CN117284721B - Intelligent decontamination method and system for rubber-plastic conveyor belt - Google Patents
Intelligent decontamination method and system for rubber-plastic conveyor belt Download PDFInfo
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- CN117284721B CN117284721B CN202311571130.6A CN202311571130A CN117284721B CN 117284721 B CN117284721 B CN 117284721B CN 202311571130 A CN202311571130 A CN 202311571130A CN 117284721 B CN117284721 B CN 117284721B
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- 238000005202 decontamination Methods 0.000 title claims abstract description 121
- 239000004033 plastic Substances 0.000 title claims abstract description 121
- 238000004140 cleaning Methods 0.000 claims abstract description 213
- 230000003588 decontaminative effect Effects 0.000 claims abstract description 106
- 238000000034 method Methods 0.000 claims abstract description 22
- 230000000694 effects Effects 0.000 claims abstract description 20
- 238000005201 scrubbing Methods 0.000 claims description 44
- 230000032683 aging Effects 0.000 claims description 15
- 238000005406 washing Methods 0.000 claims description 14
- 238000012360 testing method Methods 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000000354 decomposition reaction Methods 0.000 claims description 5
- 230000009467 reduction Effects 0.000 claims description 5
- 230000010354 integration Effects 0.000 claims description 4
- 238000013139 quantization Methods 0.000 claims description 3
- 239000002689 soil Substances 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims 1
- 238000012545 processing Methods 0.000 abstract description 3
- 238000004364 calculation method Methods 0.000 description 6
- 238000004088 simulation Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 3
- CSCPPACGZOOCGX-UHFFFAOYSA-N Acetone Chemical compound CC(C)=O CSCPPACGZOOCGX-UHFFFAOYSA-N 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 150000001298 alcohols Chemical class 0.000 description 1
- 238000009411 base construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 150000002148 esters Chemical class 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- 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
- B65G43/00—Control devices, e.g. for safety, warning or fault-correcting
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- 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
- B65G45/00—Lubricating, cleaning, or clearing devices
- B65G45/10—Cleaning devices
- B65G45/22—Cleaning devices comprising fluid applying means
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Abstract
The invention provides an intelligent decontamination method and system for a rubber-plastic conveyor belt, which relate to the technical field of data processing, wherein the method comprises the following steps: the method comprises the steps of carrying out data scanning on basic information of a rubber and plastic conveyor belt, determining stain data, establishing cleaning solution proportion information based on historical decontamination effects, establishing a cleaning solution information base, cleaning the rubber and plastic conveyor belt according to the stain data according to the cleaning solution information base, generating a cleaning path set for optimizing, generating a plurality of decontamination schemes according to optimizing results, and carrying out compensation updating on the optimal decontamination scheme according to the plurality of suboptimal decontamination schemes.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent decontamination method and system for a rubber-plastic conveyor belt.
Background
With the development of scientific technology, especially the development in rubber and plastic conveyer belt field, just need wash the dirt on rubber and plastic conveyer belt surface after using a period, on the one hand can guarantee the life of rubber and plastic conveyer belt, on the other hand can reduce the power consumption of rubber and plastic conveyer belt, and now, there is the scrubbing management and control not enough to rubber and plastic conveyer belt in prior art, leads to the technical problem that rubber and plastic conveyer belt scrubbing efficiency is low.
Disclosure of Invention
The application provides an intelligent decontamination method and system for a rubber and plastic conveyer belt, which are used for solving the technical problems that the decontamination management and control of the rubber and plastic conveyer belt is insufficient, and the decontamination efficiency of the rubber and plastic conveyer belt is low in the prior art.
In view of the foregoing, the present application provides intelligent decontamination methods and systems for rubber and plastic conveyor belts.
In a first aspect, the present application provides an intelligent decontamination method for a rubber and plastic conveyor belt, the method comprising: data scanning is carried out based on basic information of the rubber and plastic conveyor belt, and stain data are determined; establishing cleaning solution proportioning information based on the historical decontamination effect; establishing a cleaning solution information base, wherein the cleaning solution information base is obtained by integrating the cleaning solution proportioning information and the stain data; cleaning the rubber and plastic conveyor belt according to the stain data according to the cleaning solution information base to generate a cleaning path set; optimizing the cleaning path set, and generating a plurality of decontamination schemes according to the optimizing result, wherein the plurality of decontamination schemes comprise an optimal decontamination scheme and a plurality of suboptimal decontamination schemes; and after the optimal decontamination scheme is compensated and updated according to the plurality of suboptimal decontamination schemes, intelligent decontamination is carried out on the rubber and plastic conveyor belt.
In a second aspect, the present application provides an intelligent decontamination system for a rubber and plastic conveyor belt, the system comprising: the data scanning module is used for carrying out data scanning based on basic information of the rubber and plastic conveyor belt and determining stain data; the information establishing module is used for establishing cleaning solution proportioning information based on the historical decontamination effect; the data integration module is used for establishing a cleaning solution information base, and the cleaning solution information base is obtained by integrating the cleaning solution proportioning information and the stain data; the cleaning module is used for cleaning the rubber and plastic conveyor belt according to the stain data according to the cleaning solution information base to generate a cleaning path set; the scheme generation module is used for optimizing the cleaning path set and generating a plurality of decontamination schemes according to the optimizing result, wherein the plurality of decontamination schemes comprise an optimal decontamination scheme and a plurality of suboptimal decontamination schemes; and the compensation updating module is used for carrying out intelligent decontamination on the rubber and plastic conveyor belt after carrying out compensation updating on the optimal decontamination scheme according to the plurality of suboptimal decontamination schemes.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the application provides an intelligent scrubbing method and system for rubber and plastic conveyer belt relates to data processing technology field, has solved among the prior art to the scrubbing management and control of rubber and plastic conveyer belt not enough, leads to the technical problem that rubber and plastic conveyer belt scrubbing efficiency is low, has realized rationally accurate scrubbing management and control to the rubber and plastic conveyer belt, improves rubber and plastic conveyer belt scrubbing efficiency.
Drawings
FIG. 1 is a schematic flow chart of an intelligent decontamination method for a rubber and plastic conveyor belt;
FIG. 2 is a schematic diagram of a process for determining soil data in an intelligent soil removal method for a rubber and plastic conveyor belt;
FIG. 3 is a schematic flow chart of an optimal decontamination scheme for obtaining compensation in an intelligent decontamination method for a rubber and plastic conveyor belt;
fig. 4 is a schematic structural diagram of an intelligent decontamination system for a rubber and plastic conveyor belt.
Reference numerals illustrate: the system comprises a data scanning module 1, an information establishing module 2, a data integrating module 3, a cleaning module 4, a scheme generating module 5 and a compensation updating module 6.
Detailed Description
The application is through providing intelligent scrubbing method and system for rubber and plastic conveyer belt for to the scrubbing management and control of rubber and plastic conveyer belt among the solution prior art not enough, lead to the technical problem that rubber and plastic conveyer belt scrubbing efficiency is low.
Example 1
As shown in fig. 1, an embodiment of the present application provides an intelligent decontamination method for a rubber and plastic conveyor belt, the method including:
step A100: data scanning is carried out based on basic information of the rubber and plastic conveyor belt, and stain data are determined;
further, as shown in fig. 2, step a100 of the present application further includes:
step A110: the basic information of the rubber and plastic conveyor belt comprises image information, size information and color information of the rubber and plastic conveyor belt;
step A120: correcting the image information of the rubber and plastic conveyor belt according to the size information to generate a standard image of the rubber and plastic conveyor belt;
step a130: performing pixel segmentation on the standard image, scanning the color information according to a segmented image set, and determining a live color value in a rubber and plastic conveyor belt;
step A140: judging whether the live color value is in a preset color value interval or not;
step A150: and if not, carrying out noise reduction filtering on the segmented image set, judging whether the live color value is in the preset color value interval again according to the noise reduction segmented image set, and if not, generating the stain data of the rubber and plastic conveyor belt.
Further, step a150 of the present application includes:
step A151: performing wavelet decomposition on the image signals of the segmented image set to obtain wavelet coefficients of the image signals;
step a152: performing threshold quantization according to the wavelet coefficient, and determining a wavelet selection threshold of the image signal;
step A153: intercepting the wavelet coefficient according to the wavelet selection threshold, and setting zero for noise signals smaller than the wavelet selection threshold to obtain effective signal information larger than the wavelet selection threshold;
step A154: and carrying out filtering reconstruction on the effective signal information to obtain the denoising segmentation image set.
In this application, the intelligent decontamination method for a rubber-plastic conveyor belt provided in this embodiment is applied to an intelligent decontamination system for a rubber-plastic conveyor belt, so that in order to ensure the accuracy of later decontamination of the rubber-plastic conveyor belt, it is first required to determine the dirty data on the rubber-plastic conveyor belt, that is, to use the basic information of the rubber-plastic conveyor belt as reference data, where the basic information of the rubber-plastic conveyor belt includes image information, size information and color information of the rubber-plastic conveyor belt, where the image information of the rubber-plastic conveyor belt is obtained by an image acquisition device disposed in an area where the rubber-plastic conveyor belt is located, the size information of the rubber-plastic conveyor belt can be determined according to data with different widths, the width data can include 500mm, 650mm, 800mm, and the like, the color information is color information obtained by color monitoring the pixel conveyor belt in the current state, correcting the image information of the rubber-plastic conveyor belt according to the size information, namely when the image of the rubber-plastic conveyor belt acquired by the image acquisition device is distorted or distorted due to the angle or distance, the rubber-plastic conveyor belt in the image information is adjusted according to the equal proportion of the size information of the rubber-plastic conveyor belt, so that the adjusted image is recorded as a standard image of the rubber-plastic conveyor belt, further, the standard image is subjected to pixel segmentation, the color information is scanned according to a segmented image set, the standard image is equally divided, meanwhile, the first area in the image equally divided is set as a starting point, namely the obtained first area is marked as a zero area, the color information is scanned from the first area, the color information obtained in each area is matched with the color information of the pixel conveyor belt in the initial state, namely the color information when no stain exists, determining a live color value in the rubber and plastic conveyor belt, judging whether the live color value is in a preset color value interval or not, wherein the preset color value interval is defined according to a color maximum critical value and a color minimum critical value in color information of the pixel conveyor belt in an initial state, namely when no stain exists, if the live color value is in the preset color value interval, the current rubber and plastic conveyor belt is regarded as not having the signal, if the live color value is not in the preset color value interval, noise reduction filtering is carried out on a segmented image set, namely, the image signal of the segmented image set is processed by utilizing the principle of wavelet threshold denoising, the wavelet coefficient generated by the signal after wavelet transformation is carried out on the image signal of the segmented image set contains important information of the signal, the wavelet coefficient of the signal after wavelet decomposition is relatively large, the wavelet coefficient of the noise is relatively small, and the wavelet coefficient of the noise is relatively small than the wavelet coefficient of the signal.
Step A200: establishing cleaning solution proportioning information based on the historical decontamination effect;
in this application, for better carry out intelligent scrubbing to the rubber and plastic conveyer belt, therefore need carry out the record at the time interval of history in the cleaning solution that the scrubbing effect that the rubber and plastic conveyer belt carried out after the scrubbing corresponds, carry out the scrubbing effect and sort cleaning solution by good to poor to the scrubbing degree of rubber and plastic conveyer belt according to the scrubbing effect, further, carry out the extraction of solution ratio with the cleaning solution that the bit sequence is first, exemplary, this solution can be acetone, alcohols, esters etc. to carry out 1:10 with it and carry out the output as the cleaning solution ratio information of rubber and plastic conveyer belt with water after the integration, and then for realizing carrying out intelligent scrubbing to the conveyer belt and guaranteeing.
Step A300: establishing a cleaning solution information base, wherein the cleaning solution information base is obtained by integrating the cleaning solution proportioning information and the stain data;
further, step a300 of the present application further includes:
step a310: acquiring category information of the stain data, and acquiring a plurality of category information;
step A320: collecting the stain data of the plurality of types of information and matching the stain data with the cleaning solution proportioning information to obtain a plurality of cleaning solution proportioning information;
step a330: and taking the plurality of category information as a plurality of data indexes, taking the plurality of cleaning solution proportioning information as a plurality of data elements, and constructing the cleaning solution information base.
According to the method, different types of stains in the rubber-plastic conveyor belt can be obtained by detecting the stain data, further, according to different types of stains, the corresponding cleaning solutions to be matched are different, the stain data corresponding to the multiple types of information are collected and matched with the cleaning solution matching information, namely the stain removal effect of the different types of stains is compared according to the cleaning solution matching information, so that multiple cleaning solution matching information is obtained, index parameter standards of the multiple indexes are induced and integrated to obtain multiple index parameter standard sets, the multiple obtained cleaning solution matching information is used as a data index according to the multiple types of information, and a standardized comprehensive index dictionary is established, so that the standardized index dictionary covers all cleaning solution matching of stain data of the multiple types of information for removing the stain effect; constructing stain data of a plurality of types of information to perform one-to-one correspondence between the stain removal effect and a standard index dictionary; and collecting and importing a plurality of cleaning solution proportioning information, establishing a complete cleaning solution information base, and carrying out intelligent decontamination tamping on the rubber and plastic conveyor belt for the subsequent realization.
Step A400: cleaning the rubber and plastic conveyor belt according to the stain data according to the cleaning solution information base to generate a cleaning path set;
in this application, in order to promote the scrubbing effect after carrying out intelligent scrubbing to the rubber and plastic conveyer belt, then at first regard the washing solution information base of what was constructed as above as basic reference data, wash the rubber and plastic conveyer belt according to the spot data, refer to according to the dirty degree of wasing difficulty, the spot position, the spot size in the dirty data that the rubber and plastic conveyer belt contained, match in the washing solution information base, exemplary, if there is a large amount of difficult washings spot, a small amount of easy washings spot on the pixel conveyer belt, wherein, difficult washings spot refers to the spot that needs to wash at least twice, easy washings spot refers to the spot that only needs to wash once, then need connect according to the spot position of a large amount of difficult washings spot, generate initial cleaning path, and select according to the initial cleaning path and connect with the nearest spot position of easy washings spot and generate first cleaning path, use cleaning solution to wash for the first time, then directly wash for a plurality of times according to initial cleaning path and until reaching the scrubbing effect, simultaneously still can connect easy washings spot to generate second cleaning path and carry out first cleaning path, receive the dirty position according to initial cleaning path and carry out the clean spot position on the basis on the clean the required to the intelligent conveyer belt, the clean the effect is realized, the defined path is achieved to the rubber and plastic and has the effect on the basis that the cleaning belt is summarized.
Step A500: optimizing the cleaning path set, and generating a plurality of decontamination schemes according to the optimizing result, wherein the plurality of decontamination schemes comprise an optimal decontamination scheme and a plurality of suboptimal decontamination schemes;
further, step a500 of the present application further includes:
step A510: extracting the ith group of cleaning record data according to the first cleaning path;
step A520: performing fitness analysis on the ith group of cleaning record data to obtain ith group of cleaning fitness;
step a530: judging whether the i-th group cleaning fitness is greater than or equal to the i-1-th group cleaning fitness;
step a540: if the data is larger than or equal to the data, adding the i-1 group cleaning record data into an alternative data group, and if the data is smaller than the data, adding the i group cleaning record data into the alternative data group;
step A550: judging whether i meets the update period of the tabu list;
step A560: if yes, inputting the i-th group of washing fitness or the i-1-th group of washing fitness into a tabu table for updating, and judging whether the update times of the tabu table meet the preset update times;
step a570: if yes, acquiring a tabu table update value, setting the tabu table update value as the optimal decontamination scheme, and setting the data in the alternative data set as a plurality of suboptimal decontamination schemes.
Further, step a520 of the present application includes:
step a521: acquiring an ith cleaning triggering frequency characteristic and an ith cleaning triggering aging characteristic according to the ith cleaning record data;
step A522: setting a first weight for the ith group of cleaning triggering frequency characteristics and setting a second weight for the ith group of cleaning triggering aging characteristics;
step A523: and according to the first weight and the ith group of cleaning triggering frequency characteristics and the second weight and the ith group of cleaning triggering aging characteristics, solving the ith group of cleaning fitness.
In the application, firstly, randomly selecting a cleaning path in the generated cleaning path set as a first cleaning path, randomly extracting and determining an ith group of cleaning record data according to a plurality of corresponding cleaning records in the first cleaning path, respectively acquiring an ith group of cleaning trigger frequency characteristic and an ith group of cleaning trigger aging characteristic based on the ith group of cleaning record data, wherein the ith group of cleaning trigger frequency characteristic refers to the trigger frequency of decontamination cleaning in the first path, the ith group of cleaning trigger aging characteristic refers to the cleaning time length of each occurrence of the frequency in each trigger frequency, and calculating the average value of a plurality of time lengths and taking the reciprocal of the average value as the aging characteristic.
Setting a first weight for the ith group of cleaning trigger frequency features, setting a second weight for the ith group of cleaning trigger aging features, and according to the set first weight and the ith group of cleaning trigger frequency features, and the set second weight and the ith group of cleaning trigger aging features, obtaining the ith group of cleaning fitness, wherein the obtaining process can be through weighted calculation, the weighted calculation needs to be based on a large amount of data summarization and accurate weight determination, and then targeted calculation is carried out, and the weight ratio of the ith group of cleaning trigger frequency features to the ith group of cleaning trigger aging features can be a first influence coefficient: and if the second influence coefficient is 4:6, the influence parameters after the weighting calculation process are respectively 0.4 of the first influence parameter and 0.6 of the second influence parameter, the final value of the matching result is obtained according to the weighting calculation result, and the final value is used as the i-th group cleaning fitness to be output, so that the i-th group cleaning fitness is obtained.
Further judging whether the i-th group cleaning fitness is greater than or equal to the i-1-th group cleaning fitness, if the i-th group cleaning fitness is greater than or equal to the i-1-th group cleaning fitness, adding the i-1-th group cleaning record data into the alternative data group, if the i-th group cleaning fitness is less than the i-1-th group cleaning fitness, adding the i-th group cleaning record data into the alternative data group, namely comparing the cleaning fitness of two adjacent groups, and adding the group with low cleaning fitness into the alternative data group.
And judging whether the i meets the update cycle of the tabu table, wherein the tabu table is a table which takes an appropriate value as a tabu object and is continuously updated in order to prevent the search from occurring, namely, the latest cleaning fitness is counted, the oldest cleaning fitness is released from the table, if the i meets the update cycle of the tabu table, the i-th group cleaning fitness or the i-1-th group cleaning fitness is input into the tabu table for updating, judging whether the update times of the tabu table meet the preset update times, extracting a tabu initial value from the tabu table if the update times of the tabu table meet the preset update times, judging whether the i-th group cleaning fitness or the i-1-th group cleaning fitness is larger than or equal to the tabu cleaning fitness in the tabu initial value, and if the i-1-th group cleaning fitness is larger than or equal to the tabu cleaning fitness in the tabu initial value, recording the i-1-th group cleaning fitness is the tabu table for updating according to the tabu cleaning fitness, and outputting the updated tabu table as the optimal update program, and outputting the updated tabu table.
If the i-th group cleaning fitness or the i-1-th group cleaning fitness is smaller than the tabu cleaning fitness in the tabu table initial values, setting the tabu table initial values as tabu table update values, setting the tabu table update values as the optimal decontamination scheme for output, and setting the data in the alternative data group as a plurality of suboptimal decontamination schemes for output so as to be used as reference data when intelligent decontamination is carried out on the rubber and plastic conveyor belt in the later period.
Step A600: and after the optimal decontamination scheme is compensated and updated according to the plurality of suboptimal decontamination schemes, intelligent decontamination is carried out on the rubber and plastic conveyor belt.
Further, as shown in fig. 3, step a600 of the present application further includes:
step a610: acquiring the simulated cleaning information of the rubber and plastic conveyor belt subjected to simulated decontamination by the optimal decontamination scheme;
step a620: performing a decontamination test by adopting the optimal decontamination scheme to obtain actual cleaning information of the rubber and plastic conveyor belt;
step a630: calculating the errors of the simulated cleaning information of the rubber and plastic conveyor belt and the actual cleaning information of the rubber and plastic conveyor belt, traversing and selecting a suboptimal decontamination scheme to carry out decontamination test in the suboptimal decontamination schemes until the errors are smaller than an error threshold or the suboptimal decontamination schemes are traversed, and selecting the suboptimal decontamination scheme with the minimum errors as a compensation optimal decontamination scheme.
In this application, in order to carry out intelligent scrubbing to the rubber and plastic conveyer belt more accurately, avoid the optimal scrubbing scheme to fall into local optimum simultaneously, then at first carry out the compensation to the optimal scrubbing scheme according to a plurality of suboptimal scrubbing schemes in the above-mentioned alternative data group and update, refer to carrying out the simulation scrubbing to the rubber and plastic conveyer belt according to the optimal scrubbing scheme, proportioning information according to cleaning solution and spot position, the dirty washing difficulty degree carries out the simulation scrubbing, confirm the scrubbing ideal value, record it as the rubber and plastic conveyer belt simulation scrubbing information after carrying out the simulation scrubbing, further, adopt the optimal scrubbing scheme to carry out actual scrubbing test to the rubber and plastic conveyer belt, then wash the spot according to the cleaning path concentrated in order to obtain the actual scrubbing information of rubber and plastic conveyer belt, finally calculate the error of conveyer belt simulation scrubbing information and actual cleaning information, after carrying out the difference according to the error value, carry out the scrubbing test of conveyer belt according to the random selection suboptimal scrubbing scheme, until the error is less than the optimal threshold value or more than optimal scrubbing scheme is carried out the best scrubbing scheme and then carries out the best scrubbing effect is improved, the best scrubbing scheme is then carried out the best-stage according to the best intelligent scrubbing scheme is updated, the best scrubbing scheme is then carried out the best-off scheme is carried out the best-off according to the best intelligent scrubbing scheme is updated.
To sum up, the intelligent decontamination method for the rubber and plastic conveyor belt provided by the embodiment of the application at least comprises the following technical effects, and realizes reasonable and accurate decontamination control of the rubber and plastic conveyor belt, and improves the decontamination efficiency of the rubber and plastic conveyor belt.
Example 2
Based on the same inventive concept as the intelligent decontamination method for a rubber and plastic conveyor belt in the foregoing embodiments, as shown in fig. 4, the present application provides an intelligent decontamination system for a rubber and plastic conveyor belt, the system comprising:
the data scanning module 1 is used for carrying out data scanning based on basic information of the rubber and plastic conveyor belt and determining stain data;
the information establishment module 2 is used for establishing cleaning solution proportioning information based on the historical decontamination effect;
the data integration module 3 is used for establishing a cleaning solution information base, and the cleaning solution information base is obtained by integrating the cleaning solution proportioning information and the stain data;
the cleaning module 4 is used for cleaning the rubber and plastic conveyor belt according to the stain data according to the cleaning solution information base, and generating a cleaning path set;
the scheme generation module 5 is used for optimizing the cleaning path set, and generating a plurality of decontamination schemes according to the optimizing result, wherein the plurality of decontamination schemes comprise an optimal decontamination scheme and a plurality of suboptimal decontamination schemes;
and the compensation updating module 6 is used for carrying out intelligent decontamination on the rubber and plastic conveyor belt after carrying out compensation updating on the optimal decontamination scheme according to the plurality of suboptimal decontamination schemes.
Further, the system further comprises:
the information module is used for enabling the basic information of the rubber and plastic conveyor belt to comprise image information, size information and color information of the rubber and plastic conveyor belt;
the correction module is used for correcting the image information of the rubber and plastic conveyor belt according to the size information to generate a standard image of the rubber and plastic conveyor belt;
the scanning module is used for carrying out pixel segmentation on the standard image, scanning the color information according to a segmented image set and determining a live color value in the rubber and plastic conveyor belt;
the first judging module is used for judging whether the live color value is in a preset color value interval or not;
and the second judging module is used for carrying out noise reduction and filtering on the segmented image set if the live color value is not in the preset color value interval, and generating the stain data of the rubber and plastic conveyor belt if the live color value is not in the preset color value interval according to the denoising segmented image set.
Further, the system further comprises:
the wavelet decomposition module is used for carrying out wavelet decomposition on the image signals of the divided image sets to obtain wavelet coefficients of the image signals;
the threshold determining module is used for carrying out threshold quantization according to the wavelet coefficient and determining a wavelet selection threshold of the image signal;
the intercepting module is used for intercepting the wavelet coefficient according to the wavelet selection threshold value, and zeroing a noise signal smaller than the wavelet selection threshold value to obtain effective signal information larger than the wavelet selection threshold value;
and the filtering reconstruction module is used for carrying out filtering reconstruction on the effective signal information to obtain the denoising segmentation image set.
Further, the system further comprises:
the category information acquisition module is used for acquiring category information of the stain data and acquiring a plurality of category information;
the matching module is used for acquiring the stain data of the plurality of types of information and matching the stain data with the cleaning solution proportioning information to obtain a plurality of cleaning solution proportioning information;
the information base construction module is used for taking the plurality of category information as a plurality of data indexes and taking the plurality of cleaning solution proportioning information as a plurality of data elements to construct the cleaning solution information base.
Further, the system further comprises:
the extraction module is used for extracting the ith group of cleaning record data according to the first cleaning path;
the fitness analysis module is used for carrying out fitness analysis on the ith group of cleaning record data to obtain ith group of cleaning fitness;
the third judging module is used for judging whether the i-th group cleaning fitness is greater than or equal to the i-1-th group cleaning fitness or not;
the fourth judging module is used for adding the i-1 th group of cleaning record data into the alternative data group if the cleaning record data is larger than or equal to the first data group, and adding the i-1 th group of cleaning record data into the alternative data group if the cleaning record data is smaller than the first data group;
a fifth judging module, configured to judge whether i meets a tabu table update period;
the sixth judging module is used for inputting the i-th group of cleaning fitness or the i-1-th group of cleaning fitness into a tabu table for updating if the i-th group of cleaning fitness or the i-1-th group of cleaning fitness is met, and judging whether the update times of the tabu table meet the preset update times or not;
and the seventh judging module is used for acquiring updated values of the tabu list, setting the updated values as the optimal decontamination scheme and setting the data in the alternative data set as a plurality of suboptimal decontamination schemes if the updated values are met.
Further, the system further comprises:
the characteristic acquisition module is used for acquiring an ith group of cleaning triggering frequency characteristic and an ith group of cleaning triggering aging characteristic according to the ith group of cleaning record data;
the weight setting module is used for setting a first weight for the ith group of cleaning triggering frequency characteristics and setting a second weight for the ith group of cleaning triggering aging characteristics;
the fitness obtaining module is used for obtaining the i-th group cleaning fitness according to the first weight and the i-th group cleaning trigger frequency characteristic, the second weight and the i-th group cleaning trigger aging characteristic.
Further, the system further comprises:
the simulated decontamination module is used for acquiring simulated cleaning information of the rubber and plastic conveyor belt subjected to simulated decontamination by the optimal decontamination scheme;
the decontamination test module is used for carrying out decontamination test by adopting the optimal decontamination scheme to obtain actual cleaning information of the rubber and plastic conveyor belt;
the calculation module is used for calculating errors of the simulated cleaning information of the rubber and plastic conveyor belt and the actual cleaning information of the rubber and plastic conveyor belt, and in the multiple suboptimal decontamination schemes, the suboptimal decontamination scheme is selected for decontamination test in a traversing mode until the errors are smaller than an error threshold value or the multiple suboptimal decontamination schemes are completed in traversing mode, and the suboptimal decontamination scheme with the smallest errors is selected as a compensation optimal decontamination scheme.
The foregoing detailed description of the intelligent decontamination method for a rubber and plastic conveyor belt will be clear to those skilled in the art, and the device disclosed in this embodiment is relatively simple in description, and the relevant points refer to the description of the method section because it corresponds to the method disclosed in the embodiment.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An intelligent decontamination method for a rubber and plastic conveyor belt, which is characterized by comprising the following steps:
data scanning is carried out based on basic information of the rubber and plastic conveyor belt, and stain data are determined;
establishing cleaning solution proportioning information based on the historical decontamination effect;
establishing a cleaning solution information base, wherein the cleaning solution information base is obtained by integrating the cleaning solution proportioning information and the stain data;
cleaning the rubber and plastic conveyor belt according to the stain data according to the cleaning solution information base to generate a cleaning path set;
optimizing the cleaning path set, and generating a plurality of decontamination schemes according to the optimizing result, wherein the plurality of decontamination schemes comprise an optimal decontamination scheme and a plurality of suboptimal decontamination schemes;
and after the optimal decontamination scheme is compensated and updated according to the plurality of suboptimal decontamination schemes, intelligent decontamination is carried out on the rubber and plastic conveyor belt.
2. The method of claim 1, wherein the method of determining soil data based on data scanning of the base information of the rubber and plastic conveyor belt comprises:
the basic information of the rubber and plastic conveyor belt comprises image information, size information and color information of the rubber and plastic conveyor belt;
correcting the image information of the rubber and plastic conveyor belt according to the size information to generate a standard image of the rubber and plastic conveyor belt;
performing pixel segmentation on the standard image to obtain a segmented image set, scanning the color information according to the segmented image set, and determining a live color value in the rubber and plastic conveyor belt;
judging whether the live color value is in a preset color value interval or not;
and if not, carrying out noise reduction filtering on the segmented image set to obtain a denoising segmented image set, judging whether the live color value is in a preset color value interval or not again according to the denoising segmented image set, and if not, generating the stain data of the rubber and plastic conveyor belt.
3. The method of claim 2, wherein denoising the segmented image set, the method of obtaining a denoised segmented image set comprising:
performing wavelet decomposition on the image signals of the segmented image set to obtain wavelet coefficients of the image signals;
performing threshold quantization according to the wavelet coefficient, and determining a wavelet selection threshold of the image signal;
intercepting the wavelet coefficient according to the wavelet selection threshold, and setting zero for noise signals smaller than the wavelet selection threshold to obtain effective signal information larger than the wavelet selection threshold;
and carrying out filtering reconstruction on the effective signal information to obtain the denoising segmentation image set.
4. The method of claim 1, wherein the method of creating a cleaning solution information base comprises:
acquiring category information of the stain data, and acquiring a plurality of category information;
collecting the stain data of the plurality of types of information and matching the stain data with the cleaning solution proportioning information to obtain a plurality of cleaning solution proportioning information;
and taking the plurality of category information as a plurality of data indexes, taking the plurality of cleaning solution proportioning information as a plurality of data elements, and constructing the cleaning solution information base.
5. The method of claim 1, wherein optimizing the set of cleaning paths, the method of generating a plurality of decontamination schemes based on the optimization results comprises:
randomly selecting one cleaning path from the cleaning path set as a first cleaning path, and randomly extracting and determining ith group of cleaning record data according to a plurality of cleaning records corresponding to the first cleaning path;
performing fitness analysis on the ith group of cleaning record data to obtain ith group of cleaning fitness;
judging whether the i-th group cleaning fitness is greater than or equal to the i-1-th group cleaning fitness;
if the data is larger than or equal to the data, adding the i-1 group cleaning record data into an alternative data group, and if the data is smaller than the data, adding the i group cleaning record data into the alternative data group;
judging whether i meets the update period of the tabu list;
if yes, inputting the i-th group of washing fitness or the i-1-th group of washing fitness into a tabu table for updating, and judging whether the update times of the tabu table meet the preset update times;
if yes, acquiring a tabu table update value, setting the tabu table update value as the optimal decontamination scheme, and setting the data in the alternative data set as a plurality of suboptimal decontamination schemes.
6. The method of claim 5, wherein performing fitness analysis on the i-th set of cleaning log data to obtain i-th set of cleaning fitness comprises:
acquiring an ith cleaning triggering frequency characteristic and an ith cleaning triggering aging characteristic according to the ith cleaning record data;
setting a first weight for the ith group of cleaning triggering frequency characteristics and setting a second weight for the ith group of cleaning triggering aging characteristics;
and according to the first weight and the ith group of cleaning triggering frequency characteristics and the second weight and the ith group of cleaning triggering aging characteristics, solving the ith group of cleaning fitness.
7. The method of claim 1, wherein the method of compensating for updating the optimal abatement scheme in accordance with the plurality of sub-optimal abatement schemes comprises:
acquiring the simulated cleaning information of the rubber and plastic conveyor belt subjected to simulated decontamination by the optimal decontamination scheme;
performing a decontamination test by adopting the optimal decontamination scheme to obtain actual cleaning information of the rubber and plastic conveyor belt;
calculating the errors of the simulated cleaning information of the rubber and plastic conveyor belt and the actual cleaning information of the rubber and plastic conveyor belt, traversing and selecting a suboptimal decontamination scheme to carry out decontamination test in the suboptimal decontamination schemes until the errors are smaller than an error threshold or the suboptimal decontamination schemes are traversed, and selecting the suboptimal decontamination scheme with the minimum errors as a compensation optimal decontamination scheme.
8. A intelligent scrubbing system for rubber and plastic conveyer belt, its characterized in that, the system includes:
the data scanning module is used for carrying out data scanning based on basic information of the rubber and plastic conveyor belt and determining stain data;
the information establishing module is used for establishing cleaning solution proportioning information based on the historical decontamination effect;
the data integration module is used for establishing a cleaning solution information base, and the cleaning solution information base is obtained by integrating the cleaning solution proportioning information and the stain data;
the cleaning module is used for cleaning the rubber and plastic conveyor belt according to the stain data according to the cleaning solution information base to generate a cleaning path set;
the scheme generation module is used for optimizing the cleaning path set and generating a plurality of decontamination schemes according to the optimizing result, wherein the plurality of decontamination schemes comprise an optimal decontamination scheme and a plurality of suboptimal decontamination schemes;
and the compensation updating module is used for carrying out intelligent decontamination on the rubber and plastic conveyor belt after carrying out compensation updating on the optimal decontamination scheme according to the plurality of suboptimal decontamination schemes.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003149166A (en) * | 2001-11-15 | 2003-05-21 | Fuji Heavy Ind Ltd | Automatic stain detecting method, automatic cleaning method and automatic cleaning apparatus |
| WO2020022622A1 (en) * | 2018-07-23 | 2020-01-30 | 엘지전자 주식회사 | Method for controlling artificial intelligence moving robot |
| CN113229750A (en) * | 2021-04-06 | 2021-08-10 | 深圳市无限动力发展有限公司 | Sweeping and disinfecting path control method, device, equipment and medium of sweeper |
| CN113649322A (en) * | 2021-08-25 | 2021-11-16 | 北京东方星火能源科技有限公司 | Unmanned aerial vehicle cleaning system for photovoltaic panel |
| CN114970632A (en) * | 2022-06-01 | 2022-08-30 | 长春理工大学 | Pollutant removing method and device based on multi-pulse laser |
| CN115429161A (en) * | 2022-07-29 | 2022-12-06 | 云鲸智能(深圳)有限公司 | Control method, device and system of cleaning robot and storage medium |
| CN115980322A (en) * | 2023-02-09 | 2023-04-18 | 百福工业缝纫机（张家港）有限公司 | Intelligent detection method and system for fabric defects |
| CN116433217A (en) * | 2023-03-17 | 2023-07-14 | 中国大唐集团科学技术研究总院有限公司 | Distributed photovoltaic power generation management method and system |
| CN117031986A (en) * | 2023-10-10 | 2023-11-10 | 江苏通创现代建筑产业技术研究院有限公司 | Control method and system for automatic cleaning of building curtain wall |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102255712B1 (en) * | 2019-08-26 | 2021-05-26 | 엘지전자 주식회사 | An artificial intelligence robot for cleaning using pollution log and method for the same |
-
2023
- 2023-11-23 CN CN202311571130.6A patent/CN117284721B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003149166A (en) * | 2001-11-15 | 2003-05-21 | Fuji Heavy Ind Ltd | Automatic stain detecting method, automatic cleaning method and automatic cleaning apparatus |
| WO2020022622A1 (en) * | 2018-07-23 | 2020-01-30 | 엘지전자 주식회사 | Method for controlling artificial intelligence moving robot |
| CN113229750A (en) * | 2021-04-06 | 2021-08-10 | 深圳市无限动力发展有限公司 | Sweeping and disinfecting path control method, device, equipment and medium of sweeper |
| CN113649322A (en) * | 2021-08-25 | 2021-11-16 | 北京东方星火能源科技有限公司 | Unmanned aerial vehicle cleaning system for photovoltaic panel |
| CN114970632A (en) * | 2022-06-01 | 2022-08-30 | 长春理工大学 | Pollutant removing method and device based on multi-pulse laser |
| CN115429161A (en) * | 2022-07-29 | 2022-12-06 | 云鲸智能(深圳)有限公司 | Control method, device and system of cleaning robot and storage medium |
| CN115980322A (en) * | 2023-02-09 | 2023-04-18 | 百福工业缝纫机（张家港）有限公司 | Intelligent detection method and system for fabric defects |
| CN116433217A (en) * | 2023-03-17 | 2023-07-14 | 中国大唐集团科学技术研究总院有限公司 | Distributed photovoltaic power generation management method and system |
| CN117031986A (en) * | 2023-10-10 | 2023-11-10 | 江苏通创现代建筑产业技术研究院有限公司 | Control method and system for automatic cleaning of building curtain wall |
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