CN103955192B - A kind of curve form data sampling method for sewage work - Google Patents
A kind of curve form data sampling method for sewage work Download PDFInfo
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Abstract
The present invention relates to a kind of curve form data sampling method for sewage work, compared with prior art solve the defect that the big data query of browser causes the bandwidth limit of server calculating resource overload and transfer. The present invention comprises the following steps: definition sampling policy; Server parses sampling policy; Judge image data amount; Data sampling is carried out based on sampling policy; Curve plotting form. The present invention can make server end sample out from magnanimity signal data rapidly the key signal of representation signal feature.
Description
Technical field
The present invention relates to curve form data sampling techniques field, specifically a kind of curve form data sampling method for sewage work.
Background technology
In industry long distance control system, the drafting of industry curve sends inquiry request at browser to server, and acquisition number is drawn in browser execution after according to this. Industrial processes need high frequency continuously gather and preserve the industrial signal data of magnanimity, and need to review and retrieve these data in order to diagnose the actual techniques problem in production process. Particularly in sewage treatment industry, a sewage work needs every second to gather nearly thousand node datas, the data reviewed are needed to be often more than 1 year across the time, and wherein key technical index information, as particularly important in signal informations such as PH value, NH3-N, BOD, COD, electric currents, enterprise needs the curve drawn by these information datas to judge signal point exception, thus orientation problem time of origin point.
The technology taked in prior art is direct searching database, the data meeting content is all shown. This kind of method is for frequency acquisition height, and the signal data that data time span is big then brings computer system resource nervous, and the problems such as system performance decline, are not suitable for sewage treatment industry. Sewage disposal needs conventional 8 curves inquiry such as inquiry more than 1 year TP, pH, BOD, COD etc., data sampling frequency be 1 second every time. The data then all retrieved are 1*365*8*24*3600=252288000 bar. Assuming that every data needs 16 byte transmission, then need to consume nearly hundred megabyte, this all can bring great load for server or the network bandwidth. Even if server end adopts data buffering, but the load of bandwidth does not still reduce, if these 200 ten thousand data point pointwises are drawn at browser end simultaneously, not only there is no need, and bring very big load to browser.
There is part technology to propose to calculate resource and bandwidth resources load for reducing, adopt the mode of average time interval sampling and mean value calculation. Integral point data or specified time interval data are extracted in average time interval sampling, such as per half hour, gets a secondary data, although the production data curve that this kind of mode is drawn can observe the overall trend of long-time signal, but abnormal data often is ignored to fall, the abnormity point of production process cannot be searched. Mean value calculation be then to certain time interval signal data do mean value calculation, such as per hour do mean value. Adopt curve plotting in this way, although signal data overall trend can be checked, check abnormity point approximate location to a certain extent, but original signal data has been processed due to signal data, cannot accurately locate abnormity point physical location.
How to develop a kind of method that can realize the sampling of curve form data fast and become the technical problem being badly in need of solving.
Summary of the invention
It is an object of the invention to the defect causing the bandwidth limit of server calculating resource overload and transfer to solve the big data query of browser in prior art, it is provided that a kind of curve form data sampling method for sewage work solves the problem.
In order to realize above-mentioned purpose, the technical scheme of the present invention is as follows:
For a curve form data sampling method for sewage work, comprise the following steps:
Definition sampling policy, definition user-defined feature signal extracts function and sampling spot number, and browser exports number according to the sampling of browser window resolution setting signal data;
Server parses sampling policy, the Json form sampling policy script that server parses browser is sent, extracts feature sampling function, and function expression of feature being sampled does grammatical and semantic inspection, if expression formula is errorless, then by conversation strategy and configuration parameter;
Judge image data amount, retrieve qualified data, if the data amount check retrieved is less than actual needs signal point number, directly exports data, otherwise the data retrieved are sampled according to sampling policy;
Data sampling is carried out based on sampling policy;
Data are exported S set ET by curve plotting formsampData are returned to browser with Json form by coding, and browser closes SET according to output data setsampCurve plotting.
Described definition sampling policy comprises the following steps:
Defined feature signal extracts function, and characteristic signal extracts function and represents with function expression, and user is according to the function set defined feature function provided;
Calculate the unique point sampling number that fundamental function is corresponding;
Definition average time interval sampling number;
Browser, according to window size, defines output voluntarily and counts;
Browser generates Json form sampling policy and sends server end, and wherein Json script definition sampling policy form is defined as follows:
Strategy_id is sampling policy number; Avg is average time interval sampling number; Spec_cnt is whole characteristic signal sampling numbers; Output is that all sampling output is counted;
Spec is feature sampling policy set, wherein: spec_id is feature sampling policy mark; Spec_func is feature sampling function expression; Spec_samp counts for choosing based on this fundamental function expression formula.
The described data sampling that carries out based on sampling policy comprises the following steps:
By qualified data temporally span be divided into N number of data subclass, its calculation formula is as follows:
N=(Oc*Sc/Tc)+1, wherein to be data piecemeal number, Oc be N that server obtains that data export number, Tc is that server retrieves data obtain meeting search condition number of signals, Sc is that sampling policy needs signal is counted;
Data subclass is obtained average time interval sample data set by average time interval sampling respectively, first calculating sampling interval Ts, formula is as follows:
Ts=Tgap/ avg, wherein TgapFor calculating signal forming time span in data subclass, avg is average time interval sampling number;
Determine timed interval TsAfter, for data subclass signal temporally interval TsSampling, obtains data subclass average time interval sampling sample data set SETavg;
Data subclass is done data characteristics sampling according to feature policy and obtains feature sampling data,
First, data subclass use fundamental function sampling obtain Output rusults set Tempsamp;
Secondly, if signal number is greater than specific characteristic signal sampling number in the set obtained, then first selects signal according to the semanteme of fundamental function, then according to weight, data are sampled;
Finally, output characteristic sampled signal S set ETspec;
Each data acquisition is pressed signal occurs successively time sequence to merge SETavg��SETspec, obtain this data sample set and close SETsamp��
Based on sampling policy, carry out a system for data sampling, comprise Data Placement management module, operation debugging module, algorithm pond and data integration module;
Described Data Placement management module is for being divided into multiple data subclass by sampling data according to sampling policy, and is managed by each data subclass; Described operation debugging module is for managing computing pool, and for computing pool, each calculates unit according to sampling policy placement algorithm, and to management operations such as calculating cell scheduling, distribution and releases; Described algorithm pond is for setting one group of calculating unit independent parallel, and each unit algorithm that calculates is configured according to sampling policy by job scheduling module; For collecting, computing pool respectively calculates unit Output rusults to described data integration module, distributes Output rusults by task;
Described Data Placement management module is connected with algorithm pond respectively with operation debugging module, and algorithm pond is connected with data integration module.
Useful effect
A kind of curve form data sampling method for sewage work of the present invention, compared with prior art can make server end sample out from magnanimity signal data rapidly the key signal of representation signal feature. While enabling the signal trend that sewage work observes certain time span and quick position signal data abnormity point, retrieve abnormal signal point while being judged by signal overall trend thus locate solution agricultural technology problem. Set out based on browser curve plotting actual needs, at server end to the sampling of magnanimity raw data, both retained original signal data, retain again special characteristic signal, reduce server calculated load and data transfer bandwidth load. Through the data and curves of present method sample rendering, it is possible to observe the signal trend of long-time span, it is also possible to by the continuous refinement exploration time, finally retrieve abnormal signal point.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of the present invention
Fig. 2 is the connection structure iron of the data sampling system of the present invention
Wherein, the management of 1-Data Placement module, 2-job scheduling module, 3-algorithm pond, 4-data integration module.
Embodiment
For making the constitutional features to the present invention and effect of reaching have a better understanding and awareness, coordinate detailed description in order to preferred embodiment and accompanying drawing, it be described as follows:
As shown in Figure 1, a kind of curve form data sampling method for sewage work of the present invention, it is characterised in that, comprise the following steps:
The first step, definition sampling policy, definition user-defined feature signal extracts function and sampling spot number, and browser exports number according to the sampling of browser window resolution setting signal data. The data volume gathered due to data acquisition node each in sewage work is huge, and historic records is also too much. The data required for how extracting user will be considered at the method initial stage, required data will be considered the problem of forming curves form again. Therefore proposing to determine sampling spot number by user defined feature function, namely transferring involved data information by fundamental function, unrelated data information is not then transferred, thus decreases computing amount. Its concrete steps are as follows:
(1) system defined feature signal extraction function and sampling spot number voluntarily. Defined feature signal extracts function and defined feature function, and characteristic signal extracts function and represents with function expression, and user is according to the function set defined feature function provided. Function set is the algorithm assemble of symbol of the system definition that can expand, and such as: min (), max (), abs (), sqrt () etc., algorithm set constantly can be supplemented with system extension. It is that user utilizes system definition algorithm set according to the formula of specific syntax rule editing that user edits formula, such as: max(s-7.0), s expression signal point. By the definition of fundamental function, the required data volume chosen can be screened by user targetedly, thus reduces the required data volume extracted.
(2) according to fundamental function, calculate the unique point sampling number that fundamental function is corresponding, namely meet that the prerequisite of the requirement of current fundamental function is lower is had data volume.
(3) defining average time interval sampling number, average time interval sampling number is constant, sets with arranging relevant needs according to system.
(4) browser is according to window size, defines output voluntarily and counts. Owing to be shown curve form on a web browser, therefore browser then can define according to window size voluntarily, currently can export and count, lay the first stone with later stage curve plotting form.
(5) browser generates Json form sampling policy and sends server end, such as following word string: { " strategy_id ": " sw_0001 ", " avg ": 3, " spec_cnt ": 3, " output ": 1280, " spec ": [{ " spec_id ": 1, " spec_func ": " abs (s) > 1.0 ", " spec_samp ": " 2 " }, { " spec_id ": 2, " spec_func ": " min (s) ", " spec_samp ": " 1}] }.
Wherein Json script definition sampling policy form is defined as follows:
Strategy_id is sampling policy number; Avg is average time interval sampling number; Spec_cnt is whole characteristic signal sampling numbers; Output is that all sampling output is counted;
Spec is feature sampling policy set, wherein: spec_id is feature sampling policy mark; Spec_func is feature sampling function expression; Spec_samp counts for choosing based on this fundamental function expression formula.
2nd step, server parses sampling policy, the Json form sampling policy script that server parses browser is sent, extract feature sampling function, function expression of feature being sampled does grammatical and semantic inspection, if expression formula is errorless, then by conversation strategy and configuration parameter. After server receives Json form sampling policy script, the fundamental function defined for browser is resolved, and then represents and establish the definition about fundamental function between server and browser when expression formula is errorless; If when wrong, illustrate that browser definition error then waits that browser redefines.
3rd step, judges image data amount, retrieves qualified data, if the data amount check retrieved is less than actual needs signal point number, directly exports data, otherwise the data retrieved is sampled according to sampling policy. Through retrieval after, calculating qualified data volume may be fewer, and the data volume namely meeting search condition is less. If this data volume can meet the transmission condition of the environment such as current bandwidth, hardware, then do not need to carry out data sampling again, so that it may directly to transmit, curve plotting form. Relative to prior art is mentioned average time interval sampling and mean value calculation mode for, defined by search condition and fetch data, its accuracy is higher, reliability is stronger, it is also possible to there will be the big data volume meeting fundamental function, then need to carry out data sampling processing based on sampling policy.
4th step, carries out data sampling based on sampling policy. Data volume after search condition is filtered is too huge, cannot meet the transmission condition of the environment such as bandwidth, hardware, then carry out data sampling. Its concrete steps are as follows:
(1) by qualified data temporally span be divided into N number of data subclass, these data subclass are carried out concurrent sampling by system shown in Figure 2 by subsequent step, and its calculation formula is as follows:
N=(Oc*Sc/Tc)+1, wherein to be data piecemeal number, Oc be N that server obtains that data export number, Tc is that server retrieves data obtain meeting search condition number of signals, Sc is that sampling policy needs signal is counted, average time interval sampling number and all feature sampling number accumulation calculating, server show that sampling policy needs signal to count Sc. By the calculation formula of N, retrieve data is divided into N number of data subclass D by signal forming time order1������DN��
(2) due to the data singularity of sewage work, we consider to carry out the extraction of data from both macro and micro two aspect, in macroscopic view, extract data can represent Trend judgement, therefore sample for average time interval, it is possible to well overview goes out data characteristic on the whole. The sampling of data subclass is drawn the signal data group of the macrofeature of representation signal, for signal Trend judgement. Specifically data subclass is obtained average time interval sample data set by average time interval sampling respectively.
To data subclass D1������DNAverage time interval sample data set is obtained respectively, first calculating sampling interval T by average time interval samplings, formula is as follows:
Ts=Tgap/ avg, wherein TgapFor calculating signal forming time span in data subclass, avg is average time interval sampling number;
Determine timed interval TsAfter, for data subclass signal temporally interval TsSampling, obtains data subclass average time interval sampling sample data set SETavg��
(3) after macro-data is determined, can accurately not reflect curve form, therefore also need the sampling that data subclass is carried out micro-data, draw the characteristic signal data set of representation signal microscopic feature, for abnormal signal point analysis. This step and previous step macroscopic view sampling are proceed step by step, namely carry out data sampling based on sampling policy and all through the two-step pretreatment of macrofeature and microscopic feature, to be ensured the accuracy of curve.
According to feature policy, data subclass being done data characteristics sampling and obtains feature sampling data, concrete steps are as follows:
First, data subclass use fundamental function sampling obtain Output rusults set Tempsamp��
Secondly, if signal number is greater than specific characteristic signal sampling number in the set obtained, the result after namely sampling by fundamental function still cannot meet transmission needs, then first select signal according to the semanteme of fundamental function, such as: for min/max function, then according to TempsampCollective data is by minimum/maximum sequence. According to weight, data are sampled again, such as, by probability distribution weighting.
Finally, output characteristic sampled signal S set ETspec��
(4) each data acquisition is pressed signal forming time order and merges average sample data and feature sampling data, namely each data acquisition is pressed signal and occurs successively time sequence to merge SETavg��SETspec, obtain this data sample set and close SETsamp��
As shown in Figure 2, the present invention also provides a kind of system carrying out data sampling based on sampling policy, it is characterised in that: comprise Data Placement management module 1, operation debugging module 2, algorithm pond 3 and data integration module 4. Data Placement management module 1 is for being divided into multiple data subclass by sampling data according to sampling policy, and to the management of each data subclass, comprises buffer zone and create, distribute, reclaim, safeguard data subclass queue, data subclass state tracking etc. Operation debugging module 2 is for managing computing pool, and for computing pool, each calculates unit according to sampling policy placement algorithm, and to management operations such as calculating cell scheduling, distribution and releases. Algorithm pond 3 is for setting one group of calculating unit independent parallel, and each unit algorithm that calculates is configured according to sampling policy by job scheduling module, after each calculating unit completes calculation task, calculation result is given data integration module and closes 4 and and distribution. For collecting, computing pool respectively calculates unit Output rusults to data integration module 4, distributes Output rusults by task.
Data Placement management module 1 is connected with algorithm pond 3 respectively with operation debugging module 2, and algorithm pond 3 is connected with data integration module 4. Its workflow is as follows:
Input Data division is become multiple data subset by Data Placement management module 1, is each data subset allocation buffer zone, builds data subset queue, and sends to job scheduling module 2 and calculate application; Job scheduling module 2 is dispatched and is calculated unit, selects idle calculating unit, and for each calculates unit quota algorithm, mark calculates unit; Job scheduling module 2 marks and calculates unit, selects data subset D n to be loaded into algorithm pond 3 from data subset queue, and algorithm pond 3 starts to calculate; After algorithm pond 3 completes calculation task, produce to export data, and notify job scheduling module 2; Job scheduling module 2 discharges and calculates resource, and calculating unit is labeled as the free time; Notification data divides management module 1. Data Placement management module 1 Resource recovery, release data subset D n resource; Data integration module 4 is to output data integration. When confirming that one group of data sampling processing is complete, data sequence is integrated, data results is assigned.
5th step, data are exported S set ET by curve plotting formsampData are returned to browser with Json form by coding, and browser closes SET according to output data setsampCurve plotting. Data are exported S set ETsampCoding, after data being returned to browser with Json form, browser closes curve plotting form according to output data set.
Mass data is realized the sampling of configurable features strategy and average time interval sampling by the present invention. Allow the different sampling task of process at one time. Reduce server load, it is to increase the server time of response, reduce data transfer bandwidth demand. Sewage work can from average time interval sampling and feature sampling Plotting data curve, and rapid Search and Orientation signal trend and abnormal signal point, by Search and Orientation abnormal signal time of origin step by step, differentiate agricultural technology problem.
More than show and describe the ultimate principle of the present invention, the advantage of main characteristic sum the present invention. The technician of the industry should understand; the present invention is not restricted to the described embodiments; the principle of the just the present invention described in above-described embodiment and specification sheets; the present invention also has various changes and modifications without departing from the spirit and scope of the present invention, and these changes and improvements all fall in the scope of claimed the present invention. The present invention require protection domain by appending claims and etc. jljl define.
Claims (4)
1. the curve form data sampling method for sewage work, it is characterised in that, comprise the following steps:
11) defining sampling policy, definition user-defined feature signal extracts function and sampling spot number, and browser exports number according to the sampling of browser window resolution setting signal data;
12) server parses sampling policy, the Json form sampling policy script that server parses browser is sent, extracts feature sampling function, and function expression of feature being sampled does grammatical and semantic inspection, if expression formula is errorless, then by conversation strategy and configuration parameter;
13) judge image data amount, retrieve qualified data, if the data amount check retrieved is less than actual needs signal point number, directly exports data, otherwise the data retrieved are sampled according to sampling policy;
14) based on sampling policy, data sampling is carried out;
15) data are exported S set ET by curve plotting formsampData are returned to browser with Json form by coding, and browser closes SET according to output data setsampCurve plotting.
2. a kind of curve form data sampling method for sewage work according to claim 1, it is characterised in that, described definition sampling policy comprises the following steps:
21) defined feature signal extracts function, and characteristic signal extracts function and represents with function expression, and user is according to the function set defined feature function provided;
22) the unique point sampling number that fundamental function is corresponding is calculated;
23) average time interval sampling number is defined;
24) browser is according to window size, defines output voluntarily and counts;
25) browser generates Json form sampling policy and sends server end, and wherein Json script definition sampling policy form is defined as follows:
Strategy_id is sampling policy number; Avg is average time interval sampling number; Spec_cnt is whole characteristic signal sampling numbers; Output is that all sampling output is counted;
Spec is feature sampling policy set, wherein: spec_id is feature sampling policy mark; Spec_func is feature sampling function expression; Spec_samp counts for choosing based on this fundamental function expression formula.
3. a kind of curve form data sampling method for sewage work according to claim 1, it is characterised in that, the described data sampling that carries out based on sampling policy comprises the following steps:
31) by qualified data temporally span be divided into N number of data subclass, its calculation formula is as follows:
N=(Oc*Sc/Tc)+1, wherein to be data piecemeal number, Oc be N that server obtains that data export number, Tc is that server retrieves data obtain meeting search condition number of signals, Sc is that sampling policy needs signal is counted;
32) data subclass is obtained average time interval sample data set by average time interval sampling respectively, first calculating sampling interval Ts, formula is as follows:
Ts=Tgap/ avg, wherein TgapFor calculating signal forming time span in data subclass, avg is average time interval sampling number;
Determine timed interval TsAfter, for data subclass signal temporally interval TsSampling, obtains data subclass average time interval sampling sample data set SETavg;
33) data subclass is done data characteristics sampling according to feature policy and obtains feature sampling data,
First, data subclass use fundamental function sampling obtain Output rusults set Tempsamp;
Secondly, if signal number is greater than specific characteristic signal sampling number in the set obtained, then first selects signal according to the semanteme of fundamental function, then according to weight, data are sampled;
Finally, output characteristic sampled signal S set ETspec;
34) each data acquisition is pressed signal occurs successively time sequence to merge SETavg��SETspec, obtain this data sample set and close SETsamp��
4. the sampling system of a kind of curve form data sampling method for sewage work according to claim 3, it is characterised in that: comprise Data Placement management module (1), operation debugging module (2), algorithm pond (3) and data integration module (4);
Described Data Placement management module (1) is for being divided into multiple data subclass by sampling data according to sampling policy, and is managed by each data subclass; Described operation debugging module (2) is for managing computing pool, and for computing pool, each calculates unit according to sampling policy placement algorithm, and to calculating cell scheduling, distribution and release management operation; Described algorithm pond (3) is for setting one group of calculating unit independent parallel, and each unit algorithm that calculates is configured according to sampling policy by job scheduling module; For collecting, computing pool respectively calculates unit Output rusults to described data integration module (4), distributes Output rusults by task;
Described Data Placement management module (1) is connected with algorithm pond (3) respectively with operation debugging module (2), and algorithm pond (3) are connected with data integration module (4).
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| CN105243127A (en) * | 2015-09-30 | 2016-01-13 | 海天水务集团股份公司 | Report data sampling method for wastewater treatment plant |
| CN108416022B (en) * | 2018-03-07 | 2020-06-09 | 电信科学技术第五研究所有限公司 | System and method for realizing streaming data real-time fidelity curve drawing model |
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| CN101894176A (en) * | 2009-05-19 | 2010-11-24 | 三菱电机株式会社 | Rebuild the method for the distance field of sweep volume at sampled point |
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| EP2682752A1 (en) * | 2012-07-04 | 2014-01-08 | alcedo biotech GmbH | HMGA2 as a marker for diagnosing diabetes |
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