A statistical efficiency improvement method and system based on real-time judgment

Abstract

The present invention discloses a statistical efficiency improvement method and system based on real-time criteria, which utilizes a real-time criterion parser to decompose and construct a criterion object; uses the criterion object as an input parameter, designs a criterion inflection point data calculator to execute criterion inflection point collection, and obtains criterion inflection point data; based on the criterion inflection point data, determines a real-time preprocessing task for the criterion inflection point data; utilizes a criterion inflection point merger to merge the criterion inflection point data set into a criterion record set; plans and designs a real-time criterion statistical service based on the criterion inflection point data real-time preprocessing task and the criterion record set obtained by the criterion inflection point merger, improves real-time statistical efficiency, and meets the timeliness of the summary index data of power generation enterprises.

Description

Technical Field

[0001] The present invention belongs to the technical field of measurement point data statistics, and in particular relates to a method and system for improving statistical efficiency based on real-time criteria. Background Art

[0002] Real-time data is generated, changed, and stored throughout the production process of industrial enterprises. It is a type of data generated in real time, synchronously measuring the manufacturing process. Business management personnel design a large number of summary indicators based on equipment principles and process flows. Using raw historical sample data from measurement points, calculation software aggregates these indicators in real time to optimize and guide the production process, maximize benefits, and avoid risks.

[0003] When collecting statistical indicator data, it is necessary to introduce criteria to filter the time range of historical samples of measurement points. Real-time criteria are formally expressed as a judgment formula composed of one or more measurement points, and the connotation is the operating time period that meets certain conditions. For example, assuming that the load measurement point of a plant is N1TS_FH, the main steam temperature measurement point is N1ZQWD, and the main steam pressure measurement point is N1ZQYL. If you want to filter the operating time period with unit load above 300MW and main steam temperature above 550℃, the criterion expression is:

[0004] 'N1TS_FH'>=300&&'N1ZQWD'>=550

[0005] The above criterion is named PJ1. If we need to calculate the integral mean of the main steam pressure during the week when PJ1 is established, we can use the formula:

[0006] f(x)=pointAvg('N1ZQYL',time('W','W+1w-1s'),'PJ1')

[0007] The pointAvg function is a function that calculates the average value of the integral of the measurement points. Time is a time period function. W is the start of the week, and W+1w-1s is the end of the week. PJ1 is the input parameter of the pointAvg function. The function result value can only be obtained after the parameter is resolved.

[0008] The real-time criterion contains n measurement points, and each measurement point has n historical sample data in the statistical period. Therefore, the algorithm space complexity is O(n 2), with a time complexity of O(n). Most real-time measurement points change at the second level, resulting in massive amounts of data. Applying real-time metrics to aggregate indicator data without optimization will severely impact the statistical efficiency of these metrics. Business management requires simultaneous aggregation of numerous metrics. In high-concurrency scenarios, low statistical efficiency for individual metrics can severely impact the stability and reliability of real-time statistical software. Therefore, improving the statistical efficiency of real-time metrics and efficiently supporting high-concurrency scenarios has become a pressing issue in the real-time statistical process. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a statistical efficiency improvement method and system based on real-time criteria in response to the deficiencies in the above-mentioned existing technologies, so as to solve the problems of low statistical efficiency of indicator data and lack of support for high concurrency scenarios in the business management process due to real-time criterion statistics, and provide statistical services efficiently and stably for real-time criterion application needs, so that the indicator data can be synchronized with the production operation process, and the data value of the indicator can be released in time to create economic benefits for the enterprise.

[0010] The present invention adopts the following technical solutions:

[0011] A statistical efficiency improvement method based on real-time criteria includes the following steps:

[0012] S1. Decompose and construct criterion objects using a real-time criterion parser;

[0013] S2, using the criterion object obtained in step S1 as an input parameter, designing a criterion inflection point data calculator to collect criterion inflection points and obtain criterion inflection point data;

[0014] S3. Based on the criterion inflection point data obtained in step S2, determining a real-time preprocessing task for the criterion inflection point data;

[0015] S4, using a criterion inflection point merger to merge the criterion inflection point data set into a criterion record set;

[0016] S5. Based on the real-time preprocessing task of the criterion inflection point data obtained in step S3 and the criterion record set obtained by the criterion inflection point merger in step S4, plan and design a real-time criterion statistical service. Use the real-time criterion statistical service to replace the statistical samples from the original historical sample data of the real-time measurement points with the criterion inflection point feature data, so as to improve the real-time statistical efficiency of business management and control indicators.

[0017] Specifically, in step S1, the decomposition and construction of the criterion object Condition is as follows:

[0018] S101, design input parameters as real-time criterion expressions and assign them to ConditionExp;

[0019] S102. Define the array variable vars, traverse the ConditionExp designed in step S101, extract the measurement point variables from ConditionExp according to the contract enclosed in English single quotes, and add them to vars until the traversal is completed;

[0020] S103. Create a real-time criterion object Condition, assign ConditionExp to Condition.exp, and assign vars to Condition.PointVars;

[0021] S104. Return the criterion object Condition and end.

[0022] Furthermore, in step S103, the real-time criterion object Condition is specifically:

[0023] Condition=f(exp,PointVars)

[0024] Specifically, step S2 is as follows:

[0025] S201. Define the variable varsMap for temporarily storing the original sample set of the measuring point variables. Use the measuring point variable as the key and the original sample set of the measuring point as the value. Iterate through the measuring point variable set PointVars of the Condition. Read the original historical sample data of each measuring point in the period from StartTime to EndTime from the real-time database and store it in varsMap.

[0026] S202, traverse the varsMap obtained in step S201, collect the unique sample timestamp set valueTimes, traverse the original historical sample set of each measuring point variable, collect the timestamps of the historical samples, remove duplicates and sort them in ascending order, until all the original historical samples of the measuring point variables are traversed;

[0027] S203, adopting a segmented parallel processing strategy, according to the number of valueTimes Len and the number of available CPU processors nCore at that time, evenly divide the processing capacity of each segment, submit the processing task in multi-threaded mode, and traverse each segment to collect the judgment inflection point data.

[0028] Furthermore, in step S203, the inflection point data ConditionPoint is as follows:

[0029] ConditionPoint=f(valueTime,pointType)

[0030] Among them, valueTime is the inflection point time, pointType is the inflection point type, when pointType = 0, the criterion inflection point is the starting point; when pointType = 1, the criterion inflection point is the ending point; when pointType = 2, the criterion inflection point is the continuing point.

[0031] Specifically, step S3 is as follows:

[0032] S301. Set a planned processing task, read the real-time criterion configuration directory every 10 minutes, traverse and execute the criterion inflection point calculator, collect the criterion inflection point data set within each real-time criterion statistical period, and store it in a relational database;

[0033] S302: When the planned processing task is executed in real time, if the user adds a new real-time criterion, it will be automatically added to the pre-processing queue and the data will be automatically restored. For the newly added criterion, a task of restoring the inflection point data from the beginning of the year to the current time will be added to supplement the inflection point data and expand the optimization period range of the newly added criterion so that the calculation period requested by the criterion falls within the optimization period range as much as possible.

[0034] S303. The real-time pre-processing task of the criterion inflection point data runs continuously, automatically responds to the change of the criterion configuration, collects the criterion inflection point data in real time, synchronizes the criterion configuration, and modifies the criterion optimization period range.

[0035] Specifically, step S4 is as follows:

[0036] S401. Receive request parameters, including conditionName, startTime, and endTime;

[0037] S402, read the optimization period category information of the criterion according to conditionName, and distinguish the optimized period from the non-optimized period by combining the startTime to endTime period;

[0038] S403. Parallel calculation of the criterion inflection point set conditionPoints for the time period from startTime to endTime. For the unoptimized time period, the criterion inflection point calculator designed in step S2 is called to collect inflection points and add them to the conditionPoints set. For the optimized time period, all criterion inflection points are directly read from the storage data table of criterion inflection points and added to the conditionPoints set.

[0039] S404, sorting the conditionPoints set according to the chronological order;

[0040] S405: Execute the MergePoints process on the criterion inflection point set conditionPoints, and return the merged criterion record set.

[0041] Furthermore, in step S405, the condition record set ConditionRecord is specifically:

[0042] ConditionRecord=f(startPoint,endPoint)

[0043] Among them, startPoint and endPoint are specific instances of the criterion inflection point ConditionPoint, which are the starting position and the ending position respectively.

[0044] Specifically, step S5 is as follows:

[0045] S501, when starting the real-time criterion statistics service, establish an access link through the real-time database connection pool, continue loading after the connection is successful, otherwise exit;

[0046] S502: The relational database is the storage medium for real-time criterion configuration and inflection point feature data. Connect to the relational database. If the connection is successful, continue loading. Otherwise, exit.

[0047] S503, loading the criterion inflection point data real-time pre-processing task obtained in step S3, serving as the criterion inflection point data producer of the criterion statistics service, continuously and stably optimizing the production of criterion inflection point data at fixed intervals, and storing it in a relational database;

[0048] S504, loading the real-time criterion statistics service with the criterion inflection point combiner as the core obtained in step S4, starting the user consumption request interface, and waiting for consumption requests;

[0049] S505. When receiving a user consumption request, the call parameters are passed to the real-time criterion statistics service through the user request interface according to the criterion name, start time and end time, and the criterion inflection point merger is called to collect inflection points and merge them into criterion records, which are fed back to the data consumer to realize the statistical application of real-time criteria.

[0050] Another technical solution of the present invention is a statistical efficiency improvement system based on real-time criteria, comprising:

[0051] Building module, using real-time criterion parser to decompose and build criterion objects;

[0052] The collection module uses the criterion object obtained by the construction module as an input parameter, designs a criterion inflection point data calculator to perform criterion inflection point collection, and obtains criterion inflection point data;

[0053] The task module determines the real-time preprocessing task of the criterion inflection point data based on the criterion inflection point data obtained by the collection module;

[0054] A merging module, which uses a criterion inflection point merger to merge the criterion inflection point data set into a criterion record set;

[0055] The improvement module plans and designs a real-time criterion statistics service based on the real-time preprocessing task of the criterion inflection point data obtained by the task module and the criterion record set obtained by the merging module criterion inflection point combiner. The real-time criterion statistics service is used to replace the statistical samples from the original historical sample data of the real-time measurement points with the criterion inflection point feature data, thereby improving the real-time statistical efficiency of business management indicators.

[0056] Compared with the prior art, the present invention has at least the following beneficial effects:

[0057] The present invention provides a statistical efficiency improvement method based on real-time criteria. By combining real-time criterion preprocessing tasks and criterion inflection point mergers, a real-time criterion statistical service is constructed, which greatly reduces the calculation scale of real-time criterion statistics, and replaces the calculation samples from massive measurement point original samples with a small amount of inflection point feature data. It solves the problems of low statistical efficiency of indicator data and lack of support for high concurrency scenarios caused by real-time criterion statistics in the business management process, and provides statistical services for real-time criterion application needs in an efficient and stable manner, so that the indicator data is synchronized with the production operation process, and the data value of the indicator is released in time to create economic benefits for the enterprise.

[0058] Furthermore, a real-time criterion parser was constructed to parse the criterion expression into a criterion object. A real-time criterion expression is a mathematical formula composed of measurement point variables. The measurement point variables were extracted from the real-time criterion expression, their validity was verified, and the criterion object was constructed, preparing data for the design of the criterion inflection point data calculator.

[0059] Furthermore, the use of criterion expressions can meet the diverse demands of operating conditions in actual field applications. Power generation companies' on-site operational control requirements are constantly changing, and their demands for data indicators inevitably change simultaneously. Using criterion expressions allows for rapid and cost-effective responses to these demands.

[0060] Furthermore, a criterion inflection point data calculator is constructed, which is designed to take the criterion object, start time and end time as input parameters, execute the criterion inflection point calculation process, collect the criterion inflection point set of the statistical period, and provide method support for further design of criterion inflection point real-time preprocessing service.

[0061] Furthermore, the criterion inflection point data is a type of characteristic data. The result of the real-time criterion calculation is a set of criterion records, and the criterion record is actually a time period. The time period has a start time and an end time. Therefore, the criterion inflection point is divided into a start point, an end point, and a continuous point. The continuous point can be converted into a start point or an end point when merged. The real-time criterion is a definition of the interception condition of the on-site working conditions. During on-site operation, the equipment operates smoothly most of the time, which determines that the number of working condition time periods will not be large. Real-time data has the characteristics of continuous storage at the second level, massive data, and no modification is allowed. If real-time data is used to count criterion records every time, it is bound to cause a waste of computing resources. The criterion inflection point data is used as the intermediate storage data of the criterion record. The small amount of data is suitable for relational database storage, and at the same time it can meet the retrieval and merging of any statistical time period.

[0062] Furthermore, a real-time preprocessing task of the criterion inflection point data is designed. At fixed intervals, the production criterion inflection point data is continuously and stably preprocessed through the criterion inflection point data calculator and stored in the database, providing key alternative data for further optimizing and improving the statistical efficiency of real-time criteria.

[0063] Furthermore, a criterion inflection point data merger is designed, and the scale of real-time criterion statistical calculation data is replaced by a small amount of criterion inflection point data instead of a massive amount of original historical samples of measurement points, thereby greatly improving statistical efficiency and supporting high-concurrency application scenarios.

[0064] Furthermore, the criteria record set is an array of operating time periods for the real-time criteria within the statistical period, representing the statistical results of the real-time criteria. Real-time criteria are used to filter for conditions that meet the criteria. Within a period of time, such as the current day, month, or year, there may be multiple periods of conditions that meet the criteria. The structure of the criteria record set, which conforms to the statistical results of the real-time criteria, facilitates the use of real-time criteria data by consumers.

[0065] Furthermore, a real-time criteria-based statistical service has been integrated into the planning. This service automatically processes criteria inflection point data in real time and provides users with efficient consolidated statistical services based on this data. As part of the overall product design, the criteria-based statistical service is integrated into the real-time calculation software platform for business management indicators. This provides efficient statistical services for computing scenarios that rely on real-time criteria, thereby improving the statistical efficiency of indicators and ensuring that indicator results are highly synchronized with production operations. This allows the timely release of the value of indicator data, guiding enterprise production, and maximizing benefits and minimizing losses, thereby creating economic benefits for the enterprise.

[0066] In summary, the present invention significantly reduces the calculation scale of real-time criteria, which can not only ensure the accuracy of the calculation results, but also support high-concurrency application scenarios. It can serve as the basic data service for the real-time calculation software of production indicators of power generation enterprises, improve the real-time statistical efficiency of indicators, and meet the timeliness of power generation enterprises' demand for summary indicator data.

[0067] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic diagram of the present invention;

[0069] Figure 2 Flowchart of the process for starting real-time criterion statistics service. DETAILED DESCRIPTION

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0071] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0072] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0073] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A alone, A and B simultaneously, or B alone. In addition, the character " / " herein generally indicates that the associated items are in an "or" relationship.

[0074] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0075] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0076] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0077] The present invention provides a statistical efficiency improvement method based on real-time criteria, which is connected to a real-time database and shares a management access link; optimizes all configured criteria in real time, monitors criterion configuration changes, continuously and stably produces criterion inflection point data, and stores it in a relational database; combines the sample features obtained by the traversal module, processes noise data, and synchronously updates the historical sample set and sample features; according to the parameter requirements of the criterion inflection point merger and the needs of the user request scenario, encapsulates it into an interface that is convenient for user request access, receives user requests, and outputs criterion records. The present invention replaces the statistical samples from a large amount of original sample data of measurement points with a small amount of criterion inflection point data by designing a real-time criterion statistical service, significantly reducing the calculation scale of the real-time criterion, ensuring the accuracy of the calculation results, and supporting high-concurrency application scenarios. The real-time criterion statistical service can be used as the basic data service for the real-time calculation software of production indicators of power generation enterprises, improving the real-time statistical efficiency of indicators and meeting the timeliness of the summary indicator data of power generation enterprises.

[0078] See also Figure 1 The present invention provides a statistical efficiency improvement method based on real-time criteria, comprising the following steps:

[0079] S1. Design a real-time criterion parser to decompose and construct criterion objects;

[0080] First, the real-time object structure is as follows:

[0081] Condition=f(exp,PointVars)

[0082] Among them, exp is the original judgment formula of the real-time criterion, and PointVars is the set of measurement point variables contained in the real-time criterion expression.

[0083] Furthermore, the process of decomposing and constructing the Condition object structure is as follows:

[0084] S101, design input parameters as real-time criterion expressions and assign them to ConditionExp;

[0085] S102. Define the array variable vars, traverse the ConditionExp designed in step S101, extract the measurement point variables from ConditionExp according to the contract enclosed in English single quotes, and add them to vars until the traversal is completed;

[0086] S103. Create a real-time criterion object Condition, assign ConditionExp to Condition.exp, and assign vars to Condition.PointVars;

[0087] S104. Return the criterion object Condition and end.

[0088] S2. Combining the real-time criterion parser obtained in step S1, and taking the criterion object as the main input parameter, design a criterion inflection point data calculator;

[0089] The design criterion inflection point data calculator is as follows:

[0090] Through the real-time criterion parser, the criterion expression is parsed into the criterion object Condition, and the time period parameters StartTime and EndTime are used as input parameters to construct the criterion inflection point data calculator and execute the criterion inflection point collection process;

[0091] The criterion inflection point structure is designed as follows:

[0092] ConditionPoint=f(valueTime,pointType)

[0093] Among them, valueTime is the inflection point time, pointType is the inflection point type, including 0, 1, 2, etc. When pointType = 0, the criterion inflection point is the starting point; when pointType = 1, the criterion inflection point is the ending point; when pointType = 2, the criterion inflection point is the continuing point.

[0094] Furthermore, the criterion inflection point collection process is specifically as follows:

[0095] S201. Define a variable varsMap for temporarily storing the original sample set of measurement point variables. varsMap is a hash structure that uses the measurement point variable as the key and the original sample set of the measurement point as the value. It traverses the measurement point variable set PointVars of the Condition and reads the original historical sample data of each measurement point in the period from StartTime to EndTime from the real-time database and stores it in varsMap.

[0096] S202, traverse varsMap, collect the non-duplicate sample timestamp set valueTimes, traverse the original historical sample set of each measurement point variable, collect the timestamps of the historical samples, remove duplicates and sort them in ascending order, until all the original historical samples of the measurement point variables are traversed;

[0097] S203, adopting a segmented parallel processing strategy to efficiently collect criterion inflection point data.

[0098] Based on the number of valueTimes (Len) and the number of available CPU processors (nCore) at the time, the processing load for each segment is evenly divided, and the processing tasks are submitted in multi-threaded mode. Each segment is traversed to collect the judgment inflection point data.

[0099] The specific data of the inflection point collected by traversing each section are as follows:

[0100] Within the thread, execute the LoopValue process, and execute the JudgeTime process for each valueTime, judging each one and filtering and collecting the judgment inflection point data. JudgeTime and LoopValue are two reusable execution processes, described in pseudo code as follows:

[0101]

[0102]

[0103] S3, combining the criterion inflection point data calculator obtained in step S2, and designing a criterion inflection point data real-time preprocessing task;

[0104] See also Figure 2 , the real-time pre-processing tasks of the design criterion inflection point data are as follows:

[0105] S301. Set a planned processing task to read the real-time criterion configuration directory every 10 minutes, traverse and execute the criterion inflection point calculator, and collect the criterion inflection point data set within each real-time criterion statistical period and store it in the relational database;

[0106] S302: For the newly added criteria, add a processing task to restore the period from the beginning of the year to the current time, supplement the inflection point data for a period of time, and expand the optimization period range of the newly added criteria so that the criterion request calculation period falls within the optimization period range as much as possible;

[0107] S303. The real-time pre-processing task of the criterion inflection point data runs continuously, automatically responds to the change of the criterion configuration, collects the criterion inflection point data in real time, synchronizes the criterion configuration, and modifies the criterion optimization period range.

[0108] S4. Design a criterion inflection point merger to merge the criterion inflection point data set into a criterion record set;

[0109] Design a criterion inflection point merger to merge the criterion inflection point data set into a criterion record set:

[0110] S401. Receive request parameters, including conditionName (criteria name), startTime (start time), and endTime (end time);

[0111] S402: Read the optimization period category information of the criterion according to conditionName, and distinguish the optimized period from the non-optimized period in combination with the requested period (startTime to endTime);

[0112] S403. Parallel request the conditionPoints set of the time period. For unoptimized time periods, call the conditionPoints calculator designed in step S2 to collect the inflection points and add them to the conditionPoints set. For optimized time periods, directly read all the conditionPoints from the storage data table and add them to the conditionPoints set.

[0113] S404, sorting the conditionPoints set according to the chronological order;

[0114] S405, executing the MergePoints process on the criterion inflection point set conditionPoints, and returning the merged criterion record set;

[0115] The structure of the criterion record is designed as follows:

[0116] ConditionRecord=f(startPoint,endPoint)

[0117] Among them, startPoint and endPoint are specific instances of the criterion inflection point ConditionPoint, which are the starting position and the ending position respectively.

[0118] Furthermore, the MergePoints process in step S405 is described as follows using pseudo code:

[0119]

[0120]

[0121] S5. Combining the real-time preprocessing task of the criterion inflection point data obtained in step S3 and the criterion inflection point merger obtained in step S4, plan and design a real-time criterion statistical service to provide stable and efficient statistical services for the real-time statistical indicator process.

[0122] The real-time statistical services for planning and design are as follows:

[0123] S501. The real-time preprocessing task for the criterion inflection point data obtained in step S3 is used as the criterion inflection point data producer for the criterion statistics service. This task automatically begins operation when the statistics service starts, continuously and stably optimizing the production of criterion inflection point data at regular intervals and storing it in a relational database. To improve processing efficiency and stability, the real-time preprocessing task for the criterion inflection point data can be designed as an independently deployed microservice, loosely coupled with the real-time criterion statistics service, interacting through the data repository.

[0124] S502: The criterion inflection point merger obtained in step S4 is used as the core component of the real-time criterion statistics service. From the perspective of data consumers, it is encapsulated into an interface that is convenient for consumers to request access in combination with a certain protocol;

[0125] S503. When starting the real-time criterion statistics service, an access link is established through the real-time database connection pool. If the connection fails, the log information of the inability to connect to the real-time database is output, and the startup is stopped and exited directly; if the connection is successful, the loading continues, the criterion inflection point real-time preprocessing task is started, and the preprocessing of the criterion inflection point begins; the criterion inflection point merger is loaded, the user request interface is started, and data requests are waited for; when the user requests, the criterion inflection point merger of step S4 is called through the criterion statistics service according to the request parameters such as the criterion name, start time and end time, the inflection points are collected and merged into criterion records, and the records are fed back to the data consumers to realize the statistical application of real-time criteria.

[0126] In another embodiment of the present invention, a statistical efficiency improvement system based on real-time criteria is provided, which can be used to implement the above-mentioned statistical efficiency improvement method based on real-time criteria. Specifically, the statistical efficiency improvement system based on real-time criteria includes a construction module, a collection module, a task module, a merging module and an improvement module.

[0127] Among them, the construction module uses the real-time criterion parser to decompose and construct the criterion object;

[0128] The collection module uses the criterion object obtained by the construction module as an input parameter, designs a criterion inflection point data calculator to perform criterion inflection point collection, and obtains criterion inflection point data;

[0129] The task module determines the real-time preprocessing task of the criterion inflection point data based on the criterion inflection point data obtained by the collection module;

[0130] A merging module, which uses a criterion inflection point merger to merge the criterion inflection point data set into a criterion record set;

[0131] The improvement module plans and designs a real-time criterion statistics service based on the real-time preprocessing task of the criterion inflection point data obtained by the task module and the criterion record set obtained by the merging module criterion inflection point combiner. The real-time criterion statistics service is used to replace the statistical samples from the original historical sample data of the real-time measurement points with the criterion inflection point feature data, thereby improving the real-time statistical efficiency of business management indicators.

[0132] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0133] Take the actual application data of Huaneng Ruijin Power Plant as an example:

[0134] The real-time judgment configuration is as follows:

[0135] name <![CDATA[Excessive SO2 emissions from Unit #3]]> Criterion Expression 'N3DCS.30HTA20CQ001'>35&&'N3DCS.MW'>100

[0136] Assume that the judgment records on February 1, 2022 are counted, and comparisons are made between using the original sample and using the judgment inflection point.

[0137] The statistical period is from 00:00:00 to 23:59:59 on February 1, 2022.

[0138]

[0139]

[0140] Comparison of this test case shows that after optimization, the calculation scale is compressed by about 99.64%, the calculation process is reduced by 2 steps, the average time consumption is increased by about 99.84%, and the calculation results remain consistent.

[0141] In summary, the present invention is a method and system for improving statistical efficiency based on real-time criteria, which is derived from the practical summary of applications. A large number of indicators in power generation enterprises use real-time criteria to define the operating time period range of indicator data to further clarify the business guidance significance of the indicators and improve the reliability of indicator data. However, the current technical means are based on the real-time statistical criteria of the original sample data of the measurement points, which is inefficient and does not support high-concurrency scenarios, and cannot meet the power generation enterprises' demand for the timeliness of indicator data. In order to solve this problem, the present method was invented after continuous design, verification and summary, and a real-time criterion statistical service was designed to replace the statistical samples from the massive original sample data of the measurement points with a small amount of criterion inflection point data, which greatly reduces the calculation scale of the real-time criterion, and can not only ensure the accuracy of the calculation results, but also support high-concurrency application scenarios. The real-time criterion statistical service can be used as the basic data service for the real-time calculation software of the production indicators of power generation enterprises, improve the real-time statistical efficiency of the indicators, and meet the power generation enterprises' demand for the timeliness of the summary indicator data.

[0142] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0143] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 steps in the process. 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.

[0144] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0146] The above content is only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A statistical efficiency improvement method based on real-time criteria, characterized in that: The following steps are involved: S1. Use the real-time criterion parser to decompose and construct the criterion object. Specifically: S101, design input parameters to assign values ​​to real-time criterion expressions ; S102. Define array variables , traverse the design of step S101 , according to the contract where the measurement point variables are enclosed in English single quotes, Extract the measurement point variables and add , until the traversal is completed; S103, create real-time judgment object ,Will Assign to ,Will Assign to ; S104. Return the criterion object ,Finish; S2. Using the criterion object obtained in step S1 as an input parameter, a criterion inflection point data calculator is designed to collect criterion inflection points and obtain criterion inflection point data, specifically: S201. Define the variable varsMap for temporarily storing the original sample set of the measuring point variables. Use the measuring point variable as the key and the original sample set of the measuring point as the value. Iterate through the measuring point variable set PointVars of the Condition. Read the original historical sample data of each measuring point in the period from StartTime to EndTime from the real-time database and store it in varsMap. S202, traverse the varsMap obtained in step S201, collect the unique sample timestamp set valueTimes, traverse the original historical sample set of each measuring point variable, collect the timestamps of the historical samples, remove duplicates and sort them in ascending order, until all the original historical samples of the measuring point variables are traversed; S203, adopting a segmented parallel processing strategy, based on the number of valueTimes Len and the number of available CPU processors nCore at the time, evenly divides the processing load of each segment, submits the processing task in a multi-threaded mode, and traverses each segment to collect the judgment inflection point data; S3. Based on the criterion inflection point data obtained in step S2, determine the criterion inflection point data real-time preprocessing task, specifically: S301. Set a planned processing task, read the real-time criterion configuration directory every 10 minutes, traverse and execute the criterion inflection point calculator, collect the criterion inflection point data set within each real-time criterion statistical period, and store it in a relational database; S302: When the planned processing task is executed in real time, if the user adds a new real-time criterion, it is automatically added to the preprocessing queue and the data is automatically restored. For the newly added criterion, a task is added to restore the inflection point data from the beginning of the year to the current time, supplement the inflection point data, and expand the optimized power production enterprise operating condition time range for the newly added criterion so that the criterion request calculation period falls within the optimized power production enterprise operating condition time range; S303: The real-time pre-processing task of the criterion inflection point data continues to run, automatically responds to the change of the criterion configuration, collects the criterion inflection point data in real time, synchronizes the criterion configuration, and modifies the criterion to optimize the operating time range of the power production enterprise; S4, using a criterion inflection point merger to merge the criterion inflection point data set into a criterion record set; S5. Based on the real-time preprocessing task of the criterion inflection point data obtained in step S3 and the criterion record set obtained by the criterion inflection point merger in step S4, plan and design a real-time criterion statistical service. Use the real-time criterion statistical service to replace the statistical samples from the original historical sample data of the real-time measurement points with the criterion inflection point feature data, so as to improve the real-time statistical efficiency of business management and control indicators.

2. The statistical efficiency improvement method based on real-time criteria according to claim 1 is characterized in that: In step S103, the real-time judgment object Specifically: 。 3. The statistical efficiency improvement method based on real-time criteria according to claim 1 is characterized in that: In step S203, the inflection point data is judged as follows: in, The turning point moment, For the inflection point type, when =0, the inflection point is the starting point; when =1, the inflection point is the end point; when =2, the inflection point is the continuation point.

4. The statistical efficiency improvement method based on real-time criteria according to claim 1 is characterized in that: Step S4 is specifically as follows: S401. Receive request parameters, including conditionName, startTime, and endTime; S402, reading the optimized power production enterprise operating condition period category information of the criterion according to conditionName, and distinguishing the optimized power production enterprise operating condition period from the non-optimized power production enterprise operating condition period in combination with the startTime to endTime period; S403. Parallel calculation of the criterion inflection point set conditionPoints for the time period from startTime to endTime. For the time period when the power production enterprise's operating conditions are not optimized, the criterion inflection point calculator designed in step S2 is called to collect inflection points and add them to the conditionPoints set. For the time period when the power production enterprise's operating conditions are optimized, all criterion inflection points are directly read from the storage data table of the criterion inflection points and added to the conditionPoints set. S404, sorting the conditionPoints set according to the chronological order; S405: Execute the MergePoints process on the criterion inflection point set conditionPoints, and return the merged criterion record set.

5. The statistical efficiency improvement method based on real-time criteria according to claim 4 is characterized in that: In step S405, the criterion record set Specifically: in, and Both are specific instances of the criterion inflection point ConditionPoint, which are the starting position and the ending position respectively.

6. The statistical efficiency improvement method based on real-time criteria according to claim 1 is characterized in that: Step S5 is specifically as follows: S501, when starting the real-time criterion statistics service, establish an access link through the real-time database connection pool, continue loading after the connection is successful, otherwise exit; S502: The relational database is a storage medium for real-time criterion configuration and inflection point feature data. Connect to the relational database. If the connection is successful, continue loading. Otherwise, exit. S503, loading the criterion inflection point data real-time pre-processing task obtained in step S3, serving as the criterion inflection point data producer of the criterion statistics service, continuously and stably optimizing the production of criterion inflection point data at fixed intervals, and storing it in a relational database; S504, loading the real-time criterion statistics service with the criterion inflection point combiner as the core obtained in step S4, starting the user consumption request interface, and waiting for consumption requests; S505. When receiving a user consumption request, the call parameters are passed to the real-time criterion statistics service through the user request interface according to the criterion name, start time and end time, and the criterion inflection point merger is called to collect inflection points and merge them into criterion records, which are fed back to the data consumer to realize the statistical application of real-time criteria.

7. A statistical efficiency improvement system based on real-time criteria, characterized in that: include: Construction module, using real-time criterion parser to decompose and construct criterion objects, decompose and construct criterion objects Specifically: Design input parameters to be assigned to real-time criterion expressions ; Defining array variables , traverse the design of step S101 , according to the contract where the measurement point variables are enclosed in English single quotes, Extract the measurement point variables and add , until the traversal is completed; Create a real-time criterion object ,Will Assign to ,Will Assign to ; Returns the criterion object ,Finish; The collection module uses the criterion object obtained by the construction module as an input parameter, designs a criterion inflection point data calculator to perform criterion inflection point collection, and obtains criterion inflection point data; The task module determines the real-time preprocessing task of the criterion inflection point data based on the criterion inflection point data obtained by the collection module, specifically: Define the variable varsMap to temporarily store the original sample set of the measurement point variables. Use the measurement point variable as the key and the original sample set of the measurement point as the value. Iterate through the measurement point variable set PointVars of the Condition, read the original historical sample data of each measurement point in the period from StartTime to EndTime from the real-time database, and put it into varsMap. Traverse the obtained varsMap, collect the unique sample timestamp set valueTimes, traverse the original historical sample set of each measurement point variable, collect the timestamps of the historical samples, remove duplicates and sort them in ascending order until all the original historical samples of the measurement point variables are traversed; Adopting a segmented parallel processing strategy, based on the number of valueTimes (Len) and the number of available CPU processors (nCore) at the time, the processing load is evenly divided into each segment, and processing tasks are submitted in a multi-threaded mode. Each segment is traversed to collect the judgment inflection point data; The merging module uses the criterion inflection point merger to merge the criterion inflection point dataset into the criterion record set, specifically: Set up a planned processing task, read the real-time criterion configuration directory every 10 minutes, traverse and execute the criterion inflection point calculator, collect the criterion inflection point data set within each real-time criterion statistical period, and store it in a relational database; When the planned processing task is executed in real time, if the user adds a new real-time criterion, it will be automatically added to the preprocessing queue and the data will be automatically restored. For the newly added criterion, a task to restore the inflection point data from the beginning of the year to this moment will be added to supplement the inflection point data and expand the optimized power production enterprise operating condition time range for the newly added criterion so that the criterion request calculation period falls within the optimized power production enterprise operating condition time range. The real-time pre-processing task of the criterion inflection point data runs continuously, automatically responds to changes in the criterion configuration, collects the criterion inflection point data in real time, synchronizes the criterion configuration, and modifies the criterion to optimize the operating time range of the power production enterprise; The improvement module plans and designs a real-time criterion statistics service based on the real-time preprocessing task of the criterion inflection point data obtained by the task module and the criterion record set obtained by the merging module criterion inflection point combiner. The real-time criterion statistics service is used to replace the statistical samples from the original historical sample data of the real-time measurement points with the criterion inflection point feature data, thereby improving the real-time statistical efficiency of business management indicators.

Images