Flexible expansion simulation table marshalling method and system based on mixed marshalling mode and storage medium

Abstract

The invention provides an elastically extensible simulation table marshalling method and system based on a mixed marshalling mode and a storage medium, and the method comprises the steps: initializing all simulation tables of a current system to be in a static mode, and performing marshalling; calculating resources consumed by the simulation table in the static mode and the dynamic mode; searching a current available resource space, and expanding according to resource consumption; sequencing the simulation table according to resources consumed in a dynamic mode by using a greedy algorithm, and dynamically adjusting the mode; when the remaining available space resources are lower than or equal to zero, searching is stopped, and the optimal elastic resource expansion state is achieved. According to the method, the multi-modal resources are elastically expanded through mixed marshalling, so that the flexibility and the expansibility of the data processing system are improved, and meanwhile, the stability and the high efficiency of the system when facing a large amount of data processing requirements are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data communication, and more specifically, to a flexible and expandable analog meter system and a grouping method based on a hybrid grouping mode. Background Art

[0002] With the continuous advancement of smart grid and smart energy management technologies, the demand for data collection and data processing has shown explosive growth. However, the current analog meter systems on the market generally have functional limitations and insufficient flexibility, making it difficult to adapt to complex and changing field environments and diverse data simulation needs.

[0003] Traditional analog meter management methods are often limited to individual control or full control, lacking flexibility and efficiency. In large-scale smart grid systems, there are a large number of meters, which may be scattered in different geographical locations and environments. The analog meter system is required to synchronize data and control meters according to a specific grouping mode to achieve refined management.

[0004] However, the existing analog meter management system is seriously insufficient in this regard, which greatly increases the difficulty of management. Summary of the invention

[0005] The purpose of the present invention is to address the functional limitations and lack of flexibility in existing analog table management, and to propose a flexible and expandable analog table grouping method, system and storage medium based on a hybrid grouping mode; by elastically expanding multimodal resources through hybrid grouping, the flexibility and scalability of the data processing system are improved, while ensuring the stability and efficiency of the system when facing large amounts of data processing needs.

[0006] The technical solution of the present invention is:

[0007] The present invention provides a flexible and expandable analog table grouping method based on a hybrid grouping mode, the method comprising the following steps:

[0008] Identify the attributes of the simulation table;

[0009] Record the resources consumed by each attribute of each simulation table in static mode;

[0010] Record the resources consumed by each attribute of each simulation table in dynamic mode;

[0011] Perform static modal grouping on all simulation tables based on the same sampling interval and sampling data item attributes, and obtain the resources consumed under the current static modal grouping;

[0012] Search the currently available resource space. If it is greater than the resources consumed under the current static modal grouping, perform dynamic modal grouping expansion; otherwise, complete the grouping and reach the optimal elastic resource expansion state.

[0013] Furthermore, the attributes include real-time data, historical data and events; wherein,

[0014] Real-time data includes voltage, current, power factor and current electric energy indication;

[0015] Historical data includes curve data, daily frozen data and monthly frozen data;

[0016] Event generation methods include assignment and generation. In the assignment method, events are generated directly by assignment; in the generation method, corresponding events are generated based on historical data.

[0017] Furthermore, the resource C consumed by each attribute of each simulation table in static mode is expressed as follows:

[0018]

[0019] Where: j represents the attribute number, n represents the number of attributes, Δ j Indicates the resources statically consumed by the attribute numbered j.

[0020] Furthermore, the resource Z consumed by each attribute of each simulation table in dynamic mode is expressed as follows:

[0021]

[0022] Where: j represents the attribute number, n represents the number of attributes, ω j Indicates the resources dynamically consumed by the attribute numbered j.

[0023] Furthermore, the dynamic modal grouping extension includes the following steps:

[0024] Arrange the m simulation tables from small to large according to the resource Z consumed in the dynamic mode and the resource C consumed in the static mode, respectively, where m represents the total number of simulation tables;

[0025] Initialize an empty dynamic mode group as D = {};

[0026] Search for the simulation table min(C+Z) with the smallest sum of dynamic and static resources, remove the simulation table with the smallest sum of resources in the static modal group T, and update the group to T′ i , adding the simulation table with the smallest sum of the removed resources to the dynamic modal group D;

[0027] Update the free space Q after regrouping. When Q>0, continue the next round of search and update the grouping information.

[0028] Furthermore, a greedy algorithm is used to search for the simulation table min(C+Z) with the smallest sum of dynamic and static resources.

[0029] Furthermore, the free space Q after regrouping is updated using the following formula:

[0030]

[0031] Where: P represents the currently available resource space; T represents the static modal grouping, T={T1,T2,T3,…T i …T k}; where T i It refers to a set of simulation tables with the same sampling interval and sampling data item attributes. i represents the number of static groups; k represents the number of static groups; T′ i represents the updated static mode grouping, C′ i represents the resources consumed in static mode after the update; q represents the search round, Z q Indicates the resources consumed in dynamic mode after update.

[0032] Further, continuing the next round of searching and updating the grouping information is as follows;

[0033] If Q≤0 after q+1 rounds of searching, and Q≥0 in the qth round, the search is stopped immediately. At this time, the optimal elastic resource expansion state is reached, and q represents the search round;

[0034] At this time, the static modal group T is updated to T′={T′1,T′2,T′3,…T′ k}, dynamic mode group D = {D1, D2, D3, ... D q}; According to the current grouping, the static mode and dynamic mode of the simulation table are mixed to achieve the optimal resource combination.

[0035] A flexible and expandable analog meter marshaling system based on a hybrid marshaling mode, the system comprising:

[0036] Initialization management module, used to load and initialize the system;

[0037] Communication protocol management module, used to dynamically load and unload multiple communication protocols;

[0038] Communication parameter configuration module, used to configure and manage parameters of various communication channels;

[0039] A maintenance command processing module, used for receiving and executing system maintenance commands;

[0040] Task scheduling module, used to schedule system tasks;

[0041] A multimodal resource extension module, used to support all static, all dynamic or partially static and partially dynamic data processing, and implement the grouping method of the elastically expandable simulation table;

[0042] The user interaction module provides a user interface to support users in configuring communication protocols and channels, mixed grouping of data processing, and simulation and modification of meter data.

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the grouping method of the elastically expandable simulation table.

[0044] The beneficial effects of the present invention are:

[0045] The elastically scalable analog meter grouping method, system and storage medium based on the hybrid grouping mode of the present invention realizes efficient data synchronization between different protocols and channels by supporting multiple communication protocols and channels, and improves the flexibility and scalability of the data processing system under the rapid growth of data collection and processing demand in the field of smart grid and smart energy management. It provides users with an efficient, stable and flexible data synchronization solution, which is of great significance to promoting the development of smart grid and smart energy management technology. The specific priorities are as follows:

[0046] 1. Enhanced compatibility and applicability: By supporting multiple communication protocols and channels, the system of the present invention can seamlessly connect with smart meters of different manufacturers and models, greatly expanding the scope of application of the system and enabling it to adapt to diverse on-site environments and equipment requirements.

[0047] 2. Improved data processing efficiency: By adopting a hybrid grouping mode, the method of the present invention can flexibly configure and adjust the use of protocols and channels according to actual data processing requirements, thereby optimizing the data transmission path, reducing data transmission delays, and improving overall data processing efficiency.

[0048] 3. Enhanced system stability: The elastic expansion capability of multimodal resources enables the system to dynamically adjust resource allocation according to real-time load and data transmission requirements, effectively avoiding system crashes or performance degradation caused by insufficient resources, and ensuring stable operation of the system.

[0049] 4. Optimized testing and verification process: The system supports external control and data synchronization, allowing users to simulate various complex operating scenarios, providing more realistic and comprehensive data support for the testing and verification of smart meters, and improving the accuracy and effectiveness of the test.

[0050] 5. Improved user experience: The system of the present invention provides a more flexible and efficient data management method, simplifies the user's operation process, reduces the management difficulty, enables users to synchronize data and control devices more conveniently, and improves the user experience.

[0051] 6. Promote smart energy management: By achieving efficient data synchronization and refined management, the system of the present invention provides strong technical support for smart energy management, helps to improve energy utilization efficiency, reduce energy waste, and promote the development of smart energy management to a higher level.

[0052] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0054] Figure 1 A flow chart of a method for elastically scalable analog table grouping based on a hybrid grouping mode according to an embodiment of the present invention is shown.

[0055] Figure 2 The following is a diagram showing an architecture of a flexible and scalable analog meter grouping system based on a hybrid grouping mode according to an embodiment of the present invention.

[0056] Figure 3 A schematic diagram of the operation process of a flexible and scalable simulation table grouping method based on a hybrid grouping mode according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0057] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0058] Example 1

[0059] Figure 1 A flow chart of a flexible and scalable simulation table grouping method based on a hybrid grouping mode according to the present invention is shown.

[0060] like Figure 1 As shown, a flexible and scalable simulation table grouping method based on a hybrid grouping mode comprises the following steps:

[0061] The analog meter is marked with attributes; the attributes include real-time data, historical data and events; the real-time data includes voltage, current, power factor and current electric energy indication; the historical data includes curve data, daily frozen data and monthly frozen data; the event generation mode includes assignment and generation; in the assignment mode, the event is directly assigned and generated; in the generation mode, the corresponding event is generated according to the historical data;

[0062] Record the resources consumed by each attribute of each simulation table in static mode;

[0063]

[0064] Where: j represents the attribute number, n represents the number of attributes, Δ j Indicates the resources statically consumed by the attribute numbered j

[0065] Record the resources consumed by each attribute of each simulation table in dynamic mode;

[0066]

[0067] Where: j represents the attribute number, n represents the number of attributes, ω j Indicates the resources dynamically consumed by the attribute numbered j;

[0068] Perform static modal grouping on all simulation tables based on the same sampling interval and sampling data item attributes, and obtain the resources consumed under the current static modal grouping;

[0069] Search the currently available resource space. If it is greater than the resources consumed under the current static modal grouping, perform dynamic modal grouping expansion; otherwise, complete the grouping and reach the optimal elastic resource expansion state.

[0070] In one example, dynamic modal grouping expansion includes the following steps:

[0071] Arrange the m simulation tables from small to large according to the resource Z consumed in the dynamic mode and the resource C consumed in the static mode, respectively, where m represents the total number of simulation tables;

[0072] Initialize an empty dynamic mode group as D = {};

[0073] Use a greedy algorithm to search for the simulation table min(C+Z) with the smallest sum of dynamic and static resources, remove the simulation table with the smallest sum of resources in the static modal group T, update the group to T′i, and add the simulation table with the smallest sum of resources removed to the dynamic modal group D;

[0074] Update the free space Q after regrouping;

[0075]

[0076] Where: P represents the currently available resource space; T represents the static modal grouping, T={T1,T2,T3,…T i …T k}; where T i It refers to a set of simulation tables with the same sampling interval and sampling data item attributes. i represents the number of static groups; k represents the number of static groups; T′i represents the updated static mode grouping, C′ i represents the resources consumed in static mode after the update; q represents the search round, Z q Indicates the resources consumed in dynamic mode after update;

[0077] When Q>0, continue the next round of search and update the grouping information, specifically:

[0078] If Q≤0 after q+1 rounds of searching, and Q≥0 in the qth round, the search is stopped immediately. At this time, the optimal elastic resource expansion state is reached, and q represents the search round;

[0079] At this time, the static modal group T is updated to T′={T′1,T′2,T′3,…T′ k}, dynamic mode group D = {D1, D2, D3, ... D q}; According to the current grouping, the static mode and dynamic mode of the simulation table are mixed to achieve the optimal resource combination.

[0080] In this embodiment, multi-mode operation is supported, and according to the test requirements, the simulation table can be fully static, fully dynamic, partially static and partially dynamic;

[0081] Among them, static mode means that the analog meter does not run, and the system issues instructions to set a set of data for the system to use, including real-time data (voltage, current, power factor, current electric energy indication), historical data (curve, daily freeze, monthly freeze), events and other data. During the use of the analog meter, the system can issue instructions at any time to regenerate a new set of data. This mode consumes very few resources.

[0082] Among them, dynamic mode means that the analog meter starts the real running mode, and the system issues initialization instructions to set system time, voltage, current, power factor, current electric energy indication, rate period and other data; the electric energy indication simulates the actual power consumption and increases according to the voltage, current and power factor, and generates curve data, daily frozen data and monthly frozen data according to the system time. The dynamic mode consumes more resources, and the meter mode can be flexibly adjusted according to the system load and test requirements, so as to realize the elastic expansion of the analog meter software resources and ensure the stability and efficiency of the system.

[0083] Partial static and partly dynamic means that the analog meter can switch modes arbitrarily in space and time according to test needs. Specifically, the system can designate a part of the analog meter as static mode and another part as dynamic mode at the same time; or the system can first designate the analog meter as one mode, and then switch to another mode during use.

[0084] The present invention adopts a mixed grouping mode, allowing users to flexibly group multiple attributes of the simulation table according to actual needs, forming an efficient data processing link, and realizing data synchronization among the same group, multiple protocols, and multiple channels.

[0085] Example 2

[0086] Figure 2 The following is a diagram showing an architecture of a flexible and scalable analog meter grouping system based on a hybrid grouping mode according to an embodiment of the present invention.

[0087] like Figure 2 As shown, a flexible and scalable analog meter marshaling system based on a hybrid marshaling mode, the system comprises:

[0088] The initialization management module is used to load and initialize the system; specifically, it is responsible for loading and initializing other key modules when the system starts and performing a comprehensive system self-check;

[0089] The communication protocol management module is used to dynamically load and unload multiple communication protocols; specifically, this module processes the core components of the communication protocol, it supports multiple communication protocols, and can dynamically load and unload the corresponding communication protocols according to the user's choice and the actual situation of the on-site equipment;

[0090] The communication parameter configuration module is used to configure and manage the parameters of various communication channels; specifically, it is responsible for configuring and managing the parameters of the communication channels, providing flexible parameter configuration options, and monitoring the status of the communication channels in real time;

[0091] The maintenance command processing module is used to receive and execute system maintenance commands; specifically, it provides system maintenance functions, receives maintenance commands sent by users, such as restarting the system, updating software, viewing logs, etc., and performs corresponding operations. At the same time, the module also records the execution results and log information of the commands for users to view and analyze;

[0092] The task scheduling module is used to schedule system tasks; specifically, this module is the core component for implementing concurrent execution of multiple tasks. It intelligently schedules system tasks based on the priority and dependency of tasks, and reasonably arranges the execution order and time of tasks. At the same time, this module also monitors the execution status of tasks to ensure that tasks can be completed on time;

[0093] A multimodal resource extension module, used to support all static, all dynamic or partially static and partially dynamic data processing, and implement the grouping method of the elastically expandable simulation table;

[0094] The user interaction module provides a user interface to support users in configuring communication protocols and channels, mixed grouping of data processing, and simulation and modification of meter data.

[0095] In this embodiment, the system of the present invention supports multiple communication protocols, including but not limited to 698.45, 645, 376.2, Json, Modbus, etc., and can adapt to the communication requirements of different devices and systems. It can achieve multi-channel access, including but not limited to RS-232, RS-485, Ethernet, CAN, etc., and can achieve cross-channel data synchronization and stable transmission. The hybrid grouping mode is adopted to allow users to flexibly group multiple attributes of the analog table according to actual needs, form an efficient data processing link, and achieve data synchronization between the same group, multiple protocols, and multiple channels.

[0096] Example 3

[0097] Figure 3 FIG. 4 is a schematic diagram showing the operation process of a flexible scalable simulation table grouping method based on a hybrid grouping mode according to an embodiment of the present invention. Figure 3 As shown;

[0098] (1) During the software initialization phase, the system will automatically load the supported communication protocols and channel lists to ensure full compatibility of the software and the completeness of the initialization preparation work.

[0099] (2) Through the user interface, users can accurately select and configure the required communication protocols and channels to meet the communication requirements of different devices and systems and achieve personalized settings.

[0100] (3) Based on the actual needs of users, the software provides a hybrid grouping module, which allows users to flexibly configure the data processing process to achieve efficient synchronization of data between multiple protocols and multiple channels under the same grouping to meet complex and changing data processing requirements.

[0101] (4) In order to meet the data processing requirements in different application scenarios, the software is equipped with a multi-modal resource expansion module. Users can dynamically configure the static or dynamic working mode of the meter according to the system load and test requirements.

[0102] (5) As one of the core functions of the software, the data processing module allows users to comprehensively process and analyze the received data. It also supports the simulation and modification of meter data, providing users with flexible data management and testing functions.

[0103] (6) During the entire operation process, users can view the data synchronization simulation table in real time through the user interface module, clearly understand the data synchronization status between different protocols and channels, and provide an intuitive and accurate basis for data monitoring and management.

[0104] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A flexible and scalable analog meter grouping method based on a hybrid grouping mode, characterized in that: The method comprises the following steps: Identify the attributes of the simulation table; Record the resources consumed by each attribute of each simulation table in static mode; Record the resources consumed by each attribute of each simulation table in dynamic mode; Perform static modal grouping on all simulation tables based on the same sampling interval and sampling data item attributes, and obtain the resources consumed under the current static modal grouping; Search the currently available resource space. If it is greater than the resources consumed under the current static modal grouping, perform dynamic modal grouping expansion; otherwise, complete the grouping and reach the optimal elastic resource expansion state.

2. The elastically scalable analog table grouping method based on the hybrid grouping mode according to claim 1 is characterized in that: The attributes include real-time data, historical data and events; wherein, Real-time data includes voltage, current, power factor and current electric energy indication; Historical data includes curve data, daily frozen data and monthly frozen data; Event generation methods include assignment and generation. In the assignment method, events are generated directly by assignment; in the generation method, corresponding events are generated based on historical data.

3. The elastically scalable analog table grouping method based on the hybrid grouping mode according to claim 1 is characterized in that The resource C consumed by each attribute of each simulation table in static mode is expressed as follows: Where: j represents the attribute number, n represents the number of attributes, Δ j Indicates the resources statically consumed by the attribute numbered j.

4. The elastically scalable analog table grouping method based on the hybrid grouping mode according to claim 1 is characterized in that The resource Z consumed by each attribute of each simulation table in dynamic mode is expressed as follows: Where: j represents the attribute number, n represents the number of attributes, ω j Indicates the resources dynamically consumed by the attribute numbered j.

5. The elastically scalable analog table grouping method based on the hybrid grouping mode according to claim 1 is characterized in that Dynamic modal group expansion includes the following steps: Arrange the m simulation tables from small to large according to the resource Z consumed in the dynamic mode and the resource C consumed in the static mode, respectively, where m represents the total number of simulation tables; Initialize an empty dynamic mode group as D = {}; Search for the simulation table min(C+Z) with the smallest sum of dynamic and static resources, remove the simulation table with the smallest sum of resources in the static modal group T, and update the group to T i ', add the simulation table with the smallest sum of removed resources to the dynamic mode group D; Update the free space Q after regrouping. When Q>0, continue the next round of search and update the grouping information.

6. The elastically scalable analog table grouping method based on the hybrid grouping mode according to claim 5 is characterized in that Use the greedy algorithm to search for the simulation table min(C+Z) with the smallest sum of dynamic and static resources.

7. The elastically scalable analog table grouping method based on the hybrid grouping mode according to claim 5 is characterized in that The free space Q after regrouping is updated using the following formula; Where: P represents the currently available resource space; T represents the static modal grouping, T={T1,T2,T3,…T i …T k }; where T i It refers to a set of simulation tables with the same sampling interval and sampling data item attributes. i represents the number of static groups; k represents the number of static groups; T i ′ represents the updated static mode grouping, C i ′ represents the resources consumed in the static mode after the update; q represents the search round, Z q Indicates the resources consumed in dynamic mode after update.

8. The elastically scalable analog table grouping method based on the hybrid grouping mode according to claim 5 is characterized in that Continue the next round of search and update the grouping information specifically as follows; If Q≤0 after q+1 rounds of searching, and Q≥0 after q rounds, the search is stopped immediately. At this time, the optimal elastic resource expansion state is reached, and q represents the search round; At this time, the static modal group T is updated to T′={T′1,T′2,T′3,…T′ k }, dynamic mode group D = {D1, D2, D3, ... D q }; According to the current grouping, the static mode and dynamic mode of the simulation table are mixed to achieve the optimal resource combination.

9. A flexible and scalable analog meter marshaling system based on a hybrid marshaling mode, characterized in that: The system comprises: Initialization management module, used to load and initialize the system; Communication protocol management module, used to dynamically load and unload multiple communication protocols; Communication parameter configuration module, used to configure and manage parameters of various communication channels; A maintenance command processing module, used for receiving and executing system maintenance commands; Task scheduling module, used to schedule system tasks; A multimodal resource extension module, used to support all static, all dynamic or partially static and partially dynamic data processing, and implement the grouping method of the elastically scalable simulation table described in any one of claims 1-8; The user interaction module provides a user interface to support users in configuring communication protocols and channels, mixed grouping of data processing, and simulation and modification of meter data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for grouping elastically expandable simulation tables described in any one of claims 1 to 8 is implemented.

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