CN117490338A - Ship cooling unit control method and system based on deep learning - Google Patents
Ship cooling unit control method and system based on deep learning Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
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- F25D29/00—Arrangement or mounting of control or safety devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
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Abstract
The invention relates to the technical field of cooling unit control, in particular to a ship cooling unit control method and system based on deep learning, which improve the efficiency and reliability of a ship cooling unit, reduce the overheat condition of equipment and lower the maintenance cost; the method comprises the following steps: continuously acquiring working state information of a ship cooling unit according to a preset acquisition time interval, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate of the cooling unit; arranging the collected working state information in time sequence, and aligning the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate collected by different time nodes one by one to obtain a cooling efficiency characteristic matrix; and carrying out feature recognition on the cooling efficiency feature matrix by utilizing a pre-constructed cooling efficiency evaluation model to obtain cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit.
Description
Technical Field
The invention relates to the technical field of cooling unit control, in particular to a ship cooling unit control method and system based on deep learning.
Background
The ship cooling unit is a cooling system applied to equipment such as ships, maritime works and the like and is used for cooling equipment or machine parts; according to different cooling requirements and equipment types, the ship cooling unit can be composed of components such as coolers, cooling water pipelines and the like with different types and specifications.
The existing ship cooling unit control method is mostly to judge the cooling effect of the ship cooling unit through temperature change feedback of cooled equipment or machine parts; when the cooling effect is not good, the condition of the ship cooling unit is indicated for a period of time, and the adjustment and maintenance of the ship cooling unit is likely to miss the optimal adjustment time, so that the overheat hazard of cooled equipment or machine parts is caused; therefore, there is a need for a ship cooling unit control method capable of timely finding a decrease in the cooling effect of the ship cooling unit.
Disclosure of Invention
In order to solve the technical problems, the invention provides a deep learning-based ship cooling unit control method which improves the efficiency and reliability of a ship cooling unit, reduces the overheat condition of equipment and reduces the maintenance cost.
In a first aspect, the present invention provides a deep learning-based ship cooling unit control method, the method comprising:
Continuously acquiring working state information of a ship cooling unit according to a preset acquisition time interval, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate of the cooling unit;
arranging the collected working state information in time sequence, and aligning the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate collected by different time nodes one by one to obtain a cooling efficiency characteristic matrix;
performing feature recognition on the cooling efficiency feature matrix by using a pre-constructed cooling efficiency evaluation model to obtain cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit;
acquiring the energy consumption of a cooler of a ship cooling unit, wherein the time window for acquiring the energy consumption of the cooler is the same as the time window of the cooling efficiency characteristic matrix;
according to the specification information of the ship cooling unit, setting a first evaluation weight corresponding to the cooling efficiency evaluation parameter and a second evaluation weight corresponding to the energy consumption of the cooler;
according to the first evaluation weight and the second evaluation weight, weighting calculation is carried out on the cooling efficiency evaluation parameter and the energy consumption of the cooler, and a cooling unit control index is obtained;
Comparing the control index of the cooling unit with a preset threshold value: if the control index of the cooling unit exceeds a preset threshold, the current cooling unit is poor in cooling effect and needs to be correspondingly adjusted; if the control index does not exceed the preset threshold, the current cooling unit is good in cooling effect and does not need to be adjusted.
Further, the digital format of the cooling efficiency characteristic matrix is as follows:
wherein T is inN Representing the cooling medium input temperature detected by the nth time node; t (T) outN Representing the cooling medium output temperature detected by the Nth time node; p (P) 1 Representing the cooling medium pressure detected by the nth time node; f (F) 1 Representing the cooling medium flow rate detected by the nth time node.
Further, according to the first evaluation weight and the second evaluation weight, a calculation formula for weighting calculation of the cooling efficiency evaluation parameter and the cooler energy consumption is as follows:
K＝ω 1 ×Q+ω 2 ×F(E)；
wherein K represents a cooling unit control index; ω1 represents the influence weight of the cooling efficiency evaluation parameter on the cooling unit control index; ω2 represents the impact weight of chiller energy consumption on chiller plant control index; q represents a cooling efficiency evaluation parameter; e represents the energy consumption of the cooler; f (E) represents a normalized function of chiller energy consumption.
Further, the working state information acquisition of the cooling unit comprises the following steps:
a temperature sensor, a pressure sensor and a flow rate sensor are arranged in the ship cooling unit;
setting a time interval for collecting data according to the system demand and the performance evaluation demand;
continuously collecting working state information of the cooling unit according to a preset time interval, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate;
performing real-time quality control on the acquired data to remove abnormal values and noise data;
and recording the collected working state information according to time sequence and storing the working state information in a database.
Further, the method for constructing the cooling efficiency evaluation model comprises the following steps:
acquiring working state information of a cooling unit, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate;
preprocessing the acquired working state information of the cooling unit, including data cleaning, denoising, interpolation and abnormal value detection;
the method comprises the steps of extracting and diversity of characteristics of working state information, wherein the working state information comprises a training set and a testing set;
using a machine learning algorithm to construct a model framework, wherein the model framework comprises an input layer, a hidden layer and an output layer, and a cooling efficiency evaluation model is constructed;
Training the model by using the prepared training set;
evaluating the model using the prepared test set;
and deploying the trained and evaluated model into a system, and carrying out feature recognition on the cooling efficiency feature matrix to obtain cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit.
Further, the comparison and decision method of the control index of the cooling unit comprises the following steps:
defining a threshold value of a control index;
comparing the calculated control index of the cooling unit with a preset threshold value, and if the control index of the cooling unit exceeds the preset threshold value, indicating that the cooling effect of the current cooling unit is poor, and correspondingly adjusting the cooling unit; if the control index does not exceed the preset threshold, the current cooling unit is good in cooling effect and does not need to be adjusted;
according to the comparison result, when the system needs to take corresponding decision-making measures and needs to be regulated and maintained, the control index of the cooling unit exceeds a preset threshold value, and the system can trigger an alarm to inform relevant operators or an automation system to execute proper control measures; when the adjustment is not needed, the control index of the cooling unit is lower than a preset threshold value, the system maintains the current running state, and no further operation is needed.
Further, the setting influencing factors of the control index threshold include:
the threshold is determined according to the system requirements and the safety of the equipment, and different applications have different tolerances;
historical operating data and performance benchmarks can provide useful information, based on which a reasonable range of control indices can be determined;
the threshold can be set with help of the advice of professional engineers and domain specialists;
through simulation and experimentation, the relationship between different control index values and device performance can be determined, thereby providing a basis for setting the threshold.
In another aspect, the present application also provides a deep learning-based marine cooling unit control system, the system comprising:
the data acquisition module is used for continuously acquiring the working state information of the ship cooling unit according to a preset acquisition time interval, wherein the working state information comprises the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate of the cooling unit and is transmitted;
the data processing module is used for receiving the working state information, arranging the acquired working state information in time sequence, aligning the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate acquired by different time nodes one by one, acquiring a cooling efficiency characteristic matrix and transmitting the cooling efficiency characteristic matrix;
The feature recognition module is used for receiving the cooling efficiency feature matrix, carrying out feature recognition on the cooling efficiency feature matrix by utilizing a pre-constructed cooling efficiency evaluation model, obtaining cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit, and sending the cooling efficiency evaluation parameters;
the energy consumption acquisition module is used for acquiring the energy consumption of the cooler of the ship cooling unit, enabling the time window of the energy consumption of the cooler to be the same as the time window of the cooling efficiency characteristic matrix, and transmitting the energy consumption;
the weight setting module is used for setting a first evaluation weight and a second evaluation weight, setting the first evaluation weight corresponding to the cooling efficiency evaluation parameter and the second evaluation weight corresponding to the energy consumption of the cooler according to the specification information of the ship cooling unit, and sending the first evaluation weight and the second evaluation weight;
the index calculation module is used for receiving the cooling efficiency evaluation parameter, the energy consumption of the cooler, the first evaluation weight and the second evaluation weight, carrying out weighted calculation on the cooling efficiency evaluation parameter and the energy consumption of the cooler according to the first evaluation weight and the second evaluation weight to obtain a control index of the cooling unit, and sending the control index;
the decision module is used for receiving the control index of the cooling unit, comparing the control index of the cooling unit with a preset threshold value and judging the cooling effect of the current cooling unit; when the control index of the cooling unit exceeds a preset threshold, the cooling effect of the current cooling unit is determined to be poor, and corresponding adjustment operation is triggered; when the control index of the cooling unit does not exceed a preset threshold, the cooling effect of the current cooling unit is considered to be good, and adjustment is not needed.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, the computer program implementing the steps of any of the methods described above when executed by the processor.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
Compared with the prior art, the invention has the beneficial effects that: according to the method, the performance of the ship cooling unit can be monitored in real time by continuously collecting the working state information, so that the change of the cooling effect can be predicted in time; compared with the traditional method, the method can find the condition of the reduction of the cooling effect earlier, avoid missing the best adjustment time and reduce the risk of overheat of equipment;
the deep learning model is adopted to identify the cooling efficiency characteristics, so that the performance of the ship cooling unit can be estimated more accurately; the energy consumption of the cooler is considered, so that the comprehensive evaluation comprises factors of energy efficiency, not just cooling efficiency; decision making is performed based on a deep learning and data driving method, so that a system can make more accurate judgment according to a large amount of historical data and model training; when the control index of the cooling unit exceeds a preset threshold, the system can automatically trigger the adjustment operation, so that the requirement of manual intervention is reduced, and the possibility of errors is reduced; by timely maintenance and adjustment, the risk of overheat and damage of equipment can be reduced, so that maintenance cost and downtime are reduced;
In summary, the method improves the efficiency and reliability of the ship cooling unit, reduces the overheat condition of equipment and reduces the maintenance cost.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of a method of collecting operational status information for a cooling unit;
FIG. 3 is a flowchart of a method of constructing a cooling efficiency evaluation model;
FIG. 4 is a flow chart of a method of comparison and decision making of chiller unit control indexes;
fig. 5 is a block diagram of a deep learning based marine cooling unit control system.
Detailed Description
In the description of the present application, those skilled in the art will appreciate that the present application may be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Accordingly, the present application may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the present application may also be embodied in the form of a computer program product in one or more computer-readable storage media, which contain computer program code.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer magnetic disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disk read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The technical scheme of the application is that the acquisition, storage, use, processing and the like of the data meet the relevant regulations of national laws.
The present application describes methods, apparatus, and electronic devices provided by the flowchart and/or block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application is described below with reference to the drawings in the present application.
Example 1
As shown in fig. 1 to 4, the deep learning-based ship cooling unit control method of the present invention specifically includes the following steps:
s1, continuously acquiring working state information of a ship cooling unit according to a preset acquisition time interval, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate of the cooling unit;
in the field of ship cooling unit control, ensuring effective data acquisition is a key for implementing automatic control and fault detection, and in a ship cooling unit, the working state information acquisition of the cooling unit comprises the following steps:
S11, arranging a temperature sensor, a pressure sensor and a flow rate sensor in the ship cooling unit so as to monitor the working state of a cooling medium in real time;
s12, setting a data acquisition time interval according to the system demand and performance evaluation requirements, wherein a shorter time interval can provide finer data and can also cause overlarge data quantity, and a longer time interval can reduce the data quantity but also miss some instantaneous key information;
s13, continuously collecting working state information of the cooling unit according to a preset time interval, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate;
s14, performing real-time quality control on the acquired data to remove abnormal values and noise data;
and S15, recording the collected working state information according to time sequence, and storing the working state information in a database to ensure the integrity and traceability of data records, so that the subsequent analysis and processing are facilitated.
In the step, the ship cooling unit can monitor the working state of the cooling medium in real time by arranging the temperature sensor, the pressure sensor and the flow rate sensor, so that operators can know the performance and the health condition of the system at any time;
According to the requirements of system requirements and performance evaluation, different time intervals are adopted to help balance the fineness and data quantity of data, shorter time intervals provide more detailed information to help capture the instantaneous change of the system, and longer time intervals help reduce the burden of data storage and processing; the continuous collection of the working state information enables the system to know the performance of the cooling unit in real time, and is helpful for finding potential problems early;
the collected data is subjected to real-time quality control, so that abnormal values and noise can be eliminated, the accuracy of the data is ensured, and misleading data is prevented from affecting subsequent analysis and decision making; the collected data are recorded and stored in a database according to time sequence, so that the integrity and traceability of the data are ensured, and the subsequent analysis, trend analysis, performance evaluation and fault diagnosis are facilitated;
in summary, the step S1 can continuously monitor the working state of the cooling unit, and provide basic data to support subsequent cooling efficiency evaluation and automatic control, improve the efficiency of the system, reduce maintenance cost, and ensure that the device operates in a safe and reliable state.
S2, arranging the collected working state information in time sequence, and aligning the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate collected by different time nodes one by one to obtain a cooling efficiency characteristic matrix;
The cooling efficiency characteristic matrix acquisition method comprises the following steps:
s21, carrying out time alignment on data acquired by different sensors, wherein the acquisition frequencies of the different sensors are different, and the different sensors need to be mapped to the same time point;
s22, carrying out normalization processing on the data, wherein different sensors have different measuring ranges, and carrying out normalization processing on the data so as to ensure that different features have similar importance when the model is input;
s23, the data comprise a plurality of characteristics of input temperature, output temperature, pressure and flow rate of a cooling medium, and all the characteristics are arranged and organized into a cooling efficiency characteristic matrix according to time points;
the digital format of the cooling efficiency characteristic matrix is as follows:
wherein T is inN Representing the cooling medium input temperature detected by the nth time node; t (T) outN Representing the cooling medium output temperature detected by the Nth time node; p (P) 1 Representing the cooling medium pressure detected by the nth time node; f (F) 1 Representing the cooling medium flow rate detected by the nth time node; and the first group of data acquisition time nodes and the last group of data acquisition time nodes are reflected in the cooling efficiency characteristic matrix, so that a time window of the cooling efficiency characteristic matrix is obtained.
In the step, the data acquired by different sensors are time aligned, so that the consistency of the data at the same time point is ensured, the problem of data mismatch is avoided, a consistent data set is conveniently created, the subsequent analysis is easier to carry out, and meanwhile, the data integrity is ensured;
carrying out normalization processing on the data to ensure that the data of different sensors have similar importance, arranging and organizing a plurality of characteristics such as the input temperature, the output temperature, the pressure, the flow rate and the like of a cooling medium into a cooling efficiency characteristic matrix according to time points, and providing a standard input format for a deep learning model; by arranging the features in order according to time, the model can better understand the time evolution relation of the data, so that the cooling efficiency can be predicted or estimated more accurately;
in summary, the step S2 provides a clear, consistent and easy-to-process data format for subsequent modeling and analysis, improving the accuracy of understanding and predicting the working state of the cooling system.
S3, performing feature recognition on the cooling efficiency feature matrix by using a pre-constructed cooling efficiency evaluation model to obtain cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit;
The construction method of the cooling efficiency evaluation model comprises the following steps:
s31, acquiring working state information of a cooling unit, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate;
s32, preprocessing the acquired working state information of the cooling unit, including data cleaning, denoising, interpolation and abnormal value detection;
s33, carrying out feature extraction and diversity on the working state information, wherein the feature extraction and diversity comprises a training set and a testing set, and converting the original information into input which can be understood by a model;
s34, using a machine learning algorithm to construct a model framework, wherein the model framework comprises an input layer, a hidden layer and an output layer, and a cooling efficiency evaluation model is built;
s35, training the model by using the prepared training set;
s36, evaluating the model by using the prepared test set to ensure the generalization performance of the model on unseen data;
s37, deploying the trained and evaluated model into a system, and carrying out feature recognition on the cooling efficiency feature matrix to obtain cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit.
In the step, by constructing a cooling efficiency evaluation model, cooling efficiency evaluation parameters can be extracted from the working state information, and the parameters can objectively and quantitatively describe the performance of the cooling unit, not just rely on simple feedback such as temperature change and the like; the model can automatically evaluate the efficiency of the cooling unit without manual intervention, and the performance of the cooling unit can be monitored at any time and continuously, so that problems or anomalies can be found in advance;
Unlike traditional temperature feedback-based methods, the model provides more real-time feedback, and the model can detect the reduction of cooling efficiency in a short time, so that the delay of occurrence of problems is reduced, and the dangerous condition of equipment overheating is avoided;
in summary, the present step provides an automated, real-time cooling unit efficiency assessment and control method, which can discover problems in time, improve system performance, and reduce potential risks.
S4, acquiring energy consumption of a cooler of the ship cooling unit, wherein a time window for acquiring the energy consumption of the cooler is the same as a time window of the cooling efficiency characteristic matrix;
the method for acquiring the energy consumption of the cooler of the ship cooling unit comprises the following steps:
A. the method is applicable to an electric cooler, and the energy consumption condition of the cooler is deduced by monitoring the use of electric energy;
B. measuring the flow rate of the cooling medium using a flow meter, and measuring the temperatures of the inlet and the outlet using a temperature sensor, and calculating the heat load of the cooling medium in the cooler from these data, thereby estimating the energy consumption of the cooler;
C. The ship cooling system is provided with a data recording function, can record flow and temperature data of a cooling medium, and can calculate the energy consumption of the cooler through the data;
D. monitoring the energy consumption of the system by various sensors;
the obtained energy consumption data of the cooler is subjected to data cleaning and processing to remove abnormal values and fill missing data, so that the accuracy and the reliability of the data are ensured;
the first group of data acquisition time nodes and the last group of data acquisition time nodes are reflected in the cooling efficiency characteristic matrix, a time window of the cooling efficiency characteristic matrix is obtained, the time window of energy consumption of the cooler is kept consistent with the time window of the cooling efficiency characteristic matrix, the energy consumption data of the cooler in the same time window are integrated with the cooling efficiency characteristic matrix, and time stamps of the two data sets are aligned.
In the step, through acquiring the energy consumption data of the cooler, the comprehensive evaluation on the performance of the cooling system can be obtained, the time window of the energy consumption data of the cooler is ensured to be matched with the time window of the cooling efficiency characteristic matrix, the time stamps of the two data sets are aligned, and when the two data sets are analyzed, the data are ensured to be consistent at the same time point, so that the correlation analysis and the calculation of the control index can be more accurately carried out;
By acquiring the energy consumption data, the performance degradation of the cooling system can be found earlier, so that maintenance measures can be taken in advance, the risk of equipment failure is reduced, the maintenance cost is reduced, and the reliability of the system is improved;
in summary, the step S4 can comprehensively evaluate the performance of the cooling unit, discover problems in advance, improve the reliability of the system, and support more effective control strategy formulation.
S5, setting a first evaluation weight corresponding to the cooling efficiency evaluation parameter and a second evaluation weight corresponding to the energy consumption of the cooler according to specification information of the ship cooling unit;
the setting influence factors of the first evaluation weight and the second evaluation weight include:
A. the influence of different types of cooling units on weight setting is large, and the unit types comprise evaporation type, air cooling and water cooling;
B. the specification of the cooler is an influencing factor of weight setting, and the specification information of the cooler comprises the size, the material, the design and the performance of the cooler;
C. the type of the cooling medium used by the cooling unit can also influence the cooling effect, the heat capacity and the heat transfer characteristic of different media are different, and the weight is required to be set according to the characteristics of the media;
D. the specific application and requirements of using the cooling units on the ships and vessels will vary, and the cooling requirement of the ships is an important consideration for weight setting;
E. Maintenance costs and energy costs are also important considerations for weight setting, and if cost is a critical factor in a particular application, more weight needs to be assigned to the energy consumption parameters in order to better control and reduce costs;
in the step, the control method of the ship cooling unit can be customized individually according to specific situations and requirements by setting weights in the cooling efficiency and the cooler energy consumption evaluation parameters, so that the method can be flexibly adapted to different types of units, coolers with different specifications, different media and specific ship requirements;
by distributing weights, different emphasis on cooling efficiency and energy consumption is emphasized, and the performance of the cooling unit can be optimized to meet the actual requirements of ships; through weight setting, the maintenance cost and the energy consumption cost can be better controlled and reduced; through weight setting, the method has the capability of adapting to future demands, and when future regulations or ship operation standards change, the control method can be readjusted through resetting the weights so as to meet new demands;
in summary, the present step comprehensively considers a plurality of factors through weight setting, thereby improving the performance of the cooling unit, reducing the cost, and maintaining flexibility to adapt to the changing demands. The efficiency and the sustainability of the ship cooling system are facilitated to be optimized.
S6, carrying out weighted calculation on the cooling efficiency evaluation parameter and the energy consumption of the cooler according to the first evaluation weight and the second evaluation weight to obtain a cooling unit control index;
in step S6, a control index of the cooling unit is calculated, which index weights the cooling efficiency evaluation parameter and the cooler energy consumption according to the first evaluation weight and the second evaluation weight. The control index is used for judging the performance state of the current cooling unit; the following is a method of calculating a control index, comprising:
s61, acquiring a first evaluation weight and a second evaluation weight, wherein the weights are preset according to specification information and control requirements of a ship cooling unit, and reflect the relative importance of a cooling efficiency evaluation parameter and cooler energy consumption in overall performance evaluation;
s62, acquiring cooling efficiency evaluation parameters, wherein the parameters are obtained from a cooling efficiency characteristic matrix through a characteristic identification model and are used for reflecting the cooling efficiency of a cooling unit, and the parameters comprise temperature difference, flow speed and pressure parameters of a cooling medium;
s63, acquiring energy consumption of a cooler, wherein the energy consumption of the cooler is energy consumed by a cooling unit in a certain time window, the energy consumption is expressed in energy consumption units, and the energy consumption is in the forms of electric power, fuel and the like and is different according to different unit and equipment types;
S64, comprehensively considering two factors of cooling efficiency and energy consumption to obtain a single control index, and calculating a calculation formula for weighting and calculating the cooling efficiency evaluation parameter and the energy consumption of the cooler as follows:
K＝ω 1 ×Q+ω 2 ×F(E)；
wherein K represents a cooling unit control index; ω1 represents the influence weight of the cooling efficiency evaluation parameter on the cooling unit control index; ω2 represents the impact weight of chiller energy consumption on chiller plant control index; q represents a cooling efficiency evaluation parameter; e represents the energy consumption of the cooler; f (E) represents a normalized function of chiller energy consumption.
In the step, through comprehensively considering two key factors of cooling efficiency and energy consumption, comprehensive evaluation of the ship cooling unit performance is provided, and the comprehensive index can more comprehensively reflect the working state of the cooling unit; the importance of different factors can be weighed according to actual demands in the evaluation process by introducing the first evaluation weight and the second evaluation weight, and the weights can be adjusted according to the demands under different application scenes, so that the control index better accords with the actual demands;
the method can calculate the control index in real time according to a preset time window, so that the method can rapidly respond to the change of the performance of the cooling unit, and meanwhile, the weight can be adjusted according to the requirement, so that the system has certain flexibility and meets the performance evaluation requirements under different conditions; by continuously monitoring and calculating the control index, the system can timely find out the sign of the performance decline of the cooling unit, and the predictive maintenance method is helpful for finding out potential problems in advance, reducing equipment damage and downtime, and improving the reliability and stability of the system;
In conclusion, the step S6 can help maintenance and management personnel of ship equipment to find problems in time and take measures, so that safe and stable operation of the equipment is ensured.
S7, comparing the control index of the cooling unit with a preset threshold value: if the control index of the cooling unit exceeds a preset threshold, the current cooling unit is poor in cooling effect and needs to be correspondingly adjusted; if the control index does not exceed the preset threshold, the current cooling unit is good in cooling effect and does not need to be adjusted;
the comparison and decision method of the control index of the cooling unit comprises the following steps:
s71, defining a threshold value of a control index, wherein the threshold value is used for judging whether the performance of the cooling unit reaches an expected level;
s72, comparing the calculated control index of the cooling unit with a preset threshold value, wherein the comparison method comprises the following steps:
S72A, if the control index of the cooling unit is greater than or equal to a preset threshold value, the performance of the cooling unit is reduced, and the cooling effect is poor or the critical point of the poor state is approached;
S72B, if the control index of the cooling unit is lower than a preset threshold value, the performance state of the current cooling unit is in an acceptable range, the cooling effect is good, and adjustment is not needed;
S73, decision making: according to the comparison result, the system needs to take corresponding decision measures, including:
S73A, when adjustment and maintenance are needed, if the control index of the cooling unit exceeds a preset threshold value, the system triggers an alarm to inform relevant operators or an automation system to execute proper control measures;
S73B, when adjustment is not needed, if the control index of the cooling unit is lower than a preset threshold value, the system maintains the current running state and no further operation is needed;
s74, periodically executing the step to ensure that the performance of the cooling unit is continuously monitored and controlled;
more specifically, the setting influence factors of the threshold include:
a. the threshold value is determined according to the system requirement and the safety of the equipment, different applications have different tolerance, and if the equipment is irreversibly damaged by overheat, the threshold value is more strict;
b. past operational data and performance benchmarks provide useful information, based on past data, enabling a reasonable range of control indices to be determined;
c. the advice of professional engineers and domain specialists may help set thresholds;
d. through simulation and experimentation, the relationship between different control index values and device performance can be determined, thereby providing a basis for setting the threshold.
In the step, the performance state of the cooling unit can be monitored in real time by comparing the control index of the cooling unit with a preset threshold value, and the performance reduction or potential problems can be rapidly found, so that the risk of equipment damage is reduced, and the reliability of the system is improved; by monitoring the energy consumption and the control index of the cooler, the system can be adjusted when needed, so that the energy consumption is reduced, the energy efficiency is improved, and the energy waste is reduced;
when the performance is reduced, the system can trigger an alarm to inform an operator or an automation system to execute corresponding maintenance measures, so that the accuracy and the efficiency of maintenance are improved, the maintenance work is ensured to be carried out at the best time, and the unnecessary maintenance cost is reduced; the system can be adjusted according to specific requirements of equipment and applications, and does not depend on a fixed time table, so that the maintenance and adjustment are more intelligent, the adaptability is higher, and the unnecessary downtime is reduced;
the threshold is set based on various factors including safety, historical data, expert advice and simulation results, so that the decision is more scientific and reliable;
in summary, the step S7 can ensure stable operation of the ship cooling unit under various working conditions, and reduce unnecessary damage and maintenance costs.
Example two
As shown in fig. 5, the deep learning-based ship cooling unit control system of the present invention specifically comprises the following modules;
the data acquisition module is used for continuously acquiring the working state information of the ship cooling unit according to a preset acquisition time interval, wherein the working state information comprises the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate of the cooling unit and is transmitted;
the data processing module is used for receiving the working state information, arranging the acquired working state information in time sequence, aligning the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate acquired by different time nodes one by one, acquiring a cooling efficiency characteristic matrix and transmitting the cooling efficiency characteristic matrix;
the feature recognition module is used for receiving the cooling efficiency feature matrix, carrying out feature recognition on the cooling efficiency feature matrix by utilizing a pre-constructed cooling efficiency evaluation model, obtaining cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit, and sending the cooling efficiency evaluation parameters;
the energy consumption acquisition module is used for acquiring the energy consumption of the cooler of the ship cooling unit, enabling the time window of the energy consumption of the cooler to be the same as the time window of the cooling efficiency characteristic matrix, and transmitting the energy consumption;
The weight setting module is used for setting a first evaluation weight and a second evaluation weight, setting the first evaluation weight corresponding to the cooling efficiency evaluation parameter and the second evaluation weight corresponding to the energy consumption of the cooler according to the specification information of the ship cooling unit, and sending the first evaluation weight and the second evaluation weight;
the index calculation module is used for receiving the cooling efficiency evaluation parameter, the energy consumption of the cooler, the first evaluation weight and the second evaluation weight, carrying out weighted calculation on the cooling efficiency evaluation parameter and the energy consumption of the cooler according to the first evaluation weight and the second evaluation weight to obtain a control index of the cooling unit, and sending the control index;
the decision module is used for receiving the control index of the cooling unit, comparing the control index of the cooling unit with a preset threshold value and judging the cooling effect of the current cooling unit; when the control index of the cooling unit exceeds a preset threshold, the cooling effect of the current cooling unit is determined to be poor, and corresponding adjustment operation is triggered; when the control index of the cooling unit does not exceed a preset threshold, the cooling effect of the current cooling unit is considered to be good, and adjustment is not needed.
The system can continuously collect and analyze the working state information of the cooling unit, including input temperature, output temperature, pressure and flow rate, and monitor the cooling effect in real time instead of relying on temperature feedback; through the deep learning model and feature recognition, the system can timely detect the sign of the reduction of the cooling effect without waiting for the overheating of the equipment or obvious problems to take action;
When the control index of the cooling unit exceeds a preset threshold, the system can automatically trigger the adjustment operation, so that the requirement of manual intervention is reduced, and the possibility of errors is reduced; the system allows setting a first evaluation weight and a second evaluation weight, and according to the specification information of the ship cooling unit, the system can perform personalized setting according to different equipment and requirements, so that the applicability is improved; the system makes decisions based on deep learning and data driven methods, enabling the system to make more accurate decisions based on a large amount of historical data and model training, rather than relying solely on rules or thresholds; by timely maintenance and adjustment, the system helps to reduce the risk of equipment overheating and damage, thereby reducing maintenance costs and downtime; the system is a continuous monitoring and control system and is executed regularly, which is helpful for continuously optimizing the performance of the cooling unit and adapting to the continuously changing working conditions and demands;
in conclusion, the system is beneficial to improving the efficiency and reliability of the ship cooling unit, reducing the overheat condition of equipment and reducing the maintenance cost.
The various modifications and embodiments of the deep learning-based ship cooling unit control method in the first embodiment are equally applicable to the deep learning-based ship cooling unit control system of the present embodiment, and those skilled in the art will be aware of the implementation method of the deep learning-based ship cooling unit control system of the present embodiment through the foregoing detailed description of the deep learning-based ship cooling unit control method, so that the details of the implementation method of the deep learning-based ship cooling unit control system of the present embodiment will not be described in detail herein for brevity.
In addition, the application further provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the method embodiment for controlling output data are realized, and the same technical effects can be achieved, so that repetition is avoided and redundant description is omitted.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that it will be apparent to those skilled in the art that modifications and variations can be made without departing from the technical principles of the present invention, and these modifications and variations should also be regarded as the scope of the invention.
Claims (10)
1. A deep learning-based ship cooling unit control method, the method comprising:
continuously acquiring working state information of a ship cooling unit according to a preset acquisition time interval, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate of the cooling unit;
arranging the collected working state information in time sequence, and aligning the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate collected by different time nodes one by one to obtain a cooling efficiency characteristic matrix;
Performing feature recognition on the cooling efficiency feature matrix by using a pre-constructed cooling efficiency evaluation model to obtain cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit;
acquiring the energy consumption of a cooler of a ship cooling unit, wherein the time window for acquiring the energy consumption of the cooler is the same as the time window of the cooling efficiency characteristic matrix;
according to the specification information of the ship cooling unit, setting a first evaluation weight corresponding to the cooling efficiency evaluation parameter and a second evaluation weight corresponding to the energy consumption of the cooler;
according to the first evaluation weight and the second evaluation weight, weighting calculation is carried out on the cooling efficiency evaluation parameter and the energy consumption of the cooler, and a cooling unit control index is obtained;
comparing the control index of the cooling unit with a preset threshold value: if the control index of the cooling unit exceeds a preset threshold, the current cooling unit is poor in cooling effect and needs to be correspondingly adjusted; if the control index does not exceed the preset threshold, the current cooling unit is good in cooling effect and does not need to be adjusted.
2. The deep learning-based ship cooling unit control method of claim 1, wherein the digital format of the cooling efficiency characteristic matrix is:
Wherein T is inN Representing the cooling medium input temperature detected by the nth time node; t (T) outN Representing the cooling medium output temperature detected by the Nth time node; p (P) 1 Representing the cooling medium pressure detected by the nth time node; f (F) 1 Representing the cooling medium flow rate detected by the nth time node.
3. The deep learning-based ship cooling unit control method according to claim 1, wherein a calculation formula for weighting calculation of the cooling efficiency evaluation parameter and the cooler energy consumption according to the first evaluation weight and the second evaluation weight is as follows:
K＝ω 1 ×Q+ω 2 ×F(E)；
wherein K represents a cooling unit control index; ω1 represents the influence weight of the cooling efficiency evaluation parameter on the cooling unit control index; ω2 represents the impact weight of chiller energy consumption on chiller plant control index; q represents a cooling efficiency evaluation parameter; e represents the energy consumption of the cooler; f (E) represents a normalized function of chiller energy consumption.
4. The deep learning-based ship cooling unit control method according to claim 1, wherein the operation state information acquisition for the cooling unit comprises the steps of:
a temperature sensor, a pressure sensor and a flow rate sensor are arranged in the ship cooling unit;
Setting a time interval for collecting data according to the system demand and the performance evaluation demand;
continuously collecting working state information of the cooling unit according to a preset time interval, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate;
performing real-time quality control on the acquired data to remove abnormal values and noise data;
and recording the collected working state information according to time sequence and storing the working state information in a database.
5. The deep learning-based ship cooling unit control method according to claim 1, wherein the construction method of the cooling efficiency evaluation model comprises:
acquiring working state information of a cooling unit, wherein the working state information comprises cooling medium input temperature, cooling medium output temperature, cooling medium pressure and cooling medium flow rate;
preprocessing the acquired working state information of the cooling unit, including data cleaning, denoising, interpolation and abnormal value detection;
the method comprises the steps of extracting and diversity of characteristics of working state information, wherein the working state information comprises a training set and a testing set;
using a machine learning algorithm to construct a model framework, wherein the model framework comprises an input layer, a hidden layer and an output layer, and a cooling efficiency evaluation model is constructed;
Training the model by using the prepared training set;
evaluating the model using the prepared test set;
and deploying the trained and evaluated model into a system, and carrying out feature recognition on the cooling efficiency feature matrix to obtain cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit.
6. The deep learning-based ship cooling unit control method of claim 1, wherein the comparison and decision method of the cooling unit control index comprises:
defining a threshold value of a control index;
comparing the calculated control index of the cooling unit with a preset threshold value, and if the control index of the cooling unit exceeds the preset threshold value, indicating that the cooling effect of the current cooling unit is poor, and correspondingly adjusting the cooling unit; if the control index does not exceed the preset threshold, the current cooling unit is good in cooling effect and does not need to be adjusted;
according to the comparison result, when the system needs to take corresponding decision-making measures and needs to be regulated and maintained, the control index of the cooling unit exceeds a preset threshold value, and the system can trigger an alarm to inform relevant operators or an automation system to execute proper control measures; when the adjustment is not needed, the control index of the cooling unit is lower than a preset threshold value, the system maintains the current running state, and no further operation is needed.
7. The deep learning-based ship cooling unit control method of claim 6, wherein the setting influence factors of the control index threshold value include:
the threshold is determined according to the system requirements and the safety of the equipment, and different applications have different tolerances;
historical operating data and performance benchmarks can provide useful information, based on which a reasonable range of control indices can be determined;
the threshold can be set with help of the advice of professional engineers and domain specialists;
through simulation and experimentation, the relationship between different control index values and device performance can be determined, thereby providing a basis for setting the threshold.
8. A deep learning-based marine cooling unit control system, the system comprising:
the data acquisition module is used for continuously acquiring the working state information of the ship cooling unit according to a preset acquisition time interval, wherein the working state information comprises the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate of the cooling unit and is transmitted;
the data processing module is used for receiving the working state information, arranging the acquired working state information in time sequence, aligning the cooling medium input temperature, the cooling medium output temperature, the cooling medium pressure and the cooling medium flow rate acquired by different time nodes one by one, acquiring a cooling efficiency characteristic matrix and transmitting the cooling efficiency characteristic matrix;
The feature recognition module is used for receiving the cooling efficiency feature matrix, carrying out feature recognition on the cooling efficiency feature matrix by utilizing a pre-constructed cooling efficiency evaluation model, obtaining cooling efficiency evaluation parameters capable of representing the cooling efficiency of the ship cooling unit, and sending the cooling efficiency evaluation parameters;
the energy consumption acquisition module is used for acquiring the energy consumption of the cooler of the ship cooling unit, enabling the time window of the energy consumption of the cooler to be the same as the time window of the cooling efficiency characteristic matrix, and transmitting the energy consumption;
the weight setting module is used for setting a first evaluation weight and a second evaluation weight, setting the first evaluation weight corresponding to the cooling efficiency evaluation parameter and the second evaluation weight corresponding to the energy consumption of the cooler according to the specification information of the ship cooling unit, and sending the first evaluation weight and the second evaluation weight;
the index calculation module is used for receiving the cooling efficiency evaluation parameter, the energy consumption of the cooler, the first evaluation weight and the second evaluation weight, carrying out weighted calculation on the cooling efficiency evaluation parameter and the energy consumption of the cooler according to the first evaluation weight and the second evaluation weight to obtain a control index of the cooling unit, and sending the control index;
the decision module is used for receiving the control index of the cooling unit, comparing the control index of the cooling unit with a preset threshold value and judging the cooling effect of the current cooling unit; when the control index of the cooling unit exceeds a preset threshold, the cooling effect of the current cooling unit is determined to be poor, and corresponding adjustment operation is triggered; when the control index of the cooling unit does not exceed a preset threshold, the cooling effect of the current cooling unit is considered to be good, and adjustment is not needed.
9. Deep learning based ship cooling unit control electronics comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor realizes the steps in the method according to any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.
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