Method, system and equipment for statistical analysis of over-limit monitoring data in the power industry

Abstract

The present invention discloses a statistical method, system and equipment for over-limit monitoring data in the electric power industry. The method includes: defining over-limit inflection point data and storage structure; defining a parallel statistical algorithm for over-limit inflection point data; constructing an over-limit inflection point data preprocessing service based on the defined over-limit inflection point data and storage structure and the parallel statistical algorithm for over-limit inflection point data, and real-time synchronous preprocessing to generate over-limit inflection point data; based on the over-limit inflection point preprocessing data, providing an over-limit monitoring point data service based on the over-limit inflection point data and constructing an over-limit data statistical service for equipment, completing the statistics of over-limit monitoring data in the electric power industry. The method can timely, accurately and effectively improve the over-limit statistical efficiency through over-limit inflection point data, and meet the over-limit data processing and statistical needs of power plant production and operation.

Description

Technical Field

[0001] The present invention belongs to the field of real-time data application, and in particular relates to a statistical method, system and equipment for over-limit monitoring data in the electric power industry. Background Art

[0002] Over-limit monitoring is an important technical means for power generation companies to monitor the safe operation of equipment during production and operation. When the generator set is running, a large amount of temperature data is collected from the boiler wall and written into the real-time database. In order to ensure the safe operation of boiler equipment, some over-limit monitoring rules will be customized to synchronously count the number of over-limits, over-limit duration, over-limit records and other data of the collection points in real time, and summarize the over-limit monitoring data of the equipment. Based on the over-limit monitoring data, the safety work of on-site operators is evaluated and scored, so that rewards and punishments are reasonable and well-founded, and people and equipment are encouraged to operate safely and continuously.

[0003] The over-limit monitoring rules are the over-limit monitoring filtering rules designed by power generation enterprises. Common over-limit monitoring rules are shown in the following table:

[0004] Table 1 Common over-limit monitoring rules

[0005] Serial number type Limits Allow continuation(s) Penalty Mechanism 1 L3 520 30 Count once every 30 seconds 2 L2 540 30 Count once every 30 seconds 3 L1 560 30 Count once every 30 seconds 4 H1 620 30 Count once every 30 seconds 5 H2 640 30 Count once every 30 seconds 6 H3 660 30 Count once every 30 seconds

[0006] Where L represents the lower limit and H represents the upper limit. The limit value can be a constant or a dynamically calculated value. The dynamic limit value is obtained based on the collected samples and combined with the curve or calculation formula. The dynamic limit value has a wide range of adaptability, such as calculating different main steam temperature target values ​​in combination with the real-time load of the unit.

[0007] The over-limit monitoring point is composed of the over-limit collection point and the over-limit monitoring rules. The over-limit record is the period record from the beginning to the end of the over-limit at the over-limit monitoring point. Generally, the over-limit extreme value is counted during the over-limit period. The extreme value of the over-limit time represents the minimum sample value during the over-limit period, and vice versa.

[0008] At present, the main means for power generation companies to process and count over-limit data is to read historical samples from the real-time database and count them in chronological order. The amount of data from over-limit collection points is extremely large and belongs to second-level data. This method is very slow in statistical efficiency and cannot support the over-limit data statistics scenario of a large number of over-limit collection points. Moreover, each statistics is read and counted immediately, which will have a great impact on the CPU, memory, network IO, etc. of the application server, and may cause the system to crash instantly.

[0009] Nowadays, power generation companies have more and more data requirements for safety indicators, and their timeliness requirements are getting higher and higher. They need to provide real-time over-limit alarms, and quickly and efficiently obtain over-limit statistics for shifts, the day, the month, and the inter-shift month. However, current statistical processing methods can no longer meet the application scenario requirements of power generation companies for over-limit data. Summary of the invention

[0010] The technical problem to be solved by the present invention is to provide a method, storage medium and computing device for statistically analyzing over-limit monitoring data based on over-limit inflection point data in response to the above-mentioned deficiencies in the prior art. The method can improve the over-limit statistical efficiency in a timely, accurate and effective manner through the over-limit inflection point data, and meet the over-limit data processing and statistical needs of the power plant production and operation.

[0011] The present invention adopts the following technical solutions:

[0012] A statistical method for over-limit monitoring data in the power industry comprises the following steps:

[0013] Define the over-limit inflection point data and storage structure; define the parallel statistical algorithm for over-limit inflection point data;

[0014] Based on the defined over-limit inflection point data and storage structure, and the parallel statistical algorithm of over-limit inflection point data, an over-limit inflection point data preprocessing service is built to generate over-limit inflection point data through real-time synchronous preprocessing.

[0015] Based on the over-limit inflection point preprocessing data, the over-limit monitoring point data service based on the over-limit inflection point data and the construction of equipment-oriented over-limit data statistics service, the over-limit monitoring data statistics of the power industry are completed.

[0016] As a further improvement of the present invention, the definition of over-limit inflection point data and storage structure specifically includes:

[0017] The sample value X of the over-limit collection point is specifically:

[0018] X=f(t,v,q)

[0019] Among them, t is the sample time, v is the sample value, and q is the sample quality;

[0020] The specific over-limit record R is:

[0021] R=f(lt,x s ,x ex ,x e )

[0022] Among them, lt is the over-limit type, x s is the start sample value of the over-limit, x ex is the extreme sample value during the over-limit period, x e It is the over-limit end sample value. The over-limit type refers to the over-limit type defined in the over-limit monitoring rules;

[0023] Therefore, the over-limit inflection point data R is defined x Specifically:

[0024] R x =f(xTime,lt,ltv,xVal)

[0025] Among them, xTime is the time stamp of the sample value, lt is the limit type of the limit record, ltv is the limit value, and xVal is the value of the sample value;

[0026] The over-limit inflection point data is the basic data for statistical over-limit monitoring data, and further defines the over-limit inflection point data and storage structure.

[0027] As a further improvement of the present invention, each over-limit monitoring data has an independent over-limit inflection point data storage table, and the vertical division tables of the over-limit inflection point data storage table do not cross.

[0028] As a further improvement of the present invention, the parallel statistical algorithm of the over-limit inflection point data is specifically:

[0029] X={x t1 ,x t2 ,x t3 ,....x tn} is a sample set of the over-limit collection point TagA for a period of time. According to the over-limit monitoring rule Rule, the over-limit inflection point data is filtered and calculated; the number of processor cores of the computing server is obtained, and the available core number cpuNum is strategically allocated;

[0030] Get the data volume xLen of the sample set X and calculate the parallel task segmentation component jobWorkCount as follows:

[0031] jobWorkCount=xLen / cpuNum

[0032] According to jobWorkCount, the sample set X is divided into several sample set groups. A parallel task pool is established based on the Fork / Join mode. Each sample set group is submitted to the task pool to perform filtering calculations. Each calculation task cyclically filters and calculates each sample to generate corresponding over-limit inflection point data. Each sample set group generates an over-limit inflection point data set, and keeps the time continuous and orderly.

[0033] After waiting for all sample set grouping and filtering tasks to be completed, merge and sort the out-of-limit inflection point data set in ascending time order, loop through the out-of-limit inflection point data set, and retain the first inflection point of the same type according to the limit type of the inflection point, filter out the extreme inflection points, and remove the non-extreme inflection points of the same type in the middle until the limit type is different, which is recorded as the end inflection point. Loop according to this process until the traversal is completed to achieve deduplication of the out-of-limit inflection point data set.

[0034] As a further improvement of the present invention, the construction of the over-limit inflection point data preprocessing service is specifically:

[0035] Starting from the start time, set the preprocessing cycle, execute the parallel statistical algorithm of the over-limit inflection point data within the cycle, and wait for the plan to be executed outside the cycle; filter each over-limit monitoring point set in turn, first read the historical samples of the current over-limit monitoring point in this cycle, input the parallel statistical algorithm of the over-limit inflection point data, filter to generate the valid over-limit inflection point data set of the monitoring point, and then store the inflection point set in the relational database; at this point, the data conversion of the over-limit statistical sample is completed, that is, the massive sample data is converted into the over-limit inflection point data with a limited amount of data.

[0036] As a further improvement of the present invention, it also includes: when configuring to add a new over-limit monitoring point, the over-limit inflection point data preprocessing service automatically creates and completes the inflection point data recovery task from the past moment to the current moment, so that the inflection point data has been preprocessed in the recent statistical period.

[0037] As a further improvement of the present invention, the over-limit monitoring point data service based on the over-limit inflection point data is constructed as follows:

[0038] According to the name of the over-limit collection point and the name of the over-limit monitoring rule, obtain the corresponding over-limit monitoring point information;

[0039] According to the monitoring point information, find the corresponding over-limit inflection point data preprocessing data storage table;

[0040] The over-limit inflection point data preprocessing table reads all the over-limit inflection point data of the statistical period. If the start and end times of the statistical period are not included, the inflection point data of the start and end times are supplemented; the supplemented over-limit inflection point data set is traversed, connected and merged to generate an over-limit record array, and the over-limit record array is returned; the over-limit record array is applied to summarize and count the number of over-limit times, over-limit duration and over-limit deduction points.

[0041] As a further improvement of the present invention, the device-oriented over-limit statistics service is constructed as follows:

[0042] Divide the over-limit monitoring points into several groups according to the collection location, and cyclically filter and count the over-limit records of each group;

[0043] When counting the over-limit records within a group, the over-limit monitoring point is used as the cutting unit and the over-limit statistical algorithm based on the over-limit inflection point data is used to complete the statistical work; the over-limit records of each equipment group are summarized to form a set of over-limit records of the equipment; the number of over-limit times, over-limit duration and over-limit deduction points of the equipment are summarized based on the over-limit record set of the equipment.

[0044] An over-limit monitoring data statistics system for the power industry, comprising:

[0045] A definition module is used to define the over-limit inflection point data and storage structure; define the parallel statistical algorithm of the over-limit inflection point data;

[0046] The first service construction module is used to construct an over-limit inflection point data preprocessing service according to the defined over-limit inflection point data and storage structure and the parallel statistical algorithm of the over-limit inflection point data, and to generate the over-limit inflection point data by real-time synchronous preprocessing;

[0047] The second service construction module is used to provide over-limit monitoring point data services based on over-limit inflection point preprocessing data and over-limit data statistics services for equipment, so as to complete the over-limit monitoring data statistics of the power industry.

[0048] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the statistical method for over-limit monitoring data in the electric power industry when executing the computer program.

[0049] The beneficial effects of the present invention are embodied in:

[0050] The statistical method of over-limit data of the present invention first defines over-limit inflection point data and storage structure, defines a parallel statistical algorithm for over-limit inflection point data; then constructs an over-limit inflection point data preprocessing service; constructs an over-limit data statistical service based on the over-limit inflection point data; constructs an over-limit data statistical service for equipment; optimizes and improves the statistical efficiency of over-limit data, and supports the data requirements of a high concurrent access environment. Through the over-limit inflection point data, the over-limit statistical efficiency can be improved in a timely, accurate and effective manner to meet the over-limit data processing and statistical requirements of power plant production and operation. Through the present invention, electric power companies can obtain over-limit monitoring data in a timely, accurate and efficient manner, empower the production and operation process, and establish a digital safety operation barrier, while not bringing a strong impact on the real-time data system of the electric power company, reducing the direct access pressure of the real-time data system of the electric power company, and ensuring the smooth and efficient operation of the real-time data system of the electric power company. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a schematic diagram of the technical solution of the method of the present invention;

[0053] Figure 2 This is a functional module diagram of the over-limit monitor of the present invention;

[0054] Figure 3 Parallel statistical algorithm flow chart for over-limit inflection point data

[0055] Figure 4This is a schematic diagram of the structure of the power industry over-limit monitoring data statistics system according to the preferred embodiment of the present invention;

[0056] Figure 5 It is a schematic diagram of the structure of an electronic device according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0058] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0059] The importance of safe production to any enterprise is self-evident. During the production and operation of power enterprises, the healthy operation of equipment can be tracked and monitored through over-limit monitoring data. A reward and punishment mechanism can be designed based on the over-limit monitoring data to urge the formation of safety habits, truly implement safety in action, and maximize the economic value obtained from safety benefits.

[0060] like Figure 1 As shown, the present invention provides a statistical method for over-limit monitoring data in the power industry, comprising the following parts:

[0061] Define the over-limit inflection point data and storage structure;

[0062] Parallel statistical algorithms for defining over-limit inflection point data;

[0063] Build over-limit inflection point data preprocessing service;

[0064] Build over-limit data statistics service based on over-limit inflection point data;

[0065] Build an over-limit data statistics service for devices.

[0066] Specifically, the over-limit inflection point data preprocessing service is designed based on the data definition, storage structure, and parallel statistical algorithm of the over-limit inflection point, and the over-limit inflection point data is generated by real-time synchronous preprocessing. Based on the comprehensive induction of the over-limit inflection point preprocessing data, the over-limit statistical service and the over-limit data statistical service of the equipment are designed, so that the statistical sample objects are converted from the time series sample data of the over-limit collection point to the over-limit inflection point data, which optimizes and improves the statistical efficiency of the over-limit monitoring data in the power industry and supports the data requirements of the high concurrent access environment.

[0067] Specifically, the sample value X of the over-limit collection point is:

[0068] X=f(t,v,q)

[0069] Among them, t is the sample time, v is the sample value, and q is the sample quality.

[0070] The specific over-limit record R is:

[0071] R=f(lt,x s ,x ex ,x e )

[0072] Among them, lt is the over-limit type, x s is the start sample value of the over-limit, x ex is the extreme sample value during the over-limit period, x e It is the over-limit end sample value. The over-limit type refers to the over-limit type defined in the over-limit monitoring rule.

[0073] Therefore, the over-limit inflection point data R is defined x Specifically:

[0074] R x =f(xTime,lt,ltv,xVal)

[0075] Among them, xTime is the time scale of the sample value, lt is the limit type of the limit record, ltv is the limit value, and xVal is the value of the sample value. If lt=Null, this inflection point does not exceed the limit.

[0076] The over-limit inflection point data is the basic data for statistical over-limit monitoring data, and its storage structure design is as follows:

[0077] Table 2 Over-limit inflection point data storage structure table

[0078] Serial number Fields type meaning Remark 1 ValTime long Sample time xTime, primary key index 2 LimitType varchar(10) Limit type lt 3 LimitValue varchar(20) The current limit ltv 4 Val number Sample Value xVal

[0079] An independent over-limit inflection point data storage table is designed for each over-limit monitoring point, and the vertical sub-tables do not cross each other, which improves the reading and writing efficiency of the inflection point data.

[0080] Specifically, Figure 3 As shown in Figure 1, the parallel statistical algorithm for over-limit inflection point data is specifically as follows:

[0081] Assume X = {x t1 ,x t2 ,x t3 ,....x tn} is a sample set of the over-limit collection point TagA for a period of time. According to the over-limit monitoring rule Rule, the over-limit inflection point data is filtered and calculated. Multi-core design has become the mainstream of computing architecture. The number of processor cores of the computing server is obtained, and the number of available cores cpuNum is strategically allocated.

[0082] First, obtain the data volume xLen of the sample set X and calculate the parallel task segmentation component jobWorkCount, as shown below:

[0083] jobWorkCount=xLen / cpuNum

[0084] According to jobWorkCount, the sample set X is divided into several sample set groups. A parallel task pool is established based on the Fork / Join mode, and each sample set group is submitted to the task pool to perform filtering calculations. Each calculation task loops to filter and calculate each sample to generate corresponding over-limit inflection point data. In this way, each sample set group corresponds to an over-limit inflection point data set, and the time is kept continuous and orderly. After waiting for all sample set group filtering tasks to be completed, the over-limit inflection point data sets are merged and sorted in ascending order of time, and the over-limit inflection point data sets are looped through. According to the limit type of the inflection point, the first inflection point of the same type is retained, the extreme inflection points are screened out, and the non-extreme inflection points of the same type in the middle are removed until the limit type is different, which is recorded as the end inflection point. This process is repeated until the traversal is completed, and the over-limit inflection point data set is effectively deduplicated.

[0085] Specifically, the construction of over-limit inflection point data preprocessing service is as follows:

[0086] Starting from 00:00:00, the preprocessing cycle is set to 10 minutes. The parallel statistical algorithm of the over-limit inflection point data is executed within the cycle, and the plan is waited for execution outside the cycle. Each over-limit monitoring point set is filtered in turn. First, the historical samples of the current over-limit monitoring point in this cycle are read, and the over-limit inflection point data parallel statistical algorithm is input to filter and generate the valid over-limit inflection point data set of the monitoring point, and then the inflection point set is stored in the relational database. At this point, the data conversion of the over-limit statistical sample is completed, that is, the massive sample data is converted into the over-limit inflection point data with a limited amount of data.

[0087] When configuring and adding a new over-limit monitoring point, the over-limit inflection point data preprocessing service automatically creates and completes the inflection point data recovery task from the beginning of last year to the current moment, ensuring that the inflection point data has been preprocessed in the recent statistical period.

[0088] Specifically, the construction of over-limit monitoring point data service based on over-limit inflection point data is as follows:

[0089] The application requests the input of parameters such as "overlimit collection point name, overlimit monitoring rule name, statistical period", etc., and obtains the corresponding overlimit monitoring point information according to the overlimit collection point name and overlimit monitoring rule name; finds the corresponding overlimit inflection point data preprocessing data storage table according to the monitoring point information; reads all overlimit inflection point data of the statistical period from the overlimit inflection point data preprocessing table, and if it does not contain the start and end times of the statistical period, supplements the inflection point data of the start and end times; traverses the supplemented overlimit inflection point data set, connects and merges to generate an overlimit record array, and returns the overlimit record array. Based on the overlimit record array, the application summarizes and counts the number of overlimits, overlimit duration, overlimit deduction points and other data.

[0090] Specifically, building a device-oriented over-limit statistics service is as follows:

[0091] Generally, there are several over-limit monitoring points for equipment. These over-limit monitoring points are divided into several groups according to the collection location, and the over-limit records of each group are filtered and counted in a loop. When counting the over-limit records within a group, the over-limit monitoring point is used as the cutting unit and the "over-limit statistical algorithm based on over-limit inflection point data" is called in parallel to complete the statistical work quickly and efficiently. The over-limit records of each group of equipment are summarized to form an over-limit record set of the equipment; the over-limit record set of the equipment is used to summarize the number of over-limits, over-limit duration, over-limit deduction points and other data of the equipment.

[0092] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments.

[0093] like Figure 2 As shown, the over-limit monitor function module includes a real-time database access connection pool, an over-limit inflection point data preprocessing service, an over-limit monitoring point data service, an over-limit monitoring group data service and a user request interface.

[0094] When the over-limit monitor is started, it first establishes an access link through the real-time database connection pool. If the connection fails, it outputs the log information that it cannot connect to the real-time database, stops the startup and exits directly; if the connection is successful, it continues to load the over-limit inflection point data preprocessing service; the over-limit inflection point data preprocessing service starts the real-time preprocessing scheduling service, and performs preprocessing work every 10 minutes; if an interruption is found at startup, a recovery task is automatically created, and an independent recovery task is submitted to automatically complete the recovery of the over-limit inflection point data during the interruption, ensuring that the over-limit inflection point data is complete and continuous; the over-limit monitoring point data service and the over-limit monitoring group data service are loaded, ready to wait for statistical requests forwarded by the user interface; the user request interface is published based on multiple protocols, which is convenient for the client to access in multiple ways. In addition, the user request interface should be designed and packaged based on user needs to meet the user's data needs as much as possible.

[0095] Taking into account the weak concurrency of the real-time database system, a real-time database access connection pool is designed, and a multi-connection method is adopted to indirectly enhance the parallel processing capability of real-time data access, providing a basic guarantee for the stable and efficient operation of the entire over-limit monitor.

[0096] like Figure 4 As shown, another object of the present invention is to provide a statistical system for over-limit monitoring data in the power industry, comprising:

[0097] A definition module is used to define the over-limit inflection point data and storage structure; define the parallel statistical algorithm of the over-limit inflection point data;

[0098] The first service construction module is used to construct an over-limit inflection point data preprocessing service according to the defined over-limit inflection point data and storage structure and the parallel statistical algorithm of the over-limit inflection point data, and to generate the over-limit inflection point data by real-time synchronous preprocessing;

[0099] The second service construction module is used to provide over-limit monitoring point data services based on over-limit inflection point preprocessing data and over-limit data statistics services for equipment, so as to complete the over-limit monitoring data statistics of the power industry.

[0100] The method for statistically analyzing over-limit monitoring data in the electric power industry comprises the following steps:

[0101] Define the over-limit inflection point data and storage structure; define the parallel statistical algorithm for over-limit inflection point data;

[0102] Based on the defined over-limit inflection point data and storage structure, and the parallel statistical algorithm of over-limit inflection point data, an over-limit inflection point data preprocessing service is built to generate over-limit inflection point data through real-time synchronous preprocessing.

[0103] Based on the over-limit inflection point preprocessing data, the over-limit monitoring point data service based on the over-limit inflection point data and the construction of equipment-oriented over-limit data statistics service, the over-limit monitoring data statistics of the power industry are completed.

[0104] like Figure 5 As shown, the third object of the present invention is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the statistical method for over-limit monitoring data in the power industry when executing the computer program.

[0105] The method for statistically analyzing over-limit monitoring data in the electric power industry comprises the following steps:

[0106] Define the over-limit inflection point data and storage structure; define the parallel statistical algorithm for over-limit inflection point data;

[0107] Based on the defined over-limit inflection point data and storage structure, and the parallel statistical algorithm of over-limit inflection point data, an over-limit inflection point data preprocessing service is built to generate over-limit inflection point data through real-time synchronous preprocessing.

[0108] Based on the over-limit inflection point preprocessing data, the over-limit monitoring point data service based on the over-limit inflection point data and the construction of equipment-oriented over-limit data statistics service, the over-limit monitoring data statistics of the power industry are completed.

[0109] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0114] It is also obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention.

[0115] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0116] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The second and other words are used to indicate names, but not to indicate any particular order.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A statistical method for over-limit monitoring data in the power industry, characterized in that: The following steps are involved: Define the over-limit inflection point data and storage structure; define the parallel statistical algorithm for over-limit inflection point data; Based on the defined over-limit inflection point data and storage structure, and the parallel statistical algorithm of over-limit inflection point data, an over-limit inflection point data preprocessing service is built to generate over-limit inflection point data through real-time synchronous preprocessing. Based on the pre-processing data of over-limit inflection points, the over-limit monitoring point data service based on the over-limit inflection point data and the construction of equipment-oriented over-limit data statistics service, the over-limit monitoring data statistics of the power industry are completed; The definition of over-limit inflection point data and storage structure specifically includes: Sample value of the over-limit collection point Specifically: in, is the sample time, is the sample value, is the sample quality; Over limit record Specifically: in, For over-limit type, is the start sample value of the over-limit. is the extreme sample value during the over-limit period, The over-limit end sample value; the over-limit type refers to the over-limit type defined in the over-limit monitoring rules; Therefore, the over-limit inflection point data is defined Specifically: Among them, xTime is the time stamp of the sample value, lt is the limit type of the limit record, ltv is the limit value, and xVal is the value of the sample value; The over-limit inflection point data is the basic data for statistical over-limit monitoring data, and further defines the over-limit inflection point data and storage structure; Each over-limit monitoring data has an independent over-limit inflection point data storage table, and the vertical division of the over-limit inflection point data storage table does not cross; The parallel statistical algorithm for the over-limit inflection point data is specifically: It is a sample collection of the over-limit collection point TagA for a period of time. According to the over-limit monitoring rule Rule, the over-limit inflection point data is filtered and calculated; the number of processor cores of the computing server is obtained, and the available cores are strategically allocated ; Get sample collection Amount of data , calculate the parallel task cut , as follows: according to Split sample set Generate several sample set groups, establish a parallel task pool based on the Fork / Join mode, submit each sample set group to the task pool, and perform filtering calculations; each calculation task cyclically filters and calculates each sample to generate corresponding over-limit inflection point data; each sample set group generates an over-limit inflection point data set corresponding to it, and keeps the time continuous and orderly; After waiting for all sample set grouping and filtering tasks to be completed, merge and sort the out-of-limit inflection point data set in ascending time order, loop through the out-of-limit inflection point data set, and based on the limit type of the inflection point, retain the first inflection point of the same type, filter out the extreme inflection points, and remove the non-extreme inflection points of the same type in the middle until the limit type is different, which is recorded as the end inflection point. Loop according to this process until the traversal is completed to achieve deduplication of the out-of-limit inflection point data set.

2. The statistical method for over-limit monitoring data in the power industry according to claim 1 is characterized in that: The construction of over-limit inflection point data preprocessing service is specifically as follows: Starting from the start time, set the preprocessing cycle, execute the parallel statistical algorithm of the over-limit inflection point data within the cycle, and wait for the plan to be executed outside the cycle; filter each over-limit monitoring point set in turn, first read the historical samples of the current over-limit monitoring point in this cycle, input the parallel statistical algorithm of the over-limit inflection point data, filter to generate the valid over-limit inflection point data set of the monitoring point, and then store the inflection point set in the relational database; At this point, the data conversion of the over-limit statistical sample is completed, that is, the massive sample data is converted into the over-limit inflection point data with a limited amount of data.

3. The statistical method for over-limit monitoring data in the power industry according to claim 1 is characterized in that: It also includes: when configuring to add a new over-limit monitoring point, the over-limit inflection point data preprocessing service automatically creates and completes the inflection point data recovery task from the past moment to the current moment, so that the inflection point data has been preprocessed in the recent statistical period.

4. The statistical method for over-limit monitoring data in the power industry according to claim 1 is characterized in that: The construction of over-limit monitoring point data service based on over-limit inflection point data is as follows: According to the name of the over-limit collection point and the name of the over-limit monitoring rule, obtain the corresponding over-limit monitoring point information; According to the monitoring point information, find the corresponding over-limit inflection point data preprocessing data storage table; from The over-limit inflection point data preprocessing table reads all the over-limit inflection point data of the statistical period. If the start and end times of the statistical period are not included, the inflection point data of the start and end times are supplemented; the supplemented over-limit inflection point data set is traversed, connected and merged to generate an over-limit record array, and the over-limit record array is returned; the over-limit record array is applied to summarize and count the number of over-limit times, over-limit duration and over-limit deduction points.

5. The statistical method for over-limit monitoring data in the power industry according to claim 1 is characterized in that: The specific steps to build a device-oriented over-limit statistics service are as follows: Divide the over-limit monitoring points into several groups according to the collection location, and cyclically filter and count the over-limit records of each group; When counting the over-limit records within a group, the over-limit monitoring point is used as the cutting unit and the over-limit statistical algorithm based on the over-limit inflection point data is used to complete the statistical work; the over-limit records of each group of equipment are summarized to form a set of over-limit records of the equipment; The over-limit record collection of the equipment summarizes the number of times the equipment exceeds the limit, the duration of the over-limit and the over-limit deduction points.

6. A statistical system for over-limit monitoring data in the electric power industry, which implements the statistical method for over-limit monitoring data in the electric power industry as described in any one of claims 1 to 5, characterized in that: include: A definition module is used to define the over-limit inflection point data and storage structure; define the parallel statistical algorithm of the over-limit inflection point data; The first service construction module is used to construct an over-limit inflection point data preprocessing service according to the defined over-limit inflection point data and storage structure and the parallel statistical algorithm of the over-limit inflection point data, and to generate the over-limit inflection point data by real-time synchronous preprocessing; The second service construction module is used to provide over-limit monitoring point data services based on over-limit inflection point preprocessing data and over-limit data statistics services for equipment, so as to complete the over-limit monitoring data statistics of the power industry.

7. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for statistically analyzing over-limit monitoring data in the power industry as described in any one of claims 1 to 5 when executing the computer program.

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