An electric energy meter and concentrator with enhanced communication efficiency

Abstract

The present invention relates to the field of data processing technology, and specifically to an electric energy meter and concentrator for enhancing communication efficiency. The electric energy meter comprises: a data acquisition unit for collecting current operational data from multiple electric energy meters, the operational data including the size of data to be uploaded by the electric energy meters, communication delay, and the load size of each node in the channel in which each electric energy meter is located; and a data processing unit for receiving the operational data output by the acquisition unit and screening out electric energy meters for uploading data. The present invention constructs a fitness function based on the current operational data of each electric energy meter, and selects the most suitable particle based on the fitness of multiple particles with random positions, thereby obtaining a data transmission scheme for electric energy meters with high communication efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more particularly to an electric energy meter and a concentrator with enhanced communication efficiency. Background Art

[0002] In modern power systems, energy metering systems utilize a three-tiered architecture: terminal-concentrator-master station. Energy meters, acting as the terminal sensing layer, collect user electricity usage data. Concentrators aggregate regional data using multi-mode communication modules such as power line carriers and micropower wireless. Master stations integrate global data across the entire region, leveraging high-speed channels like fiber optics and 4G. This architecture forms the underlying core of the Power Internet of Things (PoI), directly supporting key services such as electricity bill settlement, load forecasting, and grid regulation. However, as smart grids evolve toward high concurrency and low latency, traditional communication mechanisms are increasingly facing significant bottlenecks in dynamic scheduling, interference mitigation, and real-time performance. For example, Chinese patent application publication number CN118280094A discloses an energy meter and concentrator with enhanced carrier communication efficiency. The meter comprises a power quality monitoring module, a circuit signal processing module, a filter parameter selection module, a filter adjustment module, and a web database. By setting monitoring cycles, the system monitors circuit quality data in real time on power lines and analyzes the power line status during the monitoring cycle to determine filter operating parameters and subsequently adjust the filters.

[0003] However, existing technologies use particle swarm algorithms to find intelligent scheduling solutions for a group of energy meter data uploads. Because the amount of data uploaded by a single energy meter is relatively small, traditional particle swarm algorithms aim to find the global optimal solution, which takes a long time to converge. The resulting intelligent scheduling solution cannot respond to dynamic changes in the communication environment, resulting in reduced energy meter communication efficiency. Improving the communication efficiency of energy meters and concentrators can significantly improve data processing speed and accuracy, helping to enhance the intelligence level of the power grid. Summary of the Invention

[0004] In order to solve the technical problem that the traditional particle swarm algorithm takes too long to converge, resulting in reduced communication efficiency of the electric energy meter, the present invention provides solutions in the following aspects.

[0005] In a first aspect, an electric energy meter for enhancing communication efficiency comprises: a data acquisition unit for acquiring operation data of a plurality of electric energy meters at a current moment, wherein the operation data comprises: the size of data to be uploaded by the electric energy meter, communication delay, and the load size of each node of the channel in which the electric energy meter is located; a data processing unit for receiving the operation data output by the acquisition unit and screening out the electric energy meter for uploading data, comprising: constructing a fitness function and a predetermined number of particles; calculating the fitness of each particle, wherein the fitness of the particle is related to the value of each dimension of the particle's position; and The optimization algorithm is iterated, and particles are randomly removed in each round of iteration, wherein the random particle removal includes: calculating the closeness between two particles and grouping the particles according to the closeness between each pair of particles to obtain multiple groups of close particles, selecting the midpoint of each group of close particles as the monitoring particle, and when the fitness of the monitoring particle in a group of close particles is greater than a predetermined multiple of the fitness of all particles in the group of close particles, randomly removing particles that do not belong to the group of close particles; after completing the predetermined number of iterations, the particle with the highest fitness is obtained and recorded as the optimal particle; the particle with the position of the optimal particle is selected as the monitoring particle. i The dimension is i The upload probability of each electric energy meter is selected, and the data of the electric energy meter with an upload probability greater than a preset probability threshold is selected for uploading, where i is a positive integer.

[0006] The present invention utilizes a particle swarm optimization (PSO) algorithm to search for an intelligent scheduling solution for a group of electricity meters with relatively high communication efficiency. Specifically, the present invention constructs a fitness function based on the current size of data to be uploaded by each electricity meter, the current communication delay, and the load of each communication node. The most suitable particle (i.e., the optimal particle) is selected based on the fitness of multiple particles with random positions, thereby achieving a data transmission solution for the electricity meters with high communication efficiency. When searching for the optimal solution using the particle swarm algorithm, the present invention accelerates particle aggregation and discards some of the remaining particles to quickly find the possible optimal solution, thereby accelerating particle convergence and more quickly finding the optimal intelligent scheduling solution.

[0007] Preferably, obtaining the position of the particle includes: randomly selecting a predetermined number of particles in the solution space, wherein the value range of each dimension of the position of the particle is [0, 1], and the speed of the particle is a random number within a preset value range; iterating through the particle swarm optimization algorithm includes: updating the speed of each particle according to a preset speed update formula; updating the position of each particle according to a preset position update formula.

[0008] Preferably, obtaining the fitness of each particle includes: taking the position of the particle as the first i The dimension is iThe upload probability of each electric energy meter corresponding to the particle and the operation data are input into the fitness function, and the function value output by the fitness function is obtained, which is recorded as the fitness of the particle.

[0009] Preferably, the fitness function is: ,in, L The value of is fitness, For the i The linearly normalized value of the upload probability of each electric energy meter at the current moment, , For the i The linear normalized value of the amount of data to be uploaded by each electric energy meter, For the i The linearly normalized value of the communication delay of each electric energy meter at the current moment, The transmission line of all electric energy meters with upload probability greater than the preset threshold at the current moment a The value of linear normalization of the load size of each node, D () is the variance function.

[0010] The fitness function constructed in the present invention can output the fitness according to the upload probability and operation data of each electric energy meter, so that the output fitness can more accurately evaluate the communication efficiency of data upload for multiple electric energy meters.

[0011] Preferably, randomly removing particles that do not belong to the group of approaching particles includes: sorting the particles that do not belong to the group of approaching particles, and then removing particles whose sequence numbers are multiples of a preset number.

[0012] Preferably, i Particles and j The particle in k The calculation formula for the stability of the change during round iteration is:

[0013] ;

[0014] in, For the i Particles and j The particle in k Stability of changes during round iterations, For the i Particles and j The particle in k -2 iteration intervals, For the i Particles and j The particle in k The spacing between round iterations, norm () is the standard normalization function, i、 j 、 k Is a positive integer.

[0015] Preferably, i Particles and j The particle in k The calculation formula for the stability of the change during round iteration is:

[0016] ;

[0017] in, For the i Particles and j The particle in k -1 iteration spacing, For the i Particles and j The particle in k Stability of changes during round iterations.

[0018] Preferably, in k During the round iteration, the formula for calculating the closeness between the i-th particle and the j-th particle is: ,in For the i Particles and j The particle in k The degree of convergence during round iterations.

[0019] By judging the stability and variation range between particles, the present invention can flexibly adjust the search direction of particles, reduce difficulties, and promote the search of particle groups in a global range.

[0020] Preferably, the distance between two particles is the Manhattan distance between the two particles, wherein in the kth iteration, the Manhattan distance between the i-th particle and the j-th particle is calculated The formula is:

[0021] ;

[0022] in, For the i The value of the first dimension corresponding to the particles, For the i The value of the second dimension corresponding to the particles, For the i The particle corresponding to N -1 dimension value, For the i The particle corresponding to N The value of the dimension, For the jThe value of the first dimension corresponding to the particles, For the j The value of the second dimension corresponding to the particles, For the j The particle corresponding to N -1 dimension value, For the j The particle corresponding to N The value of the dimension.

[0023] The present invention uses the Manhattan distance between the two particles as the distance between the two particles, so that the process of calculating the distance between the particles is simpler, thereby reducing the calculation cost.

[0024] In a second aspect, a concentrator includes an electronic device and a computer-readable storage medium; the electronic computer device includes a processor and a memory, and the memory stores a computer-readable program that can be executed by the processor; when the processor executes the computer program, it implements a unit of an electric energy meter with enhanced communication efficiency as described in any one of the above invention contents.

[0025] The beneficial effects of the present invention are:

[0026] This invention uses the particle swarm optimization (PSO) algorithm to optimize the intelligent scheduling scheme for a group of energy meters, thereby improving communication efficiency during data transmission. By constructing a precise fitness function that comprehensively considers the energy meter's upload probability, data size, communication latency, and load, it can more accurately assess and optimize transmission efficiency. This method accelerates particle aggregation and quickly finds the optimal solution, achieving efficient intelligent scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a structural block diagram of an electric energy meter with enhanced communication efficiency according to an embodiment of the present invention;

[0028] Figure 2 is a flowchart of the steps of a method for screening electric energy meters for uploading data according to an embodiment of the present invention;

[0029] Figure 3 FIG. 4 is a block diagram schematically showing the structure of an electronic device according to this embodiment. DETAILED DESCRIPTION

[0030] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] Figure 1 The figure is a structural block diagram of an electric energy meter with enhanced communication efficiency according to an embodiment of the present invention.

[0032] like Figure 1As shown, an electric energy meter with enhanced communication efficiency includes m electric energy meters, a data acquisition unit, and a data processing unit, where m is a positive integer.

[0033] Among them, the data acquisition unit is used to collect the operating data of each electricity meter at the current moment, and the operating data includes: the size of the data to be uploaded by the electricity meter, the communication delay, and the load size of each node in the channel where each electricity meter is located; the data processing unit is used to receive the operating data output by the acquisition unit and filter out the electricity meters for uploading data.

[0034] Figure 2 4 is a flowchart of the steps of a method for screening electric energy meters for uploading data according to an embodiment of the present invention.

[0035] like Figure 2 As shown, the method for screening out electric energy meters for uploading data includes steps S1 to S5.

[0036] Step S1: Construct a fitness function and a predetermined number of particles.

[0037] The fitness function is used to determine the degree of adaptability based on the upload probability and operating data of each electricity meter. The number of particle position dimensions is equal to the number of electricity meters. The particle position has multiple dimensions, and the number of dimensions is equal to the number of electricity meters. Each dimension has a value range of [0, 1]. The particle speed is a random number within a preset value range.

[0038] In one embodiment, a predetermined number of particles are randomly selected from the solution space. The upload probability for each dimension and each energy meter ranges from [0, 1], with a minimum unit value of 0.01. 300 initial particles are randomly selected from the solution space, and the initial velocity of all initial particles in each dimension is randomly generated within the range [0, 0.1].

[0039] It should be noted that generating multiple particles with random positions, i.e. generating multiple groups of energy meter scheduling schemes, means that the upload probability of each energy meter corresponding to any particle is random due to its random position.

[0040] In one embodiment, the fitness function is:

[0041] .

[0042] in, L The value of is fitness, For the i The linearly normalized value of the upload probability of each electric energy meter at the current moment, , For the i The linear normalized value of the amount of data to be uploaded by each electric energy meter, For thei The linearly normalized value of the communication delay of each electric energy meter at the current moment, The transmission line of all electric energy meters with upload probability greater than the preset threshold at the current moment a The value of linear normalization of the load size of each node, D () is the variance function.

[0043] It should be noted that Indicates the unevenness of communication network load; Indicates the i The urgency of uploading the current time of each electric energy meter, if the current time is the same as the i The higher the difference between the last upload time of each electric energy meter is, the more it is necessary to upload at the current moment, and the higher the urgency is. At this time, the fitness function value (i.e. L The higher the value of ); when the communication network load unevenness is lower, the probability of all electric energy meters with high urgency, large data volume and low latency uploading is high, the higher the fitness function value.

[0044] Step S2: Calculate the fitness of each particle.

[0045] The fitness of a particle is related to the values of each dimension of the particle's position. Each particle inputs its position and the operating data of the electric energy meter corresponding to the position into the fitness function, and the obtained value is the fitness of the particle.

[0046] In one embodiment, obtaining the fitness of each particle includes: i The dimension is i The upload probability of each electric energy meter corresponding to the particle and the operation data are input into the fitness function, and the function value output by the fitness function is obtained, which is recorded as the fitness of the particle.

[0047] Step S3: Iterate the particle swarm optimization algorithm and randomly remove particles in each round of iteration.

[0048] The random removal of particles includes: calculating the degree of proximity between two particles and grouping the particles according to the degree of proximity between each pair of particles to obtain multiple groups of approaching particles, selecting the midpoint of each group of approaching particles as a monitoring particle, and when the fitness of a monitoring particle in a group of approaching particles is greater than a predetermined multiple of the fitness of all particles in the group of approaching particles, randomly removing particles that do not belong to the group of approaching particles.

[0049] Considering that the amount of data uploaded by a single electricity meter is relatively small, the traditional particle swarm algorithm aims to find the global optimal solution, but the convergence time is too long, and the obtained intelligent scheduling solution cannot respond to the dynamic changes of the communication environment in a timely manner, resulting in reduced communication efficiency of the electricity meter. To solve the above problems, the data processing unit in the present invention randomly removes particles in each round of particle iteration, including: calculating the reduction amplitude and spacing change stability between two particles, wherein in each round of iteration, the reduction amplitude between two particles is related to the spacing between the two particles in the iteration of the round and the previous rounds, and the spacing change stability between two particles is related to the spacing change amplitude in the iteration of the round and the previous rounds; taking the product of the reduction amplitude and spacing change stability between the two particles as the proximity of the two particles, determining multiple groups of approaching particles, wherein the proximity of any two particles in a group of approaching particles is greater than a preset proximity threshold; determining the midpoint of each group of approaching particles as a monitoring particle; for a group of approaching particles, when the fitness of the monitoring particle is greater than a predetermined multiple of the maximum fitness of all particles in the group of approaching particles, randomly removing particles that do not belong to the group of approaching particles.

[0050] It should be noted that since the amount of data uploaded by a single electricity meter is relatively small, and the amount of data uploaded by all electricity meters is generally similar, and the intelligent scheduling scheme for uploading electricity meter data has high real-time requirements, when searching for the optimal solution through the particle swarm algorithm, it has a high tolerance for local optimal solutions, so it can accelerate particle aggregation and discard some of the remaining particles to quickly find the possible optimal solution, thereby achieving the effect of accelerating particle convergence and finding the best intelligent scheduling scheme faster.

[0051] In one embodiment, i Particles and j The particle in k The calculation formula for the stability of the change during round iteration is:

[0052] ;

[0053] in, For the i Particles and j The particle in k Stability of changes during round iterations, For the i Particles and j The particle in k -2 iteration intervals, For the i Particles and j The particle in k The spacing between round iterations, norm () is the standard normalization function.

[0054] It should be noted that, the smaller the distance between two particles in the current iteration is compared to the distance between two particles in the two previous iterations, the greater the reduction range of the two particles.

[0055] ;

[0056] in, For the i Particles and j The particle in k -1 iteration spacing, For the i Particles and j The particle in k Stability of changes during round iterations.

[0057] It should be noted that when the absolute difference between the reduction degree of the distance between the two particles in the previous iteration compared to the distance in the previous iteration, and the reduction degree of the distance between the two particles in the current iteration compared to the distance in the previous iteration is higher, the change of the distance between the two particles is more unstable.

[0058] In one embodiment, k During the round iteration, the formula for calculating the closeness between the i-th particle and the j-th particle is: ,in For the i Particles and j The particle in k The degree of convergence during round iterations.

[0059] It should be noted that since particle pairs with high proximity represent particles that are gradually approaching, in order to effectively find possible combinations of particles that may be gathering, it is necessary to first screen out particle pairs with high proximity. The present invention selects all particle pairs with a proximity higher than the proximity threshold as approaching particle pairs. For any approaching particle pair, when there is any particle that can form an approaching particle pair with both particles in the approaching particle pair, the particle is included in the combination of the two particles in the approaching particle pair, that is, the proximity between any two particles in a group of approaching particles is greater than the preset approach threshold. Traverse and obtain all particle combinations that can be formed by all approaching particle pairs. The empirical value of the approach threshold is 0.7.

[0060] It should be noted that, when the mean value of the closeness between any two particles in all particles in the particle combination is higher, the aggregation possibility of the particle combination is higher.

[0061] In one embodiment, randomly removing particles that do not belong to the group of approaching particles includes sorting the particles that do not belong to the group of approaching particles and removing particles whose sequence numbers are multiples of a preset number. For example, after sorting the particles that do not belong to the group of approaching particles by generation time, particles whose sequence numbers are multiples of 2 are removed.

[0062] In another implementation, setting the empirical removal rate α ,in All particle combinations are sorted from highest to lowest by their aggregation probability. The midpoints of all particles in all particle combinations are sequentially selected to generate monitoring particles. The fitness values of the monitoring particles are then obtained. If the fitness value of a monitoring particle is higher than 0.8 times (empirical coefficient) the highest fitness value of all particles other than itself, other particles that do not belong to the current particle combination are randomly removed. If the number of remaining particles in a subsequent particle combination before generating a monitoring particle is less than or equal to 1 due to the removal of particles from a particle combination with a high aggregation probability, the steps of generating monitoring particles and removing other particles are skipped to further reduce the computational complexity and accelerate particle convergence. It should be noted that since the midpoint of a particle combination with a higher aggregation probability has a higher probability of a local optimal solution, while other particles are less important in comparison, to accelerate particle convergence, the midpoint of a particle combination with a high aggregation probability should be preferentially selected to generate monitoring particles. If the fitness value of a monitoring particle is high, the probability of an optimal solution is high, and the importance of other particles is reduced. Some of the remaining particles can be randomly removed to accelerate particle swarm convergence.

[0063] In one embodiment, the spacing between two particles is the Manhattan distance between the two particles, where k In the iteration, calculate the i Particles and j The Manhattan distance between particles The formula is:

[0064] ;

[0065] in, For the i The value of the first dimension corresponding to the particles, For the i The value of the second dimension corresponding to the particles, For the i The particle corresponding to N -1 dimension value, For the i The particle corresponding to N The value of the dimension, For the j The value of the first dimension corresponding to the particles, For the j The value of the second dimension corresponding to the particles, For the j The particle corresponding to N -1 dimension value, For thej The particle corresponding to N The value of the dimension.

[0066] It should be noted that since clustered particles appear close together during iteration, it is necessary to first calculate the distance between any two particles in each iteration. Since the meter group contains a large number of meters, Manhattan distance should be used to calculate the distance to avoid spatial sparsity caused by the curse of dimensionality. Manhattan distance is a commonly used method for measuring the distance between two points.

[0067] Step S4: After completing a predetermined number of iterations, the particle with the highest fitness is obtained and recorded as the optimal particle.

[0068] It should be noted that the data upload probability of each electric energy meter in the electric energy meter group corresponding to the position of the particle with the highest fitness value when the particle swarm converges is obtained through iteration. The convergence condition of this embodiment is the preset maximum iteration rounds.

[0069] Step S5: The optimal particle position i The dimension is i The upload probability of each electric energy meter is calculated, and the data of the electric energy meter with an upload probability greater than the preset probability threshold is selected for uploading. i Is a positive integer.

[0070] It should be noted that the electricity meters with an upload probability greater than the threshold will be selected for data upload to avoid network congestion caused by too many electricity meters uploading data at the same time.

[0071] Figure 3 FIG. 4 is a block diagram schematically showing the structure of an electronic device according to this embodiment.

[0072] The present invention also provides a concentrator. The concentrator includes an electronic device and a computer-readable storage medium. Figure 3 As shown, the electronic device includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the unit of the electric energy meter with enhanced communication efficiency is implemented.

[0073] In the present invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of, accessible to, or connectable to a device. Any application or module described in the present invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

Claims

1. An electric energy meter with enhanced communication efficiency, characterized in that: include: A data acquisition unit, which is used to collect the operating data of multiple electric energy meters at the current moment, wherein the operating data includes: the size of the data to be uploaded by the electric energy meter, the communication delay, and the load size of each node of the channel where the electric energy meter is located; A data processing unit, which is used to receive the operating data output by the acquisition unit and filter out the electric energy meters for uploading the data, including: constructing a fitness function and a predetermined number of particles; The fitness function is: ,in, L The value of is fitness, For the i The linearly normalized value of the upload probability of each electric energy meter at the current moment, , For the i The linear normalized value of the amount of data to be uploaded by each electric energy meter, For the i The linearly normalized value of the communication delay of each electric energy meter at the current moment, The transmission line of all electric energy meters with upload probability greater than the preset threshold at the current moment a The value of linear normalization of the load size of each node, D () is the variance function, Indicates the urgency of uploading the i-th electric energy meter at the current moment, Indicates the difference between the current time and the last upload time of the i-th electric energy meter; Calculating the fitness of each particle, where the fitness of a particle is related to the values of each dimension of the particle's position; iterating through a particle swarm optimization algorithm, and randomly removing particles in each round of iteration, where the random particle removal includes: calculating the degree of proximity between two particles and grouping particles according to the degree of proximity between each pair of particles to obtain multiple groups of approaching particles, selecting the midpoint of each group of approaching particles as a monitoring particle, and when the fitness of a monitoring particle in a group of approaching particles is greater than a predetermined multiple of the fitness of all particles in the group of approaching particles, randomly removing particles that do not belong to the group of approaching particles; after completing a predetermined number of iterations, obtaining a particle with the highest fitness, which is recorded as the optimal particle; The optimal particle position i The dimension is i The upload probability of each electric energy meter is selected, and the data of the electric energy meter with an upload probability greater than a preset probability threshold is selected for uploading, where i is a positive integer; No. i Particles and j The particle in k The calculation formula for the reduction in round iteration is: ;in, For the i Particles and j The particle in k The reduction in the round iteration, For the i Particles and j The particle in k -2 iteration intervals, For the i Particles and j The particle in k The spacing between round iterations, norm () is the standard normalization function, i 、 j 、 k is a positive integer; No. i Particles and j The particle in k The calculation formula for the stability of the change during round iteration is: ;in, For the i Particles and j The particle in k -1 iteration spacing, For the i Particles and j The particle in k Stability of changes during round iterations; In the k During the round iteration, the formula for calculating the closeness between the i-th particle and the j-th particle is: ,in For the i Particles and j The particle in k The degree of convergence during round iterations.

2. The electric energy meter with enhanced communication efficiency according to claim 1, characterized in that: Obtaining the position of the particle includes: randomly selecting a predetermined number of particles in the solution space, wherein the value range of each dimension of the position of the particle is [0, 1], and the speed of the particle is a random number within a preset value range; iterating through the particle swarm optimization algorithm includes: updating the speed of each particle according to a preset speed update formula; and updating the position of each particle according to a preset position update formula.

3. The electric energy meter with enhanced communication efficiency according to claim 1, characterized in that: Obtaining the fitness of each particle includes: taking the position of the particle as the first i The dimension is i The upload probability of each electric energy meter corresponding to the particle and the operation data are input into the fitness function, and the function value output by the fitness function is obtained, which is recorded as the fitness of the particle.

4. The electric energy meter with enhanced communication efficiency according to claim 1, characterized in that: Randomly removing particles that do not belong to the group of approaching particles includes: sorting the particles that do not belong to the group of approaching particles, and then removing particles whose sequence numbers are multiples of a preset number.

5. The electric energy meter with enhanced communication efficiency according to claim 1, characterized in that: The distance between two particles is the Manhattan distance between the two particles, where in the kth iteration, the Manhattan distance between the i-th particle and the j-th particle is calculated The formula is: ； in, For the i The value of the first dimension corresponding to the particles, For the i The value of the second dimension corresponding to the particles, For the i The particle corresponding to N -1 dimension value, For the i The particle corresponding to N The value of the dimension, For the j The value of the first dimension corresponding to the particles, For the j The value of the second dimension corresponding to the particles, For the j The particle corresponding to N -1 dimension value, For the j The particle corresponding to N The value of the dimension.

6. A concentrator, characterized in that: including an electronic device and a computer-readable storage medium; The electronic device includes a processor and a memory, wherein the memory stores a computer-readable program executed by the processor; when the processor executes the computer program, the data processing unit of the electric energy meter with enhanced communication efficiency as described in any one of claims 1 to 5 implements the function of receiving the operating data output by the collection unit and screening out the electric energy meter for uploading data.

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