CN116611771B - Modular alternative fuel intelligent storage system - Google Patents
Modular alternative fuel intelligent storage system Download PDFInfo
- Publication number
- CN116611771B CN116611771B CN202310899455.0A CN202310899455A CN116611771B CN 116611771 B CN116611771 B CN 116611771B CN 202310899455 A CN202310899455 A CN 202310899455A CN 116611771 B CN116611771 B CN 116611771B
- Authority
- CN
- China
- Prior art keywords
- analysis
- bin
- period
- storage
- module
- 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
- 238000003860 storage Methods 0.000 title claims abstract description 101
- 239000000446 fuel Substances 0.000 title claims abstract description 36
- 238000004458 analytical method Methods 0.000 claims abstract description 150
- 239000000463 material Substances 0.000 claims abstract description 130
- 238000007599 discharging Methods 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 22
- 230000008569 process Effects 0.000 claims abstract description 20
- 238000007670 refining Methods 0.000 claims description 58
- 230000008859 change Effects 0.000 claims description 28
- 230000007246 mechanism Effects 0.000 claims description 21
- 238000012544 monitoring process Methods 0.000 claims description 21
- 230000002159 abnormal effect Effects 0.000 claims description 19
- 238000007667 floating Methods 0.000 claims description 17
- 238000011156 evaluation Methods 0.000 claims description 11
- 238000005303 weighing Methods 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 238000005070 sampling Methods 0.000 claims description 8
- 230000003247 decreasing effect Effects 0.000 claims description 7
- 238000013210 evaluation model Methods 0.000 claims description 7
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000012423 maintenance Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 239000002028 Biomass Substances 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 3
- 230000006378 damage Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000012535 impurity Substances 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 239000004484 Briquette Substances 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 210000000078 claw Anatomy 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000009365 direct transmission Effects 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 238000001125 extrusion Methods 0.000 description 1
- 230000005923 long-lasting effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 238000005507 spraying Methods 0.000 description 1
- 230000007306 turnover Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G3/00—Storing bulk material or loose, i.e. disorderly, articles
- B65G3/04—Storing bulk material or loose, i.e. disorderly, articles in bunkers, hoppers, or like containers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- 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
- G06F16/23—Updating
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Abstract
The application belongs to the technical field of fuel storage, and particularly relates to a modular intelligent storage system for alternative fuels, which comprises a bin, wherein the bin is a skid-mounted bin, and is connected with a bin control system, and the bin control system comprises: the collection module is used for obtaining warehouse data in the warehouse, wherein the warehouse data comprise material feeding speed of a feeding point, material discharging speed of a discharging point, height of materials in the warehouse and weight of the materials in the warehouse; the material level analysis module is used for analyzing the real-time data in the bin in the analysis period, judging the analysis coefficient of the material feeding and discharging in the bin and generating a feedback signal. According to the application, the process of storage can be analyzed in real time according to the condition of feeding and discharging in the storage, the storage operation condition is monitored according to the analysis result, and meanwhile, the feeding and discharging condition of the storage can be adjusted according to the actual condition, so that compared with the conventional mode, the labor input is reduced.
Description
Technical Field
The application belongs to the technical field of fuel storage, and particularly relates to a modular intelligent storage system for alternative fuels.
Background
The application of biomass fuel is mainly biomass briquette fuel, which is a novel clean fuel which is prepared by taking agriculture and forestry waste as raw materials through the processes of crushing, mixing, extruding, drying and the like, is shaped (such as block, rod and the like) and can be directly combusted, and the storage and supply of the fuel are always an important challenge.
The traditional storage system generally adopts a storage tank or storage equipment with fixed capacity and a simple feeding/discharging mode, but generally the feeding and discharging needs too much manual intervention, and especially biomass fuel is easy to occupy a large amount of space in the feeding and discharging process after being crushed, so that the original storage capacity cannot meet the requirement, in addition, the feeding and discharging control of the traditional system generally lacks the characteristics of intellectualization and automation, the flow of materials cannot be effectively managed, the continuity is ensured, and the defects are large.
Disclosure of Invention
The application aims to provide a modular alternative fuel intelligent storage system which can be tested according to trial.
The technical scheme adopted by the application is as follows:
a modular alternative fuel intelligent storage system comprising: the feed bin, the feed bin is sled dress formula feed bin, the feed bin is connected with feed bin control system, and this feed bin control system includes:
the collection module is used for obtaining warehouse data in the warehouse, wherein the warehouse data comprise material feeding speed of a feeding point, material discharging speed of a discharging point, height of materials in the warehouse and weight of the materials in the warehouse;
the material level analysis module is used for analyzing the real-time data in the bin in the analysis period, judging the analysis coefficient of the material feeding and discharging in the bin and generating a feedback signal;
the preprocessing module is used for optimizing the real-time data in the bin according to the feedback signal, feeding the optimized real-time data back to the material level analysis module again, updating the analysis coefficient by the material level analysis module and reconstructing the feedback signal;
the processing module is used for analyzing the real-time feeding and discharging conditions of the storage bin according to the updated analysis coefficient and generating an analysis report
And the evaluation module is used for evaluating the running condition of the storage bin according to the goods entering and exiting condition of the storage bin and the evaluation model, and adjusting the running condition of the storage bin by combining the evaluation result.
In a preferred embodiment, the fill level analysis module operates as follows:
acquiring real-time data of the material feeding speed of the feeding point, the height of the material in the bin and the weight of the material in the bin;
inputting the real-time data into an analysis coefficient acquisition model, and acquiring analysis coefficients of material feeding and discharging in the bin through the analysis coefficient acquisition model;
if the analysis coefficient exceeds a preset analysis coefficient threshold value, judging that the material feeding and discharging processes in the bin are abnormal in analysis, and generating a feedback signal, wherein the feedback signal is an analysis abnormal signal;
if the analysis coefficient does not exceed the preset analysis coefficient threshold value, judging that the material feeding and discharging analysis in the bin is normal, and generating a feedback signal, wherein the feedback signal is an analysis normal signal.
In a preferred scheme, the pretreatment module comprises a classification module and a refining mechanism, wherein the classification module is used for applying an operation instruction to the refining mechanism according to the analysis result of the material level analysis module;
in addition, when the feedback signal is an analysis abnormal signal, the grading module applies an execution parameter to the material-refining mechanism, the material-refining mechanism operates according to an execution standard, so that the storage data in the storage bin is updated, and the updated storage data is fed back to the material level analysis module;
when the feedback signal does not analyze the normal signal, the grading module does not send out a command, and the refining mechanism operates according to the original standard.
In a preferred embodiment, the process module runs include:
inputting the height of the materials in the bin into a first height change function, and obtaining a height increasing trend value;
inputting the height of the materials in the bin into a second height change function, and obtaining a height descending trend value;
predicting the height of the material after the next refining according to the height increasing trend value and the height decreasing trend value.
In a preferred scheme, the device further comprises a construction module, a control module and a control module, wherein the construction module is used for constructing a monitoring period and constructing an analysis period in the monitoring period according to the operation period of the bin;
the construction module operates as follows:
constructing a monitoring period, acquiring single operation time periods of the bin in the monitoring period, and marking each single operation time period as an analysis time period;
acquiring the duration of the analysis period, if the duration is smaller than a first threshold, merging the analysis period into the previous analysis period, and if the duration is larger than the first threshold, constructing a plurality of analysis periods by integral multiples of the standard analysis period threshold;
and acquiring the allowable deviation period, and then incorporating the allowable deviation period into the last analysis period.
In a preferred embodiment, the process of constructing the analysis period further comprises
Acquiring all historical storage data in an analysis period, and taking the average value of the historical storage data and calibrating the average value as data to be checked;
calibrating the data to be verified under adjacent bit numbers as parameters to be evaluated;
obtaining standard floating parameters, comparing the standard floating parameters with the parameters to be evaluated, and screening out all the parameters to be evaluated higher than the standard floating data;
acquiring all parameters to be verified corresponding to the parameters to be evaluated higher than the standard floating parameters, and calibrating the parameters to be optimized;
and acquiring analysis time periods corresponding to adjacent parameters to be optimized, calibrating the analysis time periods as reference time periods, and determining time periods between a starting node of the reference time period with the earlier bit and an ending node of the reference time period with the later bit as effective monitoring periods.
In a preferred embodiment, the operation of the evaluation module comprises:
obtaining a change trend value of the refining material in each analysis period in an effective monitoring period according to the evaluation model;
acquiring an allowable deviation value interval, and comparing with a variation trend value of the refining;
if the change trend value is in the allowable deviation value interval, judging that the bin is normal in operation;
and if the change trend value is lower than the lower limit value of the allowable deviation value interval, judging that the bin is abnormal in operation, and sending out a maintenance signal.
In a preferred scheme, the discharge gate has been seted up to one side of feed bin, the inside of feed bin just is close to the position department of discharge gate and installs last average device and lower average device, the feed inlet has been seted up to one side of the upper end of feed bin, horizontal average device and vertical average device are installed to the inside of feed bin upper end and the position department that is close to the feed inlet, automatic sampling device is installed to one side of feed bin, a plurality of weighing device are installed to the lower extreme of feed bin, the bottom average device is installed to the inside lower extreme of feed bin, a plurality of material level detection device and fire control unit of internally mounted of feed bin upper end.
In a preferred embodiment, the bottom refining apparatus is a step floor.
The application has the technical effects that:
according to the application, the storage process can be analyzed in real time according to the feeding and discharging conditions in the bin, the storage operation condition is monitored according to the analysis result, and meanwhile, the feeding and discharging conditions of the bin can be adjusted according to the actual condition, so that compared with the conventional mode, the labor input is reduced;
according to the application, through analyzing the change condition of the materials in the storage, whether hidden danger occurs in the storage process can be fed back in real time, and under the condition that the storage bin is not abnormal, the abnormality can be found timely according to the actual condition and can be processed timely.
Drawings
FIG. 1 is a system flow diagram of the present application;
FIG. 2 is a system block diagram of the present application;
FIG. 3 is a schematic view of the structure of the bin of the application;
FIG. 4 is a bottom cross-sectional view of the bin of the application;
fig. 5 is a schematic upper view of the silo of the application.
In the drawings, the names of the components represented by the reference numerals are as follows:
1. a storage bin; 2. a discharge port; 3. feeding and homogenizing devices; 4. a blanking and homogenizing device; 5. a feed inlet; 6. a transverse material homogenizing device; 7. a longitudinal material homogenizing device; 8. an automatic sampling device; 9. a weighing device; 10; a bottom material-refining device 11; a level detection device 12; a fire-fighting device.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Further, in describing the embodiments of the present application in detail, the cross-sectional view of the device structure is not partially enlarged to a general scale for convenience of description, and the schematic is only an example, which should not limit the scope of protection of the present application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Referring to fig. 1 to 2, the application provides a modular alternative fuel intelligent storage system, which comprises a bin, wherein the bin is a skid-mounted bin, and is connected with a bin control system, and the bin control system comprises:
the collection module is used for obtaining warehouse data in the warehouse, wherein the warehouse data comprise material feeding speed of a feeding point, material discharging speed of a discharging point, height of materials in the warehouse and weight of the materials in the warehouse; the analysis module here is a weighing device 9, a level detection device 11 and corresponding sensors for detecting the blanking speed.
The material level analysis module is used for analyzing real-time data in the bin in an analysis period, judging analysis coefficients of material feeding and discharging in the bin and generating a feedback signal, the material level analysis module can be computer equipment with operation capability, other computer equipment with operation capability, and the preprocessing module, the processing module and the evaluation module which are mentioned later can also be data processing units or computer equipment with operation capability;
the preprocessing module is used for optimizing the real-time data in the bin according to the feedback signal, feeding the optimized real-time data back to the material level analysis module again, updating the analysis coefficient by the material level analysis module and reconstructing the feedback signal;
the processing module is used for analyzing the real-time feeding and discharging conditions of the storage bin according to the updated analysis coefficient and generating an analysis report
And the evaluation module is used for evaluating the running condition of the storage bin according to the goods entering and exiting condition of the storage bin and the evaluation model, and adjusting the running condition of the storage bin by combining the evaluation result.
In actual biomass fuel's storage process, generally utilize conventional storage tank or storage equipment to carry out the storage, but to biomass fuel's material after especially smashing, when its piles up, the volume expansion, thereby it reduces to lead to storage utilization ratio, and to this common practice is to adopt artificial interference, extrude the material, but to biomass fuel's it is unsuitable for extrusion, especially in dark reaction stage, appropriate fluffy also can help its reaction, therefore in this embodiment, gather the storage data of feed bin inside through the collection module, and utilize the material level analysis module to judge the material accumulation condition in feed bin, so that just feedback ejection of compact storehouse business turn over material condition, simultaneously because the fuel that just gets into in the feed bin has too fluffy state, preprocessing module also can be fine handle it, so that make the material level analysis module carry out the analysis again to storage data, avoid appearing erroneous judgement phenomenon, finally, the material height in the feed bin is predicted according to the analysis coefficient, in the feed bin is often triggered when the high deviation appears in actual height, the feed bin appears in the time, the time of the feed bin is often, the time is usually long, the problem is usually triggered to the feed bin is usually appears, the time is usually, the problem is not exposed, the problem is difficult to be had in the time to the problem is not had, the problem is usually been had in the operation is not been had, the time to be had, the problem is exposed in the time to the prediction, and is not had, the time is exposed, the problem is carried out, and the problem is not had, and is exposed in time to be had.
In a preferred embodiment, the level analysis module operates as follows:
acquiring real-time data of the material feeding speed of the feeding point, the height of the material in the bin and the weight of the material in the bin;
inputting real-time data into an analysis coefficient acquisition model, and acquiring analysis coefficients of material feeding and discharging in a bin through the analysis coefficient acquisition model, wherein the coefficient acquisition model comprises a first acquisition function and a second acquisition function, the first acquisition function is x1= (a1×v+b1×h)/a1+a2, the second acquisition function is x2= (a2×v+b2×g)/a2+b2, wherein X1 and X2 are respectively the analysis coefficients of the fuel height and the fuel weight in the bin, H is the fuel height in the bin, G is the fuel weight, a1, a2, b1 and b2 are all proportionality coefficients, and specific values are actually set according to different storage equipment and are not described in detail herein;
if the analysis coefficient exceeds a preset analysis coefficient threshold value, judging that the material feeding and discharging processes in the bin are abnormal in analysis, and generating a feedback signal, wherein the feedback signal is an analysis abnormal signal;
if the analysis coefficient does not exceed the preset analysis coefficient threshold value, judging that the material feeding and discharging analysis in the bin is normal, and generating a feedback signal, wherein the feedback signal is an analysis normal signal.
In this embodiment, when the analysis coefficient exceeds the preset analysis coefficient threshold, the analysis coefficient refers to that when the fuel height analysis coefficient and the fuel weight analysis coefficient in the storage bin are both exceeded, and the condition for generating the analysis abnormal signal is that the fuel height analysis coefficient and the fuel weight analysis coefficient in the storage bin are not exceeded, or when the fuel height analysis coefficient is not exceeded, the device generates the analysis abnormal signal and feeds the analysis abnormal signal back to the user side (which may be through a server for transfer or a wired device for direct transmission), so as to remind the user side of the occurrence of an abnormal condition.
Secondly, the pretreatment module comprises a grading module and a material-refining mechanism, wherein the grading module is used for applying an operation instruction to the material-refining mechanism according to the analysis result of the material level analysis module;
in addition, when the feedback signal is an analysis abnormal signal, the grading module applies an execution parameter to the material-refining mechanism, the material-refining mechanism operates according to an execution standard, so that the storage data in the storage bin is updated, and the updated storage data is fed back to the material level analysis module;
when the feedback signal does not analyze the normal signal, the grading module does not send out a command, and the refining mechanism operates according to the original standard.
The refining mechanism in this embodiment is composed of an upper refining device 3, a lower refining device 4, a transverse refining device 6, a longitudinal refining device 7 and a bottom refining device 10, specifically, when the material level analysis module generates an analysis coefficient, the analysis coefficient is processed through the classification module, and the classification module applies an operation instruction to the refining mechanism according to the feedback signal type, wherein the applied execution parameters can be to control each component in the refining mechanism to cooperatively operate or respectively regulate and control, and how to specifically regulate and control and select can be selected according to actual conditions, so that specific redundant description is not needed, and when normal signals are analyzed, the original operation state is kept without regulation, so that the current situation is kept.
And secondly, the operation process of the processing module comprises the following steps:
inputting the height of the materials in the bin into a first height change function to obtain a height increasing trend value, wherein the first height change function expression is as follows:，/>shows a highly increasing trend value, < >>Indicates the feeding period, ++>And->Representing the height of adjacent materials;
inputting the height of the materials in the bin into a second height change function to obtain a height descending trend value, wherein the expression of the second height change function is as follows: the height decrease trend value is:，/>represents a highly decreasing trend value, +.>Indicates the refining time period, +.>And->Representing the height of adjacent materials;
wherein, the liquid crystal display device comprises a liquid crystal display device,=/>
predicting the material height after the next refining according to the height increasing trend value and the height decreasing trend value, wherein the predicted material height after the next refining is as follows:，/>indicating the material height after the next refining, < +.>Representation of the feedInterval of materials->Indicating the refining interval.
When the increasing trend and the decreasing trend of the height are equal, the refining effect is optimal, but generally, the increasing trend is gradually higher than the decreasing trend along with the increase of the height of the material, at this time, too fast the increasing trend of the height can lead to untimely refining, a comparison relation is set, and the refining is ensured，/>For the scaling factor, it is specifically not specifically limited in practice, and it is necessary to perform an optimization operation (e.g., artificial refining, decreasing the feed rate, i.e., refining two or more times followed by feeding) beyond this pair relationship.
In order to further understand and explain the operation condition of the storage bin, the intelligent storage system for the modular alternative fuel also comprises a construction module, a control module and a control module, wherein the construction module is used for constructing a monitoring period and constructing an analysis period in the monitoring period according to the operation period of the storage bin;
the construction module operates as follows:
constructing a monitoring period, acquiring single operation time periods of the bin in the monitoring period, and marking each single operation time period as an analysis time period;
acquiring the duration of an analysis period, if the duration is smaller than a first threshold, merging the analysis period into a previous analysis period, and if the duration is larger than the first threshold, constructing a plurality of analysis periods by integral multiples of a standard analysis period threshold (here, the first threshold);
the allowable deviation period (also set to the first threshold value here) is acquired, the allowable deviation period is incorporated into the last bit analysis period.
Because the time of feeding and discharging of the bin is not long-lasting, but is opened and closed in real time according to feeding and discharging requirements, when a monitoring period is constructed, a period of operation of the bin is selected as an analysis period, and the operation period of the bin is possibly short, so that the period cannot be used as the whole analysis period, when the operation period is smaller than a first threshold value (set according to actual conditions), the part of period can be fused to a previous period, when the operation period of the bin is larger than the first threshold value, the first threshold value can be directly selected as a standard analysis period, a plurality of analysis periods are constructed by integral multiples of the standard analysis period, and a remainder (namely an allowable deviation period) appears between the longer analysis period and the standard analysis period threshold value.
In addition, the process of constructing the analysis period further comprises
Acquiring all historical storage data in an analysis period, and taking the average value of the historical storage data and calibrating the average value as data to be checked;
calibrating the data to be verified under adjacent bit numbers as parameters to be evaluated;
obtaining standard floating parameters, comparing the standard floating parameters with the parameters to be evaluated, and screening out all the parameters to be evaluated higher than the standard floating data;
acquiring all parameters to be verified corresponding to the parameters to be evaluated higher than the standard floating parameters, and calibrating the parameters to be optimized;
and acquiring analysis time periods corresponding to adjacent parameters to be optimized, calibrating the analysis time periods as reference time periods, and determining time periods between a starting node of the reference time period with the earlier bit and an ending node of the reference time period with the later bit as effective monitoring periods.
Because the data of the bin in each analysis period is adopted, in general, the material inlet and outlet time of the bin is carried out according to the generation requirement, and the production process is carried out according to the production plan, namely the production process has a certain periodicity, for example, when the bin is received in summer or autumn, the use time of the bin is longer than that of other time, therefore, in different time periods, the use frequency of the bin is different, in the embodiment, firstly, the parameters to be verified in the analysis period are acquired, the adjacent parameters to be verified are used as a group of parameters to be verified, the floating value of the parameters to be verified is determined according to the difference value between the parameters to be verified, then the floating value is compared with the standard floating parameters one by one, all the parameters to be verified which are higher than the standard floating parameters are screened out, then the parameters to be verified which are corresponding to the standard floating parameters are calibrated to be optimized, and certain fluctuation exists in the time period of the opening and closing of the material inlet and outlet, the nodes to be verified with higher occurrence frequency are screened out from the storage nodes to be standard nodes, then, offset processing is carried out according to the preset evaluation period, the setting is preferably 6-8 min, then, the standard node to be verified is not used as the standard node to be different in the operation period, and the operation period can be effectively monitored.
Further, the operation of the evaluation module includes:
obtaining a change trend value of the refining material in each analysis period according to the evaluation model in an effective monitoring period, wherein the obtaining of the change trend value of the refining material is carried out by a change trend function contained in the evaluation modelObtained by the method, wherein-> 3 Indicating the change trend value of the refining material>Representing the number of analysis periods, +.>Representing interval 2 to->In a height decrease trend value, +.>Representing intervals 1 to->-a height dip trend value in 1, wherein the height dip trend value herein takes the average of all the height dip trend values within each analysis period and is input into a trend function;
acquiring an allowable deviation value interval, and comparing with a variation trend value of the refining;
if the change trend value is in the allowable deviation value interval, judging that the bin is normal in operation;
and if the change trend value is lower than the lower limit value of the allowable deviation value interval, judging that the bin is abnormal in operation, and sending out a maintenance signal.
In this embodiment, when the bin is used for a long time, the mode of comparing the multi-period height descending trend values is adopted, the efficiency of refining the fuel in the operation process of the discharging bin is reflected, because the device adopted by refining, especially the refining mechanism, tends to easily accumulate impurities, such as impurities accumulated on the bottom refining device 10 or other refining devices, or the different degrees of damage to each module of the refining mechanism can cause the actual refining trend of the fuel to change, the change is predicted by the mode, so that the problem can be found timely, and the height descending frequency of a plurality of analysis periods is counted, so that the refining change trend value can be effectively obtained, if the refining change trend value of the refining bin is larger than zero, the refining effect of the surface bin is better, if the refining change trend value is smaller than zero, the abrasion in the surface refining mechanism is larger, or other anomalies appear, the staff is reminded to maintain in time, and the situation of lack of monitoring is avoided, so that the equipment is in a serious fault is caused subsequently.
In addition, it should be emphasized here that the above embodiments of the present application are mainly directed to the calculation process in the feeding aspect, but since the feeding and the discharging are generally asynchronous, the process in the discharging is also applicable to the above embodiments, and the specific implementation only needs to change some parameters in the functions or models, and readjust the related threshold according to the past experimental data.
In addition, please refer to fig. 3 to 5, a discharge hole 2 is formed in one side of the bin 1, an upper and a lower equalizing devices 3 and 4 are installed in the bin 1 and near the discharge hole 2, the upper equalizing device 3 is located above the lower equalizing device 4, a feed inlet 5 is formed in one side of the upper end of the bin 1, a transverse equalizing device 6 and a longitudinal equalizing device 7 are installed in the bin 1 and near the feed inlet 5, an automatic sampling device 8 is installed in one side of the bin 1, a plurality of weighing devices 9 are installed at the lower end of the bin 1, a stepping floor 10 is installed at the lower end of the bin 1, and a plurality of material level detecting devices 11 and fire fighting devices 12 are installed in the bin 1.
Specifically, when materials are put in, the materials are put in the storage bin 1 through the feeding hole 5, the materials at the blanking points can be scattered in other areas of the storage bin 1 through the transverse material homogenizing device 6 and the longitudinal material homogenizing device 7, the material repose angle is erased, the utilization volume of the storage bin 1 is increased, the stepping floor 10 is operated, the materials in the storage bin 1 are driven to move towards the discharging hole 2, the upper material homogenizing device 3 and the lower material homogenizing device 4 are operated, the materials at the position of the discharging hole 2 are conveyed to the outside of the storage bin 1 for uniform feeding, and the impact caused by large-area falling of the materials is relieved;
the automatic sampling device 8 can automatically sample materials at regular time to detect the quality of the samples, wherein the automatic sampling device 8 can adopt a mechanical or pneumatic principle to drive a sampler or a sampling claw to take the materials in the storage bin 1 or grab the materials in the storage bin 1, and the sampled materials are moved into the outside of the storage bin 1 through a conveying belt or an inclined pipeline so as to facilitate the detection of the sampled materials by staff, and the automatic sampling device is the prior art and is not repeated herein;
through the weighing device 9, wherein the weighing device 9 can be a weighing sensor, and two groups, three groups or weighing sensors can be arranged at the bottom of the storage bin 1 according to the length of the storage bin 1, so that continuous weighing of materials is realized, and intelligent statistics and control are realized;
the bin level detection device 11 is arranged, wherein the bin level detection device 11 comprises a distance sensor and is used for detecting the height of materials in the bin 1, so that intelligent monitoring and management of the materials in the bin are realized;
through the fire control unit 12 of setting, wherein, fire control unit 12 is including setting up spark detector and fire control spraying system at feed bin 1 top, is prior art, and the unnecessary repetition is described here, can realize extremely quick detection and restrain flame or conflagration, avoids or reduces harm and production loss.
The storage bin 1 is designed in a container type, so that the requirements of sea transportation, train transportation or truck transportation are met, the size of the storage bin 1 can be customized according to the use requirements, and the use convenience is improved;
the upper and lower material-homogenizing devices 3 and 4 can be composed of screw rods and motors, the screw rods are driven by the motors to rotate so as to transfer materials, meanwhile, the materials are scattered by rotation, the materials are prevented from caking up, the heights of the upper and lower material-homogenizing devices 3 and 4 can be adjusted according to feeding quantity requirements by arranging electric push rods on the upper and lower material-homogenizing devices 3 and 4, and the distance can also be adjusted;
the transverse material homogenizing device 6 and the longitudinal material homogenizing device 7 can be composed of a screw rod, a porous net and a motor, wherein the porous net is arranged at the lower part of the screw rod, so that materials falling onto the porous net through a feed inlet 5 are controlled to drive the screw rod to push the materials to move on the porous net, and in the moving process, the materials fall onto a stepping floor 10 in the storage bin 1 through holes of the porous net, so that the materials at blanking points can be scattered to other areas of the storage bin 1, the material repose angle is erased, and the utilization volume of the storage bin 1 is increased;
the stepping floor 10 can be composed of a hydraulic system and a plurality of longitudinal plates, so that the longitudinal plates are divided into two groups, the two groups of longitudinal plates are controlled by the hydraulic system to uniformly reciprocate at a set speed, and the uniform feeding of materials is realized, wherein the longitudinal plates can be a sealing waterproof plate, a heavy-duty plate and the like, and the prior art is not repeated here.
The application is used when in use: the process of storage can be analyzed in real time according to the condition of feeding and discharging in the storage, the storage operation condition is monitored according to the analysis result, the feeding and discharging condition of the storage can be adjusted according to the actual condition, and compared with a conventional mode, the hidden danger can be fed back in real time through analyzing the change condition of the materials in the storage, and under the condition that the storage is not abnormal, the storage is timely discovered to be abnormal according to the actual condition and is timely processed.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
Claims (6)
1. The utility model provides a modular alternative fuel intelligent storage system which characterized in that includes: the feed bin, the feed bin is sled dress formula feed bin, the feed bin is connected with feed bin control system, and this feed bin control system includes:
the collection module is used for obtaining warehouse data in the warehouse, wherein the warehouse data comprise material feeding speed of a feeding point, material discharging speed of a discharging point, height of materials in the warehouse and weight of the materials in the warehouse;
the material level analysis module is used for analyzing the real-time data in the bin in the analysis period, judging the analysis coefficient of the material feeding and discharging in the bin and generating a feedback signal;
the preprocessing module is used for optimizing the real-time data in the bin according to the feedback signal, feeding the optimized real-time data back to the material level analysis module again, updating the analysis coefficient by the material level analysis module and reconstructing the feedback signal;
the processing module is used for analyzing the real-time feeding and discharging conditions of the storage bin according to the updated analysis coefficient and generating an analysis report
The evaluation module is used for evaluating the running condition of the storage bin according to the goods entering and exiting condition of the storage bin and the evaluation model, and adjusting the running condition of the storage bin by combining the evaluation result;
further comprises: the construction module is used for constructing a monitoring period and constructing an analysis period in the monitoring period according to the operation period of the bin;
the construction module operates as follows:
constructing a monitoring period, acquiring single operation time periods of the bin in the monitoring period, and marking each single operation time period as an analysis time period;
acquiring the duration of the analysis period, if the duration is smaller than a first threshold, merging the analysis period into the previous analysis period, and if the duration is larger than the first threshold, constructing a plurality of analysis periods by integral multiples of the standard analysis period threshold;
acquiring an allowable deviation period, and then incorporating the allowable deviation period into a final analysis period;
the process of constructing an analysis period further comprises:
acquiring all historical storage data in an analysis period, and taking the average value of the historical storage data and calibrating the average value as data to be checked;
calibrating the data to be verified under adjacent bit numbers as parameters to be evaluated;
obtaining standard floating parameters, comparing the standard floating parameters with the parameters to be evaluated, and screening out all the parameters to be evaluated higher than the standard floating data;
acquiring all parameters to be verified corresponding to the parameters to be evaluated higher than the standard floating parameters, and calibrating the parameters to be optimized;
acquiring analysis time periods corresponding to adjacent parameters to be optimized, calibrating the analysis time periods as reference time periods, and determining time periods between a starting node of the reference time period with the earlier rank and an ending node of the reference time period with the later rank as effective monitoring periods;
the operation of the evaluation module comprises:
obtaining a change trend value of the refining material in each analysis period in an effective monitoring period according to the evaluation model;
acquiring an allowable deviation value interval, and comparing with a variation trend value of the refining;
if the change trend value is in the allowable deviation value interval, judging that the bin is normal in operation;
and if the change trend value is lower than the lower limit value of the allowable deviation value interval, judging that the bin is abnormal in operation, and sending out a maintenance signal.
2. The modular alternative fuel intelligent storage system of claim 1, wherein: the operation state of the material level analysis module is as follows:
acquiring real-time data of the material feeding speed of the feeding point, the height of the material in the bin and the weight of the material in the bin;
inputting the real-time data into an analysis coefficient acquisition model, and acquiring analysis coefficients of material feeding and discharging in the bin through the analysis coefficient acquisition model;
if the analysis coefficient exceeds a preset analysis coefficient threshold value, judging that the material feeding and discharging processes in the bin are abnormal in analysis, and generating a feedback signal, wherein the feedback signal is an analysis abnormal signal;
if the analysis coefficient does not exceed the preset analysis coefficient threshold value, judging that the material feeding and discharging analysis in the bin is normal, and generating a feedback signal, wherein the feedback signal is an analysis normal signal.
3. The modular alternative fuel intelligent storage system of claim 2, wherein: the pretreatment module comprises a grading module and a material-refining mechanism, wherein the grading module is used for applying an operation instruction to the material-refining mechanism according to the analysis result of the material level analysis module;
in addition, when the feedback signal is an analysis abnormal signal, the grading module applies an execution parameter to the material-refining mechanism, the material-refining mechanism operates according to an execution standard, so that the storage data in the storage bin is updated, and the updated storage data is fed back to the material level analysis module;
when the feedback signal does not analyze the normal signal, the grading module does not send out a command, and the refining mechanism operates according to the original standard.
4. A modular alternative fuel smart storage system as defined in claim 3, wherein: the operation process of the processing module comprises the following steps:
inputting the height of the materials in the bin into a first height change function, and obtaining a height increasing trend value;
inputting the height of the materials in the bin into a second height change function, and obtaining a height descending trend value;
predicting the height of the material after the next refining according to the height increasing trend value and the height decreasing trend value.
5. The modular alternative fuel smart storage system of any one of claims 1-4, wherein: the automatic material level detection device is characterized in that a discharge hole is formed in one side of the storage bin, an upper material balancing device and a lower material balancing device are arranged in the storage bin and close to the position of the discharge hole, a feed inlet is formed in one side of the upper end of the storage bin, a transverse material balancing device and a longitudinal material balancing device are arranged in the storage bin and close to the position of the feed inlet, an automatic sampling device is arranged on one side of the storage bin, a plurality of weighing devices are arranged at the lower end of the storage bin, a bottom material balancing device is arranged at the lower end of the storage bin, and a plurality of material level detection devices and fire-fighting devices are arranged in the inner part of the upper end of the storage bin.
6. The modular alternative fuel smart storage system of claim 5, wherein: the bottom material homogenizing device is a stepping floor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310899455.0A CN116611771B (en) | 2023-07-21 | 2023-07-21 | Modular alternative fuel intelligent storage system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310899455.0A CN116611771B (en) | 2023-07-21 | 2023-07-21 | Modular alternative fuel intelligent storage system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116611771A CN116611771A (en) | 2023-08-18 |
| CN116611771B true CN116611771B (en) | 2023-09-12 |
Family
ID=87684066
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310899455.0A Active CN116611771B (en) | 2023-07-21 | 2023-07-21 | Modular alternative fuel intelligent storage system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116611771B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117311297B (en) * | 2023-11-29 | 2024-02-02 | 深圳市磐锋精密技术有限公司 | Remote monitoring management method for opening and closing hopper |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014048026A1 (en) * | 2012-09-28 | 2014-04-03 | 湖南三一智能控制设备有限公司 | Pump truck dredge control system, pump truck dredge control method, and pump truck |
| CN107601083A (en) * | 2017-09-19 | 2018-01-19 | 中国计量大学 | Straight weight-loss type material baiting method based on neutral net |
| WO2019094721A2 (en) * | 2017-11-09 | 2019-05-16 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for the industrial internet of things |
| CN112365205A (en) * | 2020-11-03 | 2021-02-12 | 中国神华能源股份有限公司哈尔乌素露天煤矿 | Intelligent warehouse matching method and system |
| CN112487567A (en) * | 2020-11-03 | 2021-03-12 | 华能国际电力股份有限公司海门电厂 | Intelligent monitoring system and method for bucket-wheel stacker reclaimer |
| CN114807596A (en) * | 2022-05-07 | 2022-07-29 | 北京首钢自动化信息技术有限公司 | Batching control method and device for ore heap |
| CN115456418A (en) * | 2022-09-16 | 2022-12-09 | 安徽华星选矿科技有限公司 | Intelligent management system for coal mine storage safety |
| CN116340112A (en) * | 2023-05-29 | 2023-06-27 | 南京优倍利科技有限公司 | Equipment state monitoring system based on big data analysis and edge calculation |
| CN116357978A (en) * | 2023-02-23 | 2023-06-30 | 北京琪玥环保科技股份有限公司 | Control method, device and equipment of feeding equipment and storage medium |
-
2023
- 2023-07-21 CN CN202310899455.0A patent/CN116611771B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014048026A1 (en) * | 2012-09-28 | 2014-04-03 | 湖南三一智能控制设备有限公司 | Pump truck dredge control system, pump truck dredge control method, and pump truck |
| CN107601083A (en) * | 2017-09-19 | 2018-01-19 | 中国计量大学 | Straight weight-loss type material baiting method based on neutral net |
| WO2019094721A2 (en) * | 2017-11-09 | 2019-05-16 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for the industrial internet of things |
| CN112365205A (en) * | 2020-11-03 | 2021-02-12 | 中国神华能源股份有限公司哈尔乌素露天煤矿 | Intelligent warehouse matching method and system |
| CN112487567A (en) * | 2020-11-03 | 2021-03-12 | 华能国际电力股份有限公司海门电厂 | Intelligent monitoring system and method for bucket-wheel stacker reclaimer |
| CN114807596A (en) * | 2022-05-07 | 2022-07-29 | 北京首钢自动化信息技术有限公司 | Batching control method and device for ore heap |
| CN115456418A (en) * | 2022-09-16 | 2022-12-09 | 安徽华星选矿科技有限公司 | Intelligent management system for coal mine storage safety |
| CN116357978A (en) * | 2023-02-23 | 2023-06-30 | 北京琪玥环保科技股份有限公司 | Control method, device and equipment of feeding equipment and storage medium |
| CN116340112A (en) * | 2023-05-29 | 2023-06-27 | 南京优倍利科技有限公司 | Equipment state monitoring system based on big data analysis and edge calculation |
Non-Patent Citations (1)
| Title |
|---|
| 首钢矿业公司烧结物料平衡控制方法研究与实现;董忠武;《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》(第4期);B023-74 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116611771A (en) | 2023-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116611771B (en) | Modular alternative fuel intelligent storage system | |
| CN107153410B (en) | A kind of intelligence sandstone aggregate production line | |
| EP1245287A2 (en) | Method for reblending sand | |
| CN101723183A (en) | Scattered material storage tank tankful detection and control method | |
| EP3859287A1 (en) | Intelligent system control and dosing device | |
| US11125071B2 (en) | Downhole failure analysis and processing method based on the particle diameter distribution of cuttings | |
| CN113559969B (en) | Multilayer drawer type automatic material preparation bin based on RFID radio frequency technology | |
| CN110834967A (en) | Continuous mixing station stock bin configured with online sampling device and sampling method | |
| DE69916393T2 (en) | DEVICE AND METHOD FOR DETERMINING THE SMALL DRILL CURRENT CURRENT | |
| CN215235855U (en) | Unmanned on duty crushing screening detection device | |
| CN213894433U (en) | Unattended system for cement production | |
| JP2017189940A (en) | Production system and production method for cement-based mixed material | |
| CN1118431A (en) | Method for metering storage in stock-bin and its apparatus | |
| CN111617879A (en) | Intelligent pollen-removing system and control method | |
| CN215179383U (en) | Broken sieve monitoring and crushing granularity measuring device | |
| CN111301998A (en) | Weighing hopper material clamping monitoring and control system and method | |
| CN115356990A (en) | Material yard equipment fault prediction method and system based on deep learning and storage medium | |
| CN212215881U (en) | Online adjustable depth grading device | |
| CN2480815Y (en) | Electronic measuring central controller | |
| CN216870276U (en) | Online real-time monitoring system for emulsion explosive quality | |
| CN220077400U (en) | Automatic monitoring device for coal bunker material level | |
| CN219273683U (en) | Screening plate falling monitoring device for coal preparation plant | |
| CN217554958U (en) | Intelligent material conveying equipment | |
| CN220708828U (en) | Automatic sampling device for machine-made sand analysis | |
| CN219173489U (en) | Safety system for bulk material conveyor and bulk material conveying 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |