CN112651645B - Method for analyzing origin environment monitoring abnormal data through whole-process tracing - Google Patents
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Abstract
The application relates to a method for analyzing origin tracing of abnormal data of production area environment monitoring in the whole process.
Description
Technical Field
The invention belongs to the technical field of agricultural environment, and particularly relates to a method for analyzing origin environment monitoring abnormal data through whole-process traceability.
Background
The environmental problem of agricultural product producing areas becomes more severe, and along with the accumulation and application of long-term monitoring data, the guarantee of data quality and the tracing of abnormal values become industrial problems. The monitoring data is a fusion database containing multiple sources, multiple directions and multiple indexes, and the current research on abnormal value tracing of the monitoring data is mostly limited to cause analysis in a sample detection stage.
The main problems of the above technical methods are: (1) The existing monitoring work abnormal data is traced to a plurality of detection results, the whole-process monitoring data is less combined for comprehensive analysis, and the tracing result is more comprehensive; (2) The quality control samples are added in the existing monitoring work to be mostly used for determining the detection capability of a laboratory, and are not applied to the source tracing analysis of abnormal monitoring data.
Disclosure of Invention
According to the method, the error index is obtained through a logistic regression algorithm by utilizing the whole-course monitoring data and combining with external quality control sample (constant value password sample, parallel double sample and mutual inspection sample) data, so that the abnormal probability of each monitoring index is further obtained, the abnormal data sequence is finally determined, and the tracing and investigation of the abnormal monitoring data are realized.
The invention aims to provide a method for analyzing the origin-producing environment monitoring abnormal data through whole-process tracing.
In order to solve the technical problem, the invention discloses a method for analyzing the origin environmental monitoring abnormal data through whole-process tracing, which comprises the following steps:
(1) Historical information acquisition
Acquiring background information and historical monitoring data;
(2) Quality control information acquisition
Acquiring sampling quality control information, acquiring sample preparation and circulation quality control information, acquiring detection quality control information,
(3) Quality control error index calculation
(3.1) error Rate calculation
Acquiring the background information, historical monitoring data, sampling quality control information, sample preparation and circulation quality control information and detection quality control information, and calculating the bit-dividing monitoring data of each specific monitoring index in all the quality control information; calculating the error rate of a specific monitoring index of the external quality control sample corresponding to each sub-position monitoring data; the error rate of the specific monitoring index of the external quality control sample refers to: when a specific monitoring index detection value of an external quality control sample of a certain quantile value has a monitoring index error, the corresponding point of the quantile value and the number of all the points below the corresponding point with the monitoring index error account for the proportion of the total number of the points; the monitoring index error of the external quality control sample refers to: the specific monitoring index detection value of the external quality control sample exceeds the indoor relative deviation range corresponding to the monitoring index;
(3.2) error index calculation
Selecting one or more models, fitting the position-divided monitoring data and the specific monitoring index error rate of the external quality control sample corresponding to the position-divided monitoring data to obtain an error index;
(4) Data exception checking
(4.1) when an external quality control sample is wrong, calculating the abnormal probability of the quality control index:
wherein Pi is the abnormal probability of the quality control index, i is the error index of the single quality control index after the external quality control sample is in error, and Σ i is the sum of the error indexes of all the quality control indexes after the external quality control sample is in error;
and (4.2) sequencing the anomaly probabilities from large to small, wherein the sequence is a tracing sequence of the abnormal monitoring data.
Further, the background information includes, but is not limited to, administrative area, crop type, soil type, land use;
further, the historical monitoring data includes, but is not limited to, monitoring point location coordinates and origin environment index detection results;
further, the sampling quality control information comprises but is not limited to sampling codes, sampling mechanisms, point location coordinate offset distances, agricultural production abnormal conditions (natural disasters and plant diseases and insect pests), yield reduction rate in season and sampling completion time;
further, the sample preparation and circulation quality control information includes, but is not limited to, sample circulation time, sample package integrity, sample state integrity, sample preparation mechanism, sample receiving amount, air drying time, air drying temperature, air drying humidity, target mesh number, sample sieving amount, material of a sample grinding cutter head, sample uniformity, sample subpackaging amount, circulation code, sample preparation duration;
further, the detection quality control information includes, but is not limited to, sample circulation time, sample packaging integrity, sample state integrity, detection mechanism, sample receiving amount, detection code, detection method, sample weighing amount, balance room humidity, test water purity, digestion acid purity, digestion temperature, single batch digestion sample amount, digestion time, detection instrument measurement and authentication residual effective period, blank value, internal control password sample error, internal control parallel sample deviation, standard curve use time, ratio of detection result to first point of standard curve, ratio of last point of standard curve to detection result, standard curve R square, quality control graph unilateral continuous batch number, and detection duration;
further, the error rate is not calculated according to three indexes of various mechanisms, codes and detection methods;
further, the model is a species sensitivity fitting model, including Weibull CDF, lognormaldCDF, gamma CDF, growth/Sigmoidal;
further, the quantile monitoring data are obtained according to an equivalent gradient method: increasing the data of the specific monitoring index in an equivalent gradient manner, extracting the monitoring result of the monitoring index corresponding to each quantile value, and calculating the fusion value of each group of indexes, wherein the quantile values are increased in a gradient manner of 1%, 2%, 3%, 4% or 5%;
further, when 5% is selected, 19 quantiles Q are calculated, namely 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%;
further, the external quality control sample comprises a constant value password sample, a parallel double sample and a mutual detection sample. On the basis of considering the difference of soil types and pollution degrees (soil and agricultural products) in different regions, the constant-value password sample is screened from more than 15 bases nationwide to collect soil and agricultural product samples (rice, wheat and corn), prepared according to the preparation requirements of relevant national standard substances, and replaced by one batch every 2 to 3 years; the parallel double samples are samples which only comprise two same subsamples in environmental monitoring and sample analysis, and the measuring result of the parallel samples reflects the precision level of the test to a certain extent; and the mutual detection is that when each detection mechanism detects samples every year, soil and agricultural product sample auxiliary samples at the same point position are randomly extracted as mutual detection samples and distributed to other designated detection mechanisms for detection.
A quality control method for a whole-process tracing analysis result of production area environment monitoring abnormal data is characterized in that quality control is carried out on the basis of one or more specific links in the first 3-10 bits of the tracing sequence of the abnormal monitoring data.
An environment monitoring optimization method for a whole-process traceability analysis result of production area environment monitoring abnormal data is characterized in that environment monitoring optimization is carried out on the basis of one or more specific links in the first 3-10 bits of an abnormal monitoring data traceability sequence.
The method for analyzing the origin environmental monitoring abnormal data through whole-process tracing has the following advantages:
1. the method is combined with the whole-course monitoring work, the source tracing analysis of the abnormal monitoring data is comprehensively carried out, the result is comprehensive and reliable, and data (case) support is provided for the source tracing analysis of the abnormal data of the later monitoring work; the related indexes with abnormal data can be mainly checked according to the tracing sequence of the abnormal monitoring data, or quality control can be enhanced aiming at the indexes;
2. according to the invention, the probability indexes which can cause abnormal monitoring data can be provided for the quality control personnel through data analysis, and the quality control personnel can screen the reasons of data abnormality according to the probability indexes, so that the resource investment such as manpower time and the like for lack of targeted whole-process inspection is greatly reduced;
3. according to the invention, probability indexes which can cause abnormal monitoring data can be provided for quality control personnel through data analysis, and monitoring personnel can screen the reasons of data abnormality according to the probability indexes, so that the acquisition mode of related monitoring indexes is changed, and the lack of targeted resource investment is greatly reduced;
drawings
FIG. 1 is a technical flowchart of a method for analyzing abnormal data of a production area environment by tracing the source in a whole process;
FIG. 2 is a plot of the site location distribution for a region of interest;
FIG. 3 is a diagram of index quantile monitoring data;
FIG. 4 is a diagram of the allowable range of analysis and measurement precision of a heavy metal detection project;
FIG. 5 is a graph of offset distance curve fit;
FIG. 6 is a graph of a yield reduction curve fit;
FIG. 7 is a graph of a package integrity curve fit;
FIG. 8 is a plot of an air-drying temperature curve fit;
FIG. 9 is a graph of a uniformity curve fit for the samples;
FIG. 10 is a digestion temperature curve fit plot;
FIG. 11 is a graph of a detection duration curve fit;
FIG. 12 is a graph showing the probability of abnormality of each index;
Detailed Description
The present invention is further described in detail below with reference to examples so that those skilled in the art can practice the invention with reference to the description.
It will be understood that terms such as "having," "including," and "comprising," when used herein, do not preclude the presence or addition of one or more other elements or groups thereof.
1. Historical information acquisition
After a certain county area is determined as a research area, 215 points are determined, as shown in fig. 2. Acquiring related background information, such as the type of crops in a research area is rice, the type of soil is red soil, and the land utilization mode is a paddy field; acquiring point location geographic information;
2. quality control information acquisition
Acquiring quality control information of each link, such as a sampling link, a sample preparation and circulation link, a detection link and the like, wherein the quality control information comprises a plurality of items, such as point offset distance, yield reduction rate, packaging integrity, air drying temperature, sample uniformity, digestion temperature, detection duration and the like;
3. quality control error index calculation
(1) Selecting index quantile values (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%) through Excel,
the results are shown in FIG. 2;
(2) Determining the grading value of the mutual detection sample soil cadmium as a research index; determining the number of error point positions smaller than each quantile value according to the allowable range of the analysis and test precision of the heavy metal detection project (figure 4), wherein more than 90% of error point positions are 7 error point positions, and more than 95% of error point positions are 18 error point positions;
(3) Selecting a Gamma CDF model for curve fitting (figures 5-11) through origin software to obtain an error index;
4. data exception checking
And calculating the sum of error indexes of all indexes and the abnormal probability thereof through excel, sequencing from large to small (figure 6), and determining the investigation sequence of quality control personnel and the specific monitoring mode of environmental monitoring.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable to various fields of endeavor for which the invention may be embodied with additional modifications as would be readily apparent to those skilled in the art, and the invention is therefore not limited to the details given herein and to the embodiments shown and described without departing from the generic concept as defined by the claims and their equivalents.
Claims (9)
1. A method for analyzing origin environmental monitoring abnormal data through whole-process tracing is characterized by comprising the following steps:
(1) Historical information acquisition
Acquiring background information and historical monitoring data;
(2) Quality control information acquisition
Obtaining sampling quality control information, obtaining sample preparation and circulation quality control information, obtaining detection quality control information, obtaining external quality control sample quality control information,
(3) Quality control error index calculation
(3.1) error Rate calculation
Acquiring the background information, historical monitoring data, sampling quality control information, sample preparation and circulation quality control information and detection quality control information, and calculating the bit-dividing monitoring data of each specific monitoring index in all the quality control information; calculating the error rate of a specific monitoring index of the external quality control sample corresponding to each sub-position monitoring data; the error rate of the specific monitoring index of the external quality control sample is as follows: when a specific monitoring index detection value of an external quality control sample of a certain quantile value has a monitoring index error, the corresponding point of the quantile value and the number of all the points below the corresponding point with the monitoring index error account for the proportion of the total number of the points; the monitoring index error of the external quality control sample refers to: the specific monitoring index detection value of the external quality control sample exceeds the indoor relative deviation range corresponding to the monitoring index;
(3.2) error index calculation
Selecting one or more models, fitting the position-divided monitoring data and the specific monitoring index error rate of the external quality control sample corresponding to the position-divided monitoring data to obtain an error index;
(4) Data exception checking
(4.1) when an external quality control sample is wrong, calculating the abnormal probability of the quality control index:
wherein Pi is the abnormal probability of the quality control index, i is the error index of the single quality control index after the external quality control sample is in error, and Σ i is the sum of the error indexes of all the quality control indexes after the external quality control sample is in error;
(4.2) sequencing the abnormal probabilities from large to small, wherein the sequence is a tracing sequence of abnormal monitoring data, and the background information comprises administrative regions, crop types, soil types and land utilization modes; the historical monitoring data comprises monitoring point position coordinates and a detection result of a producing area environment index; the sampling quality control information comprises sampling codes, a sampling mechanism, point coordinate offset distance, agricultural production abnormal conditions (natural disasters and plant diseases and insect pests), yield reduction rate in season and sampling completion time; the sample preparation and circulation quality control information comprises sample circulation time, sample packaging integrity, sample state integrity, a sample preparation mechanism, sample receiving quantity, air drying time, air drying temperature, air drying humidity, target mesh number, screened sample quantity, material of a sample grinding cutter head, sample uniformity, subpackaged sample quantity, circulation codes and sample preparation duration; the detection quality control information comprises sample circulation time, sample packaging integrity, sample state integrity, a detection mechanism, sample receiving quantity, detection codes, a detection method, sample weighing, balance room humidity, test water purity, digestion acid purity, digestion temperature, single-batch digestion sample quantity, digestion time, residual validity period of measurement and authentication of a detection instrument, a blank value, internal control code sample error, internal control parallel sample deviation, standard curve use time, a ratio of a detection result to a first point of a standard curve, a ratio of a last point of the standard curve to a detection result, a standard curve R side, unilateral continuous batch times of a quality control diagram and detection duration.
2. The method for analyzing the origin through the tracing of the abnormal data in the monitoring of the environment of the producing area according to claim 1, wherein the error rate calculation is not performed by various mechanisms, sampling coding, stream coding, detection coding and detection method indexes.
3. The method for analyzing the origin-location environment monitoring abnormal data through whole-process tracing as claimed in claim 1, wherein the quantile monitoring data are obtained according to an equivalent gradient method: and increasing the data of the specific monitoring index according to the equivalent gradient, extracting the monitoring result of the monitoring index corresponding to each quantile value, and calculating the fusion value of each group of indexes, wherein the quantile value is increased according to any value gradient of 1-10%.
4. The method as claimed in claim 3, wherein when the quantile value is 5%, 19 quantile values Q are calculated, that is, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%.
5. The method for analyzing the production area environment monitoring abnormal data through the whole tracing process as claimed in claim 1, wherein the external quality control sample comprises a constant value password sample, a parallel double sample and/or a mutual inspection sample; the model is a species sensitivity fitting model, including Weibull CDF, lognormaldCDF, gamma CDF, growth/Sigmoidal.
6. The method for analyzing the origin of the abnormal data in the monitoring of the production area environment in the whole process of tracing as claimed in claim 5, wherein the constant value password sample is prepared by screening more than 15 bases nationwide to collect soil and agricultural product samples on the basis of considering the difference of soil types and pollution degrees in different areas, preparing preparation requirements sample referring to relevant national standard substances, and changing one batch every 2-3 years; the parallel double samples are samples which only comprise two same subsamples in environment monitoring and sample analysis, and the measuring result of the parallel samples reflects the precision level of the test to a certain extent; and the mutual detection sample is that when each detection mechanism detects samples every year, soil and agricultural product sample auxiliary samples at the same point position are randomly extracted as mutual detection samples and distributed to other designated detection mechanisms for detection.
7. A quality control method based on a whole-process traceability analysis result of origin environment monitoring abnormal data is characterized in that quality control is carried out based on one or more specific links in front 3-10 bits of the traceability sequence of the abnormal monitoring data calculated by the whole-process traceability analysis method of the origin environment monitoring abnormal data according to any one of claims 1-6.
8. An environment monitoring optimization method based on a whole-process tracing analysis result of production area environment monitoring abnormal data, which is used for carrying out environment monitoring optimization based on one or more specific links in front 3-10 bits of the tracing sequence of the abnormal monitoring data calculated by the whole-process tracing analysis method of the production area environment monitoring abnormal data according to any one of claims 1-6.
9. The method for optimizing environmental monitoring based on the analysis result of the origin environmental monitoring abnormal data through the whole process of tracing is as claimed in claim 8, wherein the environmental monitoring optimization refers to the optimization and adjustment of specific monitoring steps of specific monitoring indexes in the environmental monitoring process.
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