CN116976865A - Ship maintenance device allocation management system based on big data analysis - Google Patents
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Abstract
The invention discloses a ship maintenance device allocation management system based on big data analysis, which relates to the field of ship maintenance allocation and comprises the following components: the management module is used for controlling the operation of the global functional module, and recording and managing the inventory quantity and the positions of various devices for ship maintenance; the data acquisition module is used for acquiring sensor data in each area in the ship and outputting temperature, pressure and vibration parameters of each area of the ship; the threshold module is used for setting the acquisition period and the acquisition times of the data acquisition module; the maintenance device has the advantages that the maintenance device is tracked, the threshold value of state acquisition is adjusted upwards, and the maintenance and the state after replacement of the device in the area are controlled in real time, so that loss caused by improper maintenance or unqualified devices is avoided, various types of allocation commands are reasonably classified, ordered sending of various commands is guaranteed, the efficiency and reliability of ship maintenance are improved, and the ship stopping time and maintenance cost are reduced.
Description
Technical Field
The invention relates to the technical field of ship maintenance allocation, in particular to a ship maintenance device allocation management system based on big data analysis.
Background
In the shipping and ship maintenance industries, reasonable allocation of maintenance devices is of great importance, various faults and damages to ships possibly occur in the course of navigation, the ships need to be repaired in time to ensure normal operation and safe navigation of the ships, the reasonable allocation of the devices can ensure that the devices required for maintenance can be provided in time, the delay of repair time due to the loss of the devices is avoided, the maintenance efficiency is improved, the ship berthing maintenance time is generally short, maintenance tasks are required to be completed within a limited time, the devices are prepared in advance by optimizing the allocation of the devices, and the devices are allocated to corresponding working areas according to the specific requirements of the maintenance work, so that the efficiency of the maintenance work can be improved, and the berthing time is shortened;
however, the existing ship maintenance device deployment management system has problems such as:
1. when a device in a certain area of a ship is in a problem and is replaced or maintained, the subsequent state of the device is difficult to track and analyze effectively, so that when the condition that the maintenance operation is wrong or the quality of the device is unqualified, the device is difficult to find in time, loss is caused, and the follow-up is not facilitated;
2. lacking in reasonable analysis and prediction of historical maintenance information, it is difficult to output maintenance opinions in advance according to the current running state of the ship, and it is difficult for a user to perform effective preparation work to cope with an emergency.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides the allocation management system for the ship maintenance device based on big data analysis, which can effectively solve the problems existing in the prior art when the device in a certain area of the ship is in a problem and is difficult to carry out effective tracking analysis on the subsequent state of the device after replacement or maintenance, so that when the situation that the maintenance operation is wrong or the quality of the device is unqualified occurs, the device is difficult to discover in time, causes loss, is unfavorable for tracking, lacks reasonable analysis and prediction on historical maintenance information, is difficult to carry out early output of maintenance comments according to the current running state of the ship, and is difficult for users to carry out effective preparation work to cope with the emergency.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
the invention discloses a ship maintenance device allocation management system based on big data analysis, which comprises:
the management module is used for controlling the operation of the global functional module, and recording and managing the inventory quantity and the positions of various devices for ship maintenance;
the data acquisition module is used for acquiring sensor data in each area in the ship and outputting temperature, pressure and vibration parameters of each area of the ship;
the threshold module is used for setting the acquisition period and the acquisition times of the data acquisition module;
the filtering module is used for cleaning and preprocessing the acquired data and filtering useless and erroneous data;
the identification module is used for carrying out abnormal detection on the sensor data and judging and identifying devices possibly having faults in the area;
the state analysis module is used for acquiring the current running state of the ship, extracting running characteristics, and taking the running characteristics as a reference value after calculation and analysis;
the allocation module is used for acquiring the judgment data of the identification module and the reference value of the state analysis module, editing and sending a scheduling command for the devices in real time after comprehensive analysis, and allocating the required devices from the stock to corresponding maintenance work according to the ship maintenance command;
the dispatch definition module is used for defining the sent device dispatch command and classifying the device dispatch command into a replacement, repair or purchase command for analysis;
the tracking module is used for tracking the device area which has triggered the dispatching command, and after the dispatching command is finished, the threshold module is up-regulated to acquire the period and the acquisition times, so as to perform secondary state analysis;
and the maintenance model end is used for acquiring the current reference value and the sensor data, establishing an analysis model by using a machine learning algorithm, and predicting the current device needing maintenance and the required maintenance time of the ship.
Furthermore, the authentication module is interactively connected with an alarm module through a wireless network, and the alarm module is used for monitoring the judgment state of the authentication module on the sensor data in real time and sending an alarm to the management end when abnormality is detected.
Still further, the running features extracted by the state analysis module include: the ship's travel water depth, water temperature, wind coefficient, icing coefficient, ship's historical maintenance time, maintenance type, and replaced components.
Furthermore, the maintenance model end is interactively connected with a correction module through a wireless network, and the correction module is used for receiving final output data of the identification module, the state analysis module and the allocation module as training samples, and inputting the training samples into the maintenance model end to execute model training operation after triggering.
Furthermore, the allocating module allocates the marking information to the devices, and records the flow information from the warehouse to the maintenance site by recording the identification information of the devices and tracing the allocated devices.
Furthermore, the tracking module analyzes the traceability information of the used device, acquires the quality information and the refund condition of the tracked device, and uploads the quality information and the refund condition to the management module for storage.
Furthermore, in the prediction process, the maintenance model end outputs an evaluation result through the technical indexes pointed by each reference value, refines an index system of a specific part according to an evaluation model established by an analytic hierarchy process, and establishes index weights, wherein the calculation formula is as follows:
；
wherein P represents the evaluation result parameter,represents the degree of merit of the indicated component, +.>Weight representing the i-th technical index, < ->Represents the overrule of the ith index, n represents any technical index,/for>Representing the quality score of the ith technical metric.
Further, the tracking module performs numerical scoring on the quality of the blended product, and the process is as follows:
relates to device size inspection and assembly size inspectionAnd (3) checking, if the size of the assembly size device is checked to be out of tolerance during application, performing out-of-tolerance processing, and correspondingly outputting a quality evaluation result according to the processing result, wherein: qualified products are evaluated for 100 points; if the out-of-tolerance technical index affects the performance, the out-of-tolerance product is scrapped, and the product is evaluated for 0 points; if the out-of-tolerance technical index does not influence the performance, the out-of-tolerance product is approved to be used, and the product is evaluated to be 100-And x 100 minutes, wherein T is the number of the trial and H is the total number of the products.
Furthermore, the tracking module synchronously uploads the visual interface in the tracking process to display the maintenance condition of the ship, the part scheduling plan and the real-time state.
Still further, the management module is connected with the data acquisition module and the maintenance model end through wireless network interaction, the data acquisition module is connected with the threshold module and the filter module through wireless network interaction, the filter module is connected with the discrimination module through wireless network interaction, the discrimination module is connected with the state analysis module through wireless network interaction, the state analysis module is connected with the allocation module through wireless network interaction, the allocation module is connected with the scheduling definition module through wireless network interaction, the threshold module is connected with the tracking module through wireless network interaction, and the tracking module is connected with the allocation module through wireless network interaction.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
1. the maintenance and replacement states of the devices in the area are controlled in real time by tracking the devices which are already allocated and maintained and up-regulating the threshold value of state acquisition, so that loss caused by improper maintenance or unqualified devices is avoided, various allocation commands are reasonably classified, and ordered sending of various instructions is ensured.
2. Through the measure of setting up the maintenance model, combine the analysis with ship state and the historical maintenance record that corresponds to as training sample, when the ship is in different driving states, can directly give the maintenance suggestion, so that the user makes the allotment work of device in advance, and then can improve the efficiency and the reliability of ship maintenance, reduce time and cost of maintenance of stopping the ship, and improve the wholeness ability of ship operation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of a frame of the present invention;
reference numerals in the drawings represent respectively, 1, management module; 2. a data acquisition module; 3. a threshold module; 4. a filtration module; 5. an authentication module; 6. a state analysis module; 7. a deployment module; 8. a schedule definition module; 9. a tracking module; 10. maintaining the model end; 11. a correction module; 12. and an alarm module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Example 1: the ship maintenance device deployment management system based on big data analysis of the embodiment, as shown in fig. 1, includes:
the management module 1 is used for controlling the operation of the global functional module, recording and managing the inventory quantity and the positions of various devices for ship maintenance;
the data acquisition module 2 is used for acquiring sensor data in each area in the ship and outputting temperature, pressure and vibration parameters of each area of the ship;
the threshold module 3 is used for setting the acquisition period and the acquisition times of the data acquisition module 2;
the filtering module 4 is used for cleaning and preprocessing the acquired data and filtering useless and erroneous data;
the identification module 5 is used for carrying out abnormal detection on the sensor data and judging and identifying the device which possibly has faults in the area, the identification module 5 is interactively connected with the alarm module 12 through a wireless network, and the alarm module 12 is used for monitoring the judgment state of the identification module 5 on the sensor data in real time and sending an alarm to the management end when the abnormality is detected;
the state analysis module 6 is configured to obtain a current running state of the ship, extract a running feature, perform calculation and analysis, and use the calculated and analyzed running feature as a reference value, where the running feature extracted by the state analysis module 6 includes: the ship running water area depth, the water area temperature, the wind power coefficient, the icing coefficient, the ship historical maintenance time, the maintenance type and the replaced device;
the allocating module 7 is used for acquiring the judging data of the identifying module 5 and the reference value of the state analyzing module 6, editing and transmitting a scheduling command for the devices in real time after comprehensive analysis, allocating the required devices from the stock to corresponding maintenance work according to the ship maintenance command, allocating the marking information for the devices stored in the allocating module 7, tracking after the devices are allocated by recording the identification information of the devices, and recording the flow information from the warehouse to the maintenance site;
a schedule defining module 8, configured to define the transmitted device schedule command, and classify the device schedule command as a replacement, repair or purchase command for analysis;
the tracking module 9 is used for tracking the device area which has triggered the dispatching command, and after the dispatching command is finished, the threshold module 3 is up-regulated for collecting period and collecting times, and secondary state analysis is carried out, the tracking module 9 analyzes the traceable information of the used device, acquires the quality information and the refund condition of the tracked device, and uploads the quality information and the refund condition to the management module 1 for storage, and the tracking module 9 synchronously uploads a visual interface in the tracking process, and displays the maintenance condition, the part dispatching plan and the real-time state of the ship;
the maintenance model end 10 is used for acquiring a current reference value and sensor data, establishing an analysis model by using a machine learning algorithm, predicting a device which is required to be maintained currently and required maintenance time of a ship, wherein the maintenance model end 10 is interactively connected with a correction module 11 through a wireless network, the correction module 11 is used for receiving final output data of the identification module 5, the state analysis module 6 and the allocation module 7 as training samples, putting the maintenance model end 10 into execution of model training operation after triggering, combining and analyzing the ship state and the corresponding historical maintenance record by means of a measure of setting up the maintenance model, taking the combined analysis as the training samples, and directly giving maintenance comments when the ship is in different running states, so that a user can conveniently and conveniently allocate the device in advance, further improving the efficiency and reliability of ship maintenance, reducing the ship stopping time and the maintenance cost and improving the overall performance of ship operation.
As a preferred implementation manner in this embodiment, as shown in fig. 1, the management module 1 is interactively connected with the data acquisition module 2 and the maintenance model end 10 through a wireless network, the data acquisition module 2 is interactively connected with the threshold module 3 and the filtering module 4 through a wireless network, the filtering module 4 is interactively connected with the authentication module 5 through a wireless network, the authentication module 5 is interactively connected with the state analysis module 6 through a wireless network, the state analysis module 6 is interactively connected with the allocation module 7 through a wireless network, the allocation module 7 is interactively connected with the schedule definition module 8 through a wireless network, the threshold module 3 is interactively connected with the tracking module 9 through a wireless network, and the tracking module 9 is interactively connected with the allocation module 7 through a wireless network.
In this embodiment, by tracking the apparatus that has been allocated for maintenance, the threshold value of its state acquisition is adjusted up, so as to control the maintenance and the state of the device after replacement in real time, so as to avoid the loss caused by improper maintenance or unqualified device, and reasonably classify various allocation commands, so as to ensure the orderly transmission of various instructions.
Example 2: in other aspects, in this embodiment, the tracking module 9 performs numerical scoring on the quality of the blended product, and the process is as follows:
the device size inspection and the assembly size inspection are related, if the assembly size device size inspection is out of tolerance during application, out-of-tolerance processing is performed, and a quality evaluation result is correspondingly output according to the processing result, wherein: qualified products are evaluated for 100 points; if the out-of-tolerance technical index affects the performance, the out-of-tolerance product is scrapped, and the product is evaluated for 0 points; if the out-of-tolerance technical index does not influence the performance, the out-of-tolerance product is approved to be used, and the product is evaluated to be 100-And x 100 minutes, wherein T is the number of the trial and H is the total number of the products.
Example 3: in this embodiment, in the prediction process, the maintenance model end 10 outputs the evaluation result through the technical indexes pointed by the reference values, refines the index system of the specific part according to the evaluation model established by the analytic hierarchy process, and establishes the index weight, where the calculation formula is as follows:
；
wherein P represents the evaluation result parameter,represents the degree of merit of the indicated component, +.>Weight representing the i-th technical index, < ->Represents the overrule of the ith index, n represents any technical index,/for>Representing the quality score of the ith technical metric.
In summary, the invention tracks the appliance which is already allocated and maintained, adjusts up the threshold value of state acquisition, and further controls the maintained and replaced state of the device in the area in real time, so as to avoid the loss caused by improper maintenance or unqualified device, and reasonably classifies various allocation commands to ensure the orderly transmission of various instructions;
through the measure of setting up the maintenance model, combine the analysis with ship state and the historical maintenance record that corresponds to as training sample, when the ship is in different driving states, can directly give the maintenance suggestion, so that the user makes the allotment work of device in advance, and then can improve the efficiency and the reliability of ship maintenance, reduce time and cost of maintenance of stopping the ship, and improve the wholeness ability of ship operation.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; while the invention has been described in detail with reference to the foregoing embodiments, it will be appreciated by those skilled in the art that variations may be made in the techniques described in the foregoing embodiments, or equivalents may be substituted for elements thereof; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. Ship maintenance device allocation management system based on big data analysis, which is characterized by comprising:
the management module (1) is used for controlling the operation of the global functional module, recording and managing the inventory quantity and the positions of various devices for ship maintenance;
the data acquisition module (2) is used for acquiring sensor data in each area in the ship and outputting temperature, pressure and vibration parameters of each area of the ship;
the threshold module (3) is used for setting the acquisition period and the acquisition times of the data acquisition module (2);
the filtering module (4) is used for cleaning and preprocessing the acquired data and filtering useless and erroneous data;
the identification module (5) is used for carrying out abnormal detection on the sensor data and judging and identifying the devices possibly having faults in the area;
the state analysis module (6) is used for acquiring the current running state of the ship, extracting running characteristics, and taking the running characteristics as a reference value after calculation and analysis;
the allocation module (7) is used for acquiring the judgment data of the identification module (5) and the reference value of the state analysis module (6), editing and sending a scheduling command for the devices in real time after comprehensive analysis, and allocating the required devices from the stock to corresponding maintenance work according to the ship maintenance command;
the dispatch definition module (8) is used for defining the sent device dispatch command and analyzing the device dispatch command classified as a replacement, repair or purchase command;
the tracking module (9) is used for tracking the device area which has triggered the dispatching command, and after the dispatching command is finished, the threshold module (3) is up-regulated for collecting period and collecting times, and secondary state analysis is carried out;
the maintenance model end (10) is used for acquiring the current reference value and the sensor data, establishing an analysis model by using a machine learning algorithm, and predicting the current device needing maintenance and the required maintenance time of the ship;
in the prediction process, the maintenance model end (10) outputs an evaluation result through the technical indexes pointed by each reference value, refines an index system of a specific part according to an evaluation model established by a analytic hierarchy process, and establishes index weights, wherein the calculation formula is as follows:
；
wherein P represents the evaluation result parameter,represents the degree of merit of the indicated component, +.>Weight representing the i-th technical index, < ->Represents the overrule of the ith index, n represents any technical index,/for>Representing the quality score of the ith technical metric.
2. The ship maintenance device allocation management system based on big data analysis according to claim 1, wherein the identification module (5) is interactively connected with the alarm module (12) through a wireless network, and the alarm module (12) is used for monitoring the judgment state of the identification module (5) on the sensor data in real time and sending an alarm to the management end when abnormality is detected.
3. The marine vessel repair parts deployment management system based on big data analysis according to claim 1, wherein the travel features extracted by the state analysis module (6) comprise: the ship's travel water depth, water temperature, wind coefficient, icing coefficient, ship's historical maintenance time, maintenance type, and replaced components.
4. The ship maintenance device allocation management system based on big data analysis according to claim 1, wherein the maintenance model end (10) is interactively connected with a correction module (11) through a wireless network, and the correction module (11) is used for receiving final output data of the identification module (5), the state analysis module (6) and the allocation module (7) as training samples, and is put into the maintenance model end (10) to execute model training operation after triggering.
5. The system for allocating and managing the ship maintenance devices based on big data analysis according to claim 1, wherein the allocation module (7) allocates the marking information to the devices, and records the flow information from the warehouse to the maintenance site by recording the marking information of the devices and tracing the devices after the devices are allocated.
6. The ship maintenance device allocation management system based on big data analysis according to claim 1, wherein the tracking module (9) analyzes the traceability information of the used device, obtains the quality information and the replacement condition of the tracked device, and uploads the quality information and the replacement condition to the management module (1) for storage.
7. The marine vessel repair device deployment management system based on big data analysis according to claim 1, wherein the tracking module (9) performs a numerical scoring for the deployed product quality by the process of:
the device size inspection and the assembly size inspection are related, if the assembly size device size inspection is out of tolerance during application, out-of-tolerance processing is performed, and a quality evaluation result is correspondingly output according to the processing result, wherein: qualified products are evaluated for 100 points; if the out-of-tolerance technical index affects the performance, the out-of-tolerance product is scrapped, and the product is evaluated for 0 points; if the out-of-tolerance technical index does not influence the performance, the out-of-tolerance product is approved to be used, and the product is evaluated to be 100-And x 100 minutes, wherein T is the number of the trial and H is the total number of the products.
8. The ship maintenance device allocation management system based on big data analysis according to claim 1, wherein the tracking module (9) synchronously uploads a visual interface to show the ship maintenance condition, the part scheduling plan and the real-time state in the tracking process.
9. The ship maintenance device allocation management system based on big data analysis according to claim 1, wherein the management module (1) is interactively connected with the data acquisition module (2) and the maintenance model end (10) through a wireless network, the data acquisition module (2) is interactively connected with the threshold module (3) and the filtering module (4) through the wireless network, the filtering module (4) is interactively connected with the identification module (5) through the wireless network, the identification module (5) is interactively connected with the state analysis module (6) through the wireless network, the state analysis module (6) is interactively connected with the allocation module (7) through the wireless network, the allocation module (7) is interactively connected with the scheduling definition module (8) through the wireless network, the threshold module (3) is interactively connected with the tracking module (9) through the wireless network, and the tracking module (9) is interactively connected with the allocation module (7) through the wireless network.
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| CN117371994A (en) * | 2023-12-05 | 2024-01-09 | 中国船舶集团有限公司第七一九研究所 | Ship maintenance intelligent management system based on opinion feedback |
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