Waste heat recovery system of printing and dyeing dryer

Abstract

The invention discloses a printing and dyeing dryer waste heat recovery system, which relates to the field of waste heat recovery and comprises a dryer main body, one end of the dryer main body is provided with a heat exchanger and a recovery control module, one end of the heat exchanger is provided with a recovery energy storage end, and the heat exchanger is communicated with the recovery energy storage end through a fluid pipeline. The recovery control module is in interactive connection with the heat exchanger and the recovery energy storage end through an electrical medium, the recovery control module comprises a recovery judgment module, a stage planning module, a rehearsal execution module and a correction module, and the stage planning module is used for performing planning contribution degree quantitative evaluation on fluctuation characteristics; and in combination with an abnormal feature prediction model of the rehearsal execution module, intelligent expansion or deletion of the waste heat recovery time period is realized, a traditional fixed time period recovery mode is broken through, the system dynamically adjusts the length of the recovery time period according to real-time working conditions, the waste heat recovery efficiency is improved, and meanwhile, equipment overload or energy waste caused by fluctuation is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of waste heat recovery, and specifically to a waste heat recovery system for a dyeing and drying machine. Background Art

[0002] The dyeing industry is an important part of the textile industry, involving processing raw materials through processes such as dyeing and drying to meet market demands. The drying process is often accompanied by a large amount of heat energy consumption and waste heat emissions. The operation of the drying machine requires a large amount of heat energy, usually relying on energy sources such as natural gas and electricity, resulting in high energy consumption and large emissions. Therefore, gradually improving energy efficiency and reducing the environmental burden have become an urgent need in the industry. Modern acquisition and control systems enable the waste heat recovery process to be more accurately monitored and adjusted in real time, optimizing the entire energy management system and improving economic efficiency;

[0003] In the prior art, a single waste heat recovery strategy is often adopted, which is difficult to flexibly adjust according to different cycles and specific operating conditions. As a result, under different working conditions, the recovery effect is not ideal, and it may even lead to unstable operation of the equipment. There is a lack of comprehensive data acquisition and analysis capabilities, unable to effectively identify the characteristics and factors affecting waste heat recovery, and unable to dynamically adjust in a timely manner according to the actual operating status of the drying machine and external environmental conditions. Therefore, the waste heat utilization rate will decrease, and it is also unable to effectively cope with the characteristic changes of different drying materials;

[0004] However, global data acquisition requires too much computing power, and the prior art is difficult to accurately identify the recovery periods with important contribution degrees, resulting in a time-consuming process for processing the collected recovery data, thereby affecting the timely formulation of strategies. Summary of the Invention

[0005] (1) Technical Problems to be Solved

[0006] In view of the above-mentioned disadvantages existing in the prior art, the present invention provides a waste heat recovery system for a dyeing and drying machine, which can effectively solve the problems of the prior art.

[0007] (2) Technical Solutions

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0009] The present invention discloses a waste heat recovery system for a dyeing and drying machine, including a dryer main body. One end of the dryer main body is provided with a heat exchanger and a recovery control module. One end of the heat exchanger is provided with a recovery energy storage end. The heat exchanger and the recovery energy storage end are connected through a fluid pipeline, and the fluid pipeline is used to transport a heat exchange medium. The recovery control module is connected to the heat exchanger and the recovery energy storage end through an electrical medium for interaction, where:

[0010] The recovery control module, including a recovery judgment module, a stage planning module, a preview execution module and a correction module, is used to obtain the current waste heat recovery strategy, collect the operation data of the dryer main body and the waste heat emission information in real time, identify and analyze the influencing characteristics, perform volatility analysis on the influencing characteristics in the current waste heat recovery cycle through the recovery judgment module, identify the waste heat recovery periods with fluctuations, and perform abnormal judgment on the recovery strategy. If abnormal fluctuations of the influencing characteristics are found, record and mark them. Evaluate the marked fluctuation characteristics through the stage planning module, adjust the waste heat recovery periods according to the preset planning contribution degree, expand or delete the planned recovery periods, obtain the integrated influencing characteristics within the planned recovery periods, construct a simulation prediction model through the preview execution module, input the integrated influencing characteristics, output the prediction of abnormal characteristics in the future period, and perform correction measures on the recovery periods and intensities of the predicted abnormal characteristics through the correction module, and provide alarm prompts;

[0011] The heat exchanger is used to execute the waste heat recovery strategy provided by the recovery control module and convert the working waste heat of the dryer main body;

[0012] The recovery energy storage end is used to receive the waste heat converted by the heat exchanger and store it through the heat exchange medium.

[0013] Furthermore, the recovery control module further includes: an instruction control unit, a collection module, an influence identification module and a marking alarm module. The instruction control unit is electrically connected to the collection module through an electrical medium. The collection module is electrically connected to the influence identification module through an electrical medium. The influence identification module is wirelessly interconnected with the recovery judgment module. The recovery judgment module is wirelessly interconnected with the stage planning module. The stage planning module is wirelessly interconnected with the preview execution module. The correction module is wirelessly interconnected with the preview execution module and the marking alarm module. Among them:

[0014] The instruction control unit, as the core control unit, is responsible for receiving and issuing the operation instructions of the global function module and receiving the waste heat recovery strategies of the dryer main body, the heat exchanger and the recovery energy storage end;

[0015] The collection module is used to collect the operation setting data of the dryer, monitor the waste heat emission data of the dryer in real time, and collect the attributes and moisture content data of the dried materials;

[0016] The influence identification module is used to process the collection data from the collection module, extract important influence characteristics, establish a characteristic database, and perform data marking and classification;

[0017] The marking alarm module is used to automatically trigger an alarm behavior and submit it to the instruction control unit when the data fed back by the preview execution module exceeds the preset standard threshold.

[0018] Further, sub - modules are deployed under the acquisition module, including: a fabric recognition module, a water content recognition module, and a waste heat recognition module. The fabric recognition module, the water content recognition module, and the waste heat recognition module are connected by an electrical medium, where:

[0019] The fabric recognition module is used to identify the type of fabric by the spectral information, reflectivity, and texture physical feature data of the fabric currently being dried by the dryer main body, and integrate the fabric material information recognized in the current cycle;

[0020] The water content recognition module is used to identify the water content state of the fabric currently being dried, compare the obtained real - time measurement value with the preset standard moisture content rate, and judge the drying degree of the current fabric;

[0021] The waste heat recognition module is used to monitor the exhaust gas temperature and flow rate at the outlet of the dryer main body in real - time, and calculate the recoverable waste heat energy value through the monitored temperature and flow rate data.

[0022] Further, the stage planning module calculates the planning contribution degree of the current time period based on the fluctuation intensity of a certain influencing feature. The calculation formula is:

[0023]

[0024] In the formula, R represents the planning contribution degree of the current time period, n represents the total number of influencing features, w i represents the weight coefficient of feature i, Δf i represents the fluctuation intensity of the i - th influencing feature, a represents the time decay factor, and t represents the interval between the time when the fluctuation occurs and the current time.

[0025] Further, the planning contribution degree R of the current time period in the stage planning module is compared with a preset threshold E, and the time period length ΔT is dynamically adjusted, including:

[0026] ΔT>0: It means that the time period needs to be extended, and the extended duration is k·(R - E); ΔT<0: It means that the time period needs to be shortened, and the shortened duration is P·C;

[0027] When the contribution degree of the fluctuation feature to the system is high (R>E), the recovery time period is extended;

[0028] When the contribution degree is too low (R<0.5·E), the time period is shortened;

[0029] Among them, k represents the extension coefficient, which controls the speed of time period extension, P represents the deletion ratio, which limits the maximum amplitude of time period shortening, C represents the length of the initially planned recovery time period, and E represents the contribution degree threshold.

[0030] Furthermore, sub-modules are deployed at a lower level of the correction module, including: a time period planning module and an intensity planning module. The time period planning module and the intensity planning module are interconnected through an electrical medium, where:

[0031] The time period planning module is used to receive the abnormal feature distribution output by the preview execution module and the fluctuation time period marked by the stage planning module. Based on the spatio-temporal distribution of the abnormal features, it identifies the initial time period range that needs to be adjusted. Combining with the preset planning contribution degree, it calculates whether to expand or compress the time period. If the abnormality is concentrated at the end of a certain time period, the recovery time is extended to cover the residual heat. If the abnormality is caused by equipment overload, the time period is shortened, and a time period division plan for the new recovery cycle is generated.

[0032] The intensity planning module is used to receive the abnormal feature distribution, the current operating settings of the dryer, and the attributes of the dried material. According to the peak value of waste heat emission, it dynamically adjusts the flow rate of the heat exchange medium. If the recovery energy storage end is close to the capacity limit, the recovery intensity is reduced. When multiple abnormalities occur simultaneously, the intensity resources are allocated according to the preset rules, and an intensity parameter adjustment instruction is generated.

[0033] Furthermore, the time period planning module and the intensity planning module are interconnected through a wireless network with a configuration module. The configuration module is used to integrate the time period division plan of the time period planning module and the parameter adjustment instruction of the intensity planning module to generate a complete correction strategy. Through the instruction control unit, configuration instructions are sent to the dryer main body, the heat exchanger, and the recovery energy storage end. If the real-time data still exceeds the preview threshold after configuration, the marking alarm module is triggered and rolled back to the safe configuration.

[0034] Furthermore, the preview execution module is interconnected through a wireless network with a data verification module. The data verification module is used to receive the data feedback of the acquisition module adjusted by the correction module at a preset cycle. Based on the abnormal fluctuation feature distribution, it evaluates the completion degree of the adjustment instruction. If the adjustment does not meet the expectation, an adjustment reference for the correction module is generated and submitted to the dryer main body.

[0035] Furthermore, the instruction control unit is interconnected through a wireless network with a shared storage module. The shared storage module is used to store all qualified correction cases to form a knowledge base, where: the basic information of the knowledge base includes: case number, timestamp, corresponding dryer number; the problem features of the knowledge base include: abnormal type, fluctuation time period, impact feature label; the case correction measures include: adjusted time period range, intensity correction value, comparison between the prediction result of the preview model and the actual effect; the case effect evaluation includes: energy consumption saving rate, abnormal solution rate, and verification score.

[0036] Further, the shared storage module is interconnected with an indexing module via a wireless network. The indexing module is used to provide an indexing interface, automatically extract the case keywords and effect ratings stored in the shared storage module, establish a timeline index and an exception type index. When the recovery judgment module detects a new fluctuation, the indexing module retrieves associated cases with an abnormal feature label matching degree reaching a preset threshold from the shared storage module by matching the abnormal feature labels for reference by the correction module.

[0037] (III) Beneficial Effects

[0038] Adopting the technical solution provided by the present invention, compared with the known prior art, it has the following beneficial effects:

[0039] 1. Through the stage planning module, the contribution degree of the fluctuation characteristics is quantitatively evaluated, and combined with the abnormal feature prediction model of the rehearsal execution module, the intelligent expansion or deletion of the waste heat recovery period is realized, breaking through the traditional fixed-period recovery mode, enabling the system to dynamically adjust the length of the recovery period according to the real-time working conditions, improving the waste heat recovery efficiency, and at the same time avoiding equipment overload or energy waste caused by fluctuations.

[0040] 2. By the acquisition module, the operation data of the dryer, the material properties and the environmental parameters are synchronously obtained. The key influence characteristics are extracted by the influence identification module. Through the simulation prediction model constructed by the rehearsal execution module, combined with the historical data and the multi-dimensional parameters collected in real time, the abnormal feature distribution in the future period is output, realizing the deep combination of the waste heat recovery strategy and the production process, adaptively generating the optimal strategy for different drying materials, and actively adjusting the recovery strategy through the correction module before the abnormality occurs, thereby reducing the equipment failure rate and significantly reducing the shutdown maintenance cost.

[0041] 3. Through the data verification module, the completion degree of the adjustment instruction is verified, and the successful cases are stored in the shared storage module. The indexing module provides multi-dimensional retrieval to form a knowledge base for system self-optimization, shortening the strategy debugging time under new working conditions, supporting the reuse of cases across production lines and factories, and reducing the dependence on manual experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0043] Figure 1 It is a schematic diagram of the overall framework of the present invention;

[0044] Figure 2It is a schematic framework diagram of the recovery control module in the present invention;

[0045] Figure 3 It is a schematic framework diagram of the acquisition module in the present invention;

[0046] Figure 4 It is a schematic framework diagram of the correction module in the present invention.

[0047] The labels in the figure respectively represent:

[0048] 1. Dryer main body;

[0049] 2. Recovery control module; 21. Instruction control unit; 22. Acquisition module; 201. Fabric identification module; 202. Moisture content identification module; 203. Waste heat identification module; 23. Influence identification module; 24. Recovery judgment module; 25. Stage planning module; 26. Rehearsal execution module; 27. Correction module; 701. Time period planning module; 702. Intensity planning module; 703. Configuration module; 28. Marking alarm module; 29. Data verification module; 210. Shared storage module; 211. Index module;

[0050] 3. Heat exchanger;

[0051] 4. Recovery energy storage end. Specific implementation manners

[0052] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] The present invention will be further described below with reference to the embodiments.

[0054] ① Embodiment 1

[0055] A waste heat recovery system for a dyeing dryer in this embodiment is as Figure 1 - Figure 2As shown in the figure, it includes a dryer main body 1. One end of the dryer main body 1 is equipped with a heat exchanger 3 and a recovery control module 2. One end of the heat exchanger 3 is equipped with a recovery energy storage end 4. The heat exchanger 3 is connected to the recovery energy storage end 4, and the connection method between the heat exchanger 3 and the recovery energy storage end 4 is a fluid pipeline. The fluid pipeline is used to transport the heat exchange medium. The heat exchange medium can adopt circulating water, and the absorbed waste heat is transported to the recovery energy storage end 4 through the heat exchange medium for storage. The recovery control module 2 is connected to the heat exchanger 3 and the recovery energy storage end 4 through an electrical medium. The electrical medium uses a copper core communication data line to complete the digital instruction exchange between functional modules or components. The recovery control module 2 provides terminal control. The recovery control module 2 is connected to the heat exchanger 3 and the recovery energy storage end 4 through a copper core data line, used to transmit control signals, receive sensor data in real time and send control instructions to optimize the waste heat recovery efficiency. Among them:

[0056] The recovery control module 2 includes a recovery judgment module 24, a stage planning module 25, a preview execution module 26 and a correction module 27, which is used to obtain the current waste heat recovery strategy, collect the operation data of the dryer main body 1 and the waste heat emission information in real time, identify and analyze the influencing characteristics, and perform volatility analysis on the influencing characteristics in the current waste heat recovery cycle through the recovery judgment module 24, identify the waste heat recovery time periods with fluctuations, and perform abnormal judgment on the recovery strategy. If abnormal fluctuations of the influencing characteristics are found, record and mark them. Evaluate the marked fluctuation characteristics through the stage planning module 25, adjust the waste heat recovery time periods according to the preset planning contribution degree, expand or delete the planned recovery time periods, and obtain the integrated influencing characteristics within the planned recovery time periods. Build a simulation prediction model through the preview execution module 26, input the integrated influencing characteristics, output the abnormal characteristic prediction for the future time periods, and perform correction measures on the recovery time periods and intensities of the predicted abnormal characteristics through the correction module 27, and provide an alarm prompt;

[0057] The process of volatility analysis is as follows:

[0058] The influencing characteristics include the exhaust temperature, heat exchange medium flow rate, waste heat emission intensity and environmental temperature and humidity parameters in the operation data of the dryer main body 1;

[0059] Adopt the sliding window variance calculation method, set the time window ΔT = 30 seconds, calculate the standard deviation of each characteristic parameter within the window. When the standard deviation exceeds the preset threshold, it is determined as an event of abnormal fluctuation. The preset threshold is the historical standard working condition data or set by manual customization, and establish a dynamic event marking matrix to record the start time, end time and deviation amplitude of the fluctuation;

[0060] The process of evaluating the fluctuation characteristics is as follows:

[0061] The stage planning module 25 adjusts the strategy according to the contribution degree of the abnormal period. The calculation formula is: contribution degree = proportion of historical recovered energy × weight coefficient. If the contribution degree is lower than 10%, then this period is deleted; if the contribution degree is higher than 50%, then the recovery duration of 5% of its own adjacent period is extended to compensate for the loss.

[0062] As Figure 4 shown, there are sub-modules deployed at the lower level of the correction module 27, including: the period planning module 701 and the intensity planning module 702. The period planning module 701 and the intensity planning module 702 are interconnected through an electrical medium. Among them:

[0063] The period planning module 701 is used to receive the abnormal feature distribution output by the preview execution module 26 and the fluctuating period marked by the stage planning module 25. Based on the spatio-temporal distribution of the abnormal features, it identifies the initial period range to be adjusted, combines the preset planning contribution degree, calculates whether to expand or compress the period. If the abnormality is concentrated at the end of a certain period, the recovery time is extended to cover the residual heat. If the abnormality is caused by equipment overload, then this period is shortened, and a period division plan for the new recovery cycle is generated;

[0064] The intensity planning module 702 is used to receive the abnormal feature distribution, the current operating settings of the dryer, and the properties of the dried material. According to the peak value of the waste heat emission, it dynamically adjusts the flow rate of the heat exchange medium. If the recovery energy storage end 4 is close to the capacity limit, the recovery intensity is reduced. When multiple abnormalities occur simultaneously, the intensity resources are allocated according to the preset rule: equipment safety > energy saving efficiency > period coverage, and an intensity parameter adjustment instruction is generated;

[0065] The period planning module 701 and the intensity planning module 702 are interconnected through a wireless network with a configuration module 703. The configuration module 703 is used to integrate the period division plan of the period planning module 701 and the parameter adjustment instruction of the intensity planning module 702 to generate a complete correction strategy. Through the instruction control unit 21, configuration instructions are sent to the dryer main body 1, the heat exchanger 3, and the recovery energy storage end 4. If the real-time data still exceeds the preview threshold after configuration, the marking alarm module 28 is triggered and rolled back to the safe configuration; when the configuration module 703 integrates the period division plan and the intensity parameters, it is necessary to verify whether the adjusted strategy meets the standard safety threshold constraint. If the verification passes, the instruction is issued; if it exceeds the limit or the verification fails, the safe configuration is directly called. The safe configuration is a fixed parameter set that has passed the stability verification and can cover the safety requirements under extreme working conditions. No adjustment is allowed within this period until manual intervention;

[0066] The preview execution module 26 is wirelessly interactively connected to a data verification module 29. The data verification module 29 is used to receive the data feedback of the acquisition module 22 adjusted by the correction module 27 at a preset period, evaluate the completion degree of the adjustment instruction based on the abnormal fluctuation feature distribution, and if the adjustment does not meet the expectation, generate an adjustment reference for the correction module 27 and submit it to the dryer main body 1.

[0067] The recovery control module 2 further includes: an instruction control unit 21, an acquisition module 22, an impact identification module 23, and a marking and alarm module 28. The instruction control unit 21 is connected to the acquisition module 22 through an electrical medium. The acquisition module 22 is connected to the impact identification module 23 through an electrical medium. The impact identification module 23 is wirelessly interactively connected to the recovery judgment module 24. The recovery judgment module 24 is wirelessly interactively connected to the stage planning module 25. The stage planning module 25 is wirelessly interactively connected to the preview execution module 26. The correction module 27 is wirelessly interactively connected to the preview execution module 26 and the marking and alarm module 28. The wireless interaction includes: pairing and connection of 4 / 5G network communication, Bluetooth, and Wi-Fi technology for remote transmission of the processing data and control instructions generated by each functional module or component. Among them:

[0068] The instruction control unit 21, as the core control unit, is responsible for receiving and issuing operation instructions for global functional modules, and receiving the waste heat recovery strategies of the dryer main body 1, the heat exchanger 3, and the recovery energy storage end 4.

[0069] As Figure 3 shown, the acquisition module 22 is used to acquire the operation setting data of the dryer, monitor the waste heat emission data of the dryer in real time, and collect the attribute and moisture content data of the dried items. Sub-modules are deployed under the acquisition module 22, including: a fabric identification module 201, a moisture content identification module 202, and a waste heat identification module 203. The fabric identification module 201, the moisture content identification module 202, and the waste heat identification module 203 are connected through an electrical medium. Among them:

[0070] The fabric identification module 201 is used to identify the spectral information, reflectivity, and texture physical feature data of the fabric currently being dried by the dryer main body 1, identify the type of the fabric, and integrate the fabric material information identified in the current cycle.

[0071] The moisture content identification module 202 is used to identify the moisture state of the fabric currently being dried, compare the obtained real-time measurement value with the preset standard moisture content, and judge the drying degree of the current fabric.

[0072] The waste heat identification module 203 is used to monitor the exhaust gas temperature and flow rate at the outlet of the dryer main body 1 in real time, and calculate the recoverable waste heat energy value through the monitored temperature and flow rate data.

[0073] The impact recognition module 23 is used to process the acquisition data from the acquisition module 22, extract important impact features, establish a feature database, and perform data marking and classification;

[0074] The marking alarm module 28 is used to automatically trigger an alarm behavior and submit it to the instruction control unit 21 when the data fed back by the rehearsal execution module 26 exceeds the preset standard threshold;

[0075] The heat exchanger 3 is used to execute the waste heat recovery strategy provided by the recovery control module 2 and convert the working waste heat of the dryer main body 1;

[0076] The recovery energy storage end 4 is used to receive the waste heat converted by the heat exchanger 3 and store it through the heat exchange medium.

[0077] Compared with the prior art, through the volatility analysis of the recovery judgment module 24 and the prediction model of the rehearsal execution module 26, the system can identify the abnormal fluctuations during the waste heat recovery period in real time, predict future abnormal features, improve the energy-saving efficiency compared with the traditional fixed strategy, automatically extend or compress the recovery period according to the abnormal spatio-temporal distribution, and solve the problems of waste heat waste or equipment loss caused by the fixed time period in the existing system;

[0078] Through the adaptation of operating parameters, combined with the attributes of the dried material and the real-time waste heat peak value, dynamically adjust the flow rate of the heat exchange medium and the recovery intensity, intelligently allocate resources in case of sudden overload or system conflict, avoid the common chain failures in the prior art, when an abnormal situation is detected, the system can automatically trigger an alarm, ensure the safety and operation stability of the equipment, reduce the accident risk, and in the case of multiple concurrent anomalies, the system can allocate resources preferentially according to the preset rules to ensure that the equipment safety always takes precedence over the energy-saving efficiency and time period coverage, enhancing the reliability of operation.

[0079] ② Embodiment 2

[0080] On other levels, this embodiment also provides another optimization mechanism based on Embodiment 1, specifically a calculation formula for the planning contribution degree of the current time period, which can identify the recovery time period with the optimal data contribution degree. The calculation formula is:

[0081]

[0082] In the formula, R represents the planning contribution degree of the current time period, n represents the total number of impact features, w i represents the weight coefficient of feature i, Δf i represents the fluctuation intensity of the i-th impact feature, a represents the time decay factor, which is used to reduce the influence of historical fluctuations on the current decision, and t represents the time interval between the occurrence time of the fluctuation and the current time;

[0083] The planning contribution degree R of the current time period is compared with the preset threshold E, and the time period length ΔT is dynamically adjusted, including:

[0084] ΔT > 0 indicates that an extended period is required, and the extended duration is k·(R - E); ΔT < 0 indicates that a period needs to be shortened, and the shortened duration is P·C;

[0085] When the contribution degree of the fluctuation feature to the system is high (R > E), the recovery period is extended;

[0086] When the contribution degree is too low (R < 0.5·E), the period is shortened;

[0087] Among them, k represents the extension coefficient, which controls the speed of period extension, P represents the deletion ratio, which limits the maximum amplitude of period shortening, C represents the length of the initially planned recovery period, E represents the contribution degree threshold, which is dynamically set by the system according to the historical optimal recovery efficiency.

[0088] In this embodiment, by integrating multiple fluctuation features such as temperature and moisture content, the limitation of a single index is avoided. Through the weight coefficient w i the importance of features is distinguished, and different working conditions are adapted. By comparing the actual value with the reference value, the current operating state can be reflected in real time. By setting different extension and reduction rules, the waste heat recovery period is intelligently adjusted according to the relationship between the current contribution degree and the preset threshold, enabling the system to quickly respond to changes, effectively optimize the utilization of resources, making this formula more practical and operable in the waste heat recovery system of the dyeing dryer, and contributing to the realization of efficient and stable waste heat recovery and energy efficiency management.

[0089] ③ Embodiment 3

[0090] This embodiment provides an indexing strategy for the historical storage scheme, specifically: The instruction control unit 21 is connected to the shared storage module 210 through wireless network interaction. The shared storage module 210 is used to store all corrected cases judged to be qualified to form a knowledge base. Among them: The basic information of the knowledge base includes: case number, timestamp, corresponding dryer number. The problem features of the knowledge base include: abnormal type, fluctuation period, influence feature label; The case correction measures include: adjusting the period range, intensity correction value, comparison between the prediction result of the preview model and the actual effect. The case effect evaluation includes: energy consumption saving rate, abnormal solution rate, and verification score; The shared storage module 210 is connected to the indexing module 211 through wireless network interaction. The indexing module 211 is used to provide an indexing interface, automatically extract the case keywords and effect ratings stored in the shared storage module 210, establish a time axis index and an abnormal type index. When the recovery judgment module 24 detects a new fluctuation, the indexing module 211 retrieves the associated cases with the abnormal feature label matching degree reaching the preset threshold from the shared storage module 210 for reference by the correction module 27

[0091] In this embodiment, by establishing a dynamic knowledge base to store qualified corrective cases and provide an efficient indexing interface, the efficiency of anomaly detection and response is significantly improved. Compared with the prior art, this strategy can automatically extract case keywords and effect ratings, establish a timeline and anomaly type index, enabling the system to quickly match relevant cases when a new fluctuation is detected and provide references based on historical data. This not only improves the pertinence and effectiveness of corrective measures but also further optimizes the operation efficiency and resource utilization of the dryer main body 1 by evaluating the energy consumption savings rate and anomaly resolution rate.

[0092] In summary, in the present invention, the acquisition module 22 collects fabric material, moisture content, waste gas temperature, and flow rate data in real time. The influence recognition module 23 extracts key influence features and marks and classifies them. The recycling judgment module 24 analyzes the feature volatility, identifies abnormal time periods and marks them. The stage planning module 25 calculates the time period adjustment amount based on the planning contribution formula, dynamically expands or deletes the recycling time periods. The preview execution module 26 constructs a simulation prediction model and outputs the future abnormal feature distribution. The correction module 27 generates a time period division plan and intensity adjustment instructions through the sub-module time period planning module 701, intensity planning module 702, and configuration module 703, and integrates them into a correction strategy. The heat exchanger 3 executes the strategy for waste heat conversion. The module recycling energy storage end 4 stores the converted waste heat. The data verification module 29 verifies the adjustment effect. If it does not meet the standard, it feeds back to the correction module 27 for re-optimization. Successful cases are stored as a knowledge base by the shared storage module 210. The indexing module 211 establishes a timeline and anomaly type index for subsequent matching. When the preview execution module 26 predicts that the anomaly exceeds the threshold, the marking alarm module 28 triggers a hierarchical alarm and links to the backtracking mechanism to form a closed-loop control.

[0093] Through the collaborative work of multi-level modules, the real-time monitoring, dynamic adjustment, and predictive optimization of the waste heat recovery strategy are realized, effectively improving the energy utilization efficiency and operation reliability of the system. It can collect the operation data of the dryer main body 1 in real time, analyze and identify influence features, and implement targeted corrective measures. At the same time, the system can continuously accumulate experience during operation, achieve self-learning, and thus cope with more complex operation requirements in the future. The knowledge base and indexing interface of the system enable historical cases to be traced and reused, improving the accuracy of anomaly response.

[0094] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A waste heat recovery system for a printing and dyeing dryer, characterized in that, It includes a dryer main body, with a heat exchanger and a recovery control module installed at one end of the dryer main body. A recovery energy storage end is installed at one end of the heat exchanger. The heat exchanger and the recovery energy storage end are connected through a fluid pipeline, and the fluid pipeline is used to transmit the heat exchange medium. The recovery control module is interconnected with the heat exchanger and the recovery energy storage end through an electrical medium. Among them: The recovery control module includes a recovery judgment module, a stage planning module, a preview execution module, and a correction module, which is used to obtain the current waste heat recovery strategy, collect the operating data of the dryer main body and the waste heat emission information in real time, identify and analyze the influencing characteristics, and perform volatility analysis on the influencing characteristics in the current waste heat recovery cycle through the recovery judgment module to identify the waste heat recovery periods with fluctuations, and conduct abnormal judgment on the recovery strategy. If abnormal fluctuations in the influencing characteristics are found, record and mark them. Evaluate the marked fluctuation characteristics through the stage planning module, adjust the waste heat recovery period according to the preset planning contribution degree, expand or delete the planned recovery periods, obtain the integrated influencing characteristics within the planned recovery period, construct a simulation prediction model through the preview execution module, input the integrated influencing characteristics, output the abnormal characteristic prediction for the future period, and take corrective measures on the recovery period and intensity of the predicted abnormal characteristics through the correction module, and provide an alarm prompt; The heat exchanger is used to execute the waste heat recovery strategy provided by the recovery control module and convert the working waste heat of the dryer main body; The recovery energy storage end is used to receive the waste heat converted by the heat exchanger and store it through the heat exchange medium.

2. The waste heat recovery system of a printing and dyeing dryer according to claim 1, wherein The recovery control module further includes: an instruction control unit, a collection module, an influence identification module, and a marking and alarm module. The instruction control unit is connected to the collection module through an electrical medium, the collection module is connected to the influence identification module through an electrical medium, the influence identification module is interconnected with the recovery judgment module through a wireless network, the recovery judgment module is interconnected with the stage planning module through a wireless network, the stage planning module is interconnected with the preview execution module through a wireless network, and the correction module is interconnected with the preview execution module and the marking and alarm module through a wireless network. Among them: The instruction control unit, as the core control unit, is responsible for receiving and issuing the operation instructions of the global functional modules and receiving the waste heat recovery strategies of the dryer main body, the heat exchanger, and the recovery energy storage end; The collection module is used to collect the operation setting data of the dryer, monitor the waste heat emission data of the dryer in real time, and collect the attributes and moisture content data of the dried items; The influence identification module is used to process the collection data from the collection module, extract important influencing characteristics, establish a characteristic database, and perform data marking and classification; The marking and alarm module is used to automatically trigger an alarm behavior and submit it to the instruction control unit when the data fed back by the preview execution module exceeds the preset standard threshold.

3. The waste heat recovery system of a printing and dyeing dryer according to claim 2, wherein, Sub-modules are deployed under the collection module, including: a fabric identification module, a moisture content identification module, and a waste heat identification module. The fabric identification module, the moisture content identification module, and the waste heat identification module are connected through an electrical medium. Among them: A fabric recognition module, which is used to recognize the type of fabric based on the spectral information, reflectivity, and texture physical characteristic data of the fabric currently being dried by the dryer main body, and integrate the fabric material information recognized in the current cycle; A water content recognition module, which is used to recognize the water content state of the fabric currently being dried, compare the obtained real-time measurement value with the preset standard moisture content rate, and judge the drying degree of the current fabric; A waste heat recognition module, which is used to monitor the waste gas temperature and flow rate at the outlet of the dryer main body in real time, and calculate the recoverable waste heat energy value based on the monitored temperature and flow rate data.

4. A waste heat recovery system for a printing and dyeing dryer according to claim 1, characterized in that, The stage planning module calculates the planning contribution degree of the current time period based on the fluctuation intensity of a certain influencing feature, and the calculation formula is: where R represents the planned contribution degree in the current period, n represents the total number of influencing features, w i represents the weight coefficient of feature i, Δf i represents the fluctuation intensity of the i-th influencing feature, a represents the time decay factor, and t represents the interval between the time when the fluctuation occurs and the current time.

5. The waste heat recovery system of a printing and dyeing dryer according to claim 4, characterized in that, The planning contribution degree R of the current time period in the stage planning module is compared with the preset threshold E, and the time period length ΔT is dynamically adjusted, including: ΔT>0: It means that the time period needs to be extended, and the extension duration is k·(R - E); ΔT<0: It means that the time period needs to be shortened, and the shortening duration is P·C; When the fluctuation feature has a high contribution degree to the system (R>E), the recovery time period is extended; When the contribution degree is too low (R<0.5·E), the time period is shortened; Among them, k represents the extension coefficient, which controls the speed of time period extension, P represents the deletion ratio, which limits the maximum amplitude of time period shortening, C represents the length of the initially planned recovery time period, and E represents the contribution degree threshold.

6. The waste heat recovery system of a printing and dyeing dryer according to claim 1, characterized in that, The correction module is hierarchically deployed with sub-modules, including: a time period planning module and an intensity planning module. The time period planning module and the intensity planning module are interconnected through an electrical medium, where: The time period planning module is used to receive the abnormal feature distribution output by the preview execution module and the fluctuation time period marked by the stage planning module. Based on the spatio-temporal distribution of the abnormal features, it identifies the initial time period range that needs to be adjusted, combines the preset planning contribution degree, and calculates whether to expand or compress the time period. If the abnormality is concentrated at the end of a certain time period, the recovery time is extended to cover the residual waste heat. If the abnormality is caused by equipment overload, the time period is shortened, and a time period division plan for the new recovery cycle is generated; The intensity planning module is used to receive the abnormal feature distribution, the current operating settings of the dryer, and the attributes of the dried material. According to the peak value of waste heat emission, it dynamically adjusts the flow rate of the heat exchange medium. If the recovery energy storage end is close to the capacity limit, the recovery intensity is reduced. When multiple abnormalities occur simultaneously, the intensity resources are allocated according to the preset rules, and an intensity parameter adjustment instruction is generated.

7. The waste heat recovery system of a printing and dyeing dryer according to claim 6, characterized in that, The time period planning module and the intensity planning module are interconnected through a wireless network with a configuration module. The configuration module is used to integrate the time period division plan of the time period planning module and the parameter adjustment instruction of the intensity planning module to generate a complete correction strategy, and send configuration instructions to the dryer main body, the heat exchanger, and the recovery energy storage end through the instruction control unit. If the real-time data still exceeds the preview threshold after configuration, the marked alarm module is triggered and rolled back to the safe configuration.

8. A waste heat recovery system for a printing and dyeing dryer according to claim 1, characterized in that, The preview execution module is connected to a data verification module through wireless network interaction. The data verification module is used to receive the data feedback of the acquisition module adjusted by the correction module at a preset period, evaluate the completion degree of the adjustment instruction based on the abnormal fluctuation feature distribution, and if the adjustment fails to meet the expectation, generate an adjustment reference for the correction module and submit it to the dryer main body.

9. The waste heat recovery system of a printing and dyeing dryer according to claim 2, wherein, The instruction control unit is connected to a shared storage module through wireless network interaction. The shared storage module is used to store all qualified correction cases to form a knowledge base. Among them: the basic information of the knowledge base includes: case number, timestamp, corresponding dryer number, the problem features of the knowledge base include: abnormal type, fluctuation period, influence feature label; the case correction measures include: adjustment period range, intensity correction value, comparison between the prediction result of the preview model and the actual effect, and the case effect evaluation includes: energy consumption saving rate, abnormal solution rate and verification score.

10. A waste heat recovery system for a printing and dyeing dryer according to claim 9, characterized in that, The shared storage module is connected to an index module through wireless network interaction. The index module is used to provide an index interface, automatically extract the case keywords and effect ratings stored in the shared storage module, establish a timeline index and an abnormal type index. When the recovery judgment module detects a new fluctuation, the index module retrieves the associated cases with the abnormal feature label matching degree reaching the preset threshold from the shared storage module through matching the abnormal feature labels for the reference of the correction module.

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