CN112633779B - Method for evaluating reliability of environmental monitoring data - Google Patents
Method for evaluating reliability of environmental monitoring data Download PDFInfo
- Publication number
- CN112633779B CN112633779B CN202110207648.6A CN202110207648A CN112633779B CN 112633779 B CN112633779 B CN 112633779B CN 202110207648 A CN202110207648 A CN 202110207648A CN 112633779 B CN112633779 B CN 112633779B
- Authority
- CN
- China
- Prior art keywords
- data
- site
- monitoring
- data set
- reliability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 93
- 238000000034 method Methods 0.000 title claims abstract description 23
- 230000007613 environmental effect Effects 0.000 title claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 20
- 238000011156 evaluation Methods 0.000 claims abstract description 16
- 239000003344 environmental pollutant Substances 0.000 claims description 52
- 231100000719 pollutant Toxicity 0.000 claims description 52
- 238000012423 maintenance Methods 0.000 claims description 21
- 239000000356 contaminant Substances 0.000 claims description 7
- 238000005516 engineering process Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000010801 machine learning Methods 0.000 claims description 4
- 238000012806 monitoring device Methods 0.000 claims description 2
- 238000007405 data analysis Methods 0.000 abstract description 3
- 238000012216 screening Methods 0.000 description 6
- 230000007246 mechanism Effects 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000012550 audit Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000011157 data evaluation Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000009365 direct transmission Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000005507 spraying Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Software Systems (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Operations Research (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method for evaluating the reliability of environmental monitoring data, which comprises the steps of collecting a data set at a monitoring site where environmental monitoring block chain equipment is arranged; determining a clustering site of each monitored site according to the data set; according to the data set of each monitored site and the data set of the corresponding clustering site, reliability evaluation is carried out on the monitored sites; at least writing the reliability judgment result into an environment monitoring block chain after the reliability judgment result is audited by a consensus algorithm; and the reliability judgment result comprises a credible label and a credible doubt label. The invention can carry out objective evaluation on the reliability of the environmental monitoring data at the local part of the data acquisition end, improve the real-time performance of data analysis, and improve the monitoring reliability and the monitoring data quality.
Description
Technical Field
The disclosure relates to the technical field of environmental monitoring, in particular to a method for evaluating reliability of environmental monitoring data.
Background
With the continuous deep understanding of the environment problem and the law thereof in society, the environmental problem is more and more aroused the attention of people. The quality of monitoring directly influences the authenticity and objectivity of data. The environment monitoring task credibility assessment system can assess the whole process of a monitoring task, changes the situation that whether environment monitoring data is credible or not is judged by people in the past, identifies and quantificationally assesses each component of 8 links such as a monitoring plan, field monitoring, sample management, laboratory analysis, sub-packaging, monitoring data, quality system operation and major non-conformity with work, and provides quantitative credit data for the monitoring data according to final scores.
The environment monitoring master station is used as a center to examine the operation and maintenance unit quality system, and the regional quality control center assists the master station to supervise and examine the operation and maintenance company quality system in the jurisdiction. And taking a single station as a unit, and performing percentage evaluation on the acquisition rate of the monitoring data, the qualification rate of the data quality control, the completion condition of the operation maintenance work and the like every month. The general station entrusts the operation and maintenance company to be responsible for daily operation of the station, equipment maintenance, quality control, quantity traceability and other works. The general station is used as a center to supervise the social operation and maintenance unit.
The current monitoring data reliability evaluation mode still has the following problems:
1. at present, all data are directly transmitted back to a central station, but the timeliness of the data is reduced due to the fact that the data are required to pass through a multi-layer examination and recheck program after the data are reviewed; 2. with the direct transmission of data, the operability of adjusting the data is reduced, but the recognition rate of the behavior of interfering the monitoring data by means of local water spraying, blocking of a sampling pipe and the like is still low; 3. because the judgment of the current credibility is still manually checked by depending on experience in a large scale, the time and the labor are consumed, and the judgment result is influenced by subjective judgment; 4. the central station has deployed video monitoring equipment at the monitoring station, but the video data utilization efficiency is low due to the large workload of video viewing.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method for evaluating reliability of environmental monitoring data, which overcomes the defects that the current reliability judgment is still performed by manual review by depending on experience in a large scale, time and labor are consumed, the judgment result is affected by subjective judgment, and the data evaluation timeliness is low, and can perform objective evaluation on reliability of environmental monitoring data locally at a data acquisition end, and make full use of existing data such as video data and monitoring device parameters, thereby improving real-time performance of data analysis, and improving monitoring reliability and monitoring data quality.
In order to achieve the above purpose, the invention provides the following technical scheme:
a method for evaluating the reliability of environmental monitoring data comprises the following steps:
collecting a data set at a monitoring site where environment monitoring block chain equipment is distributed;
determining a clustering site of each monitored site according to the data set;
according to the data set of each monitored site and the data set of the corresponding clustering site, reliability evaluation is carried out on the monitored sites;
at least writing the reliability judgment result into an environment monitoring block chain after the reliability judgment result is audited by a consensus algorithm; and the reliability judgment result comprises a credible label and a credible doubt label.
Further, marking the monitoring sites of multiple cities as M, and marking the data acquisition terminal of each monitoring site as MT，T＝{T 1 ,T 2 ,…T W }; the data set obtained by the data acquisition terminal is recorded as D, D ═ D 1 ,D 2 ,…D W -said dataset comprising: any one or more of site location information, pollutant concentration data, meteorological data, monitoring equipment parameter data, operation and maintenance record data and site video data; each data acquisition terminal creates an intelligent contract according to the acquired data set.
Further, utilizing an edge calculation algorithm, the edge calculation algorithm comprising: and preferentially screening m sites of the geographical position of each monitored site within a preset distance by combining the site position information in the data set with a Mahalanobis distance formula.
Further, historical pollutant concentration data and meteorological data in the data sets of the target monitoring site and the screened m sites are combined, meteorological factor similarity and pollutant factor similarity are judged, weighting calculation is carried out on the meteorological factor similarity and the pollutant factor similarity, and similarity factors are generated and sequenced; for each monitoring site M, screening the top n sites with the highest similarity factor ranking as clustering sites, and respectively recording the clustering sites as follows: { M 1 ,M 2 ,M 3 ,…M n }; the meteorological data comprises temperature, humidity, wind speed and wind direction; the historical contaminant concentration data includes site-to-site concentration correlations and site-to-site relative deviations in contaminant concentrations.
Further, the credibility assessment is performed by using the intelligent contract, and comprises a credibility assessment first stage: respectively judging the time continuity of the current pollutant concentration data by using the historical data set of the monitoring station M and the pollutant concentration data in the current data set, and comparing the time continuity with a first preset threshold value to make reliability judgment; and the pollution concentration data meeting the first preset condition is marked with a credible label, and the pollution concentration data not meeting the first preset condition enters a second credibility evaluation stage.
Further, the pollutant concentration data which do not meet the first preset condition are combined with the clustering sites { M 1 ,M 2 ,M 3 ,…M n Combining pollutant concentration data in corresponding time periods, judging the spatial continuity of the pollutant concentration of the monitoring station M through a spatial model, comparing the spatial continuity with a second preset threshold value, and judging the reliability; and the pollution concentration data meeting the second preset condition is marked with a credible label, and the pollution concentration data not meeting the second preset condition enters a third credibility evaluation stage.
Further, finding out monitoring equipment parameter data, operation and maintenance record data and site video data corresponding to the data set according to the pollutant concentration data which do not meet the second preset condition, and analyzing by using a machine learning algorithm in combination with the historical data set to make reliability judgment; and marking the pollutant concentration data judged to be credible with a credible label, and marking the incredible pollutant concentration data with a credible doubt label.
Further, performing Hash chain on the reliability judgment result, the intelligent contract and the data set, and writing the result into an environment monitoring block chain after being checked by a consensus algorithm; in the environment monitoring block chain, each block has the hash value of the previous block, and the source can be traced back.
And further, alarming the pollutant concentration data printed with the credible in-doubt label on a credibility assessment early warning platform.
Further, the alarm is matched with operation and maintenance personnel and sites where the alarm is located, a credit system of the personnel and the operation and maintenance company is formed, and the condition that the alarm cannot be tampered is protected by an environment monitoring block chain technology.
The method for evaluating the reliability of the environmental monitoring data has the advantages that: the method can effectively reduce the investment of manpower and financial resources, makes full use of the existing data, carries out objective evaluation on the reliability of the environmental monitoring data locally at the data acquisition end, improves the real-time performance of data analysis, reduces supervision links, and further effectively improves the monitoring reliability and the monitoring data quality.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of reliability evaluation of monitoring data according to an embodiment of the present invention.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be carried into practice or applied to various other specific embodiments, and various modifications and changes may be made in the details within the description and the drawings without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
Referring to fig. 1, an embodiment of the present disclosure provides a method for evaluating reliability of environmental monitoring data, including the following steps:
(1) collecting a data set at a monitoring site where environment monitoring block chain equipment is distributed;
recording monitoring sites of multiple cities as M, recording a data acquisition terminal of each monitoring site as T, and recording T as { T ═ T } 1 ,T 2 ,…T W }; the data set obtained by the data acquisition terminal is recorded as D, D ═ D 1 ,D 2 ,…D W -said dataset comprising: any one or more of site location information, pollutant concentration data, meteorological data, monitoring equipment parameter data, operation and maintenance record data and site video data; each data acquisition terminal creates an intelligent contract according to the acquired data set.
(2) Determining a clustering site of each monitored site according to the data set;
screening a plurality of clustering sub-sites within a preset distance for each monitoring site by utilizing an edge calculation algorithm and combining with similarity factors among the monitoring sites according to a data set acquired by a data acquisition terminal of each monitoring site;
the edge calculation algorithm includes: preferentially screening m sites of the geographical position of each monitored site within a preset distance by site position information in a data set and combining a Mahalanobis distance formula;
pollutant concentration data and weather in data sets of target monitoring sites and screened m sites are combinedData, judging weather factor similarity and pollutant factor similarity, performing weighted calculation on the weather factor similarity and the pollutant factor similarity, generating similarity factors and sequencing; for each monitoring site M, screening the top n sites with the highest similarity factor ranking as clustering sites, and respectively recording the clustering sites as follows: { M 1 ,M 2 ,M 3 ,…M n }; the meteorological data comprises temperature, humidity, wind speed and wind direction; the historical contaminant concentration data includes site-to-site concentration correlations and site-to-site relative deviations in contaminant concentrations.
(3) According to the data set of each monitored site and the data set of the corresponding clustered site, reliability evaluation is carried out on the monitored sites;
the credibility assessment is carried out by utilizing the intelligent contract, and comprises a credibility assessment first stage: respectively judging the time continuity of the current pollutant concentration data by using the historical data set of the monitoring station M and the pollutant concentration data in the current data set, and comparing the time continuity with a first preset threshold value to make reliability judgment; the pollution concentration data meeting the first preset condition is marked with a credible label, and the pollution concentration data not meeting the first preset condition enters a second credibility evaluation stage;
the pollutant concentration data which do not meet the first preset condition and the clustering sites { M } 1 ,M 2 ,M 3 ,…M n Combining pollutant concentration data in corresponding time periods, judging the spatial continuity of the pollutant concentration of the monitoring station M through a spatial model, comparing the spatial continuity with a second preset threshold value, and judging the reliability; the pollution concentration data meeting the second preset condition is marked with a credible label, and the pollution concentration data not meeting the second preset condition enters a third credibility evaluation stage;
finding corresponding monitoring equipment parameter data, operation and maintenance record data and site video data in the data set according to the pollutant concentration data which do not meet the second preset condition, and analyzing by using a machine learning algorithm in combination with the historical data set to make reliability judgment; and marking the pollutant concentration data judged to be credible with a credible label, and marking the incredible pollutant concentration data with a credible doubt label.
Because the current pollutant concentration data is checked mainly by manual judgment and mainly by the experience of judgment personnel, the unified objective standard is lacked. The method improves the objectivity and the accuracy of the reliability judgment of the pollutant concentration data by combining the judgment mode of the time continuity judgment of the same station and the space continuity among the clustering stations with the parameter data of the monitoring equipment, the operation and maintenance record data and the video data of the stations, and fully utilizes the equipment parameters, the video data and other data with lower utilization rate at present.
(4) At least writing the reliability judgment result into an environment monitoring block chain after the reliability judgment result is audited by a consensus algorithm; and the reliability judgment result comprises a credible label and a credible doubt label.
Performing Hash chain on the reliability judgment result, the intelligent contract and the data set, and writing the result into an environment monitoring block chain after being audited by a consensus algorithm; in the environment monitoring block chain, each block has the hash value of the previous block, and the source can be traced back.
And alarming the pollutant concentration data with the credible in-doubt label on a credibility assessment early warning platform.
And matching the alarm with operation and maintenance personnel and sites where the alarm is located to form a credit system of the personnel and the operation and maintenance company, wherein the alarm is protected by an environment monitoring block chain technology and cannot be tampered.
The consensus algorithm is based on the consensus of votes that require nodes joining the blockchain network to broadcast their trustworthy results before adding blocks to the blockchain, with data representing a site being approved only if more than 51% of the nodes in the group pass.
In the invention, the block chain technology is adopted, so that a credit certificate is not required to be issued by a center under the condition of asymmetric information, and a node created by an encryption algorithm based on internet big data is a valid node trust mechanism in a universal passing mode. The mechanism ensures that each site participates in creating a trust mechanism as a node, and only if more than 51% of nodes pass through the mechanism, a new block can be established, namely, approval is obtained; meanwhile, to tamper or counterfeit, more than 51% of nodes need to be mastered to be modified. The network is utilized to achieve consensus on the behaviors of the social monitoring mechanism through a consensus algorithm, and cryptography is used for recording on a block chain, so that an automatic monitoring mode of mutual monitoring of nodes is finally formed.
The invention aims at the technical problems that the prior assessment method has lag audit of pollutant concentration data reliability, so that the data reliability judgment lacks real-time performance, and the prior video data, monitoring equipment parameters and other data have low utilization rate. The method has the advantages that the credible label and the credible doubt label are printed on the pollutant concentration data of the monitoring station locally by utilizing edge calculation and intelligent combination, the problem that the real-time property of credibility judgment is lost is solved, and the data such as equipment parameters and video data with low utilization rate at present are fully utilized.
The present invention will be further described with reference to the following examples.
Example (b):
in the method for locally evaluating the reliability of environmental monitoring data based on the block chain technology, the step of judging that the data of a certain site in a certain city is abnormal is as follows:
(1) environment monitoring block chain equipment is arranged on a plurality of monitoring sites (hereinafter referred to as sub-stations).
The blockchain technique utilizes a blockchain data structure to verify and store data sets, utilizes a distributed node consensus algorithm to generate and update data sets, cryptographically secures data set transmission and access, utilizes smart contracts composed of automated script code to judge contaminant data trustworthiness and generate a distributed infrastructure of one of trusted tags/trusted suspicion tags.
(2) Preferentially screening m stations closest to the geographical distance of each station by combining the position information (longitude and latitude) of each station with a Mahalanobis distance formula, further judging the similarity of climate factors and the similarity of pollutant factors according to the meteorological data (temperature, humidity, wind speed, wind direction and the like) and historical pollutant concentration data (concentration correlation among stations and relative deviation of pollutant concentration among stations) of the target station and the m stations closest to the judgment, weighting and considering the two similarity factors, generating and sequencing a total similarity factor for each close station, and ranking the first n stations as clustering substations of the target substation.
(3) And judging the time continuity of the station by low-pass filtering the data of the station and the data of the past k days, and if the reliability probability of the time continuity of the data in a certain period is less than a preset threshold value, evaluating the reliability and entering the next stage. Data above the threshold is tagged with a trusted tag to wait for uplink with other data.
(4) And (3) aiming at the data with the reliability probability smaller than the preset threshold, applying the clustering substation obtained in the step (2), judging the spatial continuity of the clustering substation through a spatial model, calculating the reliability probability, and if the reliability is lower than the threshold again, evaluating the reliability and entering the next stage. Data above the threshold is tagged with a trusted tag to be uplinked with other data.
(5) And (4) analyzing the pollutant data in the suspected time period judged in the steps (3) and (4) by combining video data (image identification technology) and instrument and equipment parameters (establishing a relation between abnormal data and the equipment parameters through machine learning) in the same time period, and marking the data on the section of data with a label with a doubt if the data are found to be abnormal. Data above the threshold is tagged with a trusted tag to wait for uplink with other data.
(6) And (3) Hash chaining is carried out on the credibility/credibility doubt labels generated in the steps (3) to (5) and the data sets obtained in the step (1) and the deployed intelligent contracts, the credibility/credibility doubt labels are subjected to Hash chaining through a consensus algorithm (based on voting consensus, nodes needing to be added into the block chain network broadcast credibility results of the blocks before the blocks are added into the block chain, only more than 51% of nodes in the group pass the data indicating a certain station to be approved), the data are added onto the chain, and the pollutant concentration data marked as credibility doubt generates an alarm on a credibility assessment early warning platform.
(7) The alarm is matched with the operation and maintenance personnel and the site where the alarm is located to form a credit system of the personnel and the operation and maintenance company, and the alarm cannot be tampered by the block chain technology.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (6)
1. A method for evaluating the reliability of environmental monitoring data is characterized by comprising the following steps:
collecting a data set at a monitoring site where environment monitoring block chain equipment is distributed; the data acquisition terminal is used for acquiring a data set; each data acquisition terminal creates an intelligent contract according to the acquired data set;
determining a clustering site of each monitored site according to the data set; according to a data set acquired by a data acquisition terminal of each monitoring station, an edge calculation algorithm is utilized, M stations of the geographical position of each monitoring station within a preset distance are preferentially screened by combining a Mahalanobis distance formula through station position information in the data set, and then the first n stations with the highest ranking similarity factor are screened as clustering stations { M for each monitoring station M by combining similarity factors among the monitoring stations 1 ,M 2 ,M 3 ,…M n }；
According to the data set of each monitored site and the data set of the corresponding clustering site, reliability evaluation is carried out on the monitored sites;
the credibility assessment is carried out by utilizing the intelligent contract, and comprises a credibility assessment first stage: respectively judging the time continuity of the current pollutant concentration data by using the historical data set of the monitoring station M and the pollutant concentration data in the current data set, and comparing the time continuity with a first preset threshold value to make reliability judgment; the pollutant concentration data meeting the first preset condition are marked with a credible label, and the pollutant concentration data not meeting the first preset condition enter a second credibility evaluation stage;
the concentration of the pollutant which does not satisfy the first preset conditionData and Cluster site { M 1 ,M 2 ,M 3 ,…M n Combining pollutant concentration data in corresponding time periods, judging the spatial continuity of the pollutant concentration of the monitoring station M through a spatial model, comparing the spatial continuity with a second preset threshold value, and judging the reliability; the pollutant concentration data meeting the second preset condition is marked with a credible label, and the pollutant concentration data not meeting the second preset condition enters a third credibility evaluation stage;
at least writing the reliability judgment result into an environment monitoring block chain after the reliability judgment result is checked by a consensus algorithm; the reliability judgment result comprises a credible label and a credible doubt label; and alarming the pollutant concentration data with the credible in-doubt label on a credibility assessment early warning platform.
2. The method according to claim 1, wherein the monitoring sites of multiple cities are denoted as M, and the data acquisition terminal of each monitoring site is denoted as T, T ═ T { (T) 1 ,T 2 ,…T W }; the data set obtained by the data acquisition terminal is recorded as D, D ═ D 1 ,D 2 ,…D W -said dataset comprising: any one or more of site location information, pollutant concentration data, meteorological data, monitoring equipment parameter data, operation and maintenance record data and site video data.
3. The method of claim 1, wherein the meteorological factor similarity and the pollutant factor similarity are judged by combining historical pollutant concentration data and meteorological data in the data sets of the target monitoring station and the screened m stations, and the meteorological factor similarity and the pollutant factor similarity are subjected to weighted calculation to generate and sort the similarity factors; the meteorological data comprises temperature, humidity, wind speed and wind direction; the historical contaminant concentration data includes site-to-site concentration correlations and site-to-site relative deviations in contaminant concentration.
4. The method according to claim 1, wherein corresponding monitoring device parameter data, operation and maintenance record data and site video data in the data set are found according to the pollutant concentration data which do not meet a second preset condition, and are analyzed by a machine learning algorithm in combination with a historical data set to make a reliability judgment; and marking the pollutant concentration data judged to be credible with a credible label, and marking the incredible pollutant concentration data with a credible doubt label.
5. The method of claim 4, wherein the credibility determination result, the intelligent contract and the data set are hashed and uplinked, and are written into the environment monitoring block chain after being audited by a consensus algorithm; in an environment monitoring block chain, each block has a hash value of the previous block, and the source can be traced back.
6. The method of claim 1, further comprising matching the alarm with the operation and maintenance personnel, the site where the alarm is located, forming a credit system for the personnel and the operation and maintenance company, and protecting against tampering by environment monitoring blockchain technology.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110207648.6A CN112633779B (en) | 2021-02-25 | 2021-02-25 | Method for evaluating reliability of environmental monitoring data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110207648.6A CN112633779B (en) | 2021-02-25 | 2021-02-25 | Method for evaluating reliability of environmental monitoring data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112633779A CN112633779A (en) | 2021-04-09 |
| CN112633779B true CN112633779B (en) | 2022-08-12 |
Family
ID=75295338
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110207648.6A Active CN112633779B (en) | 2021-02-25 | 2021-02-25 | Method for evaluating reliability of environmental monitoring data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112633779B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592349A (en) * | 2021-08-11 | 2021-11-02 | 中国环境监测总站 | Intelligent monitoring system and method for environment monitoring data |
| CN114900527A (en) * | 2022-04-02 | 2022-08-12 | 中铁七局集团有限公司 | Method for monitoring state information of bridge part based on block chain |
| CN117009771B (en) * | 2023-09-26 | 2023-12-26 | 中国环境科学研究院 | Water pollution degree detection method and system suitable for park city |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190075301A (en) * | 2017-12-21 | 2019-07-01 | 중앙대학교 산학협력단 | System for resource management |
| WO2020021343A1 (en) * | 2018-07-25 | 2020-01-30 | 山东诺方电子科技有限公司 | Method for evaluating credibility of data from environmental monitoring station |
| CN110796847B (en) * | 2020-01-06 | 2021-06-11 | 北京英视睿达科技有限公司 | Block chain-based environment monitoring station operation and maintenance system and method |
| CN112217868B (en) * | 2020-09-10 | 2023-04-18 | 长江水利委员会长江科学院 | Water environment automatic monitoring and analysis evaluation method based on block chain |
-
2021
- 2021-02-25 CN CN202110207648.6A patent/CN112633779B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN112633779A (en) | 2021-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112633779B (en) | Method for evaluating reliability of environmental monitoring data | |
| CN113962659A (en) | Cloud computing-based smart power grid big data information management system and method | |
| Giglioni et al. | Autoencoders for unsupervised real‐time bridge health assessment | |
| CN111506618B (en) | Abnormal electricity consumption behavior analysis method combined with lightgbm-stacking algorithm | |
| CN113899872A (en) | Pollution source traceability system based on water quality monitoring | |
| CN113177730A (en) | Water resource traceability management system | |
| CN111784022A (en) | Short-time adjacent fog prediction method based on combination of Wrapper method and SVM method | |
| CN116932523B (en) | Platform for integrating and supervising third party environment detection mechanism | |
| CN113822781B (en) | Ecological environment supervision method and system based on block chain | |
| CN115964757A (en) | Drainage basin environment monitoring and disposal method and device based on block chain | |
| Reed et al. | Metrics as tools for bridging climate science and applications | |
| CN117522652B (en) | Human living environment vulnerability evaluation method, system, intelligent terminal and storage medium | |
| Alqudah et al. | Enhancing weather-related outage prediction and precursor discovery through attention-based multi-level modeling | |
| CN113869589A (en) | Power transmission line accident prediction method based on knowledge graph and inspection system | |
| Mascali et al. | A machine learning-based Anomaly Detection Framework for building electricity consumption data | |
| CN115481941A (en) | Multifunctional area combined intelligent security management method and system | |
| Chung et al. | Information extraction methodology by web scraping for smart cities | |
| CN112732773B (en) | Method and system for checking uniqueness of relay protection defect data | |
| CN113032949B (en) | Big data based test method | |
| CN112365563A (en) | Rainfall frequency graph set digitalization and rainstorm high-risk division visualization analysis system | |
| CN206023811U (en) | Drive test supervisory systems | |
| Xue et al. | Statistical source analysis of recurring sulfur dioxide pollution events in a chemical industrial park | |
| CN116205614B (en) | Business reconciliation management system and method for distributed scene | |
| Dai et al. | Transmission line condition assessment technology based on multi-source Parameter Fusion | |
| Assumma et al. | ANPSORT II METHOD: AN EXPERIMENTATION TO ASSESS THE RESILIENCE OF A SOCIO-ECOLOGICAL SYSTEM. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 100071 Beijing Fengtai Auto Museum West Road No. 8 Courtyard 1 Building 6 Floor 606 Applicant after: Beijing Yingshi Ruida Technology Co.,Ltd. Address before: 100160 606, 6th floor, building 1, courtyard 8, Automobile Museum West Road, Fengtai District, Beijing Applicant before: BEIJING INSIGHTS VALUE TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |