal receiving module of the smart electric energy meter to collect signal data sent by the reference node in real time and transmit the collected signal data to the data processing center; Step S2: After pre-processing the collected signal data at the data processing center, a signal data group is obtained, and feature extraction is performed on the signal data group to obtain a signal feature set; S21. Receive signal data in real time through a data processing center, identify missing values ​​in the received signal data, fill the missing values ​​using interpolation, standardize continuous variables of the signal data using mean and standard deviation, convert the signal data into a distribution with zero mean and unit standard deviation, perform standardization, and then aggregate to obtain a signal data set; Step S3: Calculate the corrected received signal strength RSSI based on the extracted signal feature set to obtain the corrected received signal value XRSSI. i , and then correct the received signal value XRSSI based on the obtained i Calculate and obtain the node distance d i ; Step S3 specifically includes S31 and S32; S31, based on the extracted signal feature set, use the signal strength change rate ROC-RSSI of the i-th signal i And the environmental noise coefficient ENC, the received signal strength RSSI is corrected to obtain the received signal correction value XRSSI i ; The received signal correction value XRSSI i Calculated using the following algorithm formula: ； Where, RSSI i Indicates the signal strength of the i-th signal, ENC i Represents the environmental noise coefficient of the i-th signal, XRSSI i represents the signal correction value of the i-th signal; S32, the node distance d i Calculated using the following algorithm formula: ； Where Pt represents the transmission power, n represents the path loss index, and the specific value is obtained by the user through field testing. i represents the path correction factor of the i-th signal; Step S4: Based on the acquired signal feature set and combined with the acquired node distance d i , respectively calculate the horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z of the electric energy meter, and then introduce the signal feature set based on the obtained horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z to perform correlation calculation to obtain the three-dimensional position (x, y, z) of the electric energy meter for positioning optimization; Step S5: Integrate with the building information model BIM to extract the actual three-dimensional position (x s ,y s , z s ), and then perform difference calculation with the obtained three-dimensional position (x, y, z) to obtain the position error value Error, and compare and evaluate the preset error threshold W with the obtained position error value Error to analyze the accuracy of the current three-dimensional position (x, y, z).

2. The method for locating a smart electric energy meter according to claim 1, wherein: The step S1 comprises the following steps: S11. Deploy multiple reference nodes at the installation site of the smart energy meter. The reference nodes are Wi-Fi access points that send wireless signals in real time. The smart energy meter receives signal data sent by the reference nodes in real time through a wireless communication module. The signal data includes signal strength RSSI, signal sending time t send , signal receiving time t arrival , signal horizontal transmission angle , signal horizontal plane angle , path correction factor MFF and environmental noise coefficient ENC; S12. Transmitting the acquired signal data to a data processing center by setting up a 4G cellular network in the wireless communication module.

3. The method for locating a smart electric energy meter according to claim 2, wherein: The step S2 comprises the following steps: S22, then calculate the signal strength RSSI and signal receiving time t in the signal data group. arrival and signal sending time t send , perform feature extraction to obtain signal strength change rate ROC-RSSI and signal arrival time TOA; S221, the signal strength change rate ROC-RSSI is calculated by recording the continuous signal strength RSSI at a time interval Δt to obtain the signal strength change rate ROC-RSSI, and the signal strength change rate ROC-RSSI is calculated using the following algorithm formula; ； Where △t represents the time interval, ROC-RSSI i Indicates the signal strength change rate of the i-th signal, RSSI i (t) represents the signal strength of the i-th signal at time t, RSSI i (t-1) represents the signal strength of the i-th signal at time t-1; S222: The signal arrival time TOA is sent via a reference node to send a wireless signal, and the signal sending time t is recorded. send , the wireless communication module receives the signal in real time and records the signal receiving time t arrival , and then perform difference calculation to obtain the signal arrival time TOA; the signal arrival time TOA is calculated by the following algorithm formula: TOA=t arrival -t send ; S23 , combining the extracted signal strength change rate ROC-RSSI and signal arrival time TOA with the signal data to generate a signal feature set.

4. The method for locating a smart electric energy meter according to claim 1, wherein: The step S4 comprises the following steps: S41, based on the signal horizontal transmission angle extracted from the signal feature set Angle with the signal horizontal plane , calculate and obtain the horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z of the smart electric energy meter; The horizontal axis coordinate x, the vertical axis coordinate y and the vertical axis coordinate z are calculated by the following algorithm formula: ; ; ; Where x0 represents the initial horizontal axis coordinate, y0 represents the initial vertical axis coordinate, z0 represents the initial vertical axis coordinate, cos represents the cosine function, and sin represents the sine function; S42. Set up an API application program interface to integrate with the building information model (BIM), determine the reference node position, and extract the actual three-dimensional position of the reference node (x s ,y s , z s ).

5. The method for locating a smart electric energy meter according to claim 4, characterized in that: S43, based on the obtained horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z, and then compared with the actual three-dimensional position (x s ,y s , z s ), the signal strength change rate ROC-RSSI and the environmental noise coefficient ENC in the signal feature set are combined to calculate the three-dimensional position (x, y, z) of the electric energy meter, and the positioning of the electric energy meter reference node is optimized; The three-dimensional position (x, y, z) is calculated using the following algorithm: ; Where, The signal strength change rate adjustment parameter is used to control the impact of the strength change rate ROC-RSSI on the total error. It represents the adjustment parameter of the environmental noise coefficient, which is used to control the influence of the environmental noise coefficient ENC on the total error. represents the minimized coordinate value, and N represents the total number of signals sent by the reference nodes.

6. The method for locating a smart electric energy meter according to claim 5, characterized in that: The step S5 comprises the following steps: S51, based on the obtained three-dimensional position (x, y, z) and the actual three-dimensional position (x s ,y s , z s ), perform correlation calculation to obtain the position error value Error, and analyze the error between the obtained positioning and the actual planned smart energy meter positioning; The position error value Error is calculated by the following algorithm formula; 。 7. The method for locating a smart electric energy meter according to claim 6, characterized in that: S52: Preset an error threshold W based on the error index, and then compare and evaluate it with the obtained position error value Error to analyze the error between the currently calculated positioning information and the actual planned positioning information; The specific evaluation plan is as follows: When the position error value Error>error threshold W, it means that there is an error between the positioning information and the actual planned positioning information. At this time, the adjustment parameter of the signal strength change rate is adjusted. and environmental noise coefficient adjustment parameters After increasing by 50%, step S43 is executed again to perform iterative calculation to optimize the positioning information accuracy; When the position error value Error ≤ error threshold W, it indicates that there is no error between the positioning information and the actual planned positioning information.

8. A positioning module for a smart electric energy meter, characterized by: It includes reference node module, smart energy meter communication module, data processing module and positioning signal adjustment module; The reference node module sends positioning signals to the surrounding smart energy meters through the WIFI signal transmitter, adjusts the transmission power to adjust the coverage range, and performs time synchronization; The smart energy meter communication module processes the received signals in real time by setting a multi-mode receiver and setting a data transmission interface to transmit the collected signal data to the data processing center; The data processing module includes a data preprocessing unit and a positioning algorithm unit; The data preprocessing unit is used to preprocess the collected signal data and then extract features to obtain a signal feature set; The preprocessing comprises receiving signal data in real time through a data processing center, identifying missing values ​​in the received signal data, filling the missing values ​​using an interpolation method, standardizing continuous variables of the signal data using a mean and a standard deviation, converting the signal data into a distribution with a zero mean and a unit standard deviation, performing a standardization process, and then aggregating the data to obtain a signal data set; The positioning algorithm unit is used to use the signal strength change rate ROC-RSSI of the i-th signal according to the extracted signal feature set i And the environmental noise coefficient ENC, the received signal strength RSSI is corrected to obtain the received signal correction value XRSSI i； The received signal correction value XRSSI i Calculated using the following algorithm formula: ； Where, RSSI i Indicates the signal strength of the i-th signal, ENC i Represents the environmental noise coefficient of the i-th signal, XRSSI i represents the signal correction value of the i-th signal; S32, node distance d i Calculated using the following algorithm formula: ； Where Pt represents the transmission power, n represents the path loss index, and the specific value is obtained by the user through field testing. i represents the path correction factor of the i-th signal; At the same time, based on the obtained signal feature set, combined with the obtained node distance d i , respectively calculate the horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z of the electric energy meter, and then introduce the signal feature set based on the obtained horizontal axis coordinate x, vertical axis coordinate y and vertical axis coordinate z to perform correlation calculation to obtain the three-dimensional position (x, y, z) of the electric energy meter for positioning optimization; The positioning signal adjustment module is used to correct and adjust the positioning signal.

9. A smart electric energy meter, characterized in that: It comprises a metering core, an adjusting core and a positioning module as claimed in claim 8.

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