CN112965961A - 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
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- C02F11/02—Biological treatment
- C02F11/04—Anaerobic treatment; Production of methane by such processes
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- G—PHYSICS
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
A big data analysis method for producing biogas by utilizing organic solid waste resources belongs to the application of information processing technology in the field of anaerobic fermentation. The system classifies and filters the information collected by a deployment place (information source place) and then sends the information to the computing center, and then the computing center reversely indexes the data so as to realize the rapid extraction and calculation of the data, and finally stores and provides the calculation result for a big data application layer for use. The data of the invention are from the internet of things, regional aquaculture waste systems and laboratories.
Description
Technical Field
The invention relates to a big data analysis method for producing biogas 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 organic solid waste resource utilization include composting, biogas production, incineration, pyrolysis, landfill and the like, wherein biogas production through anaerobic fermentation of organic wastes is a project for vigorous planting in China. At present, the main reasons of insufficient experience and immature process in the methane engineering technology in China are imperfect mathematical and chemical models of fermentation raw materials and processes. To establish a perfect mathematical and chemical model, a large amount of data needs to be collected for analysis and comparison. With the advent of the big data age, big data technology is more and more mature, but a method for analyzing disordered data by using the big data technology in the field of organic solid wastes 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 quickly and effectively processing big data acquired from the Internet of things, a regional aquaculture waste system and a laboratory and can be used for carrying out new project experience judgment and estimation according to analysis results.
Disclosure of Invention
In order to solve the problems, the invention provides a big data analysis method for producing biogas by utilizing organic solid waste resources. The invention is suitable for a distributed big data processing system, the system classifies and filters the information collected by a deployment place (information source place) and then sends the information to the computing center, then the computing center performs inverted indexing on the data so as to realize rapid extraction and computation of the data, and finally, the computation result is stored and provided for a big data application layer for use. The data of the invention are from the internet of things, regional aquaculture waste systems and laboratories.
A big data analysis method for producing biogas by utilizing organic solid waste resources comprises the following steps:
s1: a data filtering stage, wherein data are screened and filtered into a specified format according to a data source at a deployment site;
the data format of the internet of things in the specified format is as follows: (material type, feed, item number, process type, date, location, daily feed amount, daily gas production rate); the data format of the regional cultivation waste system is as follows: (material type, dung cleaning mode, position, stock date, stock quantity, type, feed and daily dung volume), and the data format of laboratory data is as follows: (material type, manure cleaning mode, process type, quality, solid content rate, density and gas yield), wherein the material type is one of specific types such as pig manure, dairy cow manure, beef manure, meat chicken manure, egg chicken manure, corn silage, corn yellow silage, biogas slurry and the like;
s2: in the data transmission stage, the corresponding type identification is added before the data format and then is sent to a computing center;
adding corresponding type identification refers to adding type identification corresponding to data before data format, LoT is data of the Internet of things, Bre is data of a regional cultivation waste system, and Lab is laboratory data;
taking the internet of things data as an example, the format of each processed data is as follows: { LoT, ("material type", "feed", "item number", "process type", "date", "position", "daily feed amount", "daily gas production amount") };
s3: a data aggregation stage, wherein the computing center aggregates data of the same type identifier into a set;
the specific operation steps of S3 are gathering data of the same type identifier into a set, where the data set of the internet of things is { LoT, ("material type", "feed", "item number", "process type", "date", "location", "daily feed amount", "daily gas production"), … }; the regional aquaculture waste system data sets are { Bre, ("material type", "manure cleaning manner", "position", "stock date", "stock quantity", "type", "feed", "daily manure volume"), ("material type", "manure cleaning manner", "position", "stock date", "stock quantity", "type", "feed", "daily manure volume"), … }; the laboratory data set is { Lab, ("material type", "feces cleaning mode", "process type", "mass", "solid content", "density", "gas yield"), … };
s4: in the data processing stage, each data corresponds to an index, and one of material type, project number, process type, feces cleaning mode, position and the like is used as a key value in each data set to reversely index the data; the index can be a physical position number corresponding to each data, and the like;
further, the specific operations of S4 are: taking the data set of the internet of things as an example, the data index values of the same "material type" are gathered in an array, that is: { LoT, (Material 1, (index 1, index 2, …)), (Material 2, (index 1, index 2, …), … }, "item number", "process type", and "location", and other data sets are also processed in the same way as the Internet of things data sets;
s5: in the data calculation stage, data are conveniently found out according to the inverted index for calculation, and calculation results are stored for application programs to use;
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 an inverted index set { LoT, (material 1, (index 1, index 2, …)) taking the ' material type ' as the key value, finding the ' material type ' as the data of the material 1, finding the internet of things data set { LoT, (' material type ', ' feed ', ' item number ', ' process type ', ' date ', ' position ', ' daily material loading amount ', ' daily gas production amount '), (material type ', ' feed ', ' item number ', ' process type ', ' date ', ' position ', ' daily material loading amount ', ' daily gas production amount '), … } in the index number as the data of the index 1 and the index 2, and obtaining a required calculation result according to a calculation formula. Part of the calculation formula and the required results are as follows:
for example: the data with the same item number can be found in the data of the Internet of things according to the inverted index set of the item number, and the unit mass gas production rate of each material is calculated by the following formula:
sigma daily gas production rate/sigma daily feed rate per unit mass
Unit mass gas production of material i ═ unit mass gas production x daily material loading of material i ÷ Σ daily material loading
The material i refers to the ith material in a plurality of different types of materials.
For example: the volume of the daily excrement of a unit can be calculated by finding data with the same material type according to a material type inverted index set in the data of the regional culture system and applying the following formula:
the volume of the excrement produced per unit is sigma daily excrement volume and sigma storage volume
For example: the unit solid content gas production can be calculated by finding the data with the same material type according to the inverted index set of the material type in laboratory data and applying the following formula:
unit solid content gas production rate ═ gas production rate ÷ mass ÷ solid content rate
For example: the method can also carry out cross calculation, the same data is taken out from the intersection of the 'feces 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 formula is as follows:
daily gas production ═ gas production ÷ (Lab) mass × (Bre) daily manure volume × (Lab) density.
The method has the advantages that firstly, big data can be quickly and effectively processed, and new project experience judgment and estimation can be carried out according to an analysis result; secondly, the results of gas production, excrement production and the like can be well predicted, and information of which material has the best gas production effect and which feed formula can improve gas production and the like to the maximum extent can be obtained according to all data of the gas production or the excrement production.
Drawings
FIG. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
Detailed Description
The system is further described below with reference to fig. 1, but the present invention is not limited to the following examples.
Example 1
The place names, item names, specific numerical values, and the like appearing in the examples are merely for convenience of explaining the present invention, and are not actually referred to.
Filtering data
The Internet of things data take the Internet of things biogas engineering in a certain county in Hebei as an example, the engineering project number XM0001 has been stably operated for 1 year, the material types are pig manure and egg chicken manure, the daily material loading amount is 10t and 5t respectively, the process type is wet fermentation, and the daily gas production amount is 9360m3. This project can collect a plurality of sensor data through the thing networking, filters other data except that the regulation data, and thing networking data processing format is: (material type, feed, item number, process type, date, location, daily feed amount, daily gas production rate); the final data processing format is: (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). This embodiment only takes two data of the internet of things project as an example.
The data of the regional culture system is taken as an example in Liaoning county, which is a big county for pig raising, and pig raising manufacturers regularly count the situation of pigs in the factory and upload the situation to the regional culture system. Wherein, the data with the specified format in the region culture system data in the system data is: the data of a certain merchant are as follows: (pig manure, water-washed manure, county 20191010,12000,3000, white pig, feed, 277). The embodiment only takes the data of the one regional culture system as an example.
Laboratory data, exemplified by laboratory data of certain university of Beijing, was processed according to the format specified in the laboratory data of the invention: (material type, dung cleaning mode, process type, quality, solid content rate, density and gas production rate), and finally certain data are processed as follows: (pig manure, water-washed manure, wet fermentation, 10,0.05,1.05,154). This example only takes this piece of laboratory data as an example.
Second, data transmission
Adding a corresponding type value before the data, sending the data to a computing center, sending Internet of things data { LoT, (pig manure, certain pig feed, XM0001, wet fermentation, 20201010, county, 10,9360) }, { LoT, (egg chicken manure, certain chicken feed, XM0001, wet fermentation, 20201010, county, 5,9360) }, sending regional culture system data { Bre, (pig manure, water-washed manure, county, 20191010,12000,3000, white pig, certain feed, 277) }, and sending laboratory data { Lab, (pig manure, water-washed manure, wet fermentation, 10,0.05,1.05,154) }.
Third, gather the data
The computing center gathers the same data into a set, all received data of the internet of things are put into the set { LoT, (pig manure, certain pig feed, XM0001, wet fermentation 20201010, county 10,9360), (layer manure, certain chicken feed, XM0001, wet fermentation 20201010, county 5,9360) }, at the moment, the computing center automatically allocates physical addresses (index values) for the two pieces of data, and the first piece of (pig manure) data index value is 001, and the second piece of (chicken manure) data index value is 002; similarly, all received regional aquaculture system data are put into a set { Bre, (pig manure, water-washed manure, county, 20191010,12000,3000, big white pig, feed, 277) }, and the index value of the data is 001 x; all received laboratory data were put into a set { Lab, (pig manure, water-washed manure, wet fermentation, 10,0.05,1.05,154) }, assuming that this data index value was 001 a.
In the data aggregation stage, when all data are stored together, each piece of data occupies one position in a computer, and each position has a 'house number', namely: an index number.
Fourthly, processing data
The calculation center takes the material type, the project number, the process type and the feces 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)), (egg chicken manure, (002)) }, the inverted index taking the item number as the key value is { LoT, (XM0001, (001,002)) }, and the inverted index taking the process type as the key value is { LoT, (wet fermentation, (001,002)) }; in the expression, in the { lot } set, (001) is the data number of the pig manure of all material types, wherein 001 is the index number, namely the position of the data in the { lot } set, and the layer manure is treated similarly.
The inverted index taking the material type as a key value in the regional culture system data set is { Bre, (pig manure, (001x)) }, the inverted index taking the manure cleaning mode as a key value is { Bre, (water-washed manure, (001x)) }, and the index number indicating the material type as the pig manure in the { Bre } set is 001 x.
The laboratory data takes the inverted index with the material type as a key value as { Lab, (pig manure, (001a)) }, takes the inverted index with the process type as a key value as { Lab, (wet fermentation, (001a)) }, and takes the inverted index with the manure cleaning mode as a key value as { Lab, (water-washed manure, (001a)) }.
Fifthly, calculating data
Analyzing and calculating the data of the inverted indexes, and solving the unit mass gas production of each material according to a 'project number' inverted index set in the data of the Internet of things, wherein the method comprises the following specific steps: in an inverted index { LoT, (XM0001, (001,002)) } with an item number as a key value, data with an item number of "XM 0001" are retrieved, and data with index numbers of 001 and 002 in an internet of things data set { LoT, (pig manure, certain pig feed, XM0001, wet fermentation, 20201010, certain county, 10,9360), (egg chicken manure, certain chicken feed, XM0001, wet fermentation, 20201010, certain county, 5,9360) }, i.e., (pig manure, certain pig feed, XM0001, wet fermentation, 20201010, certain county, 10,9360) and (egg chicken manure, certain chicken feed, XM0001, wet fermentation, 20201010, certain county, 5,9360) are found according to an index value (001,002), which is calculated according to a calculation formula:
gas production per unit mass 9360 ÷ (10+5) ÷ 624
The gas production rate per unit mass of pig manure is 624 × 10 ÷ (10+5) ═ 416
Chicken manure unit mass gas production rate 624 × 5 ÷ (10+5) ÷ 208
The volume of the daily excrement of a unit can be calculated according to a material type inverted index set in the data of the regional culture system, and the specific calculation method comprises the following steps:
unit volume of feces produced 277/12000/0.023;
the gas yield of unit solid content can be calculated in laboratory data according to a 'material type' inverted index set, and the specific calculation method is as follows:
the unit solid content gas production rate is 154 ÷ 10 ÷ 0.05 ÷ 308;
the same data are taken out from the intersection of the feces 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 formula is as follows:
daily gas production rate 154 ÷ 10 × 277 × 1.05 ═ 22551.45;
the computing center stores these calculations for use by the big data application.
Claims (5)
1. A big data analysis method for producing biogas by utilizing organic solid waste resources is characterized by comprising the following steps:
s1: a data filtering stage, wherein data are screened and filtered into a specified format according to a data source at a deployment site;
the data format of the internet of things in the specified format is as follows: (material type, feed, item number, process type, date, location, daily feed amount, daily gas production amount …); the data format of the regional cultivation waste system is as follows: (material type, dung cleaning mode, position, stock date, stock quantity, type, feed and daily dung volume …), and the data format of laboratory data is as follows: (material type, dung cleaning mode, process type, quality, solid content rate, density and gas production rate …);
s2: in the data transmission stage, the corresponding type identification is added before the data format and then is sent to a computing center;
adding corresponding type identification refers to adding type identification corresponding to data before data format, LoT is data of the Internet of things, Bre is data of a regional cultivation waste system, and Lab is laboratory data;
s3: a data aggregation stage, wherein the computing center aggregates data of the same type identifier into a set;
s4: in the data processing stage, each data corresponds to an index, and one of material type, project number, process type, feces cleaning mode, position and the like is used as a key value in each data set to reversely index the data;
s5: and in the data calculation stage, data is conveniently found out according to the inverted index for calculation, and the calculation result is stored for the application program to use.
2. The big data analysis method for biogas production by utilization of organic solid waste resources as claimed in claim 1, wherein the specific operation steps of S3 are gathering data of same type identifier into a set, the data set of internet of things is { LoT, ("material type", "feed", "project number", "process type", "date", "location", "daily feed amount" …), ("material type", "feed", "project number", "process type", "date", "location", "daily feed amount" …), … }; the regional aquaculture waste system data set is { Bre, ("material type", "feces cleaning manner", "position", "stock date", "stock quantity", "type", "feed", "daily feces volume" …), ("material type", "feces cleaning manner", "position", "stock date", "stock quantity", "type", "feed", "daily feces volume" …), … }; the laboratory data set is { Lab, ("material type", "feces cleaning mode", "process type", "mass", "solid content", "density", "gas production" …), ("material type", "feces cleaning mode", "process type", "mass", "solid content", "density", "gas production" …), … }.
3. The big data analysis method for biogas production by utilization of organic solid waste resources as claimed in claim 1, wherein the specific operations of S4 are as follows: taking the data set of the internet of things as an example, the data index values of the same "material type" are gathered in an array, that is: { LoT, (Material 1, (index 1, index 2, …)), (Material 2, (index 1, index 2, …), … }, "item number", "process type", and "location", and other data sets are also processed in the same way as the Internet of things data sets.
4. The big data analysis method for biogas production by utilization of organic solid waste resources as claimed in claim 1, wherein the specific steps of S5 are as follows: taking the internet of things data set ' material type ' as a key value to find a corresponding value as an example, in an inverted index set { LoT, (material 1, (index 1, index 2, …)) taking the ' material type ' as the key value, finding the ' material type ' as the data of the material 1, finding the internet of things data set { LoT, (' material type ', ' feed ', ' item number ', ' process type ', ' date ', ' position ', ' daily material loading amount ', ' daily gas production amount '), (material type ', ' feed ', ' item number ', ' process type ', ' date ', ' position ', ' daily material loading amount ', ' daily gas production amount '), … } in the index number as the data of the index 1 and the index 2, and obtaining a required calculation result according to a calculation formula.
5. The big data analysis method for producing biogas by utilizing organic solid waste resources, according to claim 1, is characterized in that part of the calculation formula and the required results are as follows:
the data with the same item number can be found in the data of the Internet of things according to the inverted index set of the item number, and the unit mass gas production rate of each material is calculated by the following formula:
sigma daily gas production rate/sigma daily feed rate per unit mass
Unit mass gas production of material i ═ unit mass gas production x daily material loading of material i ÷ Σ daily material loading
The material i refers to the ith material in a plurality of different materials;
the volume of the daily excrement of a unit can be calculated by finding data with the same material type according to a material type inverted index set in the data of the regional culture system and applying the following formula:
the volume of the excrement produced per unit is sigma daily excrement volume and sigma storage volume
The unit solid content gas production can be calculated by finding the data with the same material type according to the inverted index set of the material type in laboratory data and applying the following formula:
the unit solid content gas yield is gas yield ÷ mass ÷ solid content rate;
or performing cross calculation, taking out the same data from the intersection of the feces cleaning mode and the material type inverted index set, and estimating the daily gas production of the farm according to laboratory data and regional culture system data, wherein the formula is as follows:
daily gas production ═ gas production ÷ (Lab) mass × (Bre) daily manure volume × (Lab) density.
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