Multi-node communication optimization scheduling method and system suitable for electric energy meter acquisition system

Abstract

The application discloses a kind of multi-node communication optimization scheduling method and system suitable for electric energy meter acquisition system, the method is by constructing conflict risk assessment model, and using it based on communication behavior data to calculate the conflict score of each electric energy meter node, the risk value of each electric energy meter node in current scheduling period can be accurately evaluated to produce communication conflict, the higher risk value, more likely to cause communication conflict is represented, finally, the scheduling time sequence of all electric energy meter nodes is determined based on the conflict score calculation result of all electric energy meter nodes, and optimization scheduling is carried out according to the scheduling time sequence, so that the electric energy meter node with high conflict score is preferentially avoided or delayed scheduling, while the electric energy meter node with low conflict score is preferentially scheduled, which can effectively avoid triggering concentrated access in high conflict risk period, effectively reduce communication congestion and retransmission probability in a short time, thereby improving the meter reading success rate, data integrity and scheduling efficiency of electric energy meter acquisition system.

Description

Technical Field

[0001] This invention relates to the field of power communication dispatching technology, and in particular to a multi-node communication optimization dispatching method and system, electronic equipment, and computer-readable storage medium applicable to electricity meter acquisition systems. Background Technology

[0002] With the deepening of smart grid construction, energy meter acquisition systems (AMIs) have been widely deployed across the country. In densely populated areas such as urban villages and high-rise residential buildings, a single acquisition unit (such as a concentrator) typically connects hundreds of energy meter nodes. The concentrator needs to periodically complete remote data acquisition, parameter maintenance, and control command issuance for all energy meters. Its communication methods mainly include power line communication (PLC), low-power wireless communication, and RS-485. In actual operation, due to the large number of meters, heterogeneous links, and varying response capabilities, traditional acquisition systems generally use fixed polling or static time-slice scheduling methods, ignoring the communication conflicts, response timeouts, and link congestion that may result from simultaneous scheduling of multiple nodes. This conflict risk is particularly prominent in the following situations:

[0003] (1) Multiple nodes share a single physical link (like a single PLC carrier line);

[0004] (2) The response delays of different tables vary greatly, and there is response overlap;

[0005] (3) Poor stability of the table leads to frequent retries and data congestion;

[0006] (4) Communication is intensive during specific time periods (such as hourly meter reading and batch tripping).

[0007] The aforementioned issues lead to increased meter reading failure rates, response timeouts, and increased link bandwidth usage, ultimately affecting the meter reading success rate, data integrity, and scheduling efficiency of the electricity meter acquisition system. Summary of the Invention

[0008] This invention provides a multi-node communication optimization scheduling method and system applicable to electricity meter acquisition systems, as well as a computer-readable storage medium. It can effectively avoid triggering centralized access during high-conflict-risk periods, effectively reduce communication congestion and retransmission probability in a short period of time, thereby improving the meter reading success rate, data integrity and scheduling efficiency of the electricity meter acquisition system.

[0009] According to one aspect of the present invention, a multi-node communication optimization scheduling method suitable for an electricity meter data acquisition system is provided, comprising the following:

[0010] Obtain communication behavior data for all electricity meter nodes;

[0011] A conflict risk assessment model is constructed, and the conflict score of each electricity meter node is calculated based on the communication behavior data of each electricity meter node using the conflict risk assessment model.

[0012] The scheduling sequence of all electricity meter nodes is determined based on the conflict score calculation results of all electricity meter nodes, and the scheduling is optimized according to the scheduling sequence.

[0013] Furthermore, the expression for the conflict risk assessment model is:

[0014] ;

[0015] in, Indicates the first i Conflict score of each node, Indicates the first i The nodes in the most recent N Standard deviation of response delay in the next scheduling Indicates the first i The nodes in the most recent M The number of communication failures in this scheduling session Indicates the first i The retransmission ratio of each node. Indicates the direction to the first during the scheduling cycle. i The number of times a node repeatedly initiates a communication request. Indicates the scheduling of the first scheduling cycle. i The total number of attempts for each node Indicates the first i The overlap index between the scheduling windows of each node and other nodes. All of these represent weighting coefficients.

[0016] Furthermore, the overlap index for each node is calculated based on the following formula:

[0017] ;

[0018] in, t i and t j Represents a node i and nodes j Pre-scheduled timestamp, This represents the time overlap sensitivity coefficient.

[0019] Furthermore, the process of determining the scheduling sequence of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes includes the following:

[0020] A basic scheduling time benchmark is set, and the scheduling delay of each electricity meter node is calculated based on the conflict score calculation result of each electricity meter node. The scheduling trigger time of each electricity meter node is determined by combining the basic scheduling time benchmark and the scheduling delay, thereby generating the scheduling sequence of all electricity meter nodes.

[0021] Furthermore, the scheduling delay for each electricity meter node is calculated based on the following formula:

[0022] ;

[0023] in, Indicates the first i The scheduling delay of each node. Indicates the configurable maximum delay window length. Indicates the first i Conflict score of each node, This represents the maximum conflict score of all nodes in this scheduling cycle.

[0024] Furthermore, the process of determining the scheduling sequence of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes includes the following:

[0025] Based on the conflict score calculation results of each electricity meter node, the polling priority index score of each electricity meter node is calculated, and the nodes are sorted from high to low according to the polling priority index score to determine the actual polling order of all electricity meter nodes in the current scheduling cycle.

[0026] Furthermore, the polling priority score for each electricity meter node is calculated based on the following formula:

[0027] ;

[0028] in, Indicates the first i The polling priority metric score for each node. Indicates the first i Conflict score of each node, This represents the maximum conflict score of all nodes in this scheduling cycle. Indicates the first i The node business weight index of each node. Indicates the first i Link status score of each node, These are the weighting coefficients.

[0029] In addition, the present invention also provides a multi-node communication optimization scheduling system suitable for electricity meter data acquisition systems, comprising:

[0030] The data acquisition module is used to acquire communication behavior data of all electricity meter nodes;

[0031] The conflict assessment module is used to build a conflict risk assessment model and use the conflict risk assessment model to calculate the conflict score of each electricity meter node based on the communication behavior data of each electricity meter node.

[0032] The optimized scheduling module is used to determine the scheduling sequence of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes, and to perform optimized scheduling according to the scheduling sequence.

[0033] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0034] In addition, the present invention provides a computer-readable storage medium for storing a computer program for multi-node communication optimization scheduling suitable for an electricity meter acquisition system, wherein the computer program executes the steps of the method described above when running on a computer.

[0035] The present invention has the following beneficial effects:

[0036] The present invention provides a multi-node communication optimization scheduling method for electricity meter acquisition systems. By constructing a conflict risk assessment model and using this model to calculate the conflict score of each electricity meter node based on its communication behavior data, the method can accurately assess the risk of communication conflicts for each node in the current scheduling cycle. A higher risk score indicates a greater likelihood of communication conflicts. Finally, based on the conflict score calculation results of all electricity meter nodes, the scheduling sequence of all electricity meter nodes is determined, and optimized scheduling is performed according to this sequence. This prioritizes or postpones the scheduling of electricity meter nodes with high conflict scores, while prioritizing the scheduling of nodes with low conflict scores. This effectively avoids triggering concentrated access during high-conflict-risk periods, effectively reducing communication congestion and retransmission probability in short periods, thereby improving the meter reading success rate, data integrity, and scheduling efficiency of the electricity meter acquisition system.

[0037] In addition, the multi-node communication optimization scheduling system of the present invention, applicable to the electricity meter acquisition system, also has the above-mentioned advantages.

[0038] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0039] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0040] Figure 1 This is a flowchart illustrating a preferred embodiment of a multi-node communication optimization scheduling method applicable to an energy meter data acquisition system.

[0041] Figure 2 This is a schematic diagram of the module structure of a multi-node communication optimization scheduling system applicable to an energy meter acquisition system, according to another embodiment of this application. Detailed Implementation

[0042] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0043] Reference Figure 1 A preferred embodiment of this application provides a multi-node communication optimization scheduling method suitable for an energy meter data acquisition system, including the following:

[0044] Step S1: Obtain communication behavior data for all electricity meter nodes;

[0045] Step S2: Construct a conflict risk assessment model and use the conflict risk assessment model to calculate the conflict score of each electricity meter node based on the communication behavior data of each electricity meter node;

[0046] Step S3: Determine the scheduling sequence of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes, and optimize the scheduling according to the scheduling sequence.

[0047] It is understood that the multi-node communication optimization scheduling method applicable to the electricity meter acquisition system in this embodiment constructs a conflict risk assessment model and uses the model to calculate the conflict score of each electricity meter node based on the communication behavior data of each electricity meter node. This accurately assesses the risk value of each electricity meter node in the current scheduling cycle. The higher the risk value, the more likely it is to cause a communication conflict. Finally, the scheduling sequence of all electricity meter nodes is determined based on the conflict score calculation results of all electricity meter nodes, and the scheduling is optimized according to the scheduling sequence. This allows electricity meter nodes with high conflict scores to be prioritized for avoidance or postponement of scheduling, while electricity meter nodes with low conflict scores are prioritized for scheduling. This effectively avoids triggering concentrated access during high conflict risk periods, effectively reduces communication congestion and retransmission probability in a short period of time, thereby improving the meter reading success rate, data integrity, and scheduling efficiency of the electricity meter acquisition system.

[0048] In step S1, the electricity meter acquisition system includes multiple electricity meter nodes and at least one acquisition control device (such as a concentrator, edge acquisition terminal, or gateway device). Each electricity meter node connects to the system via carrier communication (PLC), low-power wireless communication, or RS-485, supporting functions such as timed meter reading, parameter configuration, and control command execution. To accurately avoid communication conflicts, the acquisition control device needs to perform detailed and continuous data collection and statistics on the communication behavior of each electricity meter node to obtain communication behavior data for all electricity meter nodes. However, the communication behavior data collected by this invention differs from the data collected in traditional network optimization scheduling. It must conform to the periodic meter reading mechanism and protocol communication characteristics of the electricity meter acquisition system. For example, when using the DL / T 645 or DL / T 698 communication protocol, the system typically uses a broadcast wake-up + point-to-point handshake polling method for data collection. This method is prone to overlapping responses or link blocking problems in scenarios with unstable links, a large number of nodes, and uncertain communication response delays. Therefore, the communication behavior data collected by this invention includes the standard deviation of response delay, the number of communication failures, the retransmission ratio, and the communication medium type and topology location.

[0049] Among them, the standard deviation of response delay is the time from when the system sends out the meter reading request message to when it receives the first meter reading message. i The time interval between valid responses from each electricity meter is denoted as . After multiple rounds of meter readings, the expected value and standard deviation can be calculated to measure the stability of the node response. The calculation formula is as follows: ,in, N This indicates the number of samplings (i.e., the most recent N schedulings). Indicates the first i The electricity meter node was recently N Average response delay in this scheduling process Indicates the first i The nodes in the most recent N Standard deviation of response delay in the next scheduling.

[0050] For the number of communication failures, count the most recent occurrences for each electricity meter node. M The number of times a response failure or data verification failure occurs in a single scheduling cycle is denoted as . F i This indicator reflects the stability of the link and the health status of the equipment.

[0051] The retransmission ratio records the number of times a communication request is repeatedly initiated for a specific energy meter within the same scheduling cycle. R i And calculate the retransmission ratio R i / N i ,in,N i This refers to the total number of attempts to schedule the node during this period. This metric indicates whether the node's link fluctuates frequently.

[0052] Regarding the communication medium type and topology location, the system synchronously records the communication channel type (such as PLC main line / branch, RS-485 serial port sequence location, wireless channel number, etc.) and physical topology location of each node. This information can be used to identify multiple adjacent nodes that are prone to conflict.

[0053] It is understandable that, compared with other fields (such as server cluster scheduling and sensor networks), the communication behavior of the electricity meter data acquisition system has obvious periodicity, strong link correlation, fixed response window, and complex protocol handshake. Therefore, the collected communication behavior data should not only focus on message success or failure and round-trip time, but should also be collected in conjunction with factors such as protocol logic, frame structure, and physical channel distribution.

[0054] Furthermore, after completing the continuous collection and structured storage of communication behavior data for all electricity meter nodes, in step S2, a risk assessment model for communication conflicts arising from electricity meter nodes in the current scheduling cycle is further constructed to guide the optimization and adjustment of subsequent scheduling timing. In electricity meter acquisition systems, communication conflicts typically manifest as multiple nodes being accessed or responded to simultaneously within an approximate time window, leading to signal superposition, overlapping responses, or buffer blockage at the receiver on the physical link. This results in data loss, increased retransmissions, or link degradation. Such problems are particularly common in carrier communication (PLC) and low-power wireless communication scenarios because their channel resources are limited and media sharing is high; once a conflict occurs, its impact may affect the entire subnet. Therefore, this invention constructs a conflict risk assessment model to quantify the risk value of communication conflicts arising from each electricity meter node in the current scheduling cycle. Its expression is:

[0055] ;

[0056] in, Indicates the first i Conflict score of each node, Indicates the first i The nodes in the most recent N The standard deviation of response delay in the sub-scheduling process characterizes its response stability. Indicates the first i The nodes in the most recent M The number of communication failures in this scheduling session Indicates the first i The retransmission ratio of each node. Indicates the direction to the first during the scheduling cycle. i The number of times a node repeatedly initiates a communication request. Indicates the scheduling of the first scheduling cycle. i The total number of attempts for each node Indicates the first i The overlap index between the scheduling windows of a node and other nodes indicates the density of its response time and scheduling moments in the time domain. All of these represent weighting coefficients.

[0057] In addition, the overlap index This is used to reflect the similarity of scheduling time or the overlap of response windows between this node and other nodes in the current scheduling plan. It can be calculated based on historical scheduling data, and the calculation formula is as follows:

[0058] ;

[0059] in, t i and t j Represents a node i and nodes j Pre-scheduled timestamp, This represents the time overlap sensitivity coefficient. Essentially, this formula is a Gaussian kernel function used to determine the concentration of the beam scheduling window.

[0060] As can be seen, the above conflict risk assessment model can accurately assess the risk value of communication conflict for each electricity meter node in the current scheduling cycle. The higher the risk value, the more likely it is to cause communication conflict, and it should be avoided or postponed in the scheduling. The conflict score calculation results of all electricity meter nodes can be directly used as a reference for the conflict risk ranking.

[0061] Optionally, in actual deployment, it is also possible to base the analysis on various metrics and conflict scores in the runtime environment. C i The statistical correlation between them is used to dynamically calculate the weight coefficients of each indicator. Specifically, various characteristic indicators (e.g., response latency standard deviation) are calculated using historical scheduling data. σ i Communication failure rate F i / M retransmission ratio R i / N i overlap o i ) and conflict rating C i Correlation coefficient between ρ j After normalization, the result is used as the basis for updating each weight coefficient. The update calculation formula is as follows: , Indicates the updated number j The higher the weighting coefficient of each indicator, the stronger its explanatory power for conflict scoring, and the system will accordingly amplify its influence factor. For example, in areas with frequent wireless interference, if the communication failure rate... F i / M and C i As the correlation increases, the weights β and γ Consequently, it increases; and in topologies with drastic response fluctuations, if σ i and C i A significant increase in the correlation will improve α The weighting of response jitter is enhanced to strengthen its guiding role in conflict risk identification. Therefore, the conflict risk assessment model of this invention also possesses quantifiable, traceable, and adaptive optimization capabilities, effectively improving the adaptability and stability of the scoring model under different communication environments.

[0062] In addition, in step S3, the process of determining the scheduling sequence of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes includes the following:

[0063] A basic scheduling time benchmark is set, and the scheduling delay of each electricity meter node is calculated based on the conflict score calculation result of each electricity meter node. The scheduling trigger time of each electricity meter node is determined by combining the basic scheduling time benchmark and the scheduling delay, thereby generating the scheduling sequence of all electricity meter nodes.

[0064] Specifically, in completing the communication conflict score for each electricity meter node... C i After calculation, this invention addresses a key issue in scheduling behavior—the concentration of access time conflicts—by proposing a dynamic scheduling delay strategy to adjust the node scheduling sequence, thereby achieving communication conflict avoidance and load shifting. In electricity meter acquisition systems, scheduling behavior is often dominated by periodic polling. The concentrator will continuously send meter reading or control command requests to multiple electricity meter nodes within a short period of time. Traditional systems use fixed or numbered polling strategies, which do not consider the response time distribution between nodes and channel contention, easily causing multiple responses to overlap in adjacent time windows, resulting in link congestion, data loss, or response conflicts. Therefore, this invention proposes a scheduling time offset strategy, which dynamically adjusts the scheduling trigger time of each node based on the conflict score calculation result.

[0065] For example, first preset a basic scheduling time base. t 0 Then, based on the conflict score of each node... Ci The scheduling delay is calculated using the following formula:

[0066] ;

[0067] in, Indicates the first i The scheduling delay of each node. This indicates the maximum configurable delay window length, typically set to 200ms. Indicates the first i Conflict score of each node, This represents the maximum conflict score for all nodes in this scheduling cycle. This is then combined with the basic scheduling time benchmark. t 0 You can get the first one. i The scheduling trigger time for each node, that is, the system will... Time to node i A communication request is initiated. Therefore, nodes with higher conflict scores are scheduled later to implement a proactive avoidance strategy, while nodes with lower conflict scores initiate access first, reducing system concurrency risks. This effectively avoids triggering concentrated access during high-conflict-risk periods, significantly reducing communication congestion and retransmission probability in short periods, thereby improving the meter reading success rate, data integrity, and scheduling efficiency of the electricity meter acquisition system. Furthermore, if communication congestion is significantly reduced under the current settings, the time required for scheduling can be shortened. To improve scheduling efficiency, if general traffic is still congested under the current settings, the time limit can be extended. To reduce instantaneous load.

[0068] Optionally, to enhance scheduling flexibility, this invention also introduces a soft-adjustment threshold ratio coefficient. ,when When, then the node i As a low-conflict node, the scheduling interval can be appropriately compressed to improve overall scheduling efficiency. The formula for calculating its scheduling delay is as follows: , This represents the compression factor, which takes values ​​between (0,1) and is used to control the degree to which low-risk nodes are clustered together during scheduling.

[0069] It is understood that in actual deployment, the dynamic scheduling delay strategy of this invention can be combined with the internal timer mechanism of the concentrator to achieve fine-grained scheduling control. Based on this strategy, the concentrator can dynamically adjust the task initiation time without changing the existing communication protocol, reducing centralized scheduling peaks and improving the dispersion of time-domain distribution among nodes. This invention has undergone actual testing. Applying this strategy in a typical 96-meter / concentrator acquisition topology can significantly reduce the number of retransmissions during high-conflict periods (by more than 30%) and the average scheduling cycle time (by more than 15%), effectively improving the system's meter reading success rate and link utilization efficiency.

[0070] Optionally, traditional electricity meter acquisition systems generally use polling scheduling based on numbering order or link structure order. While this method can meet real-time requirements when the number of nodes is small, it easily leads to the following problems in areas with a large number of nodes and complex link conditions: multiple nodes with adjacent numbers are actually on the same link branch, causing link overload due to centralized scheduling; multiple high retransmission and high latency nodes are accessed continuously, accumulating response jitter and amplifying the system cycle; critical nodes are delayed in scheduling, posing a risk of service delay. Therefore, to improve the conflict avoidance capability of scheduling timing, this invention also proposes a polling order optimization and rearrangement mechanism based on communication state awareness, used to adjust the access order of electricity meter nodes by the scheduling control equipment, avoiding the situation where high-risk nodes are centrally scheduled in the same batch due to fixed original sequences or unreasonable numbering order, which can effectively alleviate the instantaneous load pressure on communication links. The process of determining the scheduling timing of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes includes the following:

[0071] Based on the conflict score calculation results of each electricity meter node, the polling priority index score of each electricity meter node is calculated, and the nodes are sorted from high to low according to the polling priority index score to determine the actual polling order of all electricity meter nodes in the current scheduling cycle.

[0072] Specifically, the polling priority score for each electricity meter node is first calculated based on the following formula:

[0073] ;

[0074] in, Indicates the first i The polling priority metric score for each node. Indicates the first i Conflict score of each node, This represents the maximum conflict score of all nodes in this scheduling cycle. Indicates the first i The node business weight indicators for each node are pre-set based on factors such as whether the node is a key settlement account or has tripping authority. Indicates the first i Link status score of each node, These are the weighting coefficients, and It can be dynamically adjusted according to the characteristics of different transformer areas. Additionally, link status scoring... It can be composed of multiple physical and communication quality parameters, such as signal strength RSSI. i (For wireless communication only), communication success rate SR i , SR i =Number of successful attempts / Total number of attempts, Standard deviation of response latency The following model can be constructed to calculate : norm() represents the normalization function. This represents the maximum standard deviation of response delay across all nodes. , , These are the weighting coefficients. .

[0075] Then, after calculating the polling priority index scores of all electricity meter nodes, they are sorted in descending order to determine the actual polling order of all electricity meter nodes in the current scheduling cycle, thus determining the scheduling sequence of all electricity meter nodes.

[0076] It is understandable that by optimizing and rearranging the polling order as described above, nodes with good link quality, stable response, and high business weight can be scheduled first, while nodes with high conflict scores and unstable states can be scheduled later. This can avoid the situation where high-risk nodes are scheduled in the same batch due to the solidification of the original sequence or unreasonable numbering order, and further alleviate the instantaneous load pressure on the communication link.

[0077] Optionally, to further enhance the robustness of scheduling, a minimum polling interval constraint can be introduced. For example, a minimum time interval can be set for nodes that continuously poll the same physical link (such as the same branch of a PLC or the same path of an RS485). τ min This is to prevent link congestion from recurring after logical sequence optimization.

[0078] Additionally, preferably, in step S3, for conflicts with scores less than a soft adjustment threshold... For nodes with low conflict rates (i.e., low-conflict nodes), the aforementioned dynamic scheduling delay strategy is used to determine the scheduling trigger time for each electricity meter node and prioritize their scheduling. For nodes with conflict scores greater than or equal to the soft adjustment threshold, the scheduling is delayed. For nodes with high conflict (i.e., high-conflict nodes), a polling order optimization and reordering mechanism is used to dynamically adjust the polling order of each high-conflict node. This can better alleviate the instantaneous load pressure on the communication link, avoid triggering concentrated access during high-conflict risk periods, greatly reduce communication congestion and retransmission probability in a short period of time, and significantly improve the meter reading success rate, data integrity and scheduling efficiency of the electricity meter acquisition system.

[0079] In addition, such as Figure 2 As shown, another embodiment of the present invention also provides a multi-node communication optimization scheduling system suitable for an energy meter acquisition system, preferably employing the multi-node communication optimization scheduling method for an energy meter acquisition system as described above, including:

[0080] The data acquisition module is used to acquire communication behavior data of all electricity meter nodes;

[0081] The conflict assessment module is used to build a conflict risk assessment model and use the conflict risk assessment model to calculate the conflict score of each electricity meter node based on the communication behavior data of each electricity meter node.

[0082] The optimized scheduling module is used to determine the scheduling sequence of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes, and to perform optimized scheduling according to the scheduling sequence.

[0083] It is understood that the multi-node communication optimization scheduling system applicable to the electricity meter acquisition system in this embodiment constructs a conflict risk assessment model and uses the model to calculate the conflict score of each electricity meter node based on the communication behavior data of each electricity meter node. This accurately assesses the risk value of each electricity meter node in the current scheduling cycle. The higher the risk value, the more likely it is to cause a communication conflict. Finally, based on the conflict score calculation results of all electricity meter nodes, the scheduling sequence of all electricity meter nodes is determined, and the scheduling is optimized according to the scheduling sequence. This allows electricity meter nodes with high conflict scores to be prioritized for avoidance or postponement of scheduling, while electricity meter nodes with low conflict scores are prioritized for scheduling. This effectively avoids triggering concentrated access during high conflict risk periods, effectively reduces communication congestion and retransmission probability in a short period of time, thereby improving the meter reading success rate, data integrity, and scheduling efficiency of the electricity meter acquisition system.

[0084] In addition, another embodiment of the present invention provides an electronic device including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0085] In addition, another embodiment of the present invention provides a computer-readable storage medium for storing a computer program for multi-node communication optimization scheduling suitable for an energy meter acquisition system, wherein the computer program executes the steps of the method described above when running on a computer.

[0086] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical media with perforated patterns, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), any other memory chips or cartridges, or any other media readable by a computer. Instructions may further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium used to store, encode, or carry instructions for execution by a machine, and includes digital or analog carrier communication signals or intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wires, and optical fibers, which contain conductors for transmitting a bus of computer data signals.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0092] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0093] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-node communication optimization scheduling method suitable for electricity meter data acquisition systems, characterized in that, Includes the following: Obtain communication behavior data for all electricity meter nodes; A conflict risk assessment model is constructed, and based on the communication behavior data of each electricity meter node, the conflict score of each electricity meter node is calculated using the conflict risk assessment model; the expression of the conflict risk assessment model is: ; in, Indicates the first i Conflict score of each node, Indicates the first i The nodes in the most recent N Standard deviation of response delay in the next scheduling Indicates the first i The nodes in the most recent M The number of communication failures in this scheduling session Indicates the first i The retransmission ratio of each node. Indicates the direction to the first during the scheduling cycle. i The number of times a node repeatedly initiates a communication request. Indicates the scheduling of the first scheduling cycle. i The total number of attempts for each node Indicates the first i The overlap index between the scheduling windows of each node and other nodes. All represent weighting coefficients; The scheduling sequence of all electricity meter nodes is determined based on the conflict score calculation results of all electricity meter nodes, and the scheduling is optimized according to the scheduling sequence.

2. The multi-node communication optimization scheduling method for an energy meter data acquisition system as described in claim 1, characterized in that, The overlap index for each node is calculated based on the following formula: ; in, t i and t j Represents a node i and nodes j Pre-scheduled timestamp, This represents the time overlap sensitivity coefficient.

3. The multi-node communication optimization scheduling method for an energy meter data acquisition system as described in claim 1, characterized in that, The process of determining the scheduling sequence of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes includes the following: A basic scheduling time benchmark is set, and the scheduling delay of each electricity meter node is calculated based on the conflict score calculation result of each electricity meter node. The scheduling trigger time of each electricity meter node is determined by combining the basic scheduling time benchmark and the scheduling delay, thereby generating the scheduling sequence of all electricity meter nodes.

4. The multi-node communication optimization scheduling method for an energy meter data acquisition system as described in claim 3, characterized in that, The scheduling delay for each electricity meter node is calculated using the following formula: ; in, Indicates the first i The scheduling delay of each node. Indicates the configurable maximum delay window length. Indicates the first i Conflict score of each node, This represents the maximum conflict score of all nodes in this scheduling cycle.

5. The multi-node communication optimization scheduling method for an energy meter acquisition system as described in claim 1, characterized in that, The process of determining the scheduling sequence of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes includes the following: Based on the conflict score calculation results of each electricity meter node, the polling priority index score of each electricity meter node is calculated, and the nodes are sorted from high to low according to the polling priority index score to determine the actual polling order of all electricity meter nodes in the current scheduling cycle.

6. The multi-node communication optimization scheduling method for an energy meter acquisition system as described in claim 5, characterized in that, The polling priority score for each electricity meter node is calculated based on the following formula: ; in, Indicates the first i The polling priority metric score for each node. Indicates the first i Conflict score of each node, This represents the maximum conflict score of all nodes in this scheduling cycle. Indicates the first i The node business weight index of each node. Indicates the first i Link status score of each node, These are the weighting coefficients.

7. A multi-node communication optimization scheduling system suitable for electricity meter data acquisition systems, characterized in that, include: The data acquisition module is used to acquire communication behavior data of all electricity meter nodes; The conflict assessment module is used to construct a conflict risk assessment model and, based on the communication behavior data of each electricity meter node, calculate the conflict score for each electricity meter node using this model. The expression for the conflict risk assessment model is as follows: ; in, Indicates the first i Conflict score of each node, Indicates the first i The nodes in the most recent N Standard deviation of response delay in the next scheduling Indicates the first i The nodes in the most recent M The number of communication failures in this scheduling session Indicates the first i The retransmission ratio of each node. Indicates the direction to the first during the scheduling cycle. i The number of times a node repeatedly initiates a communication request. Indicates the scheduling of the first scheduling cycle. i The total number of attempts for each node Indicates the first i The overlap index between the scheduling windows of each node and other nodes. All represent weighting coefficients; The optimized scheduling module is used to determine the scheduling sequence of all electricity meter nodes based on the conflict score calculation results of all electricity meter nodes, and to perform optimized scheduling according to the scheduling sequence.

8. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium for storing a computer program for multi-node communication optimization scheduling suitable for an electricity meter data acquisition system, characterized in that, The computer program, when run on a computer, performs the steps of the method as described in any one of claims 1 to 6.

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