CN112965961B - Big data analysis method for biogas production by utilizing organic solid waste resources - Google Patents
Big data analysis method for biogas production by utilizing organic solid waste resources Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- C—CHEMISTRY; METALLURGY
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- C02F11/04—Anaerobic treatment; Production of methane by such processes
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- G—PHYSICS
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Abstract
A big data analysis method for biogas production by utilizing organic solid waste resources belongs to the application of information processing technology in the anaerobic fermentation field. The system classifies and filters information collected by a deployment site (information source site) and then sends the information to a computing center, the computing center reversely indexes data so as to achieve rapid extraction and calculation of the data, and finally, the calculation result is stored and provided for a big data application layer. The data of the invention are derived from the Internet of things, regional culture waste systems and laboratories.
Description
Technical Field
The invention relates to a big data analysis method for biogas production by utilizing organic solid waste resources, belonging to the application of an information processing technology in the field of anaerobic fermentation.
Background
The specific technical means of recycling the organic solid wastes include composting, biogas production, incineration, pyrolysis, landfill and the like, wherein biogas production by anaerobic fermentation of the organic wastes is a project of vigorous planting in China. At present, the main reasons of insufficient experience and immature process in biogas engineering technology in China are the imperfect mathematical and physical models of fermentation raw materials and processes. To build a perfect mathematical and physical model, a large amount of data needs to be collected for analysis and comparison. Along with the arrival of big data age, big data technology is also more mature, but the method for analyzing the clutter data by applying the big data technology in the organic solid waste field is not mature enough.
The invention relates to a big data analysis method for biogas production by utilizing organic solid waste resources, which is used for rapidly and effectively processing big data acquired from the internet of things, an area culture waste system and a laboratory and can be used for judging and estimating new project experience according to analysis results.
Disclosure of Invention
In order to solve the problems, the invention provides a big data analysis method for producing methane by utilizing organic solid waste resources. The invention is suitable for a large data processing system in distributed arrangement, the system classifies and filters information collected by a deployment place (information source place) and then sends the information to a computing center, the computing center reversely indexes the data so as to realize rapid extraction and calculation of the data, and finally, the calculation result is stored and provided for a large data application layer. The data of the invention are derived from the Internet of things, regional culture waste systems and laboratories.
A big data analysis method for biogas production by utilizing organic solid waste resources, the method comprising the following steps:
s1: a data filtering stage, wherein data is screened and filtered into a specified format according to data sources at a deployment site;
the data format of the internet of things in the specified format is as follows: ("material type", "feed", "project number", "process type", "date", "position", "daily feed amount", "daily gas production"); the data format of the regional culture waste system is as follows: ("material type", "manure cleaning mode", "position", "stock date", "stock quantity", "stock output quantity", "type", "feed", "daily manure volume"), laboratory data format is: the material type is one of specific types such as pig manure, dairy manure, beef cattle manure, broiler manure, laying hen manure, corn silage, biogas slurry and the like;
s2: in the data transmission stage, adding a corresponding type identifier before a data format and then sending the data format to a computing center;
the adding of the corresponding type identifier refers to adding the type identifier corresponding to the data before the data format, loT is the data of the Internet of things, bre is the data of the regional culture waste system, and Lab is the laboratory data;
taking the internet of things data as an example, each piece of data is processed in the following format: { LoT, ("Material type", "feed", "project number", "Process type", "date", "position", "daily charge", "daily gas production") };
s3: a data aggregation stage, wherein the computing center aggregates the data with the same type identifier into a set;
the specific operation step of the S3 is that the data of the same type identifier are gathered into a set, and the data set of the Internet of things is { LoT, ("material type", "feed", "project number", "process type", "date", "position", "daily feeding amount", "daily gas production"), ("material type", "feed", "project number", "process type", "date", "position", "daily feeding amount", "daily gas production amount"), "… }; the regional culture waste system data set is { Bre, ("material type", "feces clearing mode", "position", "stock date", "stock quantity", "output quantity", "type", "feed", "daily feces volume"), … }; the laboratory data set is { Lab, ("Material type", "feces removal mode", "Process type", "quality", "solid content", "density", "gas yield"), "… };
s4: in the data processing stage, each data corresponds to an index, and one of the materials, the item numbers, the process types, the manure cleaning modes, the positions and the like is used as a key value in each data set to carry out inverted indexing on the data; the index can be a physical position number corresponding to each data, etc.;
further, the specific operation of S4 is: taking the data set of the internet of things as an example, the data index values of the same material category are gathered in one array, namely: { LoT, (Material 1, (index 1, index 2, …)), (Material 2, (index 1, index 2, …), … }, inverted index of "item number", "process type", "position" is the same, and other data sets are also the same as the processing mode of the data set of the Internet of things;
s5: a data calculation stage, namely conveniently finding out data according to the inverted index to calculate, and storing calculation results for application programs;
further, the specific step of S5 is: taking the internet of things data set "material type" as a key value to find a corresponding value as an example, in inverted index set { LoT, (material 1, (index 1, index 2, …)) taking the "material type" as the key value, finding data with the "material type" as material 1, finding the internet of things data set { LoT according to the index value (index 1, index 2, …) ("material type", "feed", "item number", "process type", "date", "position", "daily feed amount", "daily gas yield"), ("material type", "feed", "item number", "process type", "date", "position", "daily feed amount", "daily gas yield") in … } with the index numbers as the data of index 1 and index 2, and calculating according to a calculation formula to obtain a required calculation result. The partial calculation formula and the required result are as follows:
for example: the data with the same item number can be found according to the inverted index set of the item number in the data of the Internet of things, and the unit mass gas production of each material is calculated by applying the following formula:
gas production per unit mass = Σdailygas production Σdailycharge
Material i gas yield per unit mass = gas yield per unit mass x daily feed rate of material i +.
The material i refers to the ith material in a plurality of different kinds of materials.
For example: according to the inverted index set of the 'material types' in the regional culture system data, the data with the same material types can be found, and the unit daily manure volume is calculated by applying the following formula:
unit fecal volume = Σdailyfecal volume Σstockvolume Σ
For example: according to the inverted index set of the material types in laboratory data, the data with the same material types can be found, and the unit solid content gas production rate is calculated by using the following formula:
gas production per solid content = gas production +.quality +.
For example: the method can also carry out cross calculation, take out the same data from the intersection of the 'manure cleaning mode' and the 'material type' inverted index set, and can estimate the daily gas production of the farm through laboratory data and regional culture system data, wherein the formula is as follows:
daily gas production= (Lab) gas production/(Lab) mass× (Bre) daily fecal volume× (Lab) density.
The invention has the advantages that (1) big data can be processed rapidly and effectively, and new project experience judgment and estimation can be performed according to analysis results; (2) the results of gas production, manure production and the like can be well predicted, and the information of which material has the best gas production effect and which feed formula can improve gas production and the like to the greatest extent can be obtained according to all data of the highest gas production or manure production.
Drawings
FIG. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
Detailed Description
The present system is further described below with reference to fig. 1, but the present invention is not limited to the following examples.
Example 1
The names of places, item names, specific numerical values, etc. appearing in the embodiments are merely for convenience of explanation of the present invention, and are not taken as actual references.
1. Filtering data
The Internet of things data take the Internet of things biogas project in certain county in Hebei as an example, the project number XM0001 is operated stably for 1 year, the material types are pig manure and laying hen manure, the daily feeding amount is respectively 10t and 5t, the process type is wet fermentation, and the daily gas yield is 9360m 3 . The project can collect a plurality of sensor data through the internet of things, other data are filtered except the specified data, and the data processing format of the internet of things is as follows: ("material type", "feed", "project number", "process type", "date", "position", "daily feed amount", "daily gas production"); the final data processing format is: (pig manure, pig feed, XM0001, wet fermentation, 20201010, county, 10,9360) (hen manure, chicken feed, XM0001, wet fermentation, 20201010, county, 5,9360). In this embodiment, only two pieces of data of the internet of things item are taken as an example.
The regional culture system data is exemplified by Liaoning county, which is the pig raising county, and pig raising manufacturer users regularly count and upload the pig cases of the pig raising manufacturer users to the regional culture system. Wherein the specified format data in the aquaculture system data is distinguished in the system data: the materials, the excrement cleaning mode, the position, the stock date, the stock quantity, the output quantity, the type, the feed and the daily excrement volume are filtered, wherein the data of a certain merchant are as follows: (pig manure, water-washed manure, county, 20191010,12000,3000, white pig, feed, 277). In this embodiment, only one region is taken as an example of the data of the cultivation system.
Laboratory data taking Beijing university laboratory data as an example, laboratory data were processed according to the format specified in the laboratory data of the present invention: ("material type", "feces cleaning mode", "process type", "quality", "solid content", "density", "gas production"), and finally some data processing is: (pig manure, water-washed manure, wet fermentation, 10,0.05,1.05,154). This example only exemplifies this piece of laboratory data.
2. Transmitting data
The method comprises the steps of adding corresponding type values before data, sending the data to a computing center, sending Internet of things data { LoT, (pig manure, pig feed, XM0001, wet fermentation, 20201010, county, 10,9360) }, { LoT, (laying hen manure, chicken feed, XM0001, wet fermentation, 20201010, county, 5,9360) }, sending regional culture system data { Bre, (pig manure, water flushing manure, county, 20191010,12000,3000, white pig, feed, 277) }, and sending laboratory data { Lab, (pig manure, water flushing manure, wet fermentation, 10,0.05,1.05,154) }.
3. Aggregating data
The computing center gathers the same data into one set, puts all received internet of things data into one set { LoT, (pig manure, certain pig feed, XM0001, wet fermentation, 20201010, certain county, 10,9360), (laying hen manure, certain chicken feed, XM0001, wet fermentation, 20201010, certain county, 5,9360) }, at which time the computer automatically allocates physical addresses (index values) for the two pieces of data, assuming that the first (pig manure) data index value is 001 and the second (chicken manure) data index value is 002; similarly, all received regional culture system data are put into a set { Bre, (pig manure, water-washed manure, county, 20191010,12000,3000, white pig, feed, 277) }, and the index value of the data is assumed to be 001x; all received laboratory data were put into a collection { Lab, (pig manure, water wash manure, wet fermentation, 10,0.05,1.05,154) }, assuming this data index value is 001a.
In the data aggregation stage, when all data are stored together, each piece of data occupies a position in a computer, and each position has a house number, namely: index number.
4. Processing data
The computing center takes the material type, the item number, the process type and the manure cleaning mode as key values in each data set to carry out inverted index on the data. The inverted index taking the material type as a key value in the data set of the Internet of things is { LoT, (pig manure, (001)), (laying hen manure, (002)) } and the inverted index taking the item number as the key value is { LoT, (XM 0001, (001,002)) } and the inverted index taking the process type as the key value is { LoT, (wet fermentation, (001,002)) } respectively; in the { lot } set, (001) is the data number of the pig manure of all material types, wherein 001 is an index number, namely the position of the data in the { lot } set, and the egg manure is the same.
The inverted index taking the material type as a key value in the regional culture system data set is { Bre, (pig manure, (001 x)) } and the inverted index taking the manure cleaning mode as a key value is { Bre, (water manure, (001 x)) } and the index number indicating that the material type in the { Bre } set is pig manure is 001x.
The inverted index of the laboratory data taking the material type as a key value is { Lab, (pig manure, (001 a)) } and the inverted index of the laboratory data taking the process type as a key value is { Lab, (wet fermentation, (001 a)) } and the inverted index of the laboratory data taking the manure cleaning mode as a key value is { Lab, (water manure flushing, (001 a)) }.
5. Calculation data
The data of the inverted index is analyzed and calculated, and the unit mass gas production of each material can be calculated according to the inverted index set of the project number in the data of the Internet of things, and the specific steps are as follows: in the inverted index { LoT, (XM 0001, (001,002)) } data of item number "XM0001" are retrieved, and the internet of things data set { LoT, (pig manure, certain pig feed, XM0001, wet fermentation, 20201010, certain county, 10,9360), (hen manure, certain chicken feed, XM0001, wet fermentation, 20201010, certain county, 5,9360) } are found from the index value (001,002), namely (pig manure, certain pig feed, XM0001, wet fermentation, 20201010, certain county, 10,9360) and (hen manure, certain chicken feed, XM0001, wet fermentation, 20201010, certain county, 5,9360), the data of index numbers 001 and 002 are calculated from the calculation formula:
air production per unit mass=9360 ≡ (10+5) =624
Pig manure gas production per unit mass=624×10/10 (10+5) =416
Chicken manure gas production per unit mass=624×5 ≡ (10+5) =208
The unit daily manure volume can be calculated according to the inverted index set of the material type in the regional culture system data, and the specific calculation method comprises the following steps:
unit fecal volume=277 ≡12000=0.023;
the unit solid content gas production amount can be calculated according to the inverted index set of the material type in laboratory data, and the specific calculation method comprises the following steps:
unit solid gas yield = 154 +.10 +.0.05 = 308;
the same data are taken out from the intersection of the 'manure cleaning mode' and the 'material type' inverted index set, the daily gas production of the farm can be estimated through laboratory data and regional culture system data, and the following formula is used:
daily gas yield = 154 ∈10×277×1.05 = 22551.45;
the computing center stores these results for use by the big data application.
Claims (1)
1. A big data analysis method for biogas production by utilizing organic solid waste resources, which is characterized by comprising the following steps:
s1: a data filtering stage, wherein data is screened and filtered into a specified format according to data sources at a deployment site;
the data format of the internet of things in the specified format is as follows: ("material type", "feed", "project number", "process type", "date", "position", "daily feed amount", "daily gas production"); the data format of the regional culture waste system is as follows: ("material type", "manure cleaning mode", "position", "stock date", "stock quantity", "stock output quantity", "type", "feed", "daily manure volume"), laboratory data format is: ("material type", "feces cleaning mode", "process type", "mass", "solids content", "density", "gas yield");
s2: in the data transmission stage, adding a corresponding type identifier before a data format and then sending the data format to a computing center;
the adding of the corresponding type identifier refers to adding the type identifier corresponding to the data before the data format, loT is the data of the Internet of things, bre is the data of the regional culture waste system, and Lab is the laboratory data;
s3: a data aggregation stage, wherein the computing center aggregates the data with the same type identifier into a set;
s4: in the data processing stage, each data corresponds to an index, and one of the materials, the item numbers, the process types, the manure cleaning modes and the positions is used as an index value in each data set to carry out inverted index on the data;
s5: a data calculation stage, namely finding out data according to the inverted index to calculate, and storing calculation results for application programs;
the specific operation step of the S3 is that the data of the same type identifier are gathered into a set, and the data set of the Internet of things is { LoT, ("material type", "feed", "project number", "process type", "date", "position", "daily feeding amount", "daily gas production"), ("material type", "feed", "project number", "process type", "date", "position", "daily feeding amount", "daily gas production amount"), "… }; the regional culture waste system data set is { Bre, ("material type", "feces clearing mode", "position", "stock date", "stock quantity", "output quantity", "type", "feed", "daily feces volume"), … }; the laboratory data set is { Lab, ("Material type", "feces removal mode", "Process type", "quality", "solid content", "density", "gas yield"), "… };
the specific operation of S4 is: taking the data set of the internet of things as an example, the data index values of the same material category are gathered in one array, namely: { LoT, (Material 1, (index 1, index 2)), (Material 2, (index 1, index 2)), … }, inverted index of "item number", "process type", "position" is the same, and other data sets are also the same as the processing mode of the data sets of the Internet of things;
the specific steps of the S5 are as follows: taking the internet of things data set 'material type' as an index value to find a corresponding value as an example, in inverted index set { LoT, (material 1, (index 1, index 2)), (material 2, (index 1, index 2)), … } the data of which the 'material type' is material 1 is found, and the data of index numbers of index 1 and index 2 in the internet of things data set { LoT, ('material type', 'feed', 'item number', 'process type', 'date', 'position', 'daily feed quantity', 'daily gas yield', 'are found according to the index value (index 1, index 2'), and the required calculation result is obtained according to the calculation formula;
the partial calculation formula and the required result are as follows:
the data with the same item number can be found according to the inverted index set of the item number in the data of the Internet of things, and the unit mass gas production of each material is calculated by applying the following formula:
gas production per unit mass = Σdailygas production Σdailycharge
Material i gas yield per unit mass = gas yield per unit mass x daily feed rate of material i +.
The material i refers to an ith material in a plurality of different types of materials;
according to the inverted index set of the 'material type' in the regional culture waste system data, the data with the same material type can be found, and the unit daily manure volume is calculated by applying the following formula:
unit fecal volume = Σdailyfecal volume Σstockvolume Σ
According to the inverted index set of the material types in laboratory data, the data with the same material types can be found, and the unit solid content gas production rate is calculated by using the following formula:
gas production per solid content = gas production +.quality +.solid content;
or cross calculation is carried out, the same data are taken out from the intersection of the 'manure cleaning mode' and the 'material type' inverted index set, the daily gas yield of the farm can be estimated through laboratory data and regional culture waste system data, and the formula is as follows:
daily gas yield = Lab gas yield +.lab mass x Bre daily fecal volume x Lab density.
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