Method for quickly and adaptively generating and collecting archive data based on operation terminal, medium and processor

Abstract

The invention discloses a method for rapidly and adaptively generating and collecting archive data based on an operation terminal, a medium and a processor, and relates to the technical field of data collection, comprising the steps of collecting data of a plurality of ammeters by the operation terminal, obtaining basic data tables of the plurality of ammeters through the operation terminal, obtaining asset data of the ammeters from all the basic data tables, generating a reading parameter table; acquiring network information data of all the electricity meters from the reading parameter table to generate a network information table; acquiring all electricity meter information from the reading parameter table, and generating an electricity meter acquisition information table; generating an electric meter acquisition time scale table containing various time scales according to the electric meter acquisition information table; generating an electricity meter electric quantity reading meter according to an acquisition item generated by the measuring point number of the electricity meter acquisition information meter; and generating the reading parameter table, the network information table, the electricity meter acquisition information table and the like on a webpage in a one-button manner to form an acquisition file. According to the invention, the most basic guarantee is provided for the acquisition stability and reliability of an electric energy data master station acquisition system.

Description

Technical Field

[0001] The present invention relates to the technical field of operation terminal data collection, and in particular to a method, a medium and a processor for quickly and adaptively generating collection archive data based on an operation terminal. Background Art

[0002] The collection of the Guangxi Zhuang Autonomous Region 102 communication protocol is different from that of other communication protocol equipment. In addition to basic file data, it still requires additional parameter data to cooperate in order to carry out collection normally. However, due to the technical personnel's unfamiliarity with business data and the business personnel's lack of understanding of development technology, the technical personnel need to manually maintain and generate each parameter table every time a collection file is generated. After generation, it is submitted to the business personnel for review, and then returned to the technical personnel for modification, and then cooperated with the installation site for communication joint debugging, etc., resulting in extremely difficult communication between the three parties on issues such as generating parameter data, troubleshooting collection anomalies, and on-site joint debugging, and the progress is slow. Summary of the invention

[0003] In view of the deficiencies in the above-mentioned prior art, the present invention provides a method, medium and processor for quickly and adaptively generating collection archive data based on a running terminal, which can quickly and accurately complete the generation of collection archives, avoid repeated queries of basic data tables to generate technical documents, improve query efficiency, and provide the most basic guarantee for the collection stability and reliability of the main station system.

[0004] The specific technical solutions are as follows:

[0005] A method for quickly and adaptively generating collection archive data based on an operation terminal includes collecting data of a plurality of electric meters by an operation terminal, reading basic data of all electric meters through the operation terminal, and generating an independent reading parameter table, a network information table, an electric meter collection information table, an electric meter collection time scale table and an electric meter power reading table based on a basic data table for query by a corresponding query program, specifically including the following steps:

[0006] S1. Obtain basic data tables of several electric meters by running the terminal, obtain electric meter asset data from all basic data tables, and generate a reading parameter table;

[0007] S2, obtaining network information data of all electric meters from the reading parameter table, and generating a network information table;

[0008] S3, obtain all the meter information from the reading parameter table and generate the meter collection information table;

[0009] S4, generating an electric meter collection time stamp table containing multiple time stamps according to the electric meter collection information table;

[0010] S5, generating an electric meter electricity reading table according to the collection items generated by the measuring point number of the electric meter collection information table;

[0011] S6. Generate the reading parameter table, network information table, meter collection information table, meter collection time scale table and meter electricity reading table on the web page in a one-click manner to form a collection file.

[0012] The above scheme further adopts hierarchical distributed construction for steps S1-S5:

[0013] 1) The electric meters are divided into multiple areas, and a domain head is set in each area; the domain head is responsible for collecting data information of the electric meters in the area;

[0014] 2) Deploy a mobile agent at the domain head to enable it to move between meters within the domain; the mobile agent reads basic data from the meter; obtains network information data of the meter from the reading parameter table; and obtains meter information from the reading parameter table;

[0015] 3) Through the distributed computing architecture, each domain head communicates and interacts with each other, coordinating data collection between domains;

[0016] The collection interval adjustment definition is: the collection window width obtained by the domain head is m, {D i |i=1,2,…,m} represents the meter data obtained at m moments; the sampling window width is defined as n, n<m, {D j |j=m-n+1,…,m} represents the meter data of the last n moments selected from the acquisition window; define ΔD m+1 is the predicted value D′ m+1 and the true value D m+1 The absolute value of the difference, ΔD m+1 =|D m+1 -D′ m+1 |; The average value of the sampling window is

[0017] Use the linear regression model for prediction, assuming that the linear regression model y = c1x1+c2x2+…+c m x m , where y is the predicted meter data value, and x1, x2, …, x m Input features, c1, c2, …, c m are the parameters of the model; the input features include historical data, time, meter load, electricity consumption patterns in the area where the meter is located, weather conditions, special events, and the equipment model and parameters of the meter;

[0018] Set the dynamic threshold T d , and use the mean μ and standard deviation σ of historical data to calculate: T d=μ+kσ, where k is an adjustment coefficient; define the current acquisition interval as S c , the last collection interval is S p ; Adjust the collection interval based on the comparison between the predicted data and the dynamic threshold:

[0019] like Indicates that the data changes greatly and the collection interval needs to be shortened. The new collection interval S c =S p ×r1, where r1<1 is the reduction coefficient;

[0020] like Indicates that the data changes smoothly and the collection interval needs to be increased. The new collection interval S c =S p ×r2, where r2>1 is the increased proportionality coefficient.

[0021] In the above scheme, in step S1, the asset data of the operating electric meter includes a device identification, a device asset number, a device name, a device communication address, a device communication protocol and a device status.

[0022] Furthermore, in the above scheme, in step S2, the network information of the electric meter includes an IP address and a device communication port.

[0023] Furthermore, in the above scheme, in step S3, the electric meter information includes an asset number, a metering point name, a primary and secondary identification, and a measuring point number.

[0024] Furthermore, in the above scheme, in step S4, the electricity meter collection information table includes an electricity meter collection time scale table of 20 time scales.

[0025] Furthermore, in the above scheme, in step S5, the collection items include four collection items: "forward active power", "forward reactive power", "reverse active power" and "reverse reactive power".

[0026] A computer-readable storage medium, the computer-readable storage medium comprising a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the method for quickly generating acquisition parameter data by an operating terminal as described in any one of claims 1 to 6.

[0027] A processor, the processor is used to run a program, wherein when the program is run, the method for quickly generating acquisition parameter data by an operating terminal as described in any one of claims 1 to 6 is executed.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention reads the basic data of all electric meters by running the terminal, and generates an independent reading parameter table, a network information table, an electric meter collection information table, an electric meter collection time scale table and an electric meter electricity reading table from the basic data table; different query programs are provided for query, thereby avoiding the need for each program to query and repeatedly query the basic data table to generate technical documents, thereby improving query efficiency.

[0030] 2. The collection files generated by the present invention can meet the collection requirements of the metering master station. At the same time, technical personnel and business personnel do not need to pay attention to the actual logic and technical logic. They can quickly generate accurate collection files with one click, thereby providing the most basic guarantee for the collection stability and reliability of the master station system.

[0031] 3. The present invention reduces manual maintenance workload through automated tools and improves the efficiency and accuracy of data collection. Through one-click file generation and sharing, the data management process is simplified. The role-customized user interface and communication and collaboration platform improve the efficiency of cross-departmental collaboration. The abnormal detection and intelligent prompt functions speed up the response and resolution of problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0033] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0036] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0037] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0038] See also Figure 1 A method for quickly and adaptively generating collection archive data based on an operation terminal includes collecting data of a plurality of electric meters by the operation terminal, reading basic data of all electric meters through the operation terminal, and generating an independent reading parameter table, a network information table, an electric meter collection information table, an electric meter collection time scale table and an electric meter power reading table based on the basic data table for query by a corresponding query program, specifically including the following steps:

[0039] S1. Obtain basic data tables of several electric meters by running the terminal, obtain electric meter asset data from all basic data tables, and generate a reading parameter table;

[0040] S2, obtaining network information data of all electric meters from the reading parameter table, and generating a network information table;

[0041] S3, obtain all the meter information from the reading parameter table and generate the meter collection information table;

[0042] S4, generating an electric meter collection time stamp table containing multiple time stamps according to the electric meter collection information table;

[0043] S5, generating an electric meter electricity reading table according to the collection items generated by the measuring point number of the electric meter collection information table;

[0044] S6. Generate the reading parameter table, network information table, meter collection information table, meter collection time scale table and meter electricity reading table on the web page in a one-click manner to form a collection file.

[0045] The above solution achieves efficient management and query of meter data by constructing interrelated data tables. By automatically generating reading parameter tables, network information tables, meter collection information tables, meter collection time scale tables, and meter power reading tables, repeated queries on basic data are reduced, and the efficiency and accuracy of data processing are improved. This correlation not only optimizes the data structure and reduces data redundancy, but also enhances the accessibility and analysis capabilities of the data, making data queries faster and more intuitive, thereby improving the technical effect of the overall system.

[0046] Here, a dynamic hierarchical distributed data acquisition algorithm adapted to the above scheme is provided, which is used to adopt hierarchical distributed construction for steps S1-S5:

[0047] 1) The electric meters are divided into multiple areas, and a domain head is set in each area; the domain head is responsible for collecting data information of the electric meters in the area. Specifically, the electric meters are divided into multiple areas according to geographical location, electricity consumption type or other relevant factors, and a domain head is set in each area. An intelligent domain head election mechanism can also be introduced. When the domain head fails or its performance degrades, a suitable new domain head can be automatically and quickly elected to ensure the continuity of data collection.

[0048] 2) Deploy mobile agents at the domain head to enable them to move between meters within the domain. Mobile agents with intelligent path planning capabilities can be used to select the optimal collection path based on the real-time status of the meter and the network topology, reducing collection time and energy consumption.

[0049] The mobile agent reads basic data from the meter, including asset data such as device identification, device asset number, device name, device communication address, device communication protocol, and device status, and generates a reading parameter table. The network information data of the meter, such as the IP address and device communication port, is obtained from the reading parameter table to generate a network information table. The meter information, including the asset number, metering point name, primary and secondary identification, and measurement point number, is obtained from the reading parameter table to generate a meter collection information table. A meter collection time stamp table containing 20 time stamps is generated based on the meter collection information table. The four collection items of "forward active power", "forward reactive power", "reverse active power", and "reverse reactive power" are generated based on the measurement point number of the meter collection information table to generate a meter electricity reading table.

[0050] 3) Through the distributed computing architecture, each domain head can communicate and interact with each other and coordinate data collection between domains.

[0051] A prediction model based on machine learning is used to make more accurate predictions of the changing trends of meter data, so that the collection interval can be adjusted more intelligently. A dynamic threshold is defined, which is automatically adjusted based on the analysis of historical data and real-time data, making the adjustment of the collection interval more flexible and adaptable to actual conditions. A feedback mechanism can also be introduced to dynamically adjust the collection strategy of the mobile agent and the coordination strategy of the central manager according to the quality and completeness of the data collected each time to improve the reliability of data collection.

[0052] The collection interval adjustment definition is: the collection window width obtained by the domain head is m, {D i |i=1,2,…,m} represents the meter data obtained at m moments; the sampling window width is defined as n, n<m, {D j|j=m-n+1,…,m} represents the meter data of the last n moments selected from the acquisition window; define ΔD m+1 is the predicted value D′ m+1 and the true value D m+1 The absolute value of the difference, ΔD m+1 =|D m+1 -D′ m+1 |; The average value of the sampling window is

[0053] Use the linear regression model for prediction, assuming that the linear regression model y = c1x1+c2x2+…+c m x m , where y is the predicted meter data value, and x1, x2, …, x m Input features, c1, c2, …, c m are the parameters of the model; the input features include historical data, time, meter load, electricity consumption patterns in the area where the meter is located, weather conditions, special events, and the equipment model and parameters of the meter; the meter load refers to the current electricity consumption, peak electricity consumption, etc., the electricity consumption pattern in the area where the meter is located refers to the peak electricity consumption period and seasonal electricity consumption changes in the area, and special events refer to holidays, large-scale events, etc. These events may lead to changes in electricity consumption behavior;

[0054] Set the dynamic threshold T d , and use the mean μ and standard deviation σ of historical data to calculate: T d =μ+kσ, where k is an adjustment coefficient; define the current acquisition interval as S c , the last collection interval is S p ; Adjust the collection interval based on the comparison between the predicted data and the dynamic threshold:

[0055] like Indicates that the data changes greatly and the collection interval needs to be shortened. The new collection interval S c =S p ×r1, where r1<1 is the reduction coefficient;

[0056] like Indicates that the data changes smoothly and the collection interval needs to be increased. The new collection interval S c =S p ×r2, where r2>1 is the increased proportionality coefficient.

[0057] The hierarchical distributed model and mobile agents improve the collection efficiency, reduce time and energy consumption, and better meet the actual application needs. In particular, the introduction of detailed data enhances data accuracy and can better grasp the trend of data changes; it can dynamically adjust according to the status of the meter and network, adapt to environmental changes, and ensure reliability; and optimize resource allocation by reasonably adjusting the time interval and collection interval.

[0058] Wherein, in step S1, the asset data of the operating electric meter includes the device identification, the device asset number, the device name, the device communication address, the device communication protocol and the device status.

[0059] Here, in step S2, the network information of the electric meter includes an IP address and a device communication port.

[0060] Wherein, in step S3, the electric meter information includes the asset number, the metering point name, the primary and secondary identifications and the measuring point number.

[0061] Here, in step S4, the electric meter collection information table includes an electric meter collection time scale table of 20 time scales.

[0062] Here, in step S5, the collection items include four collection items: "forward active power", "forward reactive power", "reverse active power" and "reverse reactive power".

[0063] The innovation of this technical solution lies in its efficient and systematic method of collecting electricity meter data, which significantly improves the efficiency and accuracy of data management through automated processes. The technical principle is to use the operating terminal to conduct in-depth analysis of the basic data of the electricity meter, and through a series of carefully designed steps, generate multi-dimensional data tables, providing a comprehensive and detailed information basis for data query and analysis.

[0064] The specific operation process is as follows:

[0065] First, the operating terminal collects comprehensive basic data of the electricity meter, including key information such as equipment identification, asset number, equipment name, communication address, communication protocol and equipment status. These data form the basis of the reading parameter table.

[0066] Extract network information such as IP address and communication port from the reading parameter table to form a network information table.

[0067] Construct the meter collection information table based on key information such as asset number and metering point name.

[0068] Based on the collection information table, a meter collection time stamp table containing 20 different time stamps and a meter power reading table for four main collection items - forward active power, forward reactive power, reverse active power and reverse reactive power - were created.

[0069] Finally, all these data tables are generated on the web page with one-click operation, forming a complete collection archive that is easy to manage and query.

[0070] In practical applications, this method can greatly enhance the remote monitoring capability of the power system, ensure the real-time and accuracy of data, and provide strong data support for the maintenance and troubleshooting of the power system. The automated data collection and collation process not only reduces the complexity of manual operations, but also greatly reduces the possibility of errors, thereby improving overall work efficiency. In smart grids, the present invention can be integrated with more smart devices and systems to achieve more efficient data exchange and processing.

[0071] The present invention can automatically generate collection parameter tables, network information tables, electric meter collection information tables, etc. for the special circumstances of the Guangxi Zhuang Autonomous Region 102 communication protocol, effectively solving the tedious and error problems of technicians when manually maintaining parameter tables, while reducing the communication barriers between business personnel and technicians due to unfamiliarity with each other's business. The introduction of automated processes not only improves the efficiency of parameter data generation, but also reduces the error rate by reducing manual operations, speeding up the progress of troubleshooting collection anomalies and on-site joint debugging. In addition, through the one-click web page generation of collection archives, rapid sharing and review of data is achieved, thereby significantly improving the efficiency and accuracy of the entire data collection process, and solving the existing communication difficulties and slow progress problems.

[0072] The present invention also discloses a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned method for quickly generating acquisition parameter data by the operating terminal, specifically according to the above-mentioned method steps.

[0073] The present invention also discloses a processor, which is used to run a program, wherein when the program is running, a method for a running terminal to quickly generate acquisition parameter data is executed, specifically according to the above method steps.

[0074] To sum up, the collection files generated by this embodiment can meet the collection requirements of the metering master station. At the same time, technical personnel and business personnel do not need to pay attention to the actual logic and technical logic. They can quickly generate accurate collection files with one click, thereby providing the most basic guarantee for the collection stability and reliability of the master station system.

[0075] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0076] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0077] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0078] If the integrated unit 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, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0079] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for quickly and adaptively generating collection archive data based on an operation terminal, comprising the operation terminal collecting data from a plurality of electric meters, characterized in that: The basic data of all electric meters are read by running the terminal, and an independent reading parameter table, network information table, electric meter collection information table, electric meter collection time scale table and electric meter power reading table are generated based on the basic data table for the corresponding query program to query, which specifically includes the following steps: S1. Obtain basic data tables of several electric meters by running the terminal, obtain electric meter asset data from all basic data tables, and generate a reading parameter table; S2, obtaining network information data of all electric meters from the reading parameter table, and generating a network information table; S3, obtain all the meter information from the reading parameter table and generate the meter collection information table; S4, generating an electric meter collection time stamp table containing multiple time stamps according to the electric meter collection information table; S5, generating an electric meter electricity reading table according to the collection items generated by the measuring point number of the electric meter collection information table; S6. Generate the reading parameter table, network information table, meter collection information table, meter collection time scale table and meter electricity reading table on the web page in a one-click manner to form a collection file.

2. A method for rapidly adaptively generating collected archive data based on an operating terminal according to claim 1, characterized in that: A hierarchical distributed construction is used for steps S1-S5: 1) The electric meters are divided into multiple areas, and a domain head is set in each area; the domain head is responsible for collecting data information of the electric meters in the area; 2) Deploy a mobile agent at the domain head to enable it to move between meters within the domain; the mobile agent reads basic data from the meter; and obtains the network information data of the meter from the reading parameter table; Obtain meter information from the reading parameter table; 3) Through the distributed computing architecture, each domain head communicates and interacts with each other, coordinating data collection between domains; The collection interval adjustment definition is: the collection window width obtained by the domain head is m, {D i |i=1,2,…,m} represents the meter data obtained at m moments; the sampling window width is defined as n, n<m, {D j |j=m-n+1, ​​..., m} represents the meter data of the last n moments selected from the acquisition window; define ΔD m+1 is the predicted value D′ m+1 and the true value D m+1 The absolute value of the difference, ΔD m+1 =|D m+1 -D′ m+1 |; The average value of the sampling window is Use the linear regression model for prediction, assuming that the linear regression model y = c1x1+c2x2+…+c m x m , where y is the predicted meter data value, and x1, x2, …, x m Input features, c1, c2, …, c m are the parameters of the model; the input features include historical data, time, meter load, electricity consumption patterns in the area where the meter is located, weather conditions, special events, and the equipment model and parameters of the meter; Set the dynamic threshold T d , and use the mean μ and standard deviation σ of historical data to calculate: T d =μ+kσ, where k is an adjustment coefficient; define the current acquisition interval as S c , the last collection interval is S p ; Adjust the collection interval based on the comparison between the predicted data and the dynamic threshold: like Indicates that the data changes greatly and the collection interval needs to be shortened. The new collection interval S c =S p ×r1, where r1<1 is the reduction coefficient; like Indicates that the data changes smoothly and the collection interval needs to be increased. The new collection interval S c =S p ×r2, where r2>1 is the increased proportionality coefficient.

3. The method for rapidly and adaptively generating collected archive data based on an operating terminal according to claim 1, characterized in that: In step S1, the asset data of the operating electric meter includes device identification, device asset number, device name, device communication address, device communication protocol and device status.

4. The method for rapidly and adaptively generating collected archive data based on an operating terminal according to claim 1, characterized in that: In step S2, the network information of the electric meter includes an IP address and a device communication port.

5. The method for rapidly and adaptively generating collected archive data based on an operating terminal according to claim 1, characterized in that: In step S3, the electric meter information includes an asset number, a metering point name, a primary and secondary identification, and a measuring point number.

6. The method for rapidly and adaptively generating collected archive data based on an operating terminal according to claim 1, characterized in that: In step S4, the electric meter collection information table includes an electric meter collection time scale table of 20 time scales.

7. The method for rapidly and adaptively generating collected archive data based on an operating terminal according to claim 1, characterized in that: In step S5, the collection items include four collection items: "forward active power", "forward reactive power", "reverse active power" and "reverse reactive power".

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute a method for quickly and adaptively generating collected archive data based on an operating terminal as described in any one of claims 1 to 6.

9. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes a method for quickly and adaptively generating collected archive data based on an operating terminal as described in any one of claims 1 to 6.

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