Positioning module and positioning method of intelligent electric energy meter and intelligent electric energy meter

Abstract

The invention discloses a positioning module and a positioning method of an intelligent electric energy meter and the intelligent electric energy meter, and relates to the field of the intelligent electric energy meter. The method comprises the steps that a signal receiving module is started, signal data sent by a reference node is collected and transmitted to a data processing center in real time, and the data processing center carries out preprocessing and feature extraction on the signal data; the method comprises the following steps: acquiring a signal feature set, correcting received signal strength RSSI, calculating to obtain a corrected signal value XRSSIi, calculating a node distance di according to the corrected signal value XRSSIi, realizing effective correction and compensation of environmental noise and a multipath effect, calculating three-dimensional coordinates (x, y, z) of an electric energy meter by using the signal feature set and the corrected signal value XRSSIi, and introducing a BIM system to carry out actual three-dimensional position (xs, ys, zs) comparison. Therefore, the positioning precision is improved, and the accurate matching of the actual position and the planned position of the electric energy meter is realized.

Description

Technical Field

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

[0002] In modern smart grid systems, accurate positioning of energy meters is a key technical challenge. As important terminal equipment in power systems, smart energy meters are widely used in homes, businesses, and industrial places. These smart energy meters can not only measure electricity, but also monitor electricity consumption data in real time, providing users with electricity consumption analysis and optimization suggestions. Specifically in a sub-area of ​​smart grids, accurate positioning of energy meters is an important basis for grid management, fault detection, and user behavior analysis.

[0003] In the Chinese invention application with application publication number CN113784305A, a positioning module, a positioning method and a smart electric energy 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 the management core in the smart electric energy 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 an external device through the NB-IoT network. The processor processes the data received and / or sent by the NB-IoT communication module to obtain the positioning information of the smart electric energy meter, or the processor processes the data received and / or sent by the NB-IoT communication module and sends the processing result to the external device through the NB-IoT communication module, so that the external device obtains the positioning information of the smart electric energy meter according to the processing result.

[0004] In combination with the prior art, the above application still has the following deficiencies:

[0005] The location method of the energy meter mainly relies on the initial record of the installation location and limited geographical information. However, as time goes by and the environment changes, the location of the energy meter may shift, making the original location information invalid. In addition, existing positioning technologies such as GPS and traditional signal strength indicator RSSI positioning methods perform poorly in complex indoor environments. These methods usually have difficulty dealing with multipath effects and signal interference problems, resulting in low positioning accuracy, especially when the signal is blocked or reflected severely. 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 solving the problems mentioned in the background technology.

[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, collect the 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 preprocessing 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 based on the received signal correction value XRSSI 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, the vertical axis coordinate y and the numerical axis coordinate z of the electric energy meter are calculated respectively, and then the signal feature set is introduced to perform correlation calculation according to the acquired horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z 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 preset the error threshold W and compare and evaluate it with the obtained position error value Error to analyze the accuracy of the current three-dimensional position (x, y, z).

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

[0014] S11. At the site where the smart meter is installed, multiple reference nodes are deployed. The reference nodes are WIFI access points that send wireless signals in real time. The smart 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 transmission time t arrival , signal receiving time t send , signal horizontal transmission angle θ, signal horizontal plane angle φ, 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 comprises the following steps:

[0018] S21, receiving signal data in real time through a data processing center, identifying missing values ​​in the received signal data, for example, if the RSSI value is empty, filling the missing values ​​using interpolation, standardizing the continuous variables of the signal data using mean and standard deviation, converting the signal data into a distribution with zero mean and unit standard deviation, performing standardization, and then summarizing to obtain a signal data group;

[0019] S22, the signal strength RSSI and signal transmission time t in the signal data group are further analyzed. arrival and signal reception 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 using the time interval t to record the continuous signal strength RSSI (t) to obtain the signal strength change rate ROC-RSSI, and the signal strength change rate ROC-RSSI is calculated by the following algorithm formula;

[0021]

[0022] Where t-1 represents the signal strength at the next time interval t;

[0023] S222: The signal arrival time TOA sends a wireless signal through a reference node, and records the signal sending time t arrival , the wireless communication module receives the signal in real time and records the signal receiving time t send , and then perform difference calculation to obtain the signal arrival time TOA; the signal arrival time TOA is calculated and obtained 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 comprises the following steps:

[0026] S31, based on the extracted signal feature set, using the signal strength change rate ROC-RSSI and the environmental noise coefficient ENC, correct the received signal strength RSSI to obtain the received signal correction value XRSSIi ;

[0027] The received signal correction value XRSSI i Calculated by 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 according to the obtained 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 index, 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 comprises the following steps:

[0035] S41, calculating and obtaining the horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z of the smart meter according to the signal horizontal transmission angle θ and the signal horizontal plane angle φ extracted from the signal feature set;

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

[0037] x=x0+di·cos(θ);

[0038] y=y0+di·sin(θ);

[0039] z=z0+di·sin(φ);

[0040] In the formula, 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 numerical 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 reference node of the electric energy meter is optimized;

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

[0044]

[0045] In the formula, α represents the adjustment parameter of the signal strength change rate, which is used to control the influence of the strength change rate ROC-RSSI on the total error, β 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, argmin x,y,z represents the minimized coordinate value, and N represents the total number of signals sent by the reference nodes.

[0046] Preferably, the step S5 comprises 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 a position error value Error, and analyze the error between the obtained positioning and the actually planned smart meter positioning;

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

[0049]

[0050] Preferably, S52, presetting an error threshold W based on the error index, and then comparing and evaluating it with the obtained position error value Error, analyzing the error between the positioning information currently calculated and obtained 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 α of the signal strength change rate and the adjustment parameter β of the environmental noise coefficient are increased by 50%, and 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 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, and performs time synchronization;

[0056] The smart meter communication module processes the received signal in real time by setting a multi-mode receiver, and sets 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 and 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, a positioning method and a smart electric energy meter for a smart electric energy meter, which have 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 the smart electric energy meter. This method effectively improves the positioning accuracy of the electric energy meter in a complex environment. Specifically, the corrected received signal strength XRSSI is used iBy using the metric to calculate the node distance di and combining multiple signal features for optimization calculation, the error caused by signal interference and reflection can be significantly reduced. This improvement in accuracy helps the grid management system to more accurately track and monitor the location of the energy meter, 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 (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 according to the error. The specific steps include: extracting the actual three-dimensional position (x s ,y s , z s ), and then calculate the difference with the obtained three-dimensional position (x, y, z) to obtain the position error value Error, and preset the error threshold W and compare and evaluate the obtained position error value Error. When the position error value Error is greater than the error threshold W, increase the adjustment parameters of the signal strength change rate and the environmental noise coefficient to perform iterative calculations to optimize the positioning information accuracy; when the position error value Error is less than or equal to the error threshold W, it indicates that 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 and improve environmental adaptability and flexibility.

[0065] (3) This method not only helps the power grid management system to accurately monitor and bill electricity consumption through the positioning of smart electricity meters, 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, shortens response time, and reduces 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 improving the overall efficiency and benefits of the system. In summary, this positioning method provides all-round 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 The figure is a schematic diagram of the steps of positioning a module of a smart electric energy meter according to the present invention. DETAILED DESCRIPTION

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

[0069] Example 1

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

[0071] Step S1, start the signal receiving module of the smart electric energy meter, collect the 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 preprocessing 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 based on the received signal correction value XRSSI 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, the vertical axis coordinate y and the numerical axis coordinate z of the electric energy meter are calculated respectively, and then the signal feature set is introduced to perform correlation calculation according to the acquired horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z 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 preset the error threshold W and compare and evaluate it 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, and effectively realizes the efficient collection and transmission of signal data. The data processing center preprocesses 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 it, realizing the effective correction and compensation of environmental noise and multipath effect. 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 electric 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 electric energy meter and the planned position. At the same time, by calculating the position error value Error in real time and comparing it with the preset error threshold W, the positioning deviation can be discovered and corrected in time. Multiple optimizations and adjustments in this process have greatly enhanced the adaptability and positioning accuracy of the system 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 site where the smart meter is installed, multiple reference nodes are deployed. The reference nodes are WIFI 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 the wireless communication module.

[0080] Signal data includes signal strength RSSI, signal transmission time t arrival , signal receiving time t send , signal horizontal transmission angle θ, signal horizontal plane angle φ, 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, receiving signal data in real time through a data processing center, identifying missing values ​​in the received signal data, for example, if the RSSI value is empty, filling the missing values ​​using interpolation, standardizing the continuous variables of the signal data using mean and standard deviation, converting the signal data into a distribution with zero mean and unit standard deviation, performing standardization, and then summarizing to obtain a signal data group;

[0083] S22, the signal strength RSSI and signal transmission time t in the signal data group are further analyzed. arrival and signal reception 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(t) at a time interval t to obtain the signal strength change rate ROC-RSSI, and the signal strength change rate ROC-RSSI is calculated by the following algorithm formula;

[0085]

[0086] Where t-1 represents the signal strength at the next time interval t;

[0087] S222, signal arrival time TOA sends a wireless signal through the reference node and records the signal sending time t arrival , the wireless communication module receives the signal in real time and records the signal receiving time t send , and then perform difference calculation to obtain the signal arrival time TOA; the signal arrival time TOA is calculated using the following algorithm formula: TOA = t arrival -t send ;

[0088] 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.

[0089] 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 signal strength change rate ROC-RSSI and 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, which significantly improves the accuracy and response speed of smart electric energy meter positioning.

[0090] Example 3

[0091] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: Step S3 includes the following steps;

[0092] S31, based on the extracted signal feature set, using the signal strength change rate ROC-RSSI and the environmental noise coefficient ENC, correct the received signal strength RSSI to obtain the received signal correction value XRSSI i ;

[0093] Received signal correction value XRSSI i Calculated by the following algorithm formula:

[0094]

[0095] 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;

[0096] S32, then correct the received signal value XRSSI according to the obtained i Combined with the path correction factor MFF in the signal feature set, the node distance di is calculated;

[0097] The node distance di is calculated using the following algorithm formula:

[0098]

[0099] 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 index, and the specific value is obtained by the user through field testing. MFF i represents the path correction factor of the i-th signal.

[0100] 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 positioning signals. 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, promoting the improvement of the intelligence level of power systems.

[0101] Example 4

[0102] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: step S4 includes the following steps;

[0103] S41, calculating and obtaining the horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z of the smart meter according to the signal horizontal transmission angle θ and the signal horizontal plane angle φ extracted from the signal feature set;

[0104] The horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z are calculated by the following algorithm formula:

[0105] x=x0+di·cos(θ);

[0106] y=y0+di·sin(θ);

[0107] z=z0+di·sin(φ);

[0108] In the formula, 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;

[0109] 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 ).

[0110] S43, based on the obtained horizontal axis coordinate x, vertical axis coordinate y and numerical 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 reference node of the electric energy meter is optimized;

[0111] The three-dimensional position (x, y, z) is calculated using the following algorithm:

[0112]

[0113] In the formula, α represents the adjustment parameter of the signal strength change rate, which is used to control the influence of the strength change rate ROC-RSSI on the total error, β 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, argmin x,y,z represents the minimized coordinate value, and N represents the total number of signals sent by the reference nodes.

[0114] In this embodiment, the three-dimensional positioning process of the smart electric energy meter of the method comprehensively considers the horizontal transmission angle of the signal and the horizontal plane angle in the signal feature set, and accurately calculates the horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z of the electric energy meter. Through the integration with the building information model BIM, the actual three-dimensional position (xs, ys, zs) of the reference node is extracted, and it is compared and optimized with the calculated coordinates, which effectively improves the positioning accuracy. This step not only ensures that the three-dimensional position (x, y, z) of the electric energy meter is more accurate, but also further optimizes the positioning result by introducing the signal strength change rate ROC-RSSI and the environmental noise coefficient ENC, significantly reducing the positioning error. This technology that comprehensively considers multiple signal characteristics can provide more stable and accurate positioning results in complex environments. In particular, in practical applications, through the integration with the BIM system, the accuracy of the positioning process has been significantly improved. The application of this technology effectively improves the spatial positioning capability of the smart electric energy meter, provides higher reliability and accuracy for equipment management and data acquisition in the smart grid, and further promotes the improvement of the intelligence and automation level of the power system.

[0115] Example 5

[0116] This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: step S5 includes the following steps;

[0117] 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 a position error value Error, and analyze the error between the obtained positioning and the actually planned smart meter positioning;

[0118] The position error value Error is calculated by the following algorithm formula;

[0119]

[0120] 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 positioning information currently calculated and obtained and the actual planned positioning information;

[0121] The specific evaluation plan is as follows:

[0122] 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 α of the signal strength change rate and the adjustment parameter β of the environmental noise coefficient are increased by 50%, and step S43 is executed again to perform iterative calculation to optimize the positioning information accuracy;

[0123] 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.

[0124] 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 of 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 the smart electric energy meter, and realizes self-correction of the positioning results by introducing an error feedback mechanism in the system. This dynamic adjustment and optimization strategy enables the ability to cope with complex environmental changes in practical applications and reduces the impact of errors. In the end, this method not only improves the positioning accuracy of the electric energy meter, but also enhances the robustness and stability of the system, providing solid support for the efficient operation of the smart grid.

[0125] A positioning module for smart electric energy meters, please refer to Figure 2 , including a reference node module, a smart meter communication module, a data processing module and a positioning signal adjustment module;

[0126] 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;

[0127] The smart 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;

[0128] The data processing module includes a data preprocessing unit and a positioning algorithm unit;

[0129] The data preprocessing unit is used to preprocess the collected signal data and then extract features to obtain a signal feature set;

[0130] The positioning algorithm unit is used to implement various positioning algorithms based on the acquired signal feature set and integrate multiple parameters for optimization calculation;

[0131] The positioning signal adjustment module is used to correct and adjust the positioning signal to optimize the impact of environmental noise and multipath effects.

[0132] An intelligent electric energy meter comprises a metering core, a regulating core and a positioning module.

[0133] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the 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 in that: The following steps are involved: Step S1, start the signal receiving module of the smart electric energy meter, collect the signal data sent by the reference node in real time, and transmit the collected signal data to the data processing center; Step S2: After preprocessing 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; 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 S4: Based on the acquired signal feature set and in combination with the acquired node distance di, the horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z of the electric energy meter are calculated respectively, and then the signal feature set is introduced to perform correlation calculation according to the acquired horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z 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 preset the error threshold W and compare and evaluate it with the obtained position error value Error to analyze the accuracy of the current three-dimensional position (x, y, z).

2. A method for locating a smart electric energy meter according to claim 1, characterized in that: The step S1 comprises the following steps: S11. At the site where the smart meter is installed, multiple reference nodes are deployed. The reference nodes are WIFI access points that send wireless signals in real time. The smart 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 transmission time t arrival , signal receiving time t send , signal horizontal transmission angle θ, signal horizontal plane angle φ, 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. A method for locating a smart electric energy meter according to claim 2, characterized in that: The step S2 comprises the following steps: S21, receiving signal data in real time through a data processing center, identifying missing values ​​of the received signal data, filling the missing values ​​using an interpolation method, standardizing the 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 standardization processing, and then summarizing to obtain a signal data group; S22, the signal strength RSSI and signal transmission time t in the signal data group are further analyzed. arrival and signal reception 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 using the time interval t to record the continuous signal strength RSSI (t) to obtain the signal strength change rate ROC-RSSI, and the signal strength change rate ROC-RSSI is calculated by the following algorithm formula; Where t-1 represents the signal strength at the next time interval t; S222: The signal arrival time TOA sends a wireless signal through a reference node, and records the signal sending time t arrival , the wireless communication module receives the signal in real time and records the signal receiving time t send , and then perform difference calculation to obtain the signal arrival time TOA; the signal arrival time TOA is calculated and obtained 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. A method for locating a smart electric energy meter according to claim 3, characterized in that: The step S3 comprises the following steps: S31, based on the extracted signal feature set, using the signal strength change rate ROC-RSSI and the environmental noise coefficient ENC, correct the received signal strength RSSI to obtain the received signal correction value XRSSI i ; The received signal correction value XRSSI i Calculated by 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, then correct the received signal value XRSSI according to the obtained i Combined with the path correction factor MFF in the signal feature set, the node distance di is calculated; The node distance di is calculated by 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.

5. The method for locating a smart electric energy meter according to claim 1, characterized in that: The step S4 comprises the following steps: S41, calculating and obtaining the horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z of the smart meter according to the signal horizontal transmission angle θ and the signal horizontal plane angle φ extracted from the signal feature set; The horizontal axis coordinate x, the vertical axis coordinate y and the numerical axis coordinate z are calculated by the following algorithm formula: x=x0+di·cos(θ); y=y0+di·sin(θ); z=z0+di·sin(φ); In the formula, 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 ).

6. A method for locating a smart electric energy meter according to claim 5, characterized in that: S43, based on the obtained horizontal axis coordinate x, vertical axis coordinate y and numerical 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 reference node of the electric energy meter is optimized; The three-dimensional position (x, y, z) is calculated by the following algorithm formula: In the formula, α represents the adjustment parameter of the signal strength change rate, which is used to control the influence of the strength change rate ROC-RSSI on the total error, β 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, argmin x,y,z represents the minimized coordinate value, and N represents the total number of signals sent by the reference nodes.

7. A method for locating a smart electric energy meter according to claim 6, 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 a position error value Error, and analyze the error between the obtained positioning and the actually planned smart meter positioning; The position error value Error is calculated by the following algorithm formula; 8. A method for locating a smart electric energy meter according to claim 7, 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, and analyze the error between the positioning information currently calculated and obtained 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 α of the signal strength change rate and the adjustment parameter β of the environmental noise coefficient are increased by 50%, and 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.

9. A positioning module for a smart electric energy meter, characterized in that: It includes reference node module, smart 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, and performs time synchronization; The smart meter communication module processes the received signal in real time by setting a multi-mode receiver, and sets 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 positioning algorithm unit is used to implement multiple positioning algorithms based on the acquired signal feature set, and integrate multiple parameters for optimization calculation; The positioning signal adjustment module is used to correct and adjust the positioning signal and optimize the influence of environmental noise and multipath effect.

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

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