An intelligent control method for ship waste heat utilization

Abstract

The present invention relates to the field of ship control technology, and specifically to an intelligent control method for ship waste heat utilization. The present invention collects and normalizes three types of parameters: waste heat outlet heat flow, cabin air temperature and pressure changes, and coolant temperature difference. By constructing an information entropy index to evaluate the contribution of various heat flow parameters to heat removal capacity, the waste heat scheduling has higher resolution and adaptability. Priority is sorted based on the thermal pressure difference data and weight values ​​of each heat conduction channel. The fuzzy comprehensive evaluation algorithm is used to dynamically identify efficient heat transfer paths. When the heat conduction channel ranking is inconsistent, a re-arrangement instruction is implemented to adjust the heat conduction channel task allocation on demand. Combined with the dual data of thermal pressure release amplitude and heat absorption capacity change, the channel without heat absorption regulation capacity is excluded and the channel priority is corrected, so that the heat flow automatically avoids invalid paths and improves heat transfer efficiency.

Description

Technical Field

[0001] The present invention relates to the field of ship control technology, and in particular to an intelligent control method for utilizing waste heat from ships. Background Art

[0002] The field of ship control technology involves the precise management and automatic adjustment of the overall operating status of the ship, mainly through the perception, analysis and execution mechanisms to achieve stable control and energy efficiency coordination of the ship under changeable sea conditions and complex mission conditions.

[0003] Among them, the traditional intelligent control method for ship waste heat utilization refers to a control method that monitors and allocates the waste heat released in high-temperature working conditions such as main engine exhaust and cooling medium during the operation of the ship, and then introduces it into the heat energy recovery device for utilization.

[0004] Traditional technology only allocates waste heat by monitoring single heat source parameters such as the main engine exhaust and cooling medium under high-temperature conditions. It lacks the ability to dynamically perceive changes within the heat flow path and make multi-factor fusion judgments. In actual operation, it fails to fully consider the differences in heat transfer states of each heat conduction channel, and is prone to problems such as inconsistency between channel sorting and actual thermal efficiency, resulting in high-efficiency paths not being prioritized. Heat flow may be retained in inefficient or blocked paths, and a closed-loop adjustment mechanism with real-time feedback data has not been established. It is difficult to match waste heat adjustment targets under load changes or environmental disturbances. For example, if the heat absorption efficiency of a channel decreases due to thermal pressure imbalance, the system cannot quickly identify and adjust it, resulting in reduced waste heat utilization efficiency and significant waste of heat energy. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent control method for waste heat utilization of ships.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an intelligent control method for ship waste heat utilization, comprising the following steps:

[0007] S1: Collect three parameters within a specified time period: the heat flux mass flow rate at the heat exhaust outlet of the ship's compartment, the temperature and pressure changes of the air medium inside the compartment, and the coolant temperature difference at the inlet and outlet of the heat exchanger. Then perform normalization processing and calculate the relative contribution of each parameter to the heat exhaust capacity of the compartment's heat flow path to obtain the heat capacity weight data.

[0008] S2: Collect the thermal pressure difference change data of the starting and ending points of each heat transfer channel of the ship within a specified time, combine the thermal capacity weight data to sort the heat transfer channel queue, compare the sorting result with the currently enabled heat transfer channel, and output the heat transfer channel reordering instruction if the order is inconsistent;

[0009] S3: Based on the set heat transfer channel rearrangement instruction, the thermal pressure difference change data of the start and end points inside the heat transfer channel is compared with the coolant heat absorption change data of the heat exchanger at the end of the heat transfer channel, and the priority of the heat transfer channel is adjusted to obtain a corrected heat transfer channel queue;

[0010] S4: Based on the corrected heat transfer channel queue, setting the valve group opening parameters for each type of waste heat transferred to the heat transfer channel, and performing task allocation to obtain a waste heat utilization control result.

[0011] The present invention has improvements in that the thermal capacity weight data includes heat flow mass weight, air temperature and pressure change weight, and coolant temperature difference weight; the heat conduction channel rearrangement instruction includes a heat conduction channel sorting change identifier, a comparison result between the current and expected heat conduction channel sequences, and a heat conduction channel switching trigger instruction; the corrected heat conduction channel queue includes a heat conduction channel priority update order, a priority tail mark heat conduction channel, and an adjusted heat conduction channel index; the waste heat utilization control result includes a leading heat conduction channel valve group opening setting, an auxiliary heat conduction channel valve group opening setting, and a valve group task instruction.

[0012] The present invention is improved in that the specific steps of S1 include:

[0013] S101: Collect the heat flux mass flow rate at the heat exhaust outlet of the ship compartment within a specified time, the temperature change and pressure change of the air medium inside the compartment, and the coolant temperature difference at the inlet and outlet of the heat exchanger to obtain a heat flux characteristic parameter set;

[0014] S102: Calculating the mean and standard deviation of each type of parameter sequence based on the heat flow characteristic parameter set, performing Z-score normalization processing, converting each type of parameter into dimensionless data, and obtaining a normalized processing result;

[0015] S103: Construct information entropy indicators for the heat flux mass flow sequence, temperature and pressure change combination sequence, and coolant temperature difference sequence after the normalization processing results, respectively, calculate the contribution of each type of indicator to the heat removal capacity of the cabin heat conduction channel through the information ratio, and obtain thermal capacity weight data.

[0016] The present invention is improved in that the specific steps of S2 include:

[0017] S201: Collecting pressure data at the starting point and end point of each heat transfer channel of the ship within a specified time, extracting the thermal pressure difference change trend of each heat transfer channel within the monitoring period in time series, and generating heat transfer channel thermal pressure change data;

[0018] S202: Assigning the thermal pressure change data of the heat transfer channels and the thermal capacity weight data to the same heat transfer channel number dimension, performing priority scoring on each heat transfer channel using a fuzzy comprehensive evaluation algorithm, and generating a heat transfer channel ranking result;

[0019] S203: calling the thermal channel priority sorting result, comparing the number position with the currently enabled thermal channel control sequence, identifying whether there is a sorting inconsistency, and outputting a thermal channel rearrangement instruction if so.

[0020] The present invention is improved in that the specific steps of S3 include:

[0021] S301: Based on the set heat conduction channel rearrangement instruction, extract the thermal pressure difference change data of the start and end points of each heat conduction channel, calculate the thermal pressure release amplitude in the continuous cycle, and obtain the thermal pressure release amplitude data of the heat conduction channel;

[0022] S302: Obtaining thermal pressure release amplitude data of the heat transfer channel and coolant heat absorption change data of the heat exchanger at the end of the heat transfer channel, comparing the two types of data with a thermal pressure release amplitude threshold and a heat absorption capacity change threshold, respectively, to identify heat transfer channels that do not have heat absorption regulation capabilities, and obtaining heat transfer channel screening results;

[0023] S303: The thermal conduction channel screening result and the thermal conduction channel priority sorting result are called to adjust the sorting position of the thermal pressure retention channel identified as the thermal pressure retention channel, update the last position of the thermal pressure retention channel in the sorting list, and obtain a corrected thermal conduction channel queue.

[0024] The present invention is improved in that the specific steps of S4 include:

[0025] S401: Based on the corrected heat transfer channel queue, matching the electric valve group number in sequence according to the heat transfer channel number, classifying each heat transfer channel, and generating a heat transfer channel task allocation result;

[0026] S402: Based on the heat transfer channel task allocation result, setting the opening parameter of the valve group corresponding to the designated priority heat transfer channel as the main channel opening setting value, and uniformly setting the remaining heat transfer channels as the non-main channel opening setting values, thereby generating a valve group opening configuration result;

[0027] S403: Based on the valve group opening configuration result, a waste heat utilization task target is assigned to each heat conduction channel, and the heat flow is guided to a path with heat absorption capacity to obtain a waste heat utilization control result.

[0028] The present invention is improved in that it further includes S5: based on the waste heat utilization control result, comparing the thermal pressure response and the heat absorption change, judging whether the current control result meets the waste heat regulation target and performing optimization switching to obtain a waste heat control optimization result;

[0029] The waste heat control optimization result includes the adjustment switching execution state, the current control effectiveness judgment conclusion, and the heat conduction channel control configuration after switching.

[0030] The present invention is improved in that the specific steps of S5 include:

[0031] S501: Based on the waste heat utilization control result, the thermal pressure response data of the air medium in each compartment and the change data of the coolant heat absorption of the heat exchanger at the end of the heat conduction channel in the current cycle are collected and uniformly sorted by channel number to obtain a thermal pressure and heat absorption response data set;

[0032] S502: Calling the thermal pressure and heat absorption response data set, determining the matching relationship between the thermal pressure fluctuation trend and the heat absorption change trend of each heat conduction channel, identifying the heat conduction channel where the fluctuation and heat absorption deviate, and obtaining a heat conduction channel deviation identification result;

[0033] S503: Based on the heat transfer channel deviation identification result, a task channel switching operation is performed, and the suboptimal channel in the corrected heat transfer channel queue is preferentially enabled to guide the residual heat, thereby obtaining a residual heat control optimization result.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are:

[0035] In the present invention, by collecting and normalizing three types of parameters, namely the heat flow at the waste heat outlet, the temperature and pressure changes of the air in the cabin, and the temperature difference of the coolant, the three types of parameters are collected and normalized, and the contribution of various heat flow parameters to the heat removal capacity is evaluated by constructing an information entropy index, so that the waste heat scheduling has higher resolution and adaptability, and priority is sorted based on the thermal pressure difference data and weight values ​​of each heat conduction channel. The fuzzy comprehensive evaluation algorithm is used to dynamically identify efficient heat transfer paths, and when the ranking of the heat conduction channels is inconsistent, a re-arrangement instruction is implemented to achieve on-demand adjustment of the heat conduction channel task allocation, and by combining the dual data of the thermal pressure release amplitude and the heat absorption capacity change, the channels that do not have the heat absorption regulation capability are excluded and the channel priority is corrected, so that the heat flow automatically avoids invalid paths, thereby improving the heat transfer efficiency. The electric valve group opening parameter setting matches the waste heat guidance target, and the path that deviates from the relationship between thermal pressure and heat absorption is identified based on real-time feedback and the channel switching is triggered, so that the waste heat utilization maintains high responsiveness and precise control in dynamic operation, thereby improving the energy recovery efficiency and control effect as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of the method of the present invention;

[0037] Figure 2 This is a detailed flow chart of step S1 of the present invention;

[0038] Figure 3 This is a detailed flow chart of step S2 of the present invention;

[0039] Figure 4 This is a detailed flow chart of step S3 of the present invention;

[0040] Figure 5This is a detailed flow chart of step S4 of the present invention;

[0041] Figure 6 This is a detailed flow chart of step S5 of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0043] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0044] See also Figure 1 The present invention provides a technical solution: an intelligent control method for utilizing waste heat from ships, comprising the following steps:

[0045] S1: Collect three parameters within a specified time period: the heat flux mass flow rate at the heat exhaust outlet of the ship's compartment, the temperature and pressure changes of the air medium inside the compartment, and the coolant temperature difference at the inlet and outlet of the heat exchanger. Then perform normalization processing and calculate the relative contribution of each parameter to the heat exhaust capacity of the compartment's heat flow path to obtain the heat capacity weight data.

[0046] S2: Collect the thermal pressure difference change data of the starting and ending points of each heat transfer channel of the ship within the specified time, combine it with the thermal capacity weight data to sort the heat transfer channel queue, compare the sorting result with the currently enabled heat transfer channel, and output the heat transfer channel reordering instruction if the order is inconsistent;

[0047] S3: Based on the set heat transfer channel rearrangement instruction, the thermal pressure difference change data at the start and end points of the heat transfer channel is compared with the coolant heat absorption change data of the heat exchanger at the end of the heat transfer channel, and the priority of the heat transfer channel is adjusted to obtain a corrected heat transfer channel queue;

[0048] S4: Based on the corrected heat transfer channel queue, the valve group opening parameters for each type of waste heat transferred to the heat transfer channel are set, and tasks are allocated to obtain the waste heat utilization control result;

[0049] The thermal capacity weight data includes the heat flow mass weight, the air temperature and pressure change weight, and the coolant temperature difference weight. The heat conduction channel rearrangement instruction includes the heat conduction channel sorting change identifier, the current and expected heat conduction channel sequence comparison results, and the heat conduction channel switching trigger instruction. The corrected heat conduction channel queue includes the heat conduction channel priority update order, the priority tail mark heat conduction channel, and the adjusted heat conduction channel index. The waste heat utilization control result includes the main heat conduction channel valve group opening setting, the auxiliary heat conduction channel valve group opening setting, and the valve group task instruction.

[0050] See also Figure 2 , the specific steps of S1 include:

[0051] S101: Collect the heat flux mass flow rate at the heat exhaust outlet of the ship compartment within a specified time, the temperature change and pressure change of the air medium inside the compartment, and the coolant temperature difference at the inlet and outlet of the heat exchanger to obtain a heat flux characteristic parameter set;

[0052] During the acquisition period, a heat flux sensor was deployed on the wall of the heat exhaust outlet pipe and the heat passing through per unit time was recorded. The acquisition frequency was set to 1 Hz. A mass flow meter was used to measure the heat flux density and the heat flux mass flow rate was obtained in combination with the outlet cross-sectional area. Thermocouples and pressure transmitters were installed in designated areas in the cabin. The changes were recorded in real time by comparing the initial cabin temperature and pressure values. For example, during the recording period, the temperature rose from 37.2°C to 39.8°C and the pressure dropped from 101.2kPa to 99.6kPa. Dual thermometers were installed at the inlet and outlet of the heat exchanger to detect the coolant inlet and outlet temperature difference. The coolant inlet was 32.5°C and the outlet was 35.4°C. The above parameters were used to form a heat flux characteristic parameter set.

[0053] S102: Calculate the mean and standard deviation of each type of parameter sequence based on the heat flow characteristic parameter set, perform Z-score normalization processing, convert each type of parameter into dimensionless data, and obtain a normalized processing result;

[0054] According to the heat flow characteristic parameter set, the mean and standard deviation of the heat flow mass flow sequence, the temperature and pressure change combination sequence and the coolant temperature difference sequence are calculated respectively. The heat flow mass flow sample sequence is [95.1, 97.3, 96.5, 94.8, 98.2] kg / h, with a mean of 96.38 and a standard deviation of 1.37. Z-score standardization is performed, using Z=(X-μ) / σ, where X is the original value, μ is the mean, and σ is the standard deviation. Each value is normalized to a dimensionless series, such as the first value Z=(95.1-96.3 8) / 1.37≈-0.93. For example, the temperature and pressure combination sequence has a temperature change of [2.1, 2.3, 2.2, 2.0, 2.5]°C and a pressure change of [-1.6, -1.5, -1.7, -1.4, -1.8] kPa. The difference sequence is uniformly calculated and then merged and normalized. The coolant temperature difference sample is [2.9, 3.0, 2.8, 3.1, 2.7]°C. Similarly, the mean of 2.9 and the standard deviation of 0.15 are calculated and then normalized to obtain the Z value sequence. The normalized results are summarized to form the final normalized result.

[0055] S103: Constructing information entropy indicators for the normalized heat flux mass flow rate sequence, temperature and pressure change combination sequence, and coolant temperature difference sequence, respectively. Calculating the contribution of each type of indicator to the heat removal capacity of the cabin heat transfer channel based on information proportion, and obtaining thermal capacity weight data.

[0056] For the heat flux mass flow sequence, temperature and pressure change combination sequence and coolant temperature difference sequence after normalization processing, the weighted information entropy method is used to evaluate their contribution to the heat removal capacity of the cabin heat conduction channel. The time period weighting and data acquisition confidence factor are introduced in the processing process to reflect the effectiveness and reliability of various parameters in different time windows. The time period weighting is manually set according to the importance of each time period in the entire heat removal process. For example, different weights are assigned to the initial, middle and final stages, such as 0.9, 1.0, and 0.8, representing that the middle stage is the most important. The data acquisition confidence factor is obtained based on a comprehensive evaluation of sensor error, data integrity, anti-interference ability, etc. Parameters with small errors and stable data have high credibility, and are usually selected between 0.85 and 1.00. First, the three types of normalized sequences A, B, and C are discretized. Suppose each normalized sequence is obtained after discretization. The value intervals are as follows: ,in Indicates the parameter category, Indicates the frequency band number, frequency Indicates the Class parameters are in The frequency of each interval is divided into three normalized value intervals using a three-segment distribution strategy. Construct three bins and further introduce the collection importance weight corresponding to each time period This parameter is weighted based on the sensor position and measurement stability. For example, the initial, middle and final stages of cabin heat removal are assigned , introducing the data collection confidence factor Its value is set based on a comprehensive evaluation of indicators such as sensor error rate, data integrity and interference immunity. For example, the error of the high-precision flow meter used for thermal mass flow is less than ±1.5%, and the data continuity is excellent, so it is set , the temperature and pressure change sequence has jumps due to some values, set , the coolant temperature difference sequence sensor has excellent stability, set , the information entropy of each parameter is calculated using the following weighted correction formula:

[0057] ;

[0058] in: : No. Weighted information entropy of class parameters; : No. The collection confidence factor of the class parameter reflects the overall credibility of the data of this class; : No. Class parameters are in The time importance weight in each time segment; : No. Class parameters are in The probability (frequency) of occurrence in a discrete interval; : The logarithm of probability with base 2, used to measure the information increment; : The number of discrete segments after normalization. 3–5 bins are recommended.

[0059] The data in heat flow mass flow sequence A is , divided into three sections, the frequency is , the time weight is , after substituting into the formula: .

[0060] The data of temperature and pressure change combination sequence B is , frequency distribution , time weight , confidence ,have to: .

[0061] The coolant temperature difference sequence C is , the frequency distribution is the same as A, the confidence level ,but: .

[0062] The total weighted information entropy of the three types of indicators is calculated as: .

[0063] Then the thermal capacity weight contribution of each parameter is obtained :

[0064] , , .

[0065] The final results show that the contributions of heat flux mass flow rate and coolant temperature difference to the heat removal capacity of the cabin heat conduction channel are basically the same, both about 33.84%, while the contribution of the temperature and pressure change combination is slightly lower, at 32.36%. This result can provide a parameter weight basis for subsequent multi-channel coupling control modeling.

[0066] See also Figure 3 , the specific steps of S2 include:

[0067] S201: Collecting pressure data at the starting point and end point of each heat transfer channel of the ship within a specified time, extracting the thermal pressure difference change trend of each heat transfer channel within the monitoring period in time series, and generating heat transfer channel thermal pressure change data;

[0068] To collect pressure data at the starting and ending points of each heat transfer channel on a ship within a specified timeframe, the monitoring period must first be clearly defined, for example, by setting a daily or weekly timeframe. The pressure sensor system is then configured to ensure real-time or periodic pressure information is acquired at both the inlet and outlet of the heat transfer channel. Each channel on the ship is numbered and identified, and the starting and ending pressure values ​​of each numbered channel at each time point are continuously recorded during the monitoring period. After obtaining the raw pressure data, data alignment and cleaning are performed to eliminate missing or abnormal records. The pressure difference at each time point is then calculated as the thermal pressure difference result. This series of differences is arranged chronologically to form thermal pressure difference trend data. This trend data can then be archived and organized by number, and possible regular changes or abnormal patterns can be extracted through time series analysis. Ultimately, the thermal pressure change data for each heat transfer channel during the monitoring period is generated. In actual ship operation scenarios, data recording can be deployed during navigation, docking, and acceleration phases, ensuring that the acquisition process covers the thermal pressure response characteristics under different operating conditions.

[0069] S202: Corresponding the thermal pressure change data and thermal capacity weight data of the heat transfer channels to the same heat transfer channel number dimension, performing priority scoring on each heat transfer channel using a fuzzy comprehensive evaluation algorithm, and generating a heat transfer channel ranking result;

[0070] After mapping the thermal pressure change data and thermal capacity weight data of the heat transfer channels to a unified channel number dimension, a nonlinear fuzzy scoring function must be constructed to quantitatively prioritize the heat transfer channels. Since the data source only includes two core input types: thermal pressure change data of the heat transfer channels and their structural thermal capacity weights, a two-factor Gaussian membership function is used as the basis for constructing the evaluation model. Considering the impact of the actual deviation of these two variables on the stability of the system control, the scoring function structure is as follows:

[0071] ;

[0072] in: : Channel number is Priority score; : The average value of the thermal pressure difference change of the channel during the monitoring period, which represents the dynamic characteristics of the thermal pressure of the channel; : The thermal pressure difference benchmark mean of the historically stable channel, which is determined by the mean of the channel samples with stable thermal pressure in recent cycles; : The tolerance value of thermal pressure difference can be set as the standard deviation of the mean thermal pressure difference of similar stable channels; : The thermal capacity weight of the channel, estimated based on the channel's heat conduction structure properties; : The base value of the thermal capacity weight, which can be set as the average thermal capacity of the ship's thermal channel structure; : Tolerance value of heat capacity weight, set as the standard deviation of heat capacity weight samples; : Evaluation weight of thermal pressure difference fluctuation term; : Evaluation weight of thermal capacity weight item.

[0073] Thermal pressure difference weight : This weight indicates the importance of evaluating the dynamic characteristics of thermal pressure. The setting principle is as follows: the larger the mean value of the thermal pressure difference change of the channel within the cycle, the more drastic the thermal state change of the channel, and more attention should be paid during the control process; if the dynamic characteristics of the channel such as the fluctuation slope and peak-to-valley mutation frequency are significantly higher, then should be increased; the thermal pressure fluctuation mean values ​​of all channels can be sorted and the channels with higher rankings can be given larger ; When dynamic mutation indicator segmentation is not used, it can be directly set and Proportional, for example, after normalization, set .

[0074] Thermal capacity weight : Indicates the load importance of the channel in the system heat conduction structure. Its setting basis is as follows: If the channel cross-sectional area is large, the thermal conductivity is high, and the effective depth is deep (or the thickness is small), its overall heat flux is large, which affects the thermal path of the global temperature control system. It should be set large; the heat capacity index can be estimated by the formula, such as ,in is the heat conduction area, is the thermal conductivity of the material, The channel depth can be increased artificially if the channel material is special or the location is critical (such as near the host or energy sink). ; can be normalized to .

[0075] Let channel number be , the average thermal pressure difference fluctuation of this channel during the monitoring period is , the stable reference value is , and its standard tolerance is ;The thermal capacity weight value is , the average capacity of the thermal structure is , whose tolerance is .

[0076] The evaluation weights are set in a normalized manner as follows:

[0077] ;

[0078] .

[0079] Calculate the Gaussian membership values ​​for each of the two terms separately:

[0080] The first item (thermal pressure difference membership):

[0081] ;

[0082] The second item (thermal capacity membership):

[0083] .

[0084] The final score is calculated as follows:

[0085] .

[0086] That is, the heat conduction channel The final priority score is .

[0087] The priority score is higher or lower, depending on the score value The relative position of all channel scores. The score value itself is in Within the interval, a value close to 1 indicates that the heat conduction channel is close to the stable reference model expected by the system in both the dynamic change of thermal pressure and the thermal capacity weight. A value close to 0 indicates that the channel deviates significantly in both aspects. In order to determine whether the priority score of a channel is "too large", a statistical distribution should be constructed based on the score values ​​of all channels, and the threshold should be divided using the sorting method or the quantile method. For example, all channel score values ​​can be sorted from high to low and divided into five sections. The scores in the top 20% can be defined as "too large scores", the scores in the bottom 20% are "too small scores", and the middle section is "medium scores". This method ensures that the judgment criteria are based on the actual characteristics of the sample distribution rather than a fixed threshold. If a fixed numerical threshold needs to be set, the mean plus standard deviation can be used to establish a too large boundary. For example, set the mean of all scores to , the standard deviation is , you can set It is the threshold boundary of the high score. Channels with priority scores exceeding this value can be identified as having significant thermal impact, active thermal pressure response and high thermal structure weight in this cycle, and should be included in the control rearrangement or priority scheduling list. This threshold setting method takes into account statistical rationality and can be flexibly adjusted according to changes in the number of channels in the system. It is not affected by the extreme value of a single channel and effectively reflects the actual proportion and degree of deviation of the score value in the overall score.

[0088] S203: calling the thermal channel priority sorting result, comparing the number position with the currently enabled thermal channel control sequence, identifying whether there is a sorting inconsistency, and outputting a thermal channel reordering instruction if so;

[0089] The thermal channel priority ranking result is called and compared with the number position of the currently enabled thermal channel control sequence. First, the thermal channel priority score and ranking list need to be obtained and uniquely mapped by number. This ranking result is saved as the target priority sequence. Then, the thermal channel number list currently enabled in the ship control system is called. This list is usually executed according to the control logic sorting, and its structure is a set of channel numbers called in sequence. After the two sequences are loaded synchronously, the index correspondence method is used to compare the number positions of the target priority ranking and the current control call sequence one by one to check whether there is any position misalignment or serious sorting inversion in the two lists. If a thermal channel is found to be at the front of the priority sorting but at the back of the control sequence, or if a channel that was originally ranked at the back is enabled first in the control, it is considered to be inconsistent in sorting, and the re-sorting logic is triggered. The system will identify all channel numbers with sorting misalignment, generate a new control call list in the order of the priority sequence, and output it as a thermal channel control re-sorting instruction. Finally, the instruction is transmitted to the control subsystem, waiting for subsequent execution instruction call.

[0090] See also Figure 4 , the specific steps of S3 include:

[0091] S301: Based on the set heat conduction channel rearrangement instruction, extract the thermal pressure difference change data of the start and end points of each heat conduction channel, calculate the thermal pressure release amplitude in the continuous cycle, and obtain the thermal pressure release amplitude data of the heat conduction channel;

[0092] Based on the set thermal channel rearrangement instruction, the channel number rearrangement sequence contained in the instruction must be parsed first. The sequence usually rearranges the thermal channel numbers in a list according to priority. Then, the start and end pressure value sequences of each thermal channel in the sequence in the current and historical monitoring cycles are extracted one by one, and the data of the continuous acquisition cycle in each channel are sliced. The starting pressure and end pressure of each channel are obtained in chronological order, and the thermal pressure difference values ​​are counted at the hour or minute level to form a complete cycle thermal pressure change array. For example, in a 48-hour cycle, sampling once an hour will form 48 groups of pressure values. Difference data, and then calculate the pressure difference change of two adjacent sampling points in turn to obtain the thermal pressure release amplitude of each period, that is, the pressure difference released by the channel between the two time points, and then perform cumulative summation or mean analysis on the release amplitudes of all periods to obtain the overall thermal pressure release amplitude data of the channel in the period. This value is used to reflect whether the channel has a thermal pressure relief trend after structural adjustment. If the release amplitude of a channel continues to converge in consecutive periods, it means that the channel has the ability to continuously release thermal pressure. On the contrary, if the release amplitude oscillates or there is no obvious release behavior, it may be marked as a thermal pressure retention state later.

[0093] S302: Obtaining thermal pressure release amplitude data of the heat transfer channel and coolant heat absorption change data of the heat exchanger at the end of the heat transfer channel, comparing the two types of data with the thermal pressure release amplitude threshold and the heat absorption capacity change threshold, respectively, to identify heat transfer channels that do not have heat absorption regulation capabilities, and obtain heat transfer channel screening results;

[0094] To obtain the thermal pressure release amplitude data of the heat conduction channel and the coolant heat absorption change data of the heat exchanger at the end of the heat conduction channel, it is necessary to first archive and organize the thermal pressure release amplitude of each heat conduction channel in a specified period and the coolant heat absorption data of the heat exchanger connected to it in the same period, and bind the two types of data according to the channel number to form a dual-indicator data group. Before processing the data group, it is necessary to preset the thermal pressure release amplitude threshold and the heat absorption capacity change threshold. The thermal pressure release threshold can be set to the mean of the release amplitudes of all channels in the current period plus or minus 0.5 times the standard deviation as the lower limit boundary for identifying "lower release amplitude". The heat absorption capacity change threshold can be set based on the system design heat absorption efficiency change range. For example, when the coolant heat absorption change amplitude is less than 5%, it can be regarded as an invalid heat absorption change. Then, the thermal pressure release data of each channel is compared with the set release threshold to determine whether its release capacity is lower than the benchmark. At the same time, it is checked whether the heat absorption change of its terminal heat exchanger in the same period is significantly less than the threshold. If both conditions are met, it is determined that the channel has thermal pressure retention and does not have effective heat absorption regulation ability, and the channel number is marked as having no regulation ability. For example, the release amplitude of channel A is 12.5, and the average release of all channels is 18.3 with a standard deviation of 3.2. The release threshold is set to 16.7, and the channel is in a low release state. If the heat absorption of the corresponding exchanger only changes by 2%, which is less than the set 5% threshold, the channel will be identified as an object without heat absorption regulation ability. Finally, the status judgment of all channels is completed and a screening result list is generated.

[0095] S303: Calling the thermal conduction channel screening results and the thermal conduction channel priority sorting results, adjusting the sorting position of the channel identified as the thermal pressure retention channel, updating the last position of the thermal pressure retention channel in the sorting list, and obtaining a corrected thermal conduction channel queue;

[0096] The thermal channel screening results and thermal channel priority sorting results are called. First, the list of thermal channel numbers identified as thermal pressure retention is loaded into memory and matched with the thermal channel sorting queue previously calculated by the fuzzy evaluation algorithm. The channels in the screening list are compared to see if they are still in the front and middle areas of the sorting list. If a channel in the front row is marked as retained, the channel sorting is determined to be mismatched and the sorting position needs to be corrected. The correction process includes removing the original index position of the retained channel from the sorting queue and inserting it to the end of the queue, while keeping the original sorting structure of other unfiltered channels unchanged. Each channel number that needs to be corrected executes the same update logic. For example, if the sorting queue is [Channel A, Channel B, Channel C, Channel D], where Channel B is a retained channel, the updated order is [Channel A, Channel C, Channel D, Channel B]. The update process must ensure that the integrity of the queue structure is not destroyed, and a new sorting queue is generated as the final control basis to form a corrected thermal channel queue.

[0097] See also Figure 5 , the specific steps of S4 include:

[0098] S401: Based on the corrected heat transfer channel queue, the electric valve group number is matched in sequence according to the heat transfer channel number, each heat transfer channel is classified, and a heat transfer channel task allocation result is generated;

[0099] Based on the corrected heat conduction channel queue, the electric valve group number is matched in sequence according to the heat conduction channel number. First, the latest channel priority sorting table is loaded, and each channel is matched to the electric valve group number one by one according to its number sequence. The matching relationship is usually preset during system design. For example, channel number A001 corresponds to electric valve number V001, channel number A002 corresponds to V002, and so on. The system compares the sorted list with the valve group list item by item in sequence. After finding that the channel number and the valve group number correspond one to one, the channels are classified to form a one-to-one mapping data. The system collects data and constructs a task allocation table with the channel number as the primary key. This table records the current priority of each channel, the corresponding valve group number, and the type of control task to be assigned. For example, the top three channels A001, A004, and A005 in the sorting order are mapped to V001, V004, and V005, respectively. The system identifies these three as the main control task channels, and the remaining channels are uniformly classified as auxiliary control task channels. Finally, a heat transfer channel task allocation result is generated that includes all channels, corresponding valve groups, and control task types for the next step of control parameter configuration.

[0100] S402: Based on the heat transfer channel task allocation result, the valve group opening parameter corresponding to the heat transfer channel with the specified priority is set as the main channel opening setting value, and the remaining heat transfer channels are uniformly set as the non-main channel opening setting values, thereby generating a valve group opening configuration result;

[0101] Based on the task allocation results of the heat transfer channel, the system sets the opening parameters of all channels. Among them, the corresponding valve group of the part marked as the main channel in the channel task allocation result needs to be set to the main channel opening value, and the remaining non-main channels are uniformly set to non-main channel opening values. The control system performs the writing operation according to the set parameter template. Usually, the main channel opening is set to a larger value, such as 80%, to ensure the smooth flow of the main heat flow path, and the non-main channel is set to a lower value, such as 30%, to assist in control. Before writing parameters, each channel completes number matching and status confirmation to avoid repeated configuration or omission. After the setting is completed, the system packages all valve group numbers, corresponding channel numbers and opening values ​​into the configuration list. For example, channel A001 corresponds to V001 set to 80%, and channel A002 corresponds to V002 set to 30%. Complete configuration records are generated in sequence, and finally the valve group opening configuration results of all heat transfer channels are completed.

[0102] S403: Based on the valve group opening configuration result, a waste heat utilization task target is assigned to each heat transfer channel, and the heat flow is directed to a path with heat absorption capacity to obtain a waste heat utilization control result;

[0103] Based on the valve group opening configuration results, the system assigns waste heat utilization tasks to each heat transfer channel. The specific process involves directing the heat flow output path of the main channel to a heat exchange structure or coolant flow path with heat absorption capacity, and connecting the auxiliary channels to auxiliary recovery devices or low-efficiency heat absorption units. Before assigning tasks, the system determines the heat absorption capacity of each path based on the previous heat absorption capacity identification results and establishes a number mapping table for paths with heat absorption capacity. It then matches the opening setpoint with the heat absorption path capacity level to ensure that the heat flow is maximized along the main channel to the path with high heat absorption capacity. For example, if the output of main channel A001-V001 is connected to coolant circulation unit C1, which has a high heat absorption capacity, the task is configured to prioritize waste heat guidance. Auxiliary channels A003-V003 are directed to the low-efficiency waste heat recovery area B3 and are assigned a low-priority waste heat conduction task. Based on the mapping of all paths and channels, the system completes the control task output file, ultimately forming the waste heat utilization control result.

[0104] See also Figure 6 , further comprising S5: based on the waste heat utilization control result, comparing the thermal pressure response and the heat absorption change, judging whether the current control result meets the waste heat regulation target and performing optimization switching to obtain the waste heat control optimization result;

[0105] The waste heat control optimization results include the adjustment switching execution status, the current control effectiveness judgment conclusion, and the heat transfer channel control configuration after switching;

[0106] The specific steps of S5 include:

[0107] S501: Based on the waste heat utilization control results, the thermal pressure response data of the air medium in each compartment and the change data of the coolant heat absorption of the heat exchanger at the end of the heat conduction channel in the current cycle are collected and uniformly sorted by channel number to obtain the thermal pressure and heat absorption response data set;

[0108] Based on the waste heat utilization control results, the system enters the data collection process. First, during the current monitoring cycle, the environmental monitoring modules installed in each section of the cabin are called to extract the air medium thermal pressure response data of each compartment. At the same time, the coolant heat absorption change data of the heat exchanger at the end of the heat transfer channel is read. These two types of data must be uniformly aligned by timestamp and bound and organized according to the heat transfer channel number to form a complete record group containing the channel number, thermal pressure response sequence, and coolant heat absorption change sequence. In specific operations, the thermal pressure response data comes from the air temperature, humidity, and local pressure change sensors in each compartment. The system obtains their numerical sequence through real-time sampling or periodic capture, and then maps and classifies them according to the channel heat flow output compartment. The heat absorption data is provided by the heat flux meter and liquid temperature difference sensor at the exchanger end. The system uniformly performs correlation matching according to the channel number. For example, channel A001 corresponds to compartment Z1 and heat exchanger X1. The system automatically generates a mapping relationship between the three and integrates their thermal pressure response and heat absorption change trends to ultimately form a thermal pressure and heat absorption response dataset.

[0109] S502: Calling the thermal pressure and heat absorption response data set, determining the matching relationship between the thermal pressure fluctuation trend and the heat absorption change trend of each heat conduction channel, identifying the heat conduction channel where the fluctuation and heat absorption deviate, and obtaining the heat conduction channel deviation identification result;

[0110] The thermal pressure and heat absorption response data sets are called to determine the matching relationship between the thermal pressure fluctuation trend and the heat absorption change trend of each heat conduction channel. The system first aligns the thermal pressure data under each channel number with the corresponding heat absorption change data according to the time axis to form two sequences. Then, the system performs fluctuation frequency extraction, fluctuation amplitude analysis and peak marking operations on the thermal pressure curve, and performs monotonic trend analysis and range statistics on the heat absorption change curve. After completing the feature analysis of the two types of sequences, the system calculates the correlation coefficient between the two sequences, such as whether the increase in thermal pressure fluctuation is accompanied by an increase in heat absorption. If it is found that the thermal pressure fluctuates rapidly but the heat absorption increases, the correlation coefficient between the two sequences is calculated. If the change is slow or steady, it is judged that the two are poorly matched, that is, the heat flow release is not absorbed and there is a heat retention problem. To avoid misjudgment, the system sets a deviation judgment threshold range. For example, if the thermal pressure fluctuation rate is greater than the set value and the heat absorption change amplitude is less than 5%, it is considered a deviation state. If this logical condition is met, the channel is recorded as a deviation channel. For example, if the average thermal pressure fluctuation amplitude of channel A003 is 1.8 units within 10 hours and the heat absorption change amplitude is only 2.5%, the system records A003 as a deviation object. Finally, after traversing all channels and completing the judgment, the thermal conduction channel deviation identification result is output.

[0111] S503: Based on the heat transfer channel deviation identification result, a task channel switching operation is performed, and the suboptimal channel in the corrected heat transfer channel queue is preferentially enabled to guide the residual heat, thereby obtaining a residual heat control optimization result;

[0112] Based on the results of the heat conduction channel deviation identification, the system executes the task channel switching operation. First, it calls the corrected heat conduction channel priority sorting queue, checks the currently enabled deviation channel numbers, and marks them as channels to be switched out in turn. At the same time, it searches for the unenabled suboptimal channels in the sorting list in order of priority as substitute heat flow paths. The system automatically completes the number replacement, task allocation relationship transfer and valve group opening synchronous update. For example, if the deviation channel is A003 and its sorting position is 4th, the system selects channel A005 with the sorting position of 5th for replacement operation. The system removes the task role of A003 in the current control instruction, resets the opening parameter of A005 to the main channel setting value, and reloads the waste heat guide path connected to it into the control instruction to ensure that the channel path, heat exchanger port and coolant interface are matched after the task switching. Finally, the replacement of all identified deviation channels and the update of control instructions are completed, and the new waste heat control optimization result is output and the next cycle execution begins.

[0113] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent control method for utilizing waste heat from ships, characterized in that: The following steps are involved: S1: Collect three parameters within a specified time period: the heat flux mass flow rate at the heat exhaust outlet of the ship's compartment, the temperature and pressure changes of the air medium inside the compartment, and the coolant temperature difference at the inlet and outlet of the heat exchanger. Then perform normalization processing and calculate the relative contribution of each parameter to the heat exhaust capacity of the compartment's heat flow path to obtain the heat capacity weight data. S2: Collect the thermal pressure difference change data of the starting and ending points of each heat transfer channel of the ship within a specified time, combine the thermal capacity weight data to sort the heat transfer channel queue, compare the sorting result with the currently enabled heat transfer channel, and output the heat transfer channel reordering instruction if the order is inconsistent; The specific steps of S2 include: S201: Collecting pressure data at the starting point and end point of each heat transfer channel of the ship within a specified time, extracting the thermal pressure difference change trend of each heat transfer channel within the monitoring period in time series, and generating heat transfer channel thermal pressure change data; S202: Assigning the thermal pressure change data of the heat transfer channels and the thermal capacity weight data to the same heat transfer channel number dimension, performing priority scoring on each heat transfer channel using a fuzzy comprehensive evaluation algorithm, and generating a heat transfer channel ranking result; S203: calling the thermal channel priority sorting result, comparing the number position with the currently enabled thermal channel control sequence, identifying whether there is a sorting inconsistency, and outputting a thermal channel re-arrangement instruction if so; The fuzzy comprehensive evaluation algorithm is used to perform priority scoring on each heat conduction channel, and the formula is used: ； The calculation channel number is Priority score ; in, is the average value of the thermal pressure difference change of the channel during the monitoring period, is the benchmark mean of the thermal pressure difference of the historical stable channel, is the tolerance value of thermal pressure difference, is the thermal capacity weight value of the channel, is the base value of thermal capacity weight, is the tolerance value of the thermal capacity weight, is the evaluation weight of the thermal pressure difference fluctuation term, is the evaluation weight of the thermal capacity weight item; S3: Based on the set heat transfer channel rearrangement instruction, the thermal pressure difference change data of the starting and ending points inside the heat transfer channel is compared with the coolant heat absorption change data of the heat exchanger at the end of the heat transfer channel, and the priority of the heat transfer channel is adjusted to obtain a corrected heat transfer channel queue; The specific steps of S3 include: S301: Based on the set heat conduction channel rearrangement instruction, extract the thermal pressure difference change data of the start and end points of each heat conduction channel, calculate the thermal pressure release amplitude in the continuous cycle, and obtain the thermal pressure release amplitude data of the heat conduction channel; S302: Obtaining thermal pressure release amplitude data of the heat transfer channel and coolant heat absorption change data of the heat exchanger at the end of the heat transfer channel, comparing the two types of data with a thermal pressure release amplitude threshold and a heat absorption capacity change threshold, respectively, to identify heat transfer channels that do not have heat absorption regulation capabilities, and obtaining heat transfer channel screening results; S303: Calling the thermal conduction channel screening result and the thermal conduction channel priority sorting result, adjusting the sorting position of the channel identified as the thermal pressure retention channel, updating the last position of the thermal pressure retention channel in the sorting list, and obtaining a corrected thermal conduction channel queue; S4: Based on the corrected heat transfer channel queue, setting the valve group opening parameters for each type of waste heat transferred to the heat transfer channel, and performing task allocation to obtain a waste heat utilization control result; S5: Based on the waste heat utilization control result, compare the thermal pressure response and the heat absorption change, determine whether the current control result meets the waste heat regulation target, and perform optimization switching to obtain the waste heat control optimization result; The thermal capacity weight data includes heat flow mass weight, air temperature and pressure change weight, and coolant temperature difference weight. The heat conduction channel rearrangement instruction includes a heat conduction channel sorting change identifier, a comparison result between the current and expected heat conduction channel sequences, and a heat conduction channel switching trigger instruction. The corrected heat conduction channel queue includes a heat conduction channel priority update order, a priority tail-marked heat conduction channel, and an adjusted heat conduction channel index. The waste heat utilization control result includes a leading heat conduction channel valve group opening setting, an auxiliary heat conduction channel valve group opening setting, and a valve group task instruction. The waste heat control optimization result includes an adjustment switching execution status, a current control effectiveness judgment conclusion, and a heat conduction channel control configuration after switching.

2. The intelligent control method for ship waste heat utilization according to claim 1, characterized in that: The specific steps of S1 include: S101: Collect the heat flux mass flow rate at the heat exhaust outlet of the ship compartment within a specified time, the temperature change and pressure change of the air medium inside the compartment, and the coolant temperature difference at the inlet and outlet of the heat exchanger to obtain a heat flux characteristic parameter set; S102: Calculating the mean and standard deviation of each type of parameter sequence based on the heat flow characteristic parameter set, performing Z-score normalization processing, converting each type of parameter into dimensionless data, and obtaining a normalized processing result; S103: Construct information entropy indicators for the heat flux mass flow sequence, temperature and pressure change combination sequence, and coolant temperature difference sequence after the normalization processing results, respectively, calculate the contribution of each type of indicator to the heat removal capacity of the cabin heat conduction channel through the information ratio, and obtain thermal capacity weight data.

3. The intelligent control method for ship waste heat utilization according to claim 2, characterized in that: To construct the information entropy index, the formula is used: ； Calculate the Weighted information entropy of class parameters , in order to construct an information entropy index that reflects the contribution of the target parameters to the thermal capacity; in, It is The collection confidence factor of the class parameters, It is Class parameters are in The time importance weight in the time segment, It is Class parameters are in The frequency of occurrence in discrete intervals, It is the logarithm of probability with base 2, which is used to measure the information increment. is the number of discrete segments after normalization.

4. The intelligent control method for ship waste heat utilization according to claim 1, characterized in that: The specific steps of S4 include: S401: Based on the corrected heat transfer channel queue, matching the electric valve group number in sequence according to the heat transfer channel number, classifying each heat transfer channel, and generating a heat transfer channel task allocation result; S402: Based on the heat transfer channel task allocation result, setting the opening parameter of the valve group corresponding to the designated priority heat transfer channel as the main channel opening setting value, and uniformly setting the remaining heat transfer channels as the non-main channel opening setting values, thereby generating a valve group opening configuration result; S403: Based on the valve group opening configuration result, a waste heat utilization task target is assigned to each heat conduction channel, and the heat flow is guided to a path with heat absorption capacity to obtain a waste heat utilization control result.

5. The intelligent control method for ship waste heat utilization according to claim 1, characterized in that: The specific steps of S5 include: S501: Based on the waste heat utilization control result, the thermal pressure response data of the air medium in each compartment and the change data of the coolant heat absorption of the heat exchanger at the end of the heat conduction channel in the current cycle are collected and uniformly sorted by channel number to obtain a thermal pressure and heat absorption response data set; S502: Calling the thermal pressure and heat absorption response data set, determining the matching relationship between the thermal pressure fluctuation trend and the heat absorption change trend of each heat conduction channel, identifying the heat conduction channel where the fluctuation and heat absorption deviate, and obtaining a heat conduction channel deviation identification result; S503: Based on the heat transfer channel deviation identification result, a task channel switching operation is performed, and the suboptimal channel in the corrected heat transfer channel queue is preferentially enabled to guide the residual heat, thereby obtaining a residual heat control optimization result.

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