CN117008559B - Data acquisition and processing method and system applied to organic fertilizer production system - Google Patents
Data acquisition and processing method and system applied to organic fertilizer production system Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 83
- 239000003895 organic fertilizer Substances 0.000 title claims abstract description 78
- 238000003672 processing method Methods 0.000 title claims abstract description 16
- 230000002159 abnormal effect Effects 0.000 claims abstract description 64
- 238000000034 method Methods 0.000 claims abstract description 31
- 238000004458 analytical method Methods 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 11
- 238000011156 evaluation Methods 0.000 claims description 24
- 238000012544 monitoring process Methods 0.000 claims description 12
- 230000005856 abnormality Effects 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 4
- 238000010223 real-time analysis Methods 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 2
- 238000011958 production data acquisition Methods 0.000 abstract 1
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 210000003608 fece Anatomy 0.000 description 3
- 208000027418 Wounds and injury Diseases 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 208000014674 injury Diseases 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 239000010871 livestock manure Substances 0.000 description 2
- 230000002547 anomalous effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000011031 large-scale manufacturing process Methods 0.000 description 1
- 244000144972 livestock Species 0.000 description 1
- 238000009304 pastoral farming Methods 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000009374 poultry farming Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 239000010865 sewage Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
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Abstract
The invention discloses a data acquisition and processing method and a system applied to an organic fertilizer production system, belonging to the technical field of organic fertilizer production data acquisition and processing, wherein the method comprises the following steps: step S1: identifying organic fertilizer production system information, and setting equipment data acquisition and emission data acquisition corresponding to the equipment data acquisition according to the organic fertilizer production system information; step S2: a display model is established, and the display model displays the equipment acquisition data and the emission acquisition data in real time; step S3: acquiring equipment acquisition data and emission acquisition data in real time, and inputting the acquired equipment acquisition data and emission acquisition data into a display model for real-time display; step S4: performing exception analysis on the equipment acquisition data and the emission acquisition data to determine whether the equipment acquisition data and the emission acquisition data have exception data; returning to the step S3 when no abnormal data exists; when there is abnormal data, go to step S5; step S5: and carrying out abnormal early warning according to a preset early warning scheme according to the abnormal data type.
Description
Technical Field
The invention belongs to the technical field of data acquisition and processing in organic fertilizer production, and particularly relates to a data acquisition and processing method and system applied to an organic fertilizer production system.
Background
With the rapid development of livestock and poultry farming, a large amount of feces and sewage are produced. Harmful elements in the feces are seriously out of standard, and are difficult to treat by a traditional returning mode. For this situation, organic fertilizer production systems have been developed. Biological manure is a social wealth and resource like petroleum, coal and ore. And the raw material resources of the organic fertilizer production line are quite rich. Through the development of the organic fertilizer production system, the method is favorable for promoting the harmless treatment, the resource utilization and the formation of commercial operation production industry chains of livestock manure, achieves the aim of industrialization, marketization and large-scale production, and effectively promotes the development of ecological agriculture and circular economy in whole market.
However, in the production process of the organic fertilizer, if corresponding equipment fails, pollution emission is easily caused, and especially for organic fertilizer production enterprises, if timely and accurate investigation cannot be performed, the pollution to the surrounding environment is easily caused when abnormal conditions occur; the invention provides a data acquisition and processing method and a system applied to an organic fertilizer production system in order to solve the problem.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a data acquisition and processing method and a system applied to an organic fertilizer production system.
The aim of the invention can be achieved by the following technical scheme:
the data acquisition and processing method applied to the organic fertilizer production system comprises the following steps:
step S1: identifying organic fertilizer production system information, and setting corresponding acquisition equipment for equipment data acquisition and emission data acquisition according to the organic fertilizer production system information;
further, the setting method of each acquisition device comprises the following steps:
establishing a monitoring library, wherein the monitoring library is used for storing data acquisition items of various organic fertilizer production equipment and corresponding data acquisition methods;
identifying various corresponding production equipment in the organic fertilizer production system information, and matching corresponding data acquisition items and data acquisition methods from the monitoring library; according to the obtained data acquisition items and the data acquisition method, carrying out acquisition equipment arrangement corresponding to equipment data acquisition;
determining emission acquisition items according to the information of the organic fertilizer production system, namely, according to the possible emission condition of the organic fertilizer production system, setting corresponding data items to be acquired, namely, emission acquisition items in a targeted manner; and setting acquisition equipment for acquiring emission data according to the emission acquisition item.
Step S2: establishing a display model, wherein the display model displays acquired equipment acquisition data and emission acquisition data in real time;
step S3: acquiring equipment acquisition data and emission acquisition data in real time, and inputting the acquired equipment acquisition data and emission acquisition data into a display model for real-time display;
step S4: performing exception analysis on the equipment acquisition data and the emission acquisition data to determine whether the equipment acquisition data and the emission acquisition data have exception data; returning to the step S3 when no abnormal data exists; when there is abnormal data, go to step S5;
further, the method for carrying out anomaly analysis on the equipment collected data comprises the following steps:
defining device acquisition data as X i ={x i1 ，x i2 ，x i3 ，……，x im I=1, 2, … …, n being a positive integer;
wherein X is i Is the equipment acquisition data of the ith organic fertilizer production equipment, x ij The single item of collection data of the j-th collection item of the i-th organic fertilizer production equipment, and m is the number of collection items corresponding to each organic fertilizer production equipment;
establishing a corresponding abnormality identification model based on historical acquisition data of the organic fertilizer production equipment, and acquiring data X of the equipment of the organic fertilizer production equipment through the established abnormality identification model i Real-time analysis is carried out to obtain corresponding abnormal data Y i ，Y i And X is i The relationship between these is as follows:
Y i =f(x i )+σ i ，i=1、2、……、n；
wherein X is i Is the ith equipment acquisition data of the organic fertilizer production equipment, Y i Is the corresponding anomaly data, sigma i Is a random error term; f (x) i ) Is an anomaly identification model.
Further, f (x i ) The expression is as follows:the ith equipment input is the organic fertilizer production equipment collects data X i Its output is the corresponding exception data Y i 。
Further, another method for performing anomaly analysis on device acquisition data includes:
presetting a coordinate system corresponding to each organic fertilizer production device, wherein a plurality of standard coordinate points are arranged in the coordinate system;
converting the acquired data of the equipment into evaluation coordinates, inputting the evaluation coordinates into a corresponding coordinate system, and identifying Euclidean distances between each evaluation coordinate and the nearest coordinate point, and marking the Euclidean distances as judgment distances; and counting the evaluation distance of each evaluation coordinate, and determining abnormal data according to the evaluation distance.
Further, the method for performing anomaly analysis on emission collection data includes:
setting emission monitoring components and corresponding standard contents;
and identifying corresponding data in the emission acquisition data in real time according to the emission monitoring component, marking the corresponding data as associated data, comparing the identified associated data with corresponding standard content in real time, and judging whether the associated data is abnormal or not.
Further, when the associated data is within the standard content allowable range, carrying out supplementary exception analysis to obtain an associated data change curve, identifying corresponding end points in the associated data change curve, and selecting A reference points by taking the end points as a benchmark, wherein A is a positive integer, and A is more than or equal to 5;
sequentially identifying corresponding curve slopes according to the appearance sequence of the reference points, and marking the corresponding curve slopes as reference slopes k v V denotes the corresponding reference point, v=1, 2, … …, a, the slope of the curve identifying the end point, labeled end slope h;
according to the formula h= |h+kmax { |k v+1 -k v The corresponding evaluation value H is calculated by I } Sign (Ht), wherein whenWhen v=a, k v+1 =h; ht is the corresponding selected k v+1 -k v The method comprises the steps of carrying out a first treatment on the surface of the The associated data having the evaluation value H greater than the threshold value X2 is regarded as abnormal data.
Step S5: and carrying out abnormal early warning according to a preset early warning scheme according to the abnormal data type.
Further, the method for carrying out abnormality early warning comprises the following steps:
establishing an early warning library, wherein the early warning library is used for storing various early warning schemes;
and matching corresponding early warning schemes according to the abnormal data types, and carrying out early warning processing according to the obtained early warning schemes.
The data acquisition processing system applied to the organic fertilizer production system comprises an acquisition module, a display module and an analysis module;
the acquisition module is used for supplementing each acquisition device according to the current organic fertilizer production system information, and acquiring device acquisition data and emission acquisition data;
the display module is used for establishing a display model, and inputting acquired equipment acquisition data and emission acquisition data into the display model for real-time display;
the analysis module is used for carrying out abnormal analysis on the equipment acquisition data and the emission acquisition data, when the equipment acquisition data and the emission acquisition data are not abnormal, corresponding operation is not carried out, and when the equipment acquisition data are abnormal, abnormal early warning is carried out according to a preset early warning scheme according to the type of the abnormal data.
Compared with the prior art, the invention has the beneficial effects that:
the invention realizes the data acquisition and processing of the organic fertilizer production system, intelligently judges whether abnormal data appear in the organic fertilizer production process, so that the abnormal data can be timely found and identified when the abnormal data appear, and the artificial participation degree can be greatly reduced through intelligent acquisition and analysis, thereby avoiding possible physical injury to staff; laying a foundation for future unmanned and less-humanized production; avoiding the abnormal discharge caused by the abnormal equipment, and causing pollution to the surrounding environment.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the data acquisition and processing method applied to the organic fertilizer production system comprises the following steps:
step S1: identifying organic fertilizer production system information, and setting equipment data acquisition and emission data acquisition corresponding acquisition equipment according to the obtained organic fertilizer production system information;
according to the specific model, working mode and the like of the organic fertilizer production system, determining what kind of acquisition equipment arrangement is needed for realizing equipment data acquisition and emission data acquisition; the data acquisition device is used for realizing the acquisition of corresponding data, the data acquisition of general equipment can be connected with corresponding equipment to acquire the data acquired by the corresponding equipment, namely, for equipment with automatic acquisition equipment, the data can be acquired directly, and if the data cannot be acquired, the corresponding acquisition equipment needs to be supplemented according to the acquisition content; and specifically, according to the information of the organic fertilizer production system, the data which should be collected by the organic fertilizer production system are matched and set, so that corresponding collection equipment supplement is carried out.
Emission data acquisition is used for emission data acquisition of an organic fertilizer production system, such as setting corresponding gas detection sensors, liquid component detection sensors and the like, and emission detection data acquisition is carried out.
Step S2: establishing a display model, wherein the display model displays acquired equipment acquisition data and emission acquisition data in real time;
the display model is established based on the current visualization technology and is used for displaying the acquired data of each acquisition device in real time, and the display of the organic fertilizer production system can be performed in a two-dimensional or three-dimensional mode according to the requirement, and corresponding display nodes are correspondingly inserted and used for displaying the corresponding acquired data.
Step S3: acquiring equipment acquisition data and emission acquisition data in real time, and inputting the acquired equipment acquisition data and emission acquisition data into a display model for real-time display;
and acquiring data by utilizing the corresponding acquisition equipment to acquire equipment acquisition data corresponding to each production equipment and emission acquisition data of the exhaust gas and the liquid.
Step S4: performing exception analysis on the equipment acquisition data and the emission acquisition data to determine whether the equipment acquisition data and the emission acquisition data have exception data; returning to the step S3 when no abnormal data exists; when there is abnormal data, go to step S5;
the method for carrying out anomaly analysis on the equipment collected data comprises the following steps:
defining device acquisition data as X i ={x i1 ，x i2 ，x i3 ，……，x im I=1, 2, … …, n being a positive integer;
wherein X is i Is the equipment acquisition data of the ith organic fertilizer production equipment, x ij The single item of collection data of the j-th collection item of the i-th organic fertilizer production equipment, and m is the number of collection items corresponding to each organic fertilizer production equipment.
The method for acquiring the abnormal data comprises the following steps:
establishing a corresponding abnormality identification model based on historical acquisition data of the organic fertilizer production equipment, and acquiring data X of the equipment of the organic fertilizer production equipment through the established abnormality identification model i Real-time analysis is carried out to obtain corresponding abnormalityData Y i ，Y i And X is i The relationship between these is as follows:
Y i =f(x i )+σ i ，i=1、2、……、n；
wherein X is i Is the ith equipment acquisition data of the organic fertilizer production equipment, Y i Is the corresponding anomaly data, sigma i Is a random error term; f (x) i ) Is an anomaly identification model;
f(x i ) The expression is as follows:the ith equipment input is the organic fertilizer production equipment collects data X i Its output is the corresponding exception data Y i 。
In another embodiment, the method may be adopted for performing the anomaly analysis of the numerical value of the device collected data, or the device collected data may be subjected to numerical conversion, that is, a corresponding conversion mode is defined manually to perform numerical conversion; the specific anomaly analysis method is as follows:
presetting a coordinate system corresponding to each organic fertilizer production device, and carrying out distinguishing setting according to each acquisition item, wherein a plurality of standard coordinate points corresponding to device acquisition data of the organic fertilizer production devices in a normal state are preset in the coordinate system;
converting the acquired data of the equipment into evaluation coordinates, inputting the evaluation coordinates into a corresponding coordinate system, and identifying Euclidean distances between each evaluation coordinate and the nearest coordinate point, wherein the nearest coordinate point can be other evaluation coordinates or standard coordinates and is marked as a judgment distance; and counting the judgment distance of each evaluation coordinate, and determining abnormal data according to the obtained judgment distance.
Judging according to the judging distance, namely judging whether the judging distance is abnormal relative to other distances or not according to the fact that v of the judging distance exceeds v of the other judging distances, and judging the judging distance as abnormal data or judging the judging distance as abnormal data when the judging distance is larger than a threshold value X1; the evaluation can be performed in various ways.
The method for carrying out anomaly analysis on emission collection data comprises the following steps:
setting emission monitoring components and corresponding standard contents; setting according to related emission standards;
and identifying corresponding data in the emission acquisition data in real time according to the emission monitoring component, marking the corresponding data as associated data, comparing the identified associated data with corresponding standard content in real time, and judging whether the associated data is abnormal or not. And the abnormal data is obtained when the standard content is exceeded.
In one embodiment, the data anomalies in the emission collection data do not necessarily all exceed the corresponding standard content, but are considered as anomalous data if the emission component varies abnormally, but they may be within the standard content tolerance, so, in order to improve the analysis accuracy, the following complementary method is proposed:
when the associated data is within the standard content allowable range, acquiring an associated data change curve, namely an associated data statistical curve; marking the corresponding point of the current associated data in the associated data change curve as a terminal point, and selecting A reference points forwards by taking the terminal point as a reference, wherein A is a positive integer, and A is more than or equal to 5; selecting each reference point A along the associated data change curve, wherein the reference points are all points recorded before; the corresponding span can be preset for selection, if the transverse axis corresponding to the end point is 10, the 5 reference points are the point positions on the associated data change curves corresponding to the positions of the transverse axes of 9, 8, 7, 6 and 5 respectively;
sequentially identifying corresponding curve slopes according to the appearance sequence of each reference point, and marking the corresponding curve slopes as reference slopes k v V denotes the corresponding reference point, v=1, 2, … …, a, the slope of the curve identifying the end point, labeled end slope h;
according to the formula h= |h+kmax { |k v+1 -k v The corresponding evaluation value H is calculated by } ×sign (Ht) |, where when v=a, k v+1 =h; ht represents Kmax { |k v+1 -k v Corresponding selected |k among | v+1 -k v K before absolute value v+1 -k v I.e. Ht is the corresponding selected k v+1 -k v The method comprises the steps of carrying out a first treatment on the surface of the When the evaluation value H is greater than the threshold value X2, abnormal data is regarded, whereas normal data is regarded.
Step S5: and carrying out abnormal early warning according to a preset early warning scheme according to the abnormal data type.
Acquiring various possible abnormal data conditions, and according to the severity, urgency and the like of the various abnormal data conditions, correspondingly setting corresponding early warning schemes, and informing corresponding technicians to check again; when the abnormal data is confirmed, emergency treatment is carried out;
summarizing the set early warning schemes and then establishing a corresponding early warning library;
and matching corresponding early warning schemes according to the abnormal data types, and carrying out early warning processing according to the obtained early warning schemes.
The invention realizes the data acquisition and processing of the organic fertilizer production system, intelligently judges whether abnormal data appear in the organic fertilizer production process, so that the abnormal data can be timely found and identified when the abnormal data appear, and the artificial participation degree can be greatly reduced through intelligent acquisition and analysis, thereby avoiding possible physical injury to staff; laying a foundation for future unmanned and less-humanized production; avoiding the abnormal discharge caused by the abnormal equipment, and causing pollution to the surrounding environment.
The data acquisition processing system applied to the organic fertilizer production system comprises an acquisition module, a display module and an analysis module;
the acquisition module is used for supplementing each acquisition device according to the current organic fertilizer production system information, and acquiring device acquisition data and emission acquisition data;
the display module is used for establishing a display model, and inputting acquired equipment acquisition data and emission acquisition data into the display model for real-time display;
the analysis module is used for carrying out abnormal analysis on the equipment acquisition data and the emission acquisition data, when the equipment acquisition data and the emission acquisition data are not abnormal, corresponding operation is not carried out, and when the equipment acquisition data are abnormal, abnormal early warning is carried out according to a preset early warning scheme according to the type of the abnormal data.
Since the above-described data acquisition processing method applied to the organic fertilizer production system has been described in detail, the specific operation method of each module is not described in detail in the present invention.
Finally, the present application also provides a corresponding embodiment of the computer readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps described in the data acquisition processing method embodiments as applied to the organic fertilizer production system described above.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (6)
1. The data acquisition and processing method applied to the organic fertilizer production system is characterized by comprising the following steps of:
step S1: identifying organic fertilizer production system information, and setting corresponding acquisition equipment for equipment data acquisition and emission data acquisition according to the organic fertilizer production system information;
step S2: establishing a display model, wherein the display model displays acquired equipment acquisition data and emission acquisition data in real time;
step S3: acquiring equipment acquisition data and emission acquisition data in real time, and inputting the acquired equipment acquisition data and emission acquisition data into a display model for real-time display;
step S4: performing exception analysis on the equipment acquisition data and the emission acquisition data to determine whether the equipment acquisition data and the emission acquisition data have exception data; returning to the step S3 when no abnormal data exists; when there is abnormal data, go to step S5;
the method for carrying out anomaly analysis on the equipment acquisition data comprises the following steps:
defining device acquisition data as X i ={x i1 ，x i2 ，x i3 ，……，x im I=1, 2, … …, n being a positive integer;
wherein X is i Is the equipment acquisition data of the ith organic fertilizer production equipment, x ij The single item of collection data of the j-th collection item of the i-th organic fertilizer production equipment, and m is the number of collection items corresponding to each organic fertilizer production equipment;
establishing a corresponding abnormality identification model based on historical acquisition data of the organic fertilizer production equipment, and acquiring data X of the equipment of the organic fertilizer production equipment through the established abnormality identification model i Real-time analysis is carried out to obtain corresponding abnormal data Y i ，Y i And X is i The relationship between these is as follows:
Y i =f(x i )+σ i ，i=1、2、……、n；
wherein X is i Is the ith equipment acquisition data of the organic fertilizer production equipment, Y i Is the corresponding anomaly data, sigma i Is a random error term; f (x) i ) Is an anomaly identification model;
f(x i ) The expression is as follows:
the ith equipment input is the organic fertilizer production equipment collects data X i Its output is the corresponding exception data Y i ；
The method for carrying out anomaly analysis on emission collection data comprises the following steps:
setting emission monitoring components and corresponding standard contents;
identifying corresponding data in emission acquisition data in real time according to emission monitoring components, marking the corresponding data as associated data, comparing the identified associated data with corresponding standard content in real time, and judging whether the associated data is abnormal or not;
when the associated data is within the standard content allowable range, carrying out supplementary exception analysis to obtain an associated data change curve, identifying corresponding end points in the associated data change curve, and selecting A reference points by taking the end points as a benchmark, wherein A is a positive integer, and A is more than or equal to 5;
sequentially identifying corresponding curve slopes according to the appearance sequence of the reference points, and marking the corresponding curve slopes as reference slopes k v V denotes the corresponding reference point, v=1, 2, … …, a, the slope of the curve identifying the end point, labeled end slope h;
according to the formula h= |h+kmax { |k v+1 -k v The corresponding evaluation value H is calculated by } ×sign (Ht) |, where when v=a, k v+1 =h; ht is the corresponding selected k v+1 -k v The method comprises the steps of carrying out a first treatment on the surface of the Regarding the associated data with the evaluation value H being greater than the threshold value X2 as abnormal data;
step S5: and carrying out abnormal early warning according to a preset early warning scheme according to the abnormal data type.
2. The data acquisition and processing method applied to an organic fertilizer production system according to claim 1, wherein the setting method of each acquisition device comprises:
establishing a monitoring library, wherein the monitoring library is used for storing data acquisition items of various organic fertilizer production equipment and corresponding data acquisition methods;
identifying various corresponding production equipment in the organic fertilizer production system information, and matching corresponding data acquisition items and data acquisition methods from the monitoring library; according to the obtained data acquisition items and the data acquisition method, carrying out acquisition equipment arrangement corresponding to equipment data acquisition;
and determining emission acquisition items according to the information of the organic fertilizer production system, and setting acquisition equipment for acquiring emission data according to the emission acquisition items.
3. The data acquisition and processing method applied to an organic fertilizer production system according to claim 1, wherein the method for performing anomaly analysis on the equipment acquisition data comprises:
presetting a coordinate system corresponding to each organic fertilizer production device, wherein a plurality of standard coordinate points are arranged in the coordinate system;
converting the acquired data of the equipment into evaluation coordinates, inputting the evaluation coordinates into a corresponding coordinate system, and identifying Euclidean distances between each evaluation coordinate and the nearest coordinate point, and marking the Euclidean distances as judgment distances; and counting the evaluation distance of each evaluation coordinate, and determining abnormal data according to the evaluation distance.
4. The data acquisition and processing method applied to an organic fertilizer production system according to claim 1, wherein the method for performing abnormality pre-warning comprises:
establishing an early warning library, wherein the early warning library is used for storing various early warning schemes;
and matching corresponding early warning schemes according to the abnormal data types, and carrying out early warning processing according to the obtained early warning schemes.
5. A data acquisition and processing system applied to an organic fertilizer production system, characterized in that the data acquisition and processing method applied to the organic fertilizer production system according to any one of claims 1 to 4 is executed, and the data acquisition and processing system comprises an acquisition module, a display module and an analysis module;
the acquisition module is used for supplementing each acquisition device according to the current organic fertilizer production system information, and acquiring device acquisition data and emission acquisition data;
the display module is used for establishing a display model, and inputting acquired equipment acquisition data and emission acquisition data into the display model for real-time display;
the analysis module is used for carrying out abnormal analysis on the equipment acquisition data and the emission acquisition data, when the equipment acquisition data and the emission acquisition data are not abnormal, corresponding operation is not carried out, and when the equipment acquisition data are abnormal, abnormal early warning is carried out according to a preset early warning scheme according to the type of the abnormal data.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the data acquisition processing method applied to an organic fertilizer production system as claimed in any one of claims 1 to 4.
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