Paper product temperature and humidity control strategy optimization method and system based on genetic algorithm

Abstract

This invention discloses a method and system for optimizing temperature and humidity control strategies for paper products based on genetic algorithms, comprising the following steps: collecting environmental data of corrugated cardboard and preprocessing it to generate an environmental parameter sequence; collecting the moisture content and warpage of the corrugated cardboard and aggregating it to generate a corrugated cardboard state sequence; generating a set of safety constraints based on the environmental parameter sequence and the corrugated cardboard state sequence; constructing a temperature and humidity control strategy template; performing evolutionary search using a genetic algorithm based on genetic coding to generate a set of candidate temperature and humidity control strategies; performing a negative example trajectory search to prune the candidate temperature and humidity control strategies and generate an optimized set of temperature and humidity control strategies; and generating the optimized temperature and humidity control strategy results based on the optimized set of temperature and humidity control strategies. This invention employs a genetic algorithm and a negative example trajectory-driven mechanism to achieve safe and provable optimization of the temperature and humidity control strategy for corrugated cardboard, possessing the advantages of strong adaptability and high stability.

Description

Technical Field

[0001] This invention relates to the field of temperature and humidity control strategy optimization, and in particular to a method and system for optimizing temperature and humidity control strategies for paper products based on genetic algorithms. Background Technology

[0002] In the current production, curing, and storage processes of corrugated cardboard, temperature and humidity control usually relies on fixed control curves or manual experience parameters. By adjusting temperature, relative humidity, and ventilation conditions in segments or at thresholds, the moisture content and structural stability of the cardboard are maintained. Some solutions introduce simple optimization algorithms or parameter correction methods based on historical data, but these mostly assume a single working condition or ideal incoming material conditions, making it difficult to systematically characterize the comprehensive impact of external environmental disturbances and batch differences in incoming materials on the evolution of the corrugated cardboard's state.

[0003] In the aforementioned existing technologies, temperature and humidity control strategies typically lack explicit constraints on the safety boundaries of corrugated cardboard. The optimization process relies mainly on performance or empirical indicators, making it difficult to avoid irreversible structural risks such as corrugation collapse and warping during the strategy generation stage. Furthermore, once the control strategy structure is set, adjustments are mostly concentrated at the parameter level, lacking a systematic correction mechanism for control modes and switching logic. This results in insufficient stability of the strategy under complex disturbance conditions, and the control effect depends on manual intervention, making it difficult to achieve provably safe automatic optimization. Summary of the Invention

[0004] One objective of this invention is to propose a method and system for optimizing the temperature and humidity control strategy of paper products based on genetic algorithms. This invention uses a genetic algorithm and a counterexample trajectory-driven mechanism to achieve safe and provable optimization of the temperature and humidity control strategy of corrugated cardboard, which has the advantages of strong adaptability and high stability.

[0005] An optimization method for temperature and humidity control strategy of paper products based on genetic algorithm according to an embodiment of the present invention includes the following steps:

[0006] Temperature, relative humidity, and air volume or ventilation rate are collected from the corrugated cardboard production area, curing area, and storage area, and pre-processed to generate an environmental parameter sequence.

[0007] The moisture content and warpage of corrugated cardboard were collected and compiled into a state sequence of corrugated cardboard.

[0008] Based on the environmental parameter sequence and the corrugated cardboard state sequence, a set of safety constraints is generated.

[0009] A temperature and humidity control strategy template is constructed, which includes a set of discrete control modes, a set of mode switching guard conditions, and a set of continuous control parameters.

[0010] According to the preset coding rules, the temperature and humidity control strategy template is mapped to a genetic code form. Based on the genetic code form, a genetic algorithm is used to perform evolutionary search to generate a set of candidate temperature and humidity control strategies.

[0011] Under the conditions of external temperature and humidity disturbance and the difference between incoming batches, a counterexample trajectory search is performed on the candidate temperature and humidity control strategy set to generate a counterexample trajectory. Based on the counterexample trajectory, the candidate temperature and humidity control strategies are structurally pruned to generate an optimized temperature and humidity control strategy set that meets the safety constraint conditions.

[0012] The target temperature and humidity control strategy is determined based on the optimized set of temperature and humidity control strategies, and is used as the result of the temperature and humidity control strategy optimization.

[0013] Optionally, the preprocessing specifically includes: timestamp alignment, anomaly removal, interpolation completion, and dimension normalization.

[0014] Optionally, the generation of the corrugated board state sequence specifically includes: pairing and associating the moisture content and warpage data of the corrugated board according to the collection time to generate the corrugated board state sequence.

[0015] Optionally, the generation of the set of security constraints specifically includes:

[0016] The environmental parameter sequence and the corrugated cardboard state sequence are matched according to the corresponding acquisition time to generate an environment-state association sequence;

[0017] In the environment-state association sequence, for each collection time point, a mapping record between the environmental parameter value and the corrugated cardboard state value is established. The mapping records under multiple collection time points are collected to generate a set of correspondences between environmental parameters and corrugated cardboard state.

[0018] In the set of correspondences, the values ​​of the corrugated cardboard state within the same or adjacent environmental parameter value ranges are statistically summarized to determine the allowable range of change of the corrugated cardboard state within each environmental parameter value range, and state constraints are generated.

[0019] In the set of correspondences, for the changes in the state of corrugated cardboard when environmental parameters change, the limiting relationship between the magnitude of the change in environmental parameters and the magnitude of the change in the state of corrugated cardboard is determined, and joint constraint conditions are generated.

[0020] By combining state constraints and joint constraints, a set of safety constraints is generated.

[0021] Optionally, the template for constructing a temperature and humidity control strategy specifically includes:

[0022] Based on the different control stages in the temperature and humidity control process of corrugated cardboard, the temperature and humidity control process is divided into stages, and multiple distinct control stages are identified. Each control stage is then labeled with a corresponding discrete control mode, generating a set of discrete control modes.

[0023] For each discrete control mode in the discrete control mode set, set the temperature regulation parameter and relative humidity regulation parameter corresponding to the discrete control mode respectively, and generate a continuous control parameter set corresponding to each discrete control mode.

[0024] In the continuous control parameter set, value boundaries are set for the continuous control parameters corresponding to each discrete control mode, so that the continuous control parameters under different discrete control modes have value ranges that can be distinguished from each other, thus generating a restricted continuous control parameter set.

[0025] Based on the sequence relationship and switching conditions between discrete control modes, the switching relationship between discrete control modes is determined among the discrete control mode sets. The switching relationship is then solidified into mode switching guard conditions, and a set of mode switching guard conditions is generated.

[0026] The discrete control mode set, the constrained continuous control parameter set, and the mode switching guard condition set are integrated to generate a temperature and humidity control strategy template.

[0027] Optionally, the generation of the candidate temperature and humidity control strategy set specifically includes:

[0028] Under the structural constraints of the temperature and humidity control strategy template, the discrete control mode set, the continuous control parameter set, and the mode switching guard condition set are uniformly encoded according to the preset coding rules to generate the initial genetic coding form.

[0029] Based on the initial genetic coding form, multiple strategy genetic coding forms are generated by instantiating and expanding the coding values ​​corresponding to the continuous control parameter set or mode switching guard condition set.

[0030] The initial genetic coding form and the strategic genetic coding form are combined to form a genetic coding population;

[0031] In the genetic coding population, the fitness of each genetic coding form is calculated according to the preset fitness evaluation rules. Based on the fitness calculation results, selection processing is performed to generate a genetic coding population that has undergone selection processing.

[0032] In the selected genetic coding population, crossover and mutation are performed on the genetic coding forms according to the preset crossover and mutation rules to generate new genetic coding forms, and the new genetic coding forms are incorporated into the genetic coding population.

[0033] In the genetic coding population, fitness calculation, selection, crossover and mutation are performed cyclically until a preset termination condition is met and the evolutionary search stops. The preset termination condition includes reaching a preset number of iterations or the genetic coding form that meets the preset screening condition remains unchanged in the genetic coding population for multiple consecutive generations.

[0034] When the evolutionary search stops, the genetic code forms in the genetic code population are mapped to the corresponding temperature and humidity control strategies. The mapped temperature and humidity control strategies are then aggregated to generate a candidate temperature and humidity control strategy set.

[0035] Optionally, the generation of the optimized temperature and humidity control strategy set specifically includes:

[0036] Under external temperature and humidity disturbance conditions and differences in incoming material batches, temperature and humidity evolution calculations are performed on each candidate temperature and humidity control strategy in the candidate temperature and humidity control strategy set to generate a set of temperature and humidity evolution trajectories.

[0037] Based on the set of temperature and humidity evolution trajectories, each temperature and humidity evolution trajectory is judged one by one according to the set of safety constraints, and the temperature and humidity evolution trajectory of corrugated cardboard exceeding the limit of the set of safety constraints is identified.

[0038] Temperature and humidity evolution trajectories that are identified as corrugated cardboard states exceeding the limits of the safety constraint set are marked as counterexample trajectories and collected to form a counterexample trajectory set.

[0039] Based on the set of counterexample trajectories, candidate temperature and humidity control strategies that generate counterexample trajectories are determined, and the discrete control modes, continuous control parameters, and mode switching guard conditions that correspond to the counterexample trajectories in the candidate temperature and humidity control strategies are located.

[0040] Based on the set of counterexample trajectories and the corresponding discrete control modes, continuous control parameters, and mode switching guard conditions, structural pruning is performed on the candidate temperature and humidity control strategies that generate counterexample trajectories. The structural pruning process includes at least one of the following operations: deleting or disabling the discrete control modes corresponding to the counterexample trajectories, reducing the value range of the continuous control parameters corresponding to the counterexample trajectories, and re-limiting the mode switching guard conditions corresponding to the counterexample trajectories, thereby generating candidate temperature and humidity control strategies that have undergone structural pruning.

[0041] Replace the original candidate temperature and humidity control strategy with the candidate temperature and humidity control strategy that has undergone structural pruning, and regenerate the temperature and humidity evolution trajectory set based on the set of replaced candidate temperature and humidity control strategies.

[0042] The newly generated set of temperature and humidity evolution trajectories is judged again based on the set of safety constraints until no more counterexample trajectories are generated in the set of candidate temperature and humidity control strategies. The optimized set of temperature and humidity control strategies that satisfy the set of safety constraints is then obtained.

[0043] Optionally, the generation of the temperature and humidity control strategy optimization results specifically includes:

[0044] Based on the optimized temperature and humidity control strategy set, for each optimized temperature and humidity control strategy, the corresponding safety margin index, control stability index and adjustment cost index are calculated, and a strategy evaluation index set corresponding to each optimized temperature and humidity control strategy is generated.

[0045] Based on the set of strategy evaluation indicators, a comprehensive evaluation of each optimized temperature and humidity control strategy is conducted, and a comprehensive evaluation result corresponding to each optimized temperature and humidity control strategy is generated.

[0046] Based on the comprehensive evaluation results, the optimized temperature and humidity control strategies in the optimized temperature and humidity control strategy set are sorted to generate a sorted sequence of optimized temperature and humidity control strategies.

[0047] In the sorted sequence of optimized temperature and humidity control strategies, the optimized temperature and humidity control strategy with the best comprehensive evaluation result is selected and determined as the optimized result of the target temperature and humidity control strategy.

[0048] A paper product temperature and humidity control strategy optimization system based on a genetic algorithm according to an embodiment of the present invention includes:

[0049] The environmental parameter acquisition module is used to collect temperature, relative humidity, and air volume or ventilation rate in the corrugated cardboard production, curing, and storage areas, and generate an environmental parameter sequence.

[0050] The corrugated cardboard state acquisition module is used to collect the moisture content and warpage of corrugated cardboard and generate a corrugated cardboard state sequence.

[0051] The safety constraint generation module is used to generate a set of safety constraint conditions based on the environmental parameter sequence and the corrugated cardboard state sequence.

[0052] The strategy template building module is used to build temperature and humidity control strategy templates that include discrete control modes, mode switching guard conditions, and continuous control parameters.

[0053] The genetic optimization module is used to map the temperature and humidity control strategy template into a genetic code form and perform an evolutionary search to generate a set of candidate temperature and humidity control strategies.

[0054] The counterexample pruning module is used to perform structural pruning on candidate temperature and humidity control strategies based on counterexample trajectories under conditions of external temperature and humidity disturbances and differences in incoming material batches, and generate an optimized set of temperature and humidity control strategies.

[0055] The strategy determination module is used to determine the target temperature and humidity control strategy from the set of optimized temperature and humidity control strategies.

[0056] The beneficial effects of this invention are:

[0057] This invention constructs a method for optimizing temperature and humidity control strategies throughout the entire process of corrugated cardboard production, maintenance, and storage. It achieves automatic generation and system optimization of temperature and humidity control strategies under conditions of complex external environmental disturbances and batch-to-batch variations in incoming materials. Compared to existing technologies that rely on fixed empirical parameters or static control curves, this invention uses environmental parameter sequences and corrugated cardboard state sequences as a basis to explicitly generate a set of safety constraints. This ensures that the temperature and humidity control strategy remains within quantifiable and verifiable safety boundaries throughout the optimization process, fundamentally reducing the risk of irreversible structural damage such as corrugation collapse and warping, and improving the consistency and stability of corrugated cardboard quality.

[0058] This invention introduces a temperature and humidity control strategy template that includes discrete control modes, mode switching guard conditions, and continuous control parameters. It then uses a genetic algorithm to encode and evolve the strategy structure, so that the optimization object is no longer limited to a single parameter level, but covers the control mode configuration and its switching logic. On this basis, through counterexample trajectory search and structure pruning mechanism, it can perform targeted correction on control strategies that trigger safety constraint violations under specific disturbance conditions, and gradually converge to obtain an optimized set of temperature and humidity control strategies that satisfy the set of safety constraint conditions. This achieves adaptive and safe convergent optimization of the control strategy structure.

[0059] After obtaining a set of safe and provable optimized temperature and humidity control strategies, this invention constructs a strategy evaluation index system based on safety margin, control stability, and adjustment cost to comprehensively evaluate and select the best strategy. This ensures that the final target temperature and humidity control strategy meets safety requirements while also considering control stability and energy consumption rationality. As a result, this invention effectively reduces the reliance on human experience in temperature and humidity control, improves the robustness and adaptability of the control strategy under complex operating conditions, and has the beneficial effects of strong feasibility, high degree of automation, and significant engineering application value. Attached Figure Description

[0060] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0061] Figure 1 This is a flowchart of a method and system for optimizing temperature and humidity control strategies for paper products based on genetic algorithms, as proposed in this invention.

[0062] Figure 2This is a schematic diagram of the temperature and humidity control strategy template structure of a paper product temperature and humidity control strategy optimization method and system based on genetic algorithm proposed in this invention.

[0063] Figure 3 This diagram illustrates the counterexample trajectory search and structural pruning of a paper product temperature and humidity control strategy optimization method and system based on genetic algorithms proposed in this invention. Detailed Implementation

[0064] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0065] refer to Figures 1-3 A method for optimizing temperature and humidity control strategies for paper products based on genetic algorithms includes the following steps:

[0066] Temperature, relative humidity, and air volume or ventilation rate are collected from the corrugated cardboard production area, curing area, and storage area, and pre-processed to generate an environmental parameter sequence.

[0067] The moisture content and warpage of corrugated cardboard were collected and compiled into a state sequence of corrugated cardboard.

[0068] Based on the environmental parameter sequence and the corrugated cardboard state sequence, a set of safety constraints is generated.

[0069] A temperature and humidity control strategy template is constructed, which includes a set of discrete control modes, a set of mode switching guard conditions, and a set of continuous control parameters.

[0070] According to the preset coding rules, the temperature and humidity control strategy template is mapped to a genetic code form. Based on the genetic code form, a genetic algorithm is used to perform evolutionary search to generate a set of candidate temperature and humidity control strategies.

[0071] Under the conditions of external temperature and humidity disturbance and the difference between incoming batches, a counterexample trajectory search is performed on the candidate temperature and humidity control strategy set to generate a counterexample trajectory. Based on the counterexample trajectory, the candidate temperature and humidity control strategies are structurally pruned to generate an optimized temperature and humidity control strategy set that meets the safety constraint conditions.

[0072] The target temperature and humidity control strategy is determined based on the optimized set of temperature and humidity control strategies, and is used as the result of the temperature and humidity control strategy optimization.

[0073] In this embodiment, the preprocessing specifically includes: timestamp alignment, anomaly removal, interpolation completion, and dimension normalization.

[0074] In this embodiment, the generation of the corrugated board state sequence specifically includes: pairing and associating the moisture content and warpage data of the corrugated board according to the collection time to generate the corrugated board state sequence.

[0075] In this embodiment, the generation of the set of security constraints specifically includes:

[0076] The environmental parameter sequence and the corrugated cardboard state sequence are matched according to the corresponding acquisition time to generate an environment-state association sequence;

[0077] In the environment-state association sequence, for each collection time point, a mapping record between the environmental parameter value and the corrugated cardboard state value is established. The mapping records under multiple collection time points are collected to generate a set of correspondences between environmental parameters and corrugated cardboard state.

[0078] In the set of correspondences, the values ​​of the corrugated cardboard state within the same or adjacent environmental parameter value ranges are statistically summarized to determine the allowable range of change of the corrugated cardboard state within each environmental parameter value range, and state constraints are generated.

[0079] The generation of the state constraints specifically includes:

[0080] In the set of correspondences between environmental parameters and corrugated cardboard states, records are grouped according to the values ​​of the environmental parameters. Records with the same or adjacent environmental parameter values ​​are grouped into a set of records corresponding to the same environmental parameter interval. For each set of records corresponding to an environmental parameter interval, the corresponding corrugated cardboard state values ​​within that interval are extracted to form a set of corrugated cardboard state values ​​that correspond one-to-one with that environmental parameter interval. In the set of corrugated cardboard state values, the corrugated cardboard state values ​​are statistically summarized to determine the upper and lower limits of the distribution of corrugated cardboard state values ​​within that environmental parameter interval. The upper and lower limits are used as the allowable range of change in the corrugated cardboard state within that environmental parameter interval, generating state constraints corresponding to that environmental parameter interval. The state constraints generated for each environmental parameter interval are aggregated to generate a set of state constraints used to limit the range of change in the corrugated cardboard state.

[0081] In the set of correspondences, for the changes in the state of corrugated cardboard when environmental parameters change, the limiting relationship between the magnitude of the change in environmental parameters and the magnitude of the change in the state of corrugated cardboard is determined, and joint constraint conditions are generated.

[0082] The generation of the joint constraint conditions specifically includes:

[0083] In the set of correspondences between environmental parameters and corrugated cardboard states, adjacent collection time points are grouped into time pairs according to the collection time sequence. For each time pair, the environmental parameter values ​​and corresponding corrugated cardboard state values ​​at the previous and subsequent collection time points are obtained. Based on the difference in the values ​​of the same environmental parameter between the previous and subsequent collection time points, the corresponding environmental parameter variation range is determined. Similarly, based on the difference in the values ​​of the same corrugated cardboard state between the previous and subsequent collection time points, the corresponding corrugated cardboard state variation range is determined, forming a corresponding record set of environmental parameter variation ranges and corrugated cardboard state variation ranges. In this corresponding record set, the minimum and maximum values ​​of the environmental parameter variation ranges are statistically analyzed. Within the range of minimum and maximum values, the variation range of environmental parameters is divided into multiple variation range intervals. For each environmental parameter variation range interval, the corresponding values ​​of corrugated cardboard state variation within that interval are collected. These values ​​are statistically summarized to determine the corresponding upper and lower limits, which serve as the allowable range for the variation range of corrugated cardboard state within that environmental parameter variation range interval. Each environmental parameter variation range interval and its corresponding allowable range for corrugated cardboard state variation are correlated to generate joint constraints that define the relationship between the variation range of environmental parameters and the variation range of corrugated cardboard state. The joint constraints generated for different environmental parameter variation range intervals are then collected to generate a set of joint constraints.

[0084] By combining state constraints and joint constraints, a set of safety constraints is generated.

[0085] In this embodiment, the construction of the temperature and humidity control strategy template specifically includes:

[0086] Based on the different control stages in the temperature and humidity control process of corrugated cardboard, the temperature and humidity control process is divided into stages, and multiple distinct control stages are identified. Each control stage is then labeled with a corresponding discrete control mode, generating a set of discrete control modes.

[0087] For each discrete control mode in the discrete control mode set, set the temperature regulation parameter and relative humidity regulation parameter corresponding to the discrete control mode respectively, and generate a continuous control parameter set corresponding to each discrete control mode.

[0088] In the continuous control parameter set, value boundaries are set for the continuous control parameters corresponding to each discrete control mode, so that the continuous control parameters under different discrete control modes have value ranges that can be distinguished from each other, thus generating a restricted continuous control parameter set.

[0089] The generation of the restricted continuous control parameter set specifically includes:

[0090] In the continuous control parameter set, for the continuous control parameters corresponding to different discrete control modes, upper and lower bounds corresponding to each discrete control mode are determined respectively; the upper and lower bounds are applied to the corresponding continuous control parameters to restrict the value range of the continuous control parameters, so that the continuous control parameters under different discrete control modes have distinct value ranges; the continuous control parameters after value range restriction are collected to generate a restricted continuous control parameter set.

[0091] Based on the sequence relationship and switching conditions between discrete control modes, the switching relationship between discrete control modes is determined among the discrete control mode sets. The switching relationship is then solidified into mode switching guard conditions, and a set of mode switching guard conditions is generated.

[0092] The generation of the mode switching guard condition set specifically includes:

[0093] In the discrete control mode set, based on the control stage sequence of each discrete control mode in the temperature and humidity control process, the allowed switching order between each discrete control mode is determined, generating an allowed switching relationship between discrete control modes. For each pair of discrete control modes in the allowed switching relationship, a corresponding mode switching condition is determined. The mode switching condition is used to limit the range of conditions that must be met when switching from one discrete control mode to another. The mode switching condition is associated with the corresponding discrete control mode switching direction to generate a mode switching guard condition describing the timing of discrete control mode switching. The mode switching guard conditions corresponding to each discrete control mode switching direction are collected to generate a mode switching guard condition set.

[0094] The discrete control mode set, the constrained continuous control parameter set, and the mode switching guard condition set are integrated to generate a temperature and humidity control strategy template.

[0095] In this embodiment, the generation of the candidate temperature and humidity control strategy set specifically includes:

[0096] Under the structural constraints of the temperature and humidity control strategy template, the discrete control mode set, the continuous control parameter set, and the mode switching guard condition set are uniformly encoded according to the preset coding rules to generate the initial genetic coding form.

[0097] The generation of the initial genetic coding form specifically includes:

[0098] Under the structural constraints of the temperature and humidity control strategy template, the coding structure of the genetic code is determined, such that the genetic code includes a mode coding segment representing discrete control modes, a parameter coding segment representing continuous control parameters, and a guard condition coding segment representing mode switching guard conditions. Each discrete control mode in the discrete control mode set is mapped to its corresponding coding bit in the mode coding segment according to a preset order, forming mode coding content characterizing the discrete control mode configuration. Each continuous control parameter in the continuous control parameter set is mapped to its corresponding coding bit in the parameter coding segment according to its parameter type and the discrete control mode it belongs to, forming parameter coding content characterizing the continuous control parameter structure. Each mode switching guard condition in the mode switching guard condition set is mapped to its corresponding coding bit in the guard condition coding segment according to its corresponding discrete control mode switching direction, forming guard condition coding content characterizing the mode switching guard condition structure. The mode coding content, parameter coding content, and guard condition coding content are combined to generate an initial genetic code corresponding to the temperature and humidity control strategy template structure.

[0099] Based on the initial genetic coding form, multiple strategy genetic coding forms are generated by instantiating and expanding the coding values ​​corresponding to the continuous control parameter set or mode switching guard condition set.

[0100] The generation of the genetic code form of the strategy specifically includes:

[0101] While keeping the initial genetic coding structure unchanged, the coding bits of the corresponding continuous control parameters in the parameter coding segment are selected. Based on the allowed value range of each continuous control parameter in the restricted continuous control parameter set, the coding values ​​of the continuous control parameters are instantiated to generate multiple sets of continuous control parameter value combinations. While keeping the initial genetic coding structure unchanged, the coding bits of the corresponding mode switching guard conditions in the guard condition coding segment are selected. Based on the allowed condition range of each mode switching guard condition in the mode switching guard condition set, the coding values ​​of the mode switching guard conditions are instantiated to generate multiple sets of mode switching guard condition value combinations. The multiple sets of continuous control parameter value combinations and the multiple sets of mode switching guard condition value combinations are mapped to the corresponding coding bits of the initial genetic coding form, generating multiple strategy genetic coding forms while keeping the coding structure unchanged.

[0102] The initial genetic coding form and the strategic genetic coding form are combined to form a genetic coding population;

[0103] In the genetic coding population, the fitness of each genetic coding form is calculated according to the preset fitness evaluation rules. Based on the fitness calculation results, selection processing is performed to generate a genetic coding population that has undergone selection processing.

[0104] In the selected genetic coding population, crossover and mutation are performed on the genetic coding forms according to the preset crossover and mutation rules to generate new genetic coding forms, and the new genetic coding forms are incorporated into the genetic coding population.

[0105] In the genetic coding population, fitness calculation, selection, crossover and mutation are performed cyclically until a preset termination condition is met and the evolutionary search stops. The preset termination condition includes reaching a preset number of iterations or the genetic coding form that meets the preset screening condition remains unchanged in the genetic coding population for multiple consecutive generations.

[0106] When the evolutionary search stops, the genetic code forms in the genetic code population are mapped to the corresponding temperature and humidity control strategies. The mapped temperature and humidity control strategies are then aggregated to generate a candidate temperature and humidity control strategy set.

[0107] In this embodiment, the generation of the optimized temperature and humidity control strategy set specifically includes:

[0108] Under external temperature and humidity disturbance conditions and differences in incoming material batches, temperature and humidity evolution calculations are performed on each candidate temperature and humidity control strategy in the candidate temperature and humidity control strategy set to generate a set of temperature and humidity evolution trajectories.

[0109] Based on the set of temperature and humidity evolution trajectories, each temperature and humidity evolution trajectory is judged one by one according to the set of safety constraints, and the temperature and humidity evolution trajectory of corrugated cardboard exceeding the limit of the set of safety constraints is identified.

[0110] Temperature and humidity evolution trajectories that are identified as corrugated cardboard states exceeding the limits of the safety constraint set are marked as counterexample trajectories and collected to form a counterexample trajectory set.

[0111] Based on the set of counterexample trajectories, candidate temperature and humidity control strategies that generate counterexample trajectories are determined, and the discrete control modes, continuous control parameters, and mode switching guard conditions that correspond to the counterexample trajectories in the candidate temperature and humidity control strategies are located.

[0112] The location of the discrete control mode, continuous control parameters, and mode switching guard conditions specifically includes:

[0113] For each counterexample trajectory in the counterexample trajectory set, a candidate temperature and humidity control strategy that generated the counterexample trajectory is determined. Based on the time index corresponding to the counterexample trajectory, the discrete control mode effective under each time index is determined in the candidate temperature and humidity control strategy, and a time correspondence is established between the counterexample trajectory and the discrete control mode. Under the time correspondence, the continuous control parameters used in the time index corresponding to the counterexample trajectory are determined, and a correspondence is established between the counterexample trajectory and the continuous control parameters. Based on the time index corresponding to the counterexample trajectory, the time index in which the discrete control mode switching occurs is determined, and the corresponding mode switching guard condition is determined in the candidate temperature and humidity control strategy, and a correspondence is established between the counterexample trajectory and the mode switching guard condition. The discrete control modes, continuous control parameters, and mode switching guard conditions that correspond to the counterexample trajectory are collected to complete the location of relevant structural elements in the candidate temperature and humidity control strategy that generated the counterexample trajectory.

[0114] Based on the set of counterexample trajectories and the corresponding discrete control modes, continuous control parameters, and mode switching guard conditions, structural pruning is performed on the candidate temperature and humidity control strategies that generate counterexample trajectories. The structural pruning process includes at least one of the following operations: deleting or disabling the discrete control modes corresponding to the counterexample trajectories, reducing the value range of the continuous control parameters corresponding to the counterexample trajectories, and re-limiting the mode switching guard conditions corresponding to the counterexample trajectories, thereby generating candidate temperature and humidity control strategies that have undergone structural pruning.

[0115] The generation of the candidate temperature and humidity control strategy specifically includes:

[0116] Based on the location results of the counterexample trajectory set and the corresponding discrete control modes, continuous control parameters, and mode switching guard conditions, the target structural elements that need structural pruning in the candidate temperature and humidity control strategies that generate counterexample trajectories are identified. For discrete control modes identified as corresponding to counterexample trajectories, these modes are removed from the candidate temperature and humidity control strategies or marked as inactive, generating candidate temperature and humidity control strategies after discrete control mode pruning. For continuous control parameters identified as corresponding to counterexample trajectories, the range of values ​​for continuous control parameters is narrowed within their allowable range, generating candidate temperature and humidity control strategies after continuous control parameter pruning. For mode switching guard conditions identified as corresponding to counterexample trajectories, the trigger condition range corresponding to the mode switching guard conditions is redefined, generating candidate temperature and humidity control strategies after mode switching guard condition pruning. The candidate temperature and humidity control strategies after discrete control mode pruning, continuous control parameter pruning, and mode switching guard condition pruning are integrated to generate candidate temperature and humidity control strategies after structural pruning.

[0117] Replace the original candidate temperature and humidity control strategy with the candidate temperature and humidity control strategy that has undergone structural pruning, and regenerate the temperature and humidity evolution trajectory set based on the set of replaced candidate temperature and humidity control strategies.

[0118] The newly generated set of temperature and humidity evolution trajectories is judged again based on the set of safety constraints until no more counterexample trajectories are generated in the set of candidate temperature and humidity control strategies. The optimized set of temperature and humidity control strategies that satisfy the set of safety constraints is then obtained.

[0119] The generation of the optimized temperature and humidity control strategy set specifically includes:

[0120] In the candidate temperature and humidity control strategy set after structural pruning, a corresponding temperature and humidity evolution trajectory set is generated for each candidate temperature and humidity control strategy. Based on the safety constraint set, each temperature and humidity evolution trajectory in the temperature and humidity evolution trajectory set is judged, and candidate temperature and humidity control strategies that still generate counterexample trajectories are identified. For candidate temperature and humidity control strategies that are identified as still generating counterexample trajectories, structural pruning is performed again, and a new temperature and humidity evolution trajectory set is generated based on the pruned candidate temperature and humidity control strategies. The temperature and humidity evolution trajectory generation and safety constraint judgment steps are repeated until there are no candidate temperature and humidity control strategies that generate counterexample trajectories in the candidate temperature and humidity control strategy set. The candidate temperature and humidity control strategies that do not generate counterexample trajectories under the termination conditions are collected to generate an optimized temperature and humidity control strategy set that satisfies the safety constraint set.

[0121] In this embodiment, the generation of the temperature and humidity control strategy optimization results specifically includes:

[0122] Based on the optimized temperature and humidity control strategy set, for each optimized temperature and humidity control strategy, the corresponding safety margin index, control stability index and adjustment cost index are calculated, and a strategy evaluation index set corresponding to each optimized temperature and humidity control strategy is generated.

[0123] The generation of the strategy evaluation index set specifically includes:

[0124] For each optimized temperature and humidity control strategy in the optimized temperature and humidity control strategy set, under the corresponding external temperature and humidity disturbance conditions and the difference between incoming material batches, a set of temperature and humidity evolution trajectories corresponding to the optimized temperature and humidity control strategy is generated. Based on the set of temperature and humidity evolution trajectories, according to the state constraints and joint constraints in the set of safety constraints, the minimum residual margin of each temperature and humidity evolution trajectory within the corresponding safety constraint boundary is determined, and a safety margin index corresponding to the optimized temperature and humidity control strategy is generated. For the changes in temperature and relative humidity between adjacent time indices, the magnitude of change and the number of times the change occurs between adjacent time indices are statistically analyzed, and a control stability index is generated to characterize the smoothness of the control output change of the optimized temperature and humidity control strategy. Based on the set of temperature and humidity evolution trajectories, for the adjustment behaviors generated by the optimized temperature and humidity control strategy during temperature adjustment, relative humidity adjustment, and ventilation adjustment, the number of occurrences and adjustment magnitude of the corresponding adjustment behaviors are statistically analyzed, and an adjustment cost index is generated to characterize the adjustment cost level of the optimized temperature and humidity control strategy. The safety margin index, control stability index, and adjustment cost index are aggregated to generate a set of strategy evaluation indices corresponding to each optimized temperature and humidity control strategy.

[0125] Based on the set of strategy evaluation indicators, a comprehensive evaluation of each optimized temperature and humidity control strategy is conducted, and a comprehensive evaluation result corresponding to each optimized temperature and humidity control strategy is generated.

[0126] The generation of the comprehensive evaluation results specifically includes:

[0127] For the safety margin index, control stability index, and regulation cost index corresponding to each optimized temperature and humidity control strategy in the strategy evaluation index set, each index is converted into a numerical index in the same evaluation direction. Based on the numerical index in the same evaluation direction, the safety margin index, control stability index, and regulation cost index are numerically synthesized to generate a comprehensive evaluation result corresponding to each optimized temperature and humidity control strategy.

[0128] Based on the comprehensive evaluation results, the optimized temperature and humidity control strategies in the optimized temperature and humidity control strategy set are sorted to generate a sorted sequence of optimized temperature and humidity control strategies.

[0129] In the sorted sequence of optimized temperature and humidity control strategies, the optimized temperature and humidity control strategy with the best comprehensive evaluation result is selected and determined as the optimized result of the target temperature and humidity control strategy.

[0130] A system for optimizing temperature and humidity control strategies for paper products based on genetic algorithms, comprising:

[0131] The environmental parameter acquisition module is used to collect temperature, relative humidity, and air volume or ventilation rate in the corrugated cardboard production, curing, and storage areas, and generate an environmental parameter sequence.

[0132] The corrugated cardboard state acquisition module is used to collect the moisture content and warpage of corrugated cardboard and generate a corrugated cardboard state sequence.

[0133] The safety constraint generation module is used to generate a set of safety constraint conditions based on the environmental parameter sequence and the corrugated cardboard state sequence.

[0134] The strategy template building module is used to build temperature and humidity control strategy templates that include discrete control modes, mode switching guard conditions, and continuous control parameters.

[0135] The genetic optimization module is used to map the temperature and humidity control strategy template into a genetic code form and perform an evolutionary search to generate a set of candidate temperature and humidity control strategies.

[0136] The counterexample pruning module is used to perform structural pruning on candidate temperature and humidity control strategies based on counterexample trajectories under conditions of external temperature and humidity disturbances and differences in incoming material batches, and generate an optimized set of temperature and humidity control strategies.

[0137] The strategy determination module is used to determine the target temperature and humidity control strategy from the set of optimized temperature and humidity control strategies.

[0138] Example 1:

[0139] To verify the feasibility of this invention in practice, it was applied to a large-scale integrated corrugated cardboard production and warehousing manufacturing scenario. This scenario includes a continuously operating corrugated cardboard production section, a curing section after forming, and a warehousing section before finished products are put into storage. Each section has significant differences in spatial location, equipment configuration, and environmental conditions. In actual operation, external climate conditions are constantly changing, and different batches of incoming materials have objective differences in the moisture content, fiber structure, and processing response characteristics of the base paper. This makes it difficult for traditional temperature and humidity control methods based on fixed empirical parameters to adapt in a timely manner. Corrugated cardboard is prone to problems such as warping accumulation, increased risk of corrugation collapse, and amplified quality fluctuations in the later stages of production and storage. Moreover, control personnel need to frequently intervene manually, and the adjustment process is lagging and lacks a basis for safety boundaries.

[0140] In this application scenario, environmental sensing devices are first deployed in the production, curing, and storage areas to continuously collect parameters related to temperature, relative humidity, and ventilation. The collected multi-source environmental data is then time-aligned and anomaly-handled to form a unified environmental parameter sequence. Simultaneously, during the paperboard production and curing process, the moisture content and warpage of the corrugated paperboard are collected synchronously and correlated according to the collection time to construct a corrugated paperboard state sequence. Based on this, through joint analysis of the environmental parameter sequence and the corrugated paperboard state sequence, a set of safety constraints reflecting the safe operating boundary of the corrugated paperboard is automatically generated. This allows the system to clearly distinguish between normal evolution states and potential risk states without relying on human experience for judgment.

[0141] In this scenario, a temperature and humidity control strategy template suitable for the production and storage environment is constructed. This strategy template divides discrete control modes based on different control stages and configures corresponding continuous control parameters and mode switching guard conditions for each control mode. By mapping this strategy template into a genetic code form, a genetic algorithm is introduced to perform evolutionary search on the control strategy structure. The system can generate multiple candidate temperature and humidity control strategies under given structural constraints. In actual operation, the system applies the candidate strategies to the current environmental disturbance conditions and incoming material batch conditions to calculate the evolution process of the corrugated cardboard state. Based on the set of safety constraints, it automatically identifies counterexample trajectories that generate safety risks. For the identified counterexample trajectories, the system further locates the control mode, control parameters, or mode switching conditions that cause the risks and performs pruning on the corresponding strategy structure, thereby gradually eliminating potential risk control paths and enabling the control strategy to evolve towards a safe convergence direction through repeated corrections.

[0142] After running continuously for a period of time, the system can form a set of optimized temperature and humidity control strategies that meet the set of safety constraints. Based on this, the system comprehensively evaluates each optimized strategy. During the evaluation process, the system combines the safety margin performance of corrugated cardboard under different environmental disturbances, the stability of control output, and the overall cost characteristics of adjustment behavior to conduct a unified evaluation of each strategy. Thus, the target temperature and humidity control strategy is determined without human intervention. This target strategy is directly applied to the linkage control of the production section, curing section, and storage section, enabling the temperature and humidity adjustment process to automatically adjust with environmental changes and differences in incoming materials.

[0143] Through continuous operation and observation in this practical application scenario, it can be found that compared with the original control method that relies on experience, the method provided by this invention can significantly reduce the probability of corrugated cardboard entering an irreversible structural damage state during the curing and storage stages. The control process is more stable, the environmental adjustment response is more coordinated, and the frequency of manual intervention is significantly reduced. At the same time, since the control strategy has embedded safety constraints and counterexample correction mechanisms in the generation process, the system can still maintain stable operation when facing sudden environmental changes and will not cause a chain of quality problems due to parameter mismatch. This fully demonstrates the applicability, stability and engineering promotion value of this invention in complex practical scenarios.

[0144] Table 1. Overall performance comparison results of the method of the present invention and traditional temperature and humidity control methods.

[0145]

[0146] As shown in Table 1, the method of the present invention demonstrates stable and reasonable improvement effects on multiple key indicators throughout the overall operation cycle of corrugated cardboard production, maintenance, and storage. Moreover, the improvement range of each indicator remains within the range achievable in engineering practice. In terms of product structural safety, the warping over-limit incidence rate of the method of the present invention is reduced from 6.8% of the traditional method to 3.9%, and the number of times the risk of corrugation collapse is triggered is reduced from 14 times per month to 8 times. This indicates that through the structural pruning mechanism driven by the set of safety constraints and counterexample trajectories, the control strategy systematically avoids high-risk states during the generation stage, thereby effectively reducing the probability of irreversible structural damage.

[0147] Regarding the stability of corrugated cardboard, the average moisture content fluctuation range under traditional methods is ±1.7%, while the method of this invention converges this fluctuation range to ±1.1%. This indicates that the temperature and humidity control strategy can maintain a more stable state evolution under different environmental disturbances and batch differences of incoming materials. This improvement is not achieved by simply tightening the control parameters, but rather stems from the optimization of the control mode switching logic at the control strategy structure level, making the temperature and humidity adjustment process smoother and avoiding frequent large-scale adjustments.

[0148] From an operation and maintenance perspective, traditional methods rely on human experience to correct parameters, resulting in a monthly manual adjustment frequency of up to 18 times. The method of this invention reduces this frequency to 9 times, a significant reduction. This result reflects that the genetic algorithm evolutionary search combined with the counterexample trajectory structure pruning mechanism enables the control strategy to maintain its adaptive capability under complex operating conditions, significantly reducing the dependence on human intervention and improving the system's automation level and operational consistency.

[0149] Regarding the stability and energy consumption of the control process, the relative value of the control output change amplitude of the method of the present invention is reduced from 1.00 to 0.72, and the relative value of environmental regulation energy consumption per unit output is reduced from 1.00 to 0.86. This indicates that, under the premise of ensuring safety and stability, the control strategy avoids ineffective or excessive regulation. This improvement comes from the fact that the control strategy considers safety margin, control stability and regulation cost simultaneously during the optimization process, so that the final selected target temperature and humidity control strategy achieves balance under multiple objective constraints, rather than simply pursuing rapid response.

[0150] Regarding the ability to cope with abnormal operating conditions, the method of this invention shortens the recovery time from 6.5 hours to 3.8 hours and extends the stable operation cycle of the control strategy from 18 days to 27 days. This indicates that when there are sudden changes in the environment or significant differences in incoming materials, the control strategy can return to the safe operating range more quickly and maintain its effectiveness for a longer period of time. This effect is mainly due to the safety convergence mechanism driven by counterexamples, which ensures that the control strategy fully covers potential disturbances during the optimization stage, thereby enhancing the overall robustness.

[0151] The above comparison results show that the present invention does not achieve performance improvement through extreme optimization of a single indicator, but rather achieves synergistic improvement in quality stability, operational safety, and energy consumption rationality by introducing safety constraints, structural evolution, and counterexample correction mechanisms in the control strategy generation stage. It has high engineering feasibility and practical promotion value.

[0152] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing temperature and humidity control strategies for paper products based on genetic algorithms, characterized in that, Includes the following steps: Temperature, relative humidity, and air volume or ventilation rate are collected from the corrugated cardboard production area, curing area, and storage area, and pre-processed to generate an environmental parameter sequence. The moisture content and warpage of corrugated cardboard were collected and compiled into a state sequence of corrugated cardboard. Based on the environmental parameter sequence and the corrugated cardboard state sequence, a set of safety constraints is generated. A temperature and humidity control strategy template is constructed, which includes a set of discrete control modes, a set of mode switching guard conditions, and a set of continuous control parameters. According to the preset coding rules, the temperature and humidity control strategy template is mapped to a genetic code form. Based on the genetic code form, a genetic algorithm is used to perform evolutionary search to generate a set of candidate temperature and humidity control strategies. Under the conditions of external temperature and humidity disturbance and the difference between incoming batches, a counterexample trajectory search is performed on the candidate temperature and humidity control strategy set to generate a counterexample trajectory. Based on the counterexample trajectory, the candidate temperature and humidity control strategies are structurally pruned to generate an optimized temperature and humidity control strategy set that satisfies the set of safety constraints. The target temperature and humidity control strategy is determined based on the optimized set of temperature and humidity control strategies, and is used as the optimization result of the temperature and humidity control strategy. The generation of the optimized temperature and humidity control strategy set specifically includes: Under external temperature and humidity disturbance conditions and differences in incoming material batches, temperature and humidity evolution calculations are performed on each candidate temperature and humidity control strategy in the candidate temperature and humidity control strategy set to generate a set of temperature and humidity evolution trajectories. Based on the set of temperature and humidity evolution trajectories, each temperature and humidity evolution trajectory is judged one by one according to the set of safety constraints, and the temperature and humidity evolution trajectory of corrugated cardboard exceeding the limit of the set of safety constraints is identified. Temperature and humidity evolution trajectories that are identified as corrugated cardboard states exceeding the limits of the safety constraint set are marked as counterexample trajectories and collected to form a counterexample trajectory set. Based on the set of counterexample trajectories, candidate temperature and humidity control strategies that generate counterexample trajectories are determined, and the discrete control modes, continuous control parameters, and mode switching guard conditions that correspond to the counterexample trajectories in the candidate temperature and humidity control strategies are located. Based on the set of counterexample trajectories and the corresponding discrete control modes, continuous control parameters, and mode switching guard conditions, structural pruning is performed on the candidate temperature and humidity control strategies that generate counterexample trajectories. The structural pruning process includes at least one of the following operations: deleting or disabling the discrete control modes corresponding to the counterexample trajectories, reducing the value range of the continuous control parameters corresponding to the counterexample trajectories, and re-limiting the mode switching guard conditions corresponding to the counterexample trajectories, thereby generating candidate temperature and humidity control strategies that have undergone structural pruning. Replace the original candidate temperature and humidity control strategy with the candidate temperature and humidity control strategy that has undergone structural pruning, and regenerate the temperature and humidity evolution trajectory set based on the set of replaced candidate temperature and humidity control strategies. The newly generated set of temperature and humidity evolution trajectories is judged again based on the set of safety constraints until no more counterexample trajectories are generated in the set of candidate temperature and humidity control strategies. The optimized set of temperature and humidity control strategies that satisfy the set of safety constraints is then obtained.

2. The method for optimizing temperature and humidity control strategies for paper products based on genetic algorithms according to claim 1, characterized in that, The preprocessing specifically includes: timestamp alignment, anomaly removal, interpolation completion, and dimension normalization.

3. The method for optimizing temperature and humidity control strategies for paper products based on genetic algorithms according to claim 1, characterized in that, The generation of the corrugated cardboard state sequence specifically includes: pairing and associating the moisture content and warpage data of the corrugated cardboard according to the collection time to generate the corrugated cardboard state sequence.

4. The method for optimizing temperature and humidity control strategies for paper products based on genetic algorithms according to claim 1, characterized in that, The generation of the set of security constraints specifically includes: The environmental parameter sequence and the corrugated cardboard state sequence are matched according to the corresponding acquisition time to generate an environment-state association sequence; In the environment-state association sequence, for each collection time point, a mapping record between the environmental parameter value and the corrugated cardboard state value is established. The mapping records under multiple collection time points are collected to generate a set of correspondences between environmental parameters and corrugated cardboard state. In the set of correspondences, the values ​​of the corrugated cardboard state within the same or adjacent environmental parameter value ranges are statistically summarized to determine the allowable range of change of the corrugated cardboard state within each environmental parameter value range, and state constraints are generated. In the set of correspondences, for the changes in the state of corrugated cardboard when environmental parameters change, the limiting relationship between the magnitude of the change in environmental parameters and the magnitude of the change in the state of corrugated cardboard is determined, and joint constraint conditions are generated. By combining state constraints and joint constraints, a set of safety constraints is generated.

5. The method for optimizing temperature and humidity control strategies for paper products based on genetic algorithms according to claim 1, characterized in that, The specific components of the temperature and humidity control strategy template include: Based on the different control stages in the temperature and humidity control process of corrugated cardboard, the temperature and humidity control process is divided into stages, and multiple distinct control stages are identified. Each control stage is then labeled with a corresponding discrete control mode, generating a set of discrete control modes. For each discrete control mode in the discrete control mode set, set the temperature regulation parameter and relative humidity regulation parameter corresponding to the discrete control mode respectively, and generate a continuous control parameter set corresponding to each discrete control mode. In the continuous control parameter set, value boundaries are set for the continuous control parameters corresponding to each discrete control mode, so that the continuous control parameters under different discrete control modes have value ranges that can be distinguished from each other, thus generating a restricted continuous control parameter set. Based on the sequence relationship and switching conditions between discrete control modes, the switching relationship between discrete control modes is determined among the discrete control mode sets. The switching relationship is then solidified into mode switching guard conditions, and a set of mode switching guard conditions is generated. The discrete control mode set, the constrained continuous control parameter set, and the mode switching guard condition set are integrated to generate a temperature and humidity control strategy template.

6. The method for optimizing temperature and humidity control strategies for paper products based on genetic algorithms according to claim 1, characterized in that, The generation of the candidate temperature and humidity control strategy set specifically includes: Under the structural constraints of the temperature and humidity control strategy template, the discrete control mode set, the continuous control parameter set, and the mode switching guard condition set are uniformly encoded according to the preset coding rules to generate the initial genetic coding form. Based on the initial genetic coding form, multiple strategy genetic coding forms are generated by instantiating and expanding the coding values ​​corresponding to the continuous control parameter set or mode switching guard condition set. The initial genetic coding form and the strategic genetic coding form are combined to form a genetic coding population; In the genetic coding population, the fitness of each genetic coding form is calculated according to the preset fitness evaluation rules. Based on the fitness calculation results, selection processing is performed to generate a genetic coding population that has undergone selection processing. In the selected genetic coding population, crossover and mutation are performed on the genetic coding forms according to the preset crossover and mutation rules to generate new genetic coding forms, and the new genetic coding forms are incorporated into the genetic coding population. In the genetic coding population, fitness calculation, selection, crossover and mutation are performed cyclically until a preset termination condition is met and the evolutionary search stops. The preset termination condition includes reaching a preset number of iterations or the genetic coding form that meets the preset screening condition remains unchanged in the genetic coding population for multiple consecutive generations. When the evolutionary search stops, the genetic code forms in the genetic code population are mapped to the corresponding temperature and humidity control strategies. The mapped temperature and humidity control strategies are then aggregated to generate a candidate temperature and humidity control strategy set.

7. The method for optimizing temperature and humidity control strategies for paper products based on genetic algorithms according to claim 1, characterized in that, The generation of the optimized temperature and humidity control strategy results specifically includes: Based on the optimized temperature and humidity control strategy set, for each optimized temperature and humidity control strategy, the corresponding safety margin index, control stability index and adjustment cost index are calculated, and a strategy evaluation index set corresponding to each optimized temperature and humidity control strategy is generated. Based on the set of strategy evaluation indicators, a comprehensive evaluation of each optimized temperature and humidity control strategy is conducted, and a comprehensive evaluation result corresponding to each optimized temperature and humidity control strategy is generated. Based on the comprehensive evaluation results, the optimized temperature and humidity control strategies in the optimized temperature and humidity control strategy set are sorted to generate a sorted sequence of optimized temperature and humidity control strategies. In the sorted sequence of optimized temperature and humidity control strategies, the optimized temperature and humidity control strategy with the best comprehensive evaluation result is selected and determined as the optimized result of the target temperature and humidity control strategy.

8. A system for optimizing temperature and humidity control strategies for paper products based on genetic algorithms, comprising executing the method for optimizing temperature and humidity control strategies for paper products based on genetic algorithms as described in any one of claims 1 to 7, characterized in that, include: The environmental parameter acquisition module is used to collect temperature, relative humidity, and air volume or ventilation rate in the corrugated cardboard production, curing, and storage areas, and generate an environmental parameter sequence. The corrugated cardboard state acquisition module is used to collect the moisture content and warpage of corrugated cardboard and generate a corrugated cardboard state sequence. The safety constraint generation module is used to generate a set of safety constraint conditions based on the environmental parameter sequence and the corrugated cardboard state sequence. The strategy template building module is used to build temperature and humidity control strategy templates that include discrete control modes, mode switching guard conditions, and continuous control parameters. The genetic optimization module is used to map the temperature and humidity control strategy template into a genetic code form and perform an evolutionary search to generate a set of candidate temperature and humidity control strategies. The counterexample pruning module is used to perform structural pruning on candidate temperature and humidity control strategies based on counterexample trajectories under conditions of external temperature and humidity disturbances and differences in incoming material batches, and generate an optimized set of temperature and humidity control strategies. The strategy determination module is used to determine the target temperature and humidity control strategy from the set of optimized temperature and humidity control strategies.

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