A method for automatically identifying sampling flow characteristic parameters

Abstract

The application discloses a kind of sampling flow characteristic parameter automatic identification method, solve the problem of distinguishing sampling process characteristic parameter by sampling flow, introduce multi-dimensional space scale fixed-length sliding window queue, its sample element includes sampling flow and its corresponding time stamp, using the rule of first-in first-out order dequeuing, after each sampling flow scanning collection period ends, the sample element that current sampling flow and corresponding time stamp are composed is pressed into fixed-length sliding window queue;Sampling terminal application program is according to sampling process flow trend and peak constraint rule to the current fixed-length sliding queue sampling flow sequence is judged, to extract sampling point and sampling peak, sampling starting point, sampling termination point and other related characteristic information;According to sampling peak statistics sampling point sampling times, according to adjacent sampling point peak corresponding time stamp determines sampling interval period, and combines the reference constraint condition determined by sampling basic process condition, determine the working mode of current sampling flow.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of process industry production process parameter detection and calculation, more particularly to the technical field of identification calculation and automatic extraction method of liquid sampling flow characteristic parameters of a production process test and analysis system such as ore dressing, building materials, sewage treatment, papermaking, sugar making, etc. BACKGROUND

[0002] In industrial production, the liquid slurry sampling method and sampling adjustment parameters directly affect the sampling uniformity and test result representativeness of the product quality control link, and at the same time can reflect the advantages and disadvantages of the current production process technical index from multiple spatial regions and time dimensions, which is the main evaluation standard for determining whether the production condition is in the best state or stable state, and is also an important basis for implementing dynamic adjustment of production process parameters. Accurate understanding of the sampling frequency and sampling times of the production process has special significance for production index real-time prediction, process condition control and production process operation decision-making.

[0003] The ultimate goal of liquid slurry sampling is to make the sampling process as uniform as possible under the premise of ensuring the sampling shift cumulative amount, and to reduce the product detection error caused by sampling unevenness. In industrial production, the sampling process usually has the characteristics of randomness, small flow (single sampling volume 20-35 ml), short duration (10-25 ml), complex working conditions (high temperature, high humidity, corrosive), etc. Influenced by factors such as production site working environment and installation conditions, in order to improve environmental adaptability and reduce the failure rate of sampling equipment, the process sampling under complex working conditions such as actual industrial production process and mine enterprise ore dressing process is mostly only equipped with a flow detection device for sampling state.

[0004] Fluid slurry sampling under complex conditions, in order to ensure the reliability of the sampling process, control execution mechanisms are rarely installed on the sampling device. Influenced by the time-varying characteristics of the working condition state such as production process operation parameter adjustment and abnormal fluctuation of process conditions, the shift cumulative sampling amount is difficult to reach the expected index in most cases. In order to monitor the state of the sampling process online, improve the sampling inspection operation efficiency, and solve the problems of intermittent micro-flow real-time detection and high-reliability transmission under complex working conditions, the sampling parameters and working state can be provided to the front-line test and analysis inspection and maintenance personnel in time. A flow detector is installed at the overflow of the slurry sampling to detect the state of the sampling online.

[0005] According to the detection results of the sampling flow meter, the current working state of the sampling machine and its detection flow at the sampling point can be judged in real time, but the sampling times per shift, the sampling point maintenance times and other parameters that can comprehensively reflect the sampling characteristics cannot be obtained. SUMMARY

[0006] The application aims at solving the problem of identifying the sampling process characteristic parameters by the sampling flow, and provides a sampling flow characteristic parameter automatic identification method.

[0007] In order to achieve the above-mentioned purpose, the application specifically adopts the following technical scheme:

[0008] The sampling flow characteristic parameter automatic identification method includes three parts of data detection and collection, feature extraction and parameter comprehensive calculation, analyzes and processes the collection results from the sampling flow of the sampling operation area, obtains the characteristic parameters such as the sampling point peak value and the sampling point interval time, and determines the current working state of sampling on this basis.

[0009] 1) In the sampling flow collection period, the field terminal operation station collects the 4-20mA flow detection signal from the sampling machine flow meter through the analog channel in real time, and constructs the real-time identification window queue under the multi-dimensional space scale of the sampling flow sample domain;

[0010] 2) According to the sampling flow characteristics, set the rising limit, the falling limit, the flow peak value coefficient and other initialization parameters;

[0011] 3) Combined with the process characteristics and the trend monotonic change characteristics of the sampling flow fast, small and random, search the sampling domain space to determine whether there is a sampling domain; mainly includes the following three cases:

[0012] a. The sampling sample flow is monotonically increasing and decreasing (single peak value of flow curve);

[0013] b. The sampling sample flow is non-monotonically increasing (double peak value) in the rising section, and is monotonically decreasing in the falling section;

[0014] c. The sampling sample flow is monotonically increasing in the rising section, and is non-monotonically decreasing (double peak value) in the falling section.

[0015] 4) Calculate the sampling start position and the termination position of the flow sample in the sampling domain;

[0016] 5) Calculate the mean value of the sampling flow sample in the sampling domain, extract the maximum peak value of the sampling point and the corresponding time point, and store them in the corresponding data table of the local database;

[0017] 6) Use the last sampling point data in the historical data table to calculate the sampling interval period;

[0018] 7) Judge the sampling state, if the calculated sampling interval period is less than the preset sampling period, the current sampling machine is in the maintenance mode, and the maintenance parameters are recorded.

[0019] Further, at the flow sampling time t, the flow detection signal F(t) from the sampler flow sensor is accessed to the sampling terminal application through the analog channel, and is matched with the sampling flow output time T(t) to form the sample element (F(t), T(t)) of the sampling flow sample domain Ω0 set. The sample element (F(t), T(t)) is pressed into the sample sequence according to the first-in first-out principle, and the historical sample element is moved backward. The last sample element (F(t-N0+1), T(t-N0+1)) is removed from the sampling flow sample domain Ω0 set.

[0020] Further, the sampling flow parameter initialization value is set in the sampling terminal application to complete the initialization. The multi-dimensional space scale fixed-length sliding window sequence length n=50, the continuous decreasing or increasing point number N c =8, the first peak determination coefficient C1=1.55, the second peak determination coefficient C2=1.15, the sampling period lower limit T min =60s, and the sampling period upper limit T max =7200s are set.

[0021] Further, the sample element of the sampling flow sample domain Ω0 set is searched according to the following constraint condition rule to confirm whether there is a sampling point in the current sample domain:

[0022] 1) For the sampling flow domain, there is only one peak value of the flow trend, that is, the flow detection value is monotonically increased to the peak value and then monotonically decreased, and the single peak sample domain Q0 is as follows:

[0023] ① The flow signal in the sample domain Q0 is monotonically and continuously increased to the peak value The number of flow collection points in the sample domain increasing section is greater than N c ;

[0024] ② After reaching the peak value, it is continuously and monotonically decreased, and the number of flow collection points in the sample domain decreasing section is greater than N c ;

[0025] The peak value meeting the conditions ① and ② is greater than C1 times the average value of the flow signal in the non-sample domain, that is, the following relationship (1-4) is met:

[0026] In the flow sampling domain Ω0, the average value of the flow signal sequence in the non-sample domain is shown in formula (1), and meets the condition required by formula (2);

[0027]

[0028]

[0029] For the sample domain Q0monotonically increasing interval, the constraint condition of formula (3) is met;

[0030]

[0031] For the sample domain Q0monotonically decreasing interval, the constraint condition of formula (4) is met;

[0032]

[0033] 2) For the sampling flow domain, there may be multiple peaks in the flow trend. According to the production process characteristics of the sampling flow, under normal conditions, no more than 2 peaks are allowed to appear, that is, the detection flow is monotonically decreasing in the decreasing segment in the sampling area, but it is not monotonous in the increasing segment, resulting in a sample domain Q0with 2 peaks:

[0034] When condition ② is met but condition ① is not met, the maximum peak needs to be greater than the peak of the rising segment, that is, the second highest peak C2 times, and the sum of the data sampling points before the inflection point of the trough and the data sampling points from the inflection point of the trough to the peak is not less than N C , that is, the constraint is as follows:

[0035]

[0036]

[0037] 3) For the sampling flow domain, there may be multiple peaks in the flow trend. According to the production process characteristics of the sampling flow, under normal conditions, no more than 2 peaks are allowed to appear, that is, the detection flow is monotonically increasing in the increasing segment in the sampling area, but it is not monotonous in the decreasing segment, resulting in a sample domain Q0with 2 peaks:

[0038] When condition ① is met but condition ② is not met, the maximum peak needs to be greater than C1 times the average of the flow signal in the non-sample domain, and the maximum peak also needs to be greater than C2 times the second highest peak in the decreasing segment. The peak and data sampling points meet the constraint conditions shown in the following formulas (7-8):

[0039]

[0040]

[0041] 4) According to the judgment criteria of step 3), if there is a sampling point in the sampling flow sample domain Ω0set, extract the sampling feature peak Sampling sample start point Sampling sample end point and other parameters, according to the position distribution of m0, n0 in the sampling flow sample domain Ω0set, the corresponding sampling sample start point elements sampling sample peak element sampling sample end point element wherein the constraint condition given by formula (9) is satisfied;

[0042]

[0043] Further, the sampling terminal application program searches the local data table to extract the historical sampling feature peak value of the adjacent sampling point feature information corresponding to the current sampling point historical sampling sample start point historical sampling sample end point According to the rule stipulated in formula (10), the sampling interval period T(0) at the current time is calculated;

[0044]

[0045] Further, according to the sampling interval period and the rule set in formula (11), the working state of the current sampling machine is judged;

[0046] T(0)≤T min ,(a)

[0047] T(0)>T min &T(0)<T max ,(b)

[0048] T(0)≥T max ,(c) (11)

[0049] If the rule (a) in formula (11) is satisfied, the current sampling is in the operation and maintenance mode; if the rule (b) in formula (11) is satisfied, the current sampling is in the normal working mode; if the rule (c) in formula (11) is satisfied, the current sampling is in the operation and flow interruption mode.

[0050] Further, according to the identification result of the state mode, if the sampling works in the maintenance mode, formula (12) is calculated, and the calculation result information is stored in the local data table; if the sampling works in the normal mode, the sampling point calculation result information is stored in the local data table;

[0051]

[0052] The technical principle of the application is as follows:

[0053] (1) Using the sampling machine flow sensor analog channel to output the real-time measurement value of the sampling flow to the sampling terminal application program, combining the sampling time sequence to form a multi-dimensional spatial scale fixed-length sliding window queue covering the sampling flow and the sampling time sequence, as the basic information source for dynamic search and related feature extraction of the sampling point. (2) According to the sampling point flow change process characteristics, the sampling point is identified according to the agreed sampling peak extraction rule in the sampling flow fixed-length sliding window queue, and the sampling point peak value, sampling start position, sampling end position and corresponding time stamp are calculated and calculated for the flow sequence with sampling point. For the flow sequence without sampling point, the sampling set sequence is updated after the flow data collection scanning is completed. (3) The adjacent sampling point peak value, sampling start position, sampling end position and corresponding time stamp are read from the historical data table, and the difference value is calculated with the current sampling sampling point peak time stamp to obtain the current sampling interval period. According to the sampling process basic characteristics and the set sampling period limit extreme value, if the current sampling interval period is less than the minimum value of the sampling period, the current sampling machine is in maintenance mode; if the current sampling interval period is greater than the maximum value of the sampling period, the current sampling machine is in the state of flow break; if the current sampling interval period is greater than the minimum value of the sampling period and less than the maximum value of the sampling period, the sampling machine is in normal sampling state. The output sampling interval period and the sampling machine mode can provide necessary information support for the daily inspection of the operator, and improve the inspection efficiency of the sampling process.

[0054] The beneficial effects of the present application are as follows:

[0055] (1) The present application is suitable for detecting parameter typical feature extraction in production processes such as metallurgy, chemical industry, building materials, etc., and is mainly used to solve the problems of sampling frequency statistics and sampling state recognition of non-powered liquid slurry sampling;

[0056] (2) The present application gives a sliding window queue construction method for real-time identification of sampling points based on the multi-dimensional spatial scale of sampling flow sample domain, and proposes a feature active classification solver with high recognition rate and simple application for different sampling conditions;

[0057] (3) The present application gives a sampling interval period calculation method, that is, according to the time sequence historical information corresponding to the adjacent sampling point peak value to obtain the current sampling period;

[0058] (4) According to the calculated sampling interval period and the upper and lower limit values of the process set sampling period, the current sampling state is pre-judged and accurately determined (normal, flow break and maintenance), and on this basis, the corresponding parameters are calculated and reported to the inspection platform, providing information support for targeted daily inspection of inspection personnel. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 It is the basic structure diagram of sampling parameter identification in the present application;

[0060] Figure 2 is a single-peak sampling flow time series graph in the present application;

[0061] Figure 3 is a first type of multi-peak sampling flow time series in the present application;

[0062] Figure 4 is a second type of multi-peak sampling flow time series in the present application. DETAILED DESCRIPTION

[0063] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0064] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0065] EMBODIMENT

[0066] WITH REFERENCE TO Figure 1The sampling flow characteristic parameter extraction method in the application firstly detects the instantaneous flow detection signal value output by the sampling machine flowmeter through the analog channel, relies on the sampling machine field terminal operation station application program (hereinafter referred to as "sampling terminal application program"), and sets a multi-dimensional space scale fixed-length sliding window queue locally to store the sampling flow output signal and the corresponding sampling time. The sampling terminal application program searches and analyzes whether there is a sampling point in the flow domain sample within the sampling flow collection scanning period according to the set initial sampling flow sequence upper and lower limits, flow peak value coefficient, sampling period upper and lower limit extreme value and other parameters, extracts the sampling flow peak value and the corresponding time on this basis, and determines the sampling sample start and end points and the time stamp. Since the sampling process is affected by other known or unknown factors of the production process, the sampling occurrence time and the size of the sampling flow have obvious randomness and uncertainty. According to the flow peak value and the corresponding time searched by the current sampling flow sample, the sampling interval period is calculated and updated in combination with the last time flow peak value characteristic parameter information of the sampling point. In order to obtain and present the sampling working condition state in real time, the sampling interval period calculated is automatically identified by the classification comparison enumeration method according to the set sampling period upper and lower limit value, the current sampling working mode is located, and the related parameters are recorded. The method involved in the application is as follows:

[0067] A sampling flow characteristic parameter automatic identification method, comprising the following steps:

[0068] (1) At the flow sampling t moment, the flow detection signal F(t) from the sampling machine flow sensor passes through the analog channel and accesses the sampling terminal application program, and is matched with the sampling flow output time T(t) to form the sampling flow sample domain Ω0 set sample element (F(t), T(t)), wherein the sampling flow sample domain Ω0 set is pressed into the sample sequence according to the first-in first-out principle, and the historical sample elements are shifted in this way. The last sample element (F(t-N0+1), T(t-N0+1)) is removed from the sampling flow sample domain Ω0 set;

[0069] (2) Set the sampling flow parameter initialization value in the sampling terminal application program, and complete the initialization, wherein the multi-dimensional space scale fixed-length sliding window sequence length n is set to 50, the continuous decreasing or increasing point number N is set to 8, the first peak value determination coefficient C1 is set to 1.55, the second peak value determination coefficient C2 is set to 1.15, the sampling period lower limit T is set to 60s, and the sampling period upper limit T is set to 7200s; c min max

[0070] (3) The sampling flow sample domain Ω0 set sample element is searched according to the following constraint condition rule to confirm whether there is a sampling point in the current sample domain: ​​​

[0071] 1) For Figure 2 The sampled flow domain shown has one and only one peak, meaning the flow detection value first monotonically increases to the peak and then monotonically decreases, resulting in a single peak sample domain Q0.

[0072] ① The flow signal within the sample domain Q0 increases monotonically and continuously until it reaches its peak value. The number of flow collection points in the increasing segment of the sample domain is greater than N c ;

[0073] ② After reaching the peak, the flow rate continuously and monotonically decreases, and the number of flow rate collection points in the decreasing segment of the sample domain is greater than N. c ;

[0074] The peak values ​​satisfying conditions ① and ② must be greater than C1 times the mean value of the flow signal outside the sample domain, i.e., satisfy the following relationship (1-4):

[0075] Within the flow sampling domain Ω0, the non-sample domain flow signal sequence The mean is shown in equation (1), and it satisfies the conditions required by equation (2).

[0076]

[0077]

[0078] For the sample domain Q0, which is monotonically increasing, the constraint condition of equation (3) is satisfied.

[0079]

[0080] For the sample domain Q0, which is a monotonically decreasing interval, the constraint condition of equation (4) is satisfied.

[0081]

[0082] 2) The flow trend within the sampled flow domain may have multiple peaks. Based on the characteristics of the sampling process and production technology, under normal conditions, no more than two peaks are allowed. For example... Figure 3 The scenario shown illustrates a situation where the detected flow rate decreases monotonically in the decreasing segment of the sampling region but is not monotonically increasing in the increasing segment, resulting in a sample domain Q0 with two peaks.

[0083] When condition ② is met, but condition ① is not met, the maximum peak value must be greater than C² times the peak of the rising segment (i.e., the second highest peak value). Simultaneously, the sum of the number of data sampling points in the corresponding rising segment before the trough inflection point and the number of data sampling points from the trough inflection point to the peak value must not be less than N. C This satisfies the constraints shown below;

[0084]

[0085]

[0086] 3) For the flow trend in the sampling flow domain, there can be multiple peaks. According to the sampling flow production process characteristics, under normal conditions, at most, no more than 2 peaks are allowed to occur. As shown in the case, the detection flow is monotonically increasing in the increasing segment in the sampling region, but non-monotonically decreasing in the decreasing segment, resulting in a case with 2 peak sample domains Q0: Figure 4

[0087] When condition ① is met but condition ② is not met, the maximum peak value needs to be greater than C1 times the average flow signal in the non-sample domain, and at the same time, the maximum peak value also needs to be greater than C2 times the second highest peak value in the descending segment. The peak value and the number of data sampling points meet the constraint conditions (7-8) as shown below.

[0088]

[0089]

[0090] 4) According to the judgment criteria in step 3), if there is a sampling point in the sampling flow sample domain Ω0set, extract the sampling characteristic peak value sampling sample start point sampling sample end point and other parameters, according to the position distribution of m0, n0 in the sampling flow sample domain Ω0set, respectively get the corresponding sampling sample start point elements sampling sample peak value element sampling sample end point element where the constraint condition given by equation (9) is satisfied;

[0091]

[0092] 5) The sampling terminal application searches the local data table to extract the historical sampling characteristic peak value of the adjacent sampling point feature information corresponding to the current sampling point historical sampling sample start point historical sampling sample end point According to the rules specified in equation (10), calculate the sampling interval period T(0) at the current time;

[0093]

[0094] 6) According to the sampling interval period and the setting rule in equation (11), judge the working state of the current sampling machine;

[0095]

[0096] ​If the rule (a) in formula (11) is satisfied, the current sampling is in the operation and maintenance mode; if the rule (b) in formula (11) is satisfied, the current sampling is in the normal working mode; if the rule (c) in formula (11) is satisfied, the current sampling is in the operation and flow interruption mode;

[0097] For the identification result of the state mode, if the sampling works in the maintenance mode, calculation is performed according to formula (12), and the calculation result information is stored in the local data table; if the sampling works in the normal mode, the sampling point calculation result information is stored in the local data table;

[0098]

[0099] At the flow sampling time t+1, the sampling terminal application program receives the sampling flow F(t) and the corresponding time stamp T(t) from the flow sensor, forms a sample (F(t), T(t)), enters the sample (F(t-1), T(t-1)) corresponding to the sample flow sample domain Ω0 set, and returns to step 1) to perform the sampling point judgment of the next period.

[0100] In the above steps and expressions, the symbols used are as follows: Ω0 is a flow sample domain set, Ω0={(F(t-i), T(t-i)) T |(F(t-1), T(t-1)) T ,…,(F(t-N0), T(t-N0)) T};

[0101] t is the current sampling flow detection and output time to the terminal application program;

[0102] F(t-1) is the sampling flow detection output result signal at the last time, with the unit of mA;

[0103] T(t) / T(0) is the time stamp corresponding to the current sampling flow result, with the structure of yyyy-MM-dd hh:mm:ss; wherein

[0104] T(t-1) / T(1) is the time stamp corresponding to the sampling flow result at the last time, with the structure of yyyy-MM-dd hh:mm:ss; wherein

[0105] N0 is the sample length in the Ω0 flow sample domain set;

[0106] (F(t-i), T(t-i)), i=1,…,N0 is the sample element of the Ω0 flow sample domain set;

[0107] N c is the number of continuously increasing or decreasing points in the sampling point sample;

[0108] C1, C2 are the determination coefficients of the first and second sampling flow peak value, respectively;

[0109] Q0 is the sampling sample domain, is the non-sample domain flow signal sequence, and the specific expression range is shown in Figures 2-4 ;

[0110] T min is the lower limit value of the sampling period set by the operator according to the working characteristics of the sampling machine and the process conditions, T max is the upper limit value of the sampling period set by the operator according to the working characteristics of the sampling machine and the process conditions, and is the basic reference quantity for determining the working mode of the sampling machine;

[0111] is the starting point of the sampling flow domain Ω0 (fixed-length sliding window queue), is the starting point flow of the sampling sample domain Q0, is the first flow peak value in the sampling sample domain, is the second flow peak value in the sampling sample domain, is the terminal point flow of the sampling sample domain Q0; is the minimum flow of the monotonically decreasing segment of the second flow peak value in the sampling sample domain; is the starting flow of the monotonically increasing segment of the second flow peak value in the sampling sample domain;

[0112] is the position of the starting point of the sampling sample domain Q0 in the fixed-length sliding window queue;

[0113] m0 is the position of the first flow peak value of the sampling sample domain Q0 in the fixed-length sliding window queue;

[0114] m1 is the position of the second flow peak value of the sampling sample domain Q0 in the fixed-length sliding window queue;

[0115] n0 is the position of the terminal point of the sampling sample domain Q0 in the fixed-length sliding window queue, generally n0=N0-1;

[0116] is the mean value of the non-sample domain flow signal sequence ;

[0117] m ac is the position of the minimum flow of the monotonically decreasing segment of the second peak value in the sampling sample domain Q0 in the fixed-length sliding window queue;

[0118] m de is the position of the starting point of the monotonically increasing segment of the second peak value in the sampling sample domain Q0 in the fixed-length sliding window queue;

[0119] is the sampling flow feature peak value of the last historical sampling point, which is at the position m1 corresponding to the historical fixed-length sliding window queue;

[0120] is the sampling starting point flow of the last historical sampling point, which is at the position m1 corresponding to the historical fixed-length sliding window queue;

[0121] is the sampling sample end point flow of the last historical sampling point, which is at the position n1 corresponding to the historical fixed-length sliding window queue, generally satisfying n1=N0-1.

Claims

1. A method for automatically identifying characteristic parameters of sampled flow rate, characterized in that, The method comprises three parts: data detection and acquisition, feature extraction, and parameter comprehensive calculation. It analyzes and processes the collected data from the sampling flow rate in the sampling work area to obtain the characteristic parameters of the sampling points, and determines the current working status of the sampling based on these parameters. The automatic identification method specifically includes the following steps: 1) During the sampling flow acquisition period, the field terminal operation station acquires the 4-20mA flow detection signal from the sampler flow meter in real time through the analog signal channel, and constructs an instant recognition window queue under the multi-dimensional spatial scale of the sampling flow sample domain. 2) Set initialization parameters based on the sampled flow characteristics; 3) Combining the process characteristics of fast, small, and random sampling flow rates with the monotonic trend of change, search the sampling domain space to determine whether a sampling domain exists; 4) Calculate the starting and ending positions of the flow samples within the sampling domain; 5) Calculate the mean of the sampled flow samples within the sampling domain, extract the maximum peak value and corresponding time point of the sampling point, and store them in the corresponding data table of the local database; 6) Calculate the sampling interval period using the data from the previous sampling point in the historical data table; 7) Determine the sampling status. If the calculated sampling interval period is less than the preset sampling period, the current sampler is in maintenance mode. Record the maintenance parameters. In step 1), at the time of flow sampling t, the flow detection signal F(t) from the flow sensor of the sampler is connected to the sampling terminal application through the analog channel and matched with the sampling flow output time T(t) to form the sample elements (F(t), T(t)) of the sampled flow sample domain Ω0 set. The sample elements (F(t), T(t)) of the sampled flow sample domain Ω0 set are pushed into the sample sequence according to the first-in-first-out principle. At the same time, the historical sample elements are shifted to the next position. The last sample element (F(t-N0+1), T(t-N0+1)) is removed from the sampled flow sample domain Ω0 set. In the sampling terminal application, set the initial value of the sampling flow parameter to complete the initialization. This includes setting the length of the multi-dimensional spatial scale fixed-length sliding window sequence n = 50, and the number of continuously decreasing or increasing points N. c =8, the coefficient of determination for the first peak C1 = 1.55, the coefficient of determination for the second peak C2 = 1.15, and the lower limit of the sampling period T. min =60s, upper limit of sampling period T max =7200s.

2. The method for automatic identification of sampling flow characteristic parameters according to claim 1, characterized in that, The sample elements of the sample domain Ω0 set are traversed and searched according to the following constraint rules to confirm whether there is a sampling point in the current sample domain: 1) For the case where the flow trend within the sampled flow domain has one and only one peak, i.e. the flow detection value first monotonically increases to the peak and then monotonically decreases, and there is a single peak sample domain Q0: ① The flow signal within the sample domain Q0 increases monotonically and continuously until it reaches its peak value. The number of flow collection points in the increasing segment of the sample domain is greater than N c ; ②After reaching the peak, the flow rate continuously and monotonically decreases, and the number of flow collection points in the decreasing segment of the sample domain is greater than Nc; The peak values ​​satisfying conditions ① and ② must be greater than C1 times the mean value of the flow signal outside the sample domain, i.e., satisfy the following relationship (1-4): Within the flow sampling domain Ω0, the non-sample domain flow signal sequence The mean is shown in equation (1), and satisfies the conditions required by equation (2); For the sample domain Q0 monotonically increasing interval, the constraint condition of equation (3) is satisfied; For the sample domain Q0, which is a monotonically decreasing interval, the constraint condition of equation (4) is satisfied. 2) For the flow trend within the sampled flow domain, there may be multiple peaks. Based on the characteristics of the sampling process and production technology, under normal conditions, no more than two peaks are allowed. That is, the detected flow rate decreases monotonically in the decreasing segment of the sampling area, but is not monotonically increasing in the increasing segment, resulting in a sample domain Q0 with two peaks: When condition ② is met, but condition ① is not met, the maximum peak value must be greater than the peak of the rising segment, i.e., C2 times the second highest peak value. At the same time, the sum of the number of data sampling points in the corresponding rising segment before the trough inflection point and the number of data sampling points from the trough inflection point to the peak value must not be less than N. C This satisfies the constraints shown below; 3) For the flow trend within the sampled flow domain, there may be multiple peaks. Based on the characteristics of the sampling process and production technology, under normal conditions, no more than two peaks are allowed. That is, the detected flow rate is monotonically increasing in the increasing segment of the sampling area, but not monotonically decreasing in the decreasing segment, resulting in a sample domain Q0 with two peaks: When condition ① is met but condition ② is not met, the maximum peak value must be greater than C1 times the average value of the flow signal in the non-sample domain, and the maximum peak value must also be greater than C2 times the value of the second peak value in the falling segment. Its peak value and the number of data sampling points meet the constraints shown in the following formula (7-8). 4) Based on the judgment criteria in step 3), if there are sampling points in the sampled flow sample domain Ω0 set, then extract the sampling characteristic peak value. Sampling start point Sampling endpoint Parameters, according to The positional distribution of m0 and n0 within the sampled flow sample domain Ω0 set yields the corresponding sampling sample starting point elements. Sample peak element Sampling sample termination point element Wherein the constraints given by equation (9) are satisfied; 3. The method for automatic identification of sampling flow characteristic parameters according to claim 2, characterized in that, The sampling terminal application searches the local data table and extracts historical sampling feature peaks of the feature information of neighboring sampling points corresponding to the current sampling point. Historical sampling sample starting point Historical sampling endpoint Calculate the sampling interval period T(0) at the current moment according to the rules agreed upon in equation (10); 4. The method for automatic identification of sampling flow characteristic parameters according to claim 3, characterized in that, Based on the sampling interval period and the rule set by formula (11), determine the current working status of the sampler; If rule (a) in equation (11) is satisfied, the current sample is in operation and maintenance mode; if rule (b) in equation (11) is satisfied, the current sample is in normal operation mode; if rule (c) in equation (11) is satisfied, the current sample is in operation and disconnection mode.

5. The method for automatic identification of sampling flow characteristic parameters according to claim 4, characterized in that, For the identification results of the state mode, if the sampling work is in maintenance mode, the calculation is performed according to formula (12), and the calculation result information is stored in the local data table; If the sampling operation is in normal mode, the calculation results of the sampling points are stored in the local data table; 6. The method for automatic identification of sampling flow characteristic parameters according to claim 1, characterized in that, The characteristic parameters include the peak value of the sampling point and the sampling point interval time.

7. The method for automatic identification of sampling flow characteristic parameters according to claim 1, characterized in that, The initialization parameters in step 2) include the upper limit, lower limit, and peak flow coefficient.

Images