Flow control method and system for optimizing ammeter data collection

Abstract

The invention discloses a flow control method and system for optimizing ammeter data collection, and relates to the technical field of power network data collection, and the method comprises the steps: carrying out the first parameter adjustment of a first object according to a first execution state, collecting the feature data of a second object, and carrying out the first adjustment processing of the first object according to the feature data. The second object realizes first target optimization by adjusting own data in real time, and the first object performs first parameter adjustment again according to the first target optimization. According to the method, the cache utilization coefficient and price of the power gateway are dynamically adjusted, so that efficient utilization of cache resources is realized. Through the application of a matching mechanism, the data traffic is adjusted in real time to optimize the use of the power gateway cache, the traffic balance of power network data collection is ensured, and the cache overload or resource waste is avoided. And better effects are achieved in the aspects of system stability and reasonable configuration of power gateway resources.

Description

Technical Field

[0001] The present invention relates to the technical field of power network data collection, and in particular to a flow control method and system for optimizing meter data collection. Background Art

[0002] In recent years, the power network has developed rapidly, and the network scale, data types, and data traffic in the power network have all grown rapidly. However, the resources of the power network are limited. A large amount of data traffic in the power network exceeds the network's processing capacity, which may cause congestion in the power network, resulting in increased data packet delay, decreased network throughput, and even data packet loss. These problems have led to a decrease in the data collection efficiency of the power network, restricting the development of the power industry.

[0003] From the perspective of the power network, to improve the data collection efficiency of the power network, it is necessary to improve the network utility of the power network and maximize the utilization of the power network without causing congestion in the power network. The power gateway in the power network has a cache to store data traffic that exceeds the gateway capacity. On the one hand, if the cache of the power gateway is empty, it means that the network is not fully utilized. On the other hand, if the cache of the power gateway is large, it means that the power network is under-loaded.

[0004] The current flow control method in the power network lacks control over the power gateway cache, which often puts the power gateway in a saturated load state, affecting the performance of the network. Therefore, the current flow control model collected by the meter has certain defects in performance. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: the existing flow control method in the power network lacks control over the power gateway cache, and how to solve the problem of network performance affected by the saturated load state of the power gateway.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a flow control method for optimizing meter data collection, comprising adjusting a first parameter of a first object and a second object according to a first execution state;

[0008] Collecting feature data of the second object and performing a first adjustment process on the first object according to the feature data;

[0009] The second object adjusts its own data in real time and establishes an optimization model in combination with the first object, and optimizes the first target through the optimization model;

[0010] The first object again adjusts the first parameters according to the first target optimization.

[0011] 3. As a preferred solution of the flow control method for optimizing meter data collection described in the present invention, the second object is an intermediate transmission device, the second object processes the data of the first object in the terminal device and transmits it to the first object. The first object achieves the first goal through the second object, the data output by the first object is the input data of the second object, the data is processed by the second object and then returned to the first object, so that the first object readjusts its own output data.

[0012] The first parameter adjustment includes determining an execution state and adjusting parameter data of a first object in a first execution state.

[0013] As a preferred solution of the flow control method for optimizing meter data collection described in the present invention, the first adjustment process includes resetting the adjusted first object parameter data according to the second object characteristic data.

[0014] As a preferred solution of the flow control method for optimizing meter data collection described in the present invention, wherein: the first target optimization includes the second object adjusting the second object characteristic data according to its own data and providing real-time feedback to the first object to realize the matching process.

[0015] As a preferred solution of the flow control method for optimizing meter data collection described in the present invention, the adjustment process includes setting the first object according to the second characteristic data to obtain the first object parameters, and setting the initialized first object according to the first object parameters.

[0016] As a preferred solution of the flow control method for optimizing meter data collection described in the present invention, the matching process includes grading the feature data of the first object and the second object by calculation so that the feature data of the high-priority second object matches the feature data of the high-priority first object.

[0017] The classification process is to establish a model through the first object and the second object. Both parties reach the optimal state through continuous adjustment, maximize resource utilization and improve efficiency.

[0018] As a preferred solution of the flow control method for optimizing meter data collection described in the present invention, the adjustment process includes re-adjusting the first parameter according to the matching process and obtaining excellent parameters with high priority.

[0019] Another object of the present invention is to provide a flow control system for optimizing meter data collection, which can solve the problems of unreasonable data flow control and inefficient cache resource utilization in the current power network through a dynamic control mechanism based on the power gateway cache utilization coefficient and pricing adjustment.

[0020] As a preferred solution of the flow control system for optimizing meter data collection described in the present invention, it includes: an electricity meter node module, a power gateway node module, and a data flow control and optimization module.

[0021] The meter node module is used to send data packets to the main station and adjust the sending rate according to the price and network conditions of the power gateway. The meter node dynamically adjusts the data sending rate according to historical data and real-time network information to ensure efficient and stable data transmission.

[0022] The power gateway node module is used to manage cache data packets and adjust cache utilization, and optimize the use of data transmission paths and cache space by controlling the cache utilization coefficient and price.

[0023] The data flow control and optimization module is used to integrate the information of the meter nodes and the power gateway nodes, adjust the data flow in real time, optimize the use of the power gateway cache, ensure the flow balance of power network data collection, and prevent overload or waste of resources.

[0024] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a flow control method for optimizing data collection of electric meters.

[0025] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a flow control method for optimizing meter data collection.

[0026] Beneficial effects of the present invention: The flow control method for optimizing meter data collection provided by the present invention realizes efficient use of cache resources by dynamically adjusting the power gateway cache utilization coefficient and price. By using the matching mechanism, the data flow is adjusted in real time to optimize the use of the power gateway cache, ensuring the flow balance of power network data collection and avoiding cache overload or resource waste. By controlling the cache utilization coefficient and price, the use of data transmission paths and cache space is optimized. The present invention achieves better results in data transmission efficiency, system stability and reasonable allocation of power gateway resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0028] Figure 1 An overall flow chart of a flow control method for optimizing meter data collection provided in the first embodiment of the present invention.

[0029] Figure 2 An optimization control diagram of a flow control method for optimizing meter data collection provided in a second embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0031] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a flow control method for optimizing meter data collection, comprising:

[0032] S1: adjusting first parameters of a first object and a second object according to a first execution state.

[0033] Specifically: the first parameter adjustment includes determining the execution state and adjusting the parameter data of the first object in the first execution state. In this embodiment: specifically, determining whether it is in the initialization state through the power gateway node and the meter node, and initializing the meter node and the power gateway node when in the initialization state.

[0034] In this embodiment, the first object is a power system, the first execution state is a running state and an initialization state, and further, the first parameter adjustment includes an electric meter node and a power gateway node of the power system. The electric meter node includes voltage, current, power, etc. The power gateway node includes data transmission rate, control signal, etc.

[0035] In an optional embodiment, the first parameter adjustment may also be a parameter reset or a real-time adjustment. For these two adjustment methods, the quantitative characteristic data may include the following current adjustment parameters and voltage fluctuation ranges: parameter reset, current adjustment parameters: maximum current value, current adjustment step, etc. Voltage fluctuation range: voltage adjustment period, maximum voltage fluctuation amplitude, etc. Real-time adjustment, current adjustment parameters: current current value, current change rate, etc. Voltage fluctuation range: instantaneous voltage fluctuation, voltage stability, etc.

[0036] S2: Collect feature data of the second object and perform a first adjustment process on the first object according to the feature data.

[0037] Specifically: the first adjustment process includes resetting the adjusted first object parameter data according to the second object characteristic data. In this embodiment: specifically, the initialized meter node data is set according to the power gateway node monitoring cache space and the number of data packets.

[0038] In this embodiment, the first object is an electric meter node, and the second object is a power gateway node. Further, the characteristic data of the second object includes the number of data packets and the visual cache space of the power gateway node. The number of data packets includes the size of the data packets, etc. The monitoring cache space includes the usage rate of the cache space, etc.

[0039] In an optional embodiment, the second object characteristic data may also be a data delay parameter or a network bandwidth utilization rate. For these two types of characteristic data, the quantitative characteristic data may include the following transmission efficiency indicators and resource usage: data delay parameter, transmission efficiency indicators: average delay time, maximum delay time, etc. Resource usage: delay impact range, delay occurrence frequency, etc. Network bandwidth utilization rate, transmission efficiency indicators: actual bandwidth utilization rate, bandwidth allocation efficiency, etc. Resource usage: bandwidth utilization fluctuation, peak bandwidth demand, etc.

[0040] S3: The second object adjusts its own data in real time and establishes an optimization model in combination with the first object, and optimizes the first target through the optimization model.

[0041] Specifically: the first target optimization includes that the second object adjusts the second object characteristic data according to its own data and provides real-time feedback to the first object to realize the matching process. In this embodiment: specifically, the power gateway node monitors the cache space and the number of data packets, dynamically adjusts the price feedback meter node to ensure the reasonable use of the cache space, and dynamically adjusts the price by calculating the priority of different meter nodes and the cache competition, so that high-priority data packets obtain better transmission conditions.

[0042] In this embodiment, the first target optimization is to dynamically adjust the data transmission priority of the meter node. Further, the first target optimization includes the adjustment strategy and feedback mechanism of the second object feature data. The adjustment strategy includes dynamic price adjustment, priority calculation, etc. The feedback mechanism includes transmission delay monitoring, real-time feedback optimization, etc.

[0043] In an optional embodiment, the first target optimization may also be bandwidth allocation optimization or data congestion relief. For these two optimization methods, the quantitative characteristic data may include the following transmission efficiency parameters and resource usage parameters: bandwidth allocation optimization, transmission efficiency parameters: allocated bandwidth utilization, transmission rate change, etc. Resource usage parameters: bandwidth competition index, peak demand satisfaction rate, etc. Data congestion relief, transmission efficiency parameters: congestion occurrence frequency, data loss rate, etc. Resource usage parameters: cache utilization, network load balancing coefficient, etc.

[0044] S4: The first object adjusts the first parameters again according to the first target optimization.

[0045] Specifically: the adjustment process includes, according to the matching process, re-adjusting the first parameter, and obtaining a better parameter with a high priority. In this embodiment: specifically, the electric meter node calculates a new transmission rate according to the matching result, and completes the allocation of the transmission rate.

[0046] In this embodiment, the adjustment process is to adjust the priority of the first parameter, the first parameter is the sending rate and the sending priority, and further, the adjustment process includes the allocation rule and the priority optimization mechanism of the first parameter. The allocation rule includes dynamic sending rate allocation, bandwidth allocation strategy, etc. The priority optimization mechanism includes priority adjustment based on real-time load, feedback optimization mechanism, etc.

[0047] In an optional embodiment, the adjustment process may also be dynamic parameter allocation or real-time load optimization. For these two adjustment methods, the quantitative characteristic data may include the following load balancing indicators and response time parameters: Dynamic parameter allocation, load balancing indicators: balancing coefficient, network congestion, etc. Response time parameters: task completion time, system delay, etc. Real-time load optimization, load balancing indicators: real-time load balancing degree, packet loss rate, etc. Response time parameters: average delay, peak delay, etc.

[0048] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides an algorithm implementation process of a flow control method for optimizing meter data collection.

[0049] S1: Meter node implementation process;

[0050] Initialize a sending rate greater than or equal to 0 for sending data packets to the master station and record the sending rate. l And the number of power gateways required for its own transmission path, calculate the total data transmission path price p s , the calculation formula is p s =∑p l According to the total data transmission path price and utility function U s (x s ) and calculation rules to calculate the new sending rate x s After receiving the new power gateway price, return to the calculation path price link.

[0051] Furthermore, the master station is the destination of the data packets sent by the meter node, and the sent data packets reach the master station through the power gateway node. The utility function is a monotonically increasing, quadratically differentiable concave function, which describes the meter node sending rate, and its setting rule is Among them U s ' -1 Indicates U s ' is the inverse function of .

[0052] S2: Power gateway node implementation process

[0053] Initialize a power gateway node price greater than 0, record the price to the power gateway node, initialize the number of packets cached by the power gateway to 0, and record the number of packets to the power gateway node. Within a time interval Δt, according to the number of packets cached by the power gateway, l and the upper limit of the packet space cached by the power gateway Q l , get the utilization coefficient η of the power gateway cache. Count the number of data packets q entering the power gateway within Δt l , the number of packets cached by the power gateway and the rate capacity of the power gateway c l The power gateway price is worked out based on the power gateway cache utilization coefficient. The new power gateway price is sent to the meter node. Within the new Δt, the power gateway cache utilization coefficient link is returned and re-executed in sequence.

[0054] Furthermore, the utilization coefficient of the power gateway cache is calculated as follows:

[0055]

[0056] The pricing rules for the power gateway are as follows:

[0057] p l (t+Δt)=max{(p l (t)-β(η(Q l-B l )+c l Δt-q l )),0}

[0058] Where β represents the step size of price adjustment.

[0059] S3: Introducing the game theory model in the first objective optimization process can be used to simulate the competition and cooperation between the meter nodes and the power gateway nodes, and maximize the efficiency and fairness of the entire power gateway system by formulating pricing strategies. The price adjustment strategy can be solved by using Nash equilibrium or leader-follower game.

[0060] Specifically, assume that the participants are players: power gateway nodes and meter nodes. Strategy space: pricing strategy of power gateway and sending rate of meter nodes (i.e., the result of competition). Utility function: The goal of power gateway is to maximize the reasonable utilization of cache and system efficiency, while the goal of meter nodes is to minimize transmission cost and ensure the priority transmission of high-priority data.

[0061] In the framework of game theory, the power gateway node dynamically adjusts the price according to the competition of the meter nodes and the status of its own cache space. Specifically, the power gateway node determines the pricing strategy by calculating the "bid" (i.e., the sending rate) of each meter node and the load of network traffic.

[0062] The game model constructed through pricing adjustment is as follows.

[0063]

[0064] P gateway is the pricing of the power gateway node, λ is the adjustment factor that controls the sensitivity of price changes, C used is the current cache usage space of the power gateway, C threshold is the capacity threshold of the power gateway cache. Exceeding this threshold may cause resource overload. i is the sending rate of the meter node i, and N is the number of meter nodes connected to the power gateway node.

[0065] Example 3 is an embodiment of the present invention, which provides a flow control method for optimizing meter data collection. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0066] First, this embodiment experimentally verifies the flow control method for optimizing meter data collection, specifically testing the pricing mechanism and data flow optimization effect between the power gateway and the meter node. In this experiment, the pricing strategy of the power gateway is dynamically adjusted through game theory models (especially Nash equilibrium and leader-follower game), so as to achieve efficient utilization of the cache space of the power gateway and optimization of data transmission of the meter node. The main parameters involved in the experiment include the cache space utilization of the power gateway node, the pricing of the power gateway node, and the sending rate of the meter node.

[0067] Initial settings:

[0068] Assume that the system consists of 10 electricity meter nodes and 1 power gateway node. The data transmission rate of each electricity meter node is S i and the pricing of the power gateway node P gateway It needs to be adjusted according to the real-time network status. The upper limit of the cache space of the power gateway node is 2000MB, and the current cache usage is 1500MB. The system will simulate the data transmission process under different load conditions and calculate the pricing strategy of the power gateway based on the game theory model.

[0069] According to the status of the power gateway node and the meter node, the price of the power gateway node and the sending rate of the meter node are initialized. The price of the power gateway node is initialized to 0.05 units / MB, and the sending rate of the meter node is initialized to 200MB / s.

[0070] The cache space utilization of the power gateway node and the sending rate of the meter node are collected. Based on the collected feature data, the power gateway node will dynamically adjust its price, and the meter node will adjust its sending rate according to the pricing strategy of the power gateway.

[0071] In the experiment, the power gateway node adjusted its pricing in real time according to the sending rate and cache usage of the meter node to optimize the transmission conditions of data traffic and the overall efficiency of the system.

[0072] The meter node provides feedback based on the pricing of the power gateway node, adjusts its sending rate, and continues to optimize the system based on the new strategy. The adjusted results are used to calculate new pricing strategies and sending rates, thereby achieving a reasonable allocation of resources.

[0073] Through the above experimental steps, the system can optimize the use of the cache space of the power gateway node and ensure that the high-priority data packets of the meter node are transmitted first.

[0074] Table 1 Adjustment data table

[0075]

[0076]

[0077] It can be seen from the experimental data table that by using the optimization method in the present invention, the cache space of the power gateway node is reasonably utilized, the data transmission delay is gradually reduced, and the overall transmission efficiency is improved. The specific analysis is as follows:

[0078] As the usage of cache space gradually increases, the pricing of the power gateway node increases. It gradually increases from 0.05 units / MB in the initial state to 0.07 units / MB in the final state. This price adjustment shows that as the usage of cache space increases, the power gateway node will adjust the pricing according to the game theory model, thereby prompting the meter node to optimize its sending rate and ensure that resources are reasonably allocated. The cache utilization rate of the power gateway node gradually increases from 75% in the initial state to 100%. As the cache utilization rate increases, the power gateway node will adjust the pricing to avoid cache overload and ensure that the priority and transmission efficiency of the data packet are reasonably balanced. As the price of the power gateway node is adjusted, the sending rate of the meter node gradually decreases. Through the introduction of the game theory model, the meter node adjusts its sending rate under limited resources, optimizing the overall data flow control of the system. With the optimization of power gateway pricing and cache space management, the data transmission delay gradually decreases, and finally in the final adjustment state, the data transmission delay is reduced to 110 milliseconds and the transmission efficiency is increased to 780Mbps. Through the application of the game theory model, high-priority data packets obtain better transmission conditions, thereby improving the overall transmission efficiency.

[0079] Embodiment 4 is an embodiment of the present invention, which provides a flow control system for optimizing the collection of electricity meter data, including an electricity meter node module, a power gateway node module, and a data flow control and optimization module.

[0080] The meter node module is used to send data packets to the main station and adjust the sending rate according to the price and network conditions of the power gateway. The meter node dynamically adjusts the data sending rate according to historical data and real-time network information to ensure efficient and stable data transmission.

[0081] The power gateway node module is used to manage cache data packets and adjust cache utilization, and optimize the use of data transmission paths and cache space by controlling the cache utilization coefficient and price.

[0082] The data flow control and optimization module is used to integrate the information of the meter nodes and the power gateway nodes, adjust the data flow in real time, optimize the use of the power gateway cache, ensure the flow balance of power network data collection, and prevent overload or waste of resources.

[0083] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0084] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0085] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0086] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A flow control method for optimizing meter data collection, characterized in that: include: Performing first parameter adjustment on the first object and the second object according to the first execution state; Collecting feature data of the second object and performing a first adjustment process on the first object according to the feature data; The second object adjusts its own data in real time and establishes an optimization model in combination with the first object, and optimizes the first target through the optimization model; The first object again adjusts the first parameters according to the first target optimization.

2. The flow control method for optimizing the collection of electric meter data according to claim 1, characterized in that: The second object is an intermediate transmission device, and the second object processes the data of the first object in the terminal device and transmits it to the first object; The first object achieves the first goal through the second object, the output data of the first object is the input data of the second object, the data is processed by the second object and then returned to the first object, so that the first object readjusts its own output data; The first parameter adjustment includes determining an execution state and adjusting parameter data of a first object in a first execution state.

3. The flow control method for optimizing the collection of electric meter data as claimed in claim 2, characterized in that: The first adjustment process includes resetting the adjusted first object parameter data according to the second object feature data.

4. The flow control method for optimizing the collection of electric meter data as claimed in claim 3, characterized in that: The first target optimization includes that the second object adjusts the second object feature data according to its own data and provides real-time feedback to the first object to realize the matching process.

5. The flow control method for optimizing the collection of electric meter data as claimed in claim 4, characterized in that: The adjustment process includes setting the first object according to the second characteristic data to obtain first object parameters, and setting the initialized first object according to the first object parameters.

6. The flow control method for optimizing the collection of electric meter data as claimed in claim 5, characterized in that: The matching process includes ranking the first object and the second object feature data by calculation so that the high priority second object feature data matches the high priority first object; The classification process is to establish a model through the first object and the second object. Both parties reach the optimal state through continuous adjustment, maximize resource utilization and improve efficiency.

7. The flow control method for optimizing the collection of electric meter data as claimed in claim 6, characterized in that: The adjustment process includes re-adjusting the first parameter according to the matching process and obtaining excellent parameters with high priority.

8. A system using the flow control method for optimizing meter data collection as claimed in any one of claims 1 to 7, characterized in that: Including meter node module, power gateway node module, data flow control and optimization module; The meter node module is used to send data packets to the master station and adjust the sending rate according to the price and network status of the power gateway. The meter node dynamically adjusts the data sending rate according to historical data and real-time network information to ensure efficient and stable data transmission; The power gateway node module is used to manage cache data packets and adjust cache utilization, and optimize the use of data transmission paths and cache space by controlling the cache utilization coefficient and price; The data flow control and optimization module is used to integrate the information of the meter nodes and the power gateway nodes, adjust the data flow in real time, optimize the use of the power gateway cache, ensure the flow balance of power network data collection, and prevent overload or waste of resources.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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