CN114967629A - Remote monitoring system of sludge treatment device - Google Patents
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- CN114967629A CN114967629A CN202210827244.1A CN202210827244A CN114967629A CN 114967629 A CN114967629 A CN 114967629A CN 202210827244 A CN202210827244 A CN 202210827244A CN 114967629 A CN114967629 A CN 114967629A
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- 239000010802 sludge Substances 0.000 title claims abstract description 42
- 238000012544 monitoring process Methods 0.000 title claims abstract description 40
- 238000004458 analytical method Methods 0.000 claims abstract description 38
- 238000007726 management method Methods 0.000 claims abstract description 31
- 238000013500 data storage Methods 0.000 claims abstract description 15
- 230000003993 interaction Effects 0.000 claims abstract description 14
- 238000013528 artificial neural network Methods 0.000 claims description 28
- 238000012545 processing Methods 0.000 claims description 27
- 238000003860 storage Methods 0.000 claims description 14
- 238000012549 training Methods 0.000 claims description 14
- 238000012360 testing method Methods 0.000 claims description 12
- 238000004088 simulation Methods 0.000 claims description 9
- 238000012423 maintenance Methods 0.000 claims description 7
- 238000012795 verification Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 5
- 238000000034 method Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 3
- 230000007812 deficiency Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 230000003203 everyday effect Effects 0.000 claims description 2
- 238000010606 normalization Methods 0.000 claims description 2
- 238000005457 optimization Methods 0.000 claims description 2
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 238000006243 chemical reaction Methods 0.000 claims 1
- 238000007405 data analysis Methods 0.000 abstract description 3
- 238000003745 diagnosis Methods 0.000 abstract description 2
- 239000003921 oil Substances 0.000 description 15
- 239000010801 sewage sludge Substances 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000003344 environmental pollutant Substances 0.000 description 2
- 231100000719 pollutant Toxicity 0.000 description 2
- 239000003570 air Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000010779 crude oil Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000004171 remote diagnosis Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F11/00—Treatment of sludge; Devices therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31359—Object oriented model for fault, quality control
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention discloses a remote monitoring system of a sludge treatment device, belonging to the technical field of remote monitoring, and comprising a sludge treatment device, a management platform, an acquisition and classification module, a remote monitoring module, an operation analysis module, a data storage module, a user interaction module, a display screen and a mobile terminal, wherein the sludge treatment device is used for treating sludge; the invention can realize the functions of equipment management, remote fault diagnosis, historical data analysis and the like, and can greatly improve the input-output ratio of equipment management, improve the accuracy of information and reduce personnel configuration so as to achieve the aims of reducing the management cost and improving the management quality.
Description
Technical Field
The invention relates to the technical field of remote monitoring, in particular to a remote monitoring system of a dirty oil sludge treatment device.
Background
The oil sludge is a pollutant generated in each stage of crude oil processing, not only has complex components, but also has extremely strong pollution force, is a main pollutant generated in the processes of petroleum development, production and processing, and can cause serious pollution to water, soil and air in the environment once being discharged into the ecological environment if the oil sludge cannot be reasonably treated. With the rapid development of industrial information technology and internet technology, modern industry is greatly advancing towards the direction of unmanned and intelligent, and the operation state of equipment is mastered at all times, which is very important for the unmanned and intelligent industry. The field environment is severe, the deployment position of the equipment is complex, the fault time is uncertain, the sludge treatment device is a skid-mounted mobile device, local management is only carried out on the sludge treatment device, and the requirement of cross-region monitoring cannot be met. When the dirty oil sludge treatment device has problems, enterprises need to send equipment maintenance personnel to go to the site for treatment. The headquarters of the companies cannot timely acquire the real-time data of the remote-end sludge treatment device, and are difficult to timely analyze and make effective decisions on equipment, thereby seriously hindering technicians from normally monitoring the running condition of the equipment on a project site. Therefore, it is necessary to develop a remote monitoring system for a set of sludge treatment device by using internet technology.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a remote monitoring system of a greasy dirt processing device.
In order to achieve the purpose, the invention adopts the following technical scheme: the remote monitoring system of the sludge treatment device comprises the sludge treatment device, a management platform, an acquisition and classification module, a remote monitoring module, an operation analysis module, a data storage module, a user interaction module, a display screen and a mobile terminal; the device comprises a sewage sludge treatment device, a sewage sludge treatment device and a control device, wherein the sewage sludge treatment device is used for treating sewage sludge; the management platform is used for receiving login information of workers, identifying the login information of the workers, monitoring the safety environment in real time, receiving data transmitted by each submodule, and processing each group of data to generate display data; the acquisition and classification module is used for acquiring the data of the dirty oil sludge processing device and classifying the acquired data; the remote monitoring module is used for collecting the working state of the greasy dirt processing device in real time and feeding back the collected image information to the operation analysis module;
the operation analysis module is used for receiving the image information fed back by the remote monitoring module and analyzing and feeding back the working state of the greasy dirt processing device; the data storage module is used for receiving and sorting and storing each group of data; the user interaction module is used for receiving user operation information and retrieving and selecting the display information of the management platform according to the user operation information;
the display screen is used for receiving all groups of data of the current day and feeding the data back to a user for viewing in the form of images; the mobile terminal is used for remotely monitoring and checking the dirty oil sludge treatment device by a user, and meanwhile, the corresponding operation information can be sent by a worker through the mobile terminal.
As a further scheme of the present invention, the data classification of the collection and classification module specifically comprises the following steps: the method comprises the following steps: the acquisition and classification module is in communication connection with sensors in the sump oil sludge treatment device and receives data sent by each group of sensors in real time; step two: after receiving each group of data, the collecting and classifying module classifies according to the running time, the processing capacity of the oil sludge and the service life of the oil sludge processing device; step three: the acquisition and classification module automatically constructs a data record table, simultaneously leads each classified group of data into the data record table, simultaneously marks the acquisition time of each group of data, and updates the data record table once every day.
As a further scheme of the present invention, the specific steps of the analysis and feedback of the operation analysis module are as follows: step (1): the operation analysis module constructs an analysis neural network, trains and optimizes the analysis neural network, and receives image information acquired by the remote monitoring module; step (2): the operation analysis module carries out geometric correction and feature extraction processing on each group of image information, a test set is constructed according to each group of processed image information, and meanwhile, the optimized analysis neural network receives the test set; and (3): and the analysis neural network converts the non-binary data in the test set into binary data, normalizes the binary data, performs performance evaluation on the result meeting the expected value, draws an operation curve graph, analyzes the operation curve graph, generates a related device fault report, and feeds the device fault report back to a worker.
As a further scheme of the invention, the analyzing neural network training optimization comprises the following specific steps: the first step is as follows: the method comprises the steps that a neural network is analyzed to collect running data of a past device, a simulation data set is constructed, then a group of simulation data is selected as verification data, and the verification data is repeatedly used for verifying the precision of the neural network; the second step is that: selecting any subset as a test set for each group of simulation data, then taking the rest subsets as a training set, carrying out primary prediction on each group of data, and outputting the data with the best prediction result as the optimal parameter; the third step: the training data set is standardized according to the optimal parameters, and finally the training samples are transmitted to an analytical neural network for analysis, and the analytical neural network is accessed for multiple times
And (5) performing iterative training.
As a further scheme of the present invention, the data storage module comprises the following specific steps:
s1: the data storage module receives the data recording table, the image information, the operation curve graph and the device fault report, and integrates and classifies four groups of data generated in the same day into a group of storage data sets;
s2: collecting maintenance information of a user on the greasy dirt processing device, and inputting the maintenance information into corresponding storage data set;
s3: and sequencing the groups of storage data sets according to the generation time, and simultaneously dividing a group of storage space for the storage data sets to store data.
As a further scheme of the invention, the specific steps of the user interaction module retrieval and selection are as follows:
p1: the user interaction module generates a related control instruction according to the received operation information and simultaneously sends the control instruction to the management platform;
p2: the management platform receives the control instruction, analyzes and judges the control instruction, and judges the operation authority of the worker;
p3: if the control instruction is within the operation authority of the worker, the control instruction is in communication connection with the related sub-modules, corresponding data is called, and then the data is fed back to the worker to be checked, if the control instruction is not within the operation authority of the worker, the execution of the control instruction is forbidden, and meanwhile, the 'authority deficiency' of the worker is fed back.
Compared with the prior art, the invention has the beneficial effects that: the invention collects each group of data of the oil sludge treatment device through the remote monitoring module and the collection and classification module, simultaneously operates the analysis module to construct an analysis neural network, and simultaneously receives
Analyzing image information acquired by a remote monitoring module, converting non-binary data in each group of image information into binary data by a neural network, normalizing the binary data, evaluating the performance of the result meeting the expected value, drawing an operation curve graph, analyzing the operation curve graph, generating a fault report of a relevant device, feeding the fault report of the device back to a worker, and simultaneously feeding the fault report of the device to the worker
The staff sends the relevant operation information to the user interaction module through the mobile terminal, the staff interaction module feeds the operation information back to the management platform, and the management platform feeds back the relevant information after analyzing the identity authority of the staff, so that the functions of equipment management, fault remote diagnosis, historical data analysis and the like can be realized, meanwhile, the input-output ratio of the equipment management can be greatly improved, and the information is improved
Accuracy, and personnel configuration are reduced, so that the aims of reducing management cost and improving management quality are fulfilled.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a system block diagram of a remote monitoring system of a contaminated oil sludge treatment apparatus according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, the remote monitoring system of the sludge treatment device comprises a sludge treatment device, a management platform, an acquisition and classification module, a remote monitoring module, an operation analysis module, a data storage module, a user interaction module, a display screen and a mobile terminal; wherein, dirty oil sludge processing apparatus is used for handling dirty oil sludge.
The management platform is used for receiving login information of workers, identifying the login information of the workers, monitoring the safety environment in real time, receiving data transmitted by each submodule, and processing each group of data to generate display data.
The collecting and classifying module is used for collecting the data of the greasy dirt processing device and classifying the collected data.
The device comprises a collection classification module, a dirty oil and mud treatment device, a data recording table, a data storage module, a data transmission module, a data storage module and a data transmission module.
The remote monitoring module is used for collecting the working state of the dirty oil sludge processing device in real time and feeding back the collected image information to the operation analysis module.
The operation analysis module is used for receiving the image information fed back by the remote monitoring module and analyzing and feeding back the working state of the greasy dirt processing device.
Specifically, the operation analysis module constructs an analysis neural network, trains and optimizes the analysis neural network, receives image information acquired by the remote monitoring module, performs geometric correction and feature extraction processing on each group of image information, constructs a test set according to the processed image information, receives the test set by the optimized analysis neural network, converts non-binary data in the test set into binary data by the analysis neural network, performs normalization processing on the binary data, performs performance evaluation on results meeting expected values, draws an operation curve graph, analyzes the operation curve graph, generates a related device fault report, and feeds the device fault report back to a worker.
It should be further explained that the analysis neural network collects past device operation data, constructs a simulation data set, then selects a group of simulation data as verification data, and repeatedly uses the verification data to verify the accuracy of the analysis neural network, and selects any subset as a test set for each group of simulation data, then selects the rest subsets as a training set, and predicts each group of data once, and outputs the data with the best prediction result as the optimal parameter, then standardizes the training data set according to the optimal parameter, finally transmits the training sample to the analysis neural network for analysis, and carries out iterative training on the analysis neural network for many times.
The data storage module is used for receiving each group of data and sorting and storing each group of data.
Specifically, the data storage module receives a data recording table, image information, an operation curve chart and a device fault report, integrates and classifies four groups of data generated on the same day into a group of storage data sets, collects maintenance information of the greasy dirt processing device by a user, records the maintenance information into the corresponding storage data sets, sorts the storage data sets according to the generation time,
and simultaneously, dividing a group of storage spaces for data storage.
And the user interaction module is used for receiving the user operation information and retrieving and selecting the display information of the management platform according to the user operation information.
Specifically, the user interaction module generates a related control instruction according to the received operation information, simultaneously sends the control instruction to the management platform, then the management platform receives the control instruction and analyzes and judges the control instruction, meanwhile, the operation authority of the staff is judged, if the control command is in the operation authority of the staff, then the control command is in communication connection with the related sub-modules, corresponding data is called, and then the data is fed back to a worker for checking, if the control command is not in the operation authority of the worker, the execution of the control instruction is forbidden, meanwhile, the 'authority deficiency' of the working personnel is fed back, the functions of equipment management, remote fault diagnosis, historical data analysis and the like can be realized, meanwhile, the input-output ratio of equipment management can be greatly improved, the information accuracy is improved, and the personnel configuration is reduced, so that the aims of reducing the management cost and improving the management quality are fulfilled.
The display screen is used for receiving various groups of data of the current day and feeding the data back to the user for viewing in the form of images.
The mobile terminal is used for the user to carry out remote monitoring and check on the greasy dirt processing device, and meanwhile, the staff can send corresponding operation information through the mobile terminal.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered as the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (6)
1. The remote monitoring system of the sludge treatment device is characterized by comprising a sludge treatment device, a management platform, an acquisition and classification module, a remote monitoring module, an operation analysis module, a data storage module, a user interaction module, a display screen and a mobile terminal; the oil-contaminated sludge treatment device is used for treating the oil-contaminated sludge; the management platform is used for receiving login information of workers, identifying the login information, monitoring the safety environment in real time, receiving data transmitted by each sub-module, and processing each group of data to generate display data; the collecting and classifying module is used for collecting the data of the greasy dirt processing device and classifying the collected data; the remote monitoring module is used for acquiring the working state of the sump oil sludge treatment device in real time
Collecting and feeding back the collected image information to an operation analysis module; the operation analysis module is used for receiving the image information fed back by the remote monitoring module and analyzing and feeding back the working state of the greasy dirt processing device; the data storage module is used for receiving and sorting and storing each group of data; the user interaction module is used for receiving user operation information and retrieving and selecting the display information of the management platform according to the user operation information; the display screen is used for receiving various groups of data on the current day and feeding the data back to a user for viewing in the form of images; the mobile terminal is used for remotely monitoring and checking the dirty oil sludge treatment device by a user, and meanwhile, the corresponding operation information can be sent by a worker through the mobile terminal.
2. The remote monitoring system of the sludge treatment device according to claim 1, wherein the data classification of the collection and classification module comprises the following steps: the method comprises the following steps: the acquisition and classification module is in communication connection with sensors in the sump oil sludge treatment device and receives data sent by each group of sensors in real time; step two: after receiving all the groups of data, the collecting and classifying module classifies according to the running time, the sludge treatment capacity and the use duration of the sludge treatment device; step three: the acquisition and classification module automatically constructs a data record table, simultaneously leads each classified group of data into the data record table, simultaneously marks the acquisition time of each group of data, and updates the data record table once every day.
3. The remote monitoring system for the sludge treatment facility as claimed in claim 1, wherein the operation analysis module analyzes the feedback by the following steps: step (1): the operation analysis module constructs and analyzes the neural network and trains the neural network
Meanwhile, receiving image information acquired by the remote monitoring module; step (2): the operation analysis module carries out geometric correction and feature extraction processing on each group of image information, a test set is constructed according to the processed image information, and meanwhile, the optimized analysis neural network receives the test set; and (3): analyzing non-binary data conversion of test set into binary by neural network
And then carrying out normalization processing on the device, carrying out performance evaluation on the result meeting the expected value, drawing an operation curve graph, analyzing the operation curve graph, generating a related device fault report, and feeding the device fault report back to the working personnel.
4. The remote monitoring system of the sludge treatment device according to claim 3, wherein the analyzing neural network training optimization comprises the following specific steps:
the first step is as follows: the method comprises the steps that a neural network is analyzed to collect running data of a past device, a simulation data set is constructed, then a group of simulation data is selected as verification data, and the verification data is repeatedly used for verifying the precision of the neural network;
the second step is that: selecting any subset as a test set for each group of simulation data, then taking the rest subsets as a training set, carrying out primary prediction on each group of data, and outputting the data with the best prediction result as the optimal parameter;
the third step: and carrying out standardization processing on the training data set according to the optimal parameters, finally conveying the training samples to an analysis neural network for analysis, and carrying out iterative training on the analysis neural network for multiple times.
5. The remote monitoring system of the sludge treatment device according to claim 3, wherein the data storage module comprises the following steps:
s1: the data storage module receives the data recording table, the image information, the operation curve graph and the device fault report, and integrates and classifies four groups of data generated in the same day into a group of storage data sets;
s2: collecting maintenance information of a user on the greasy dirt processing device, and inputting the maintenance information into corresponding storage data set;
s3: and sequencing the groups of storage data sets according to the generation time, and simultaneously dividing a group of storage space for the storage data sets to store data.
6. The remote monitoring system for sludge treatment facility according to claim 3 wherein said user interaction module search selection comprises the following steps:
p1: the user interaction module generates a related control instruction according to the received operation information and simultaneously sends the control instruction to the management platform;
p2: the management platform receives the control instruction, analyzes and judges the control instruction, and judges the operation authority of the worker;
p3: if the control instruction is within the operation authority of the worker, the control instruction is in communication connection with the related sub-modules, corresponding data is called, and then the data is fed back to the worker to be checked, if the control instruction is not within the operation authority of the worker, the execution of the control instruction is forbidden, and meanwhile, the 'authority deficiency' of the worker is fed back.
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