Method and device for controlling poison resistance consistency in solid hydrogen storage alloy batch preparation, electronic equipment and medium

Abstract

The invention discloses a method and device for controlling poison resistance consistency in solid hydrogen storage alloy batch preparation, electronic equipment and a medium, and belongs to the technical field of hydrogen energy storage and material preparation. The method comprises the following steps: acquiring a performance prediction model of a production system, wherein the performance prediction model is constructed based on a time trajectory of historical process parameters of production equipment and anti-poisoning performance indexes of the produced hydrogen storage alloy; on the basis of the performance prediction model, an upper-layer decision controller is constructed with the purpose of minimizing the difference of the anti-poisoning performance indexes of the hydrogen storage alloy produced by the multiple pieces of production equipment; the time track of the historical process parameters is optimized through an upper decision controller, and the optimal time track of the process parameters of the production system is obtained; and on the basis of the optimal time trajectory, the multiple production devices are controlled through a lower-layer model prediction controller. According to the method, the consistency of anti-poisoning performance indexes of mass production of the hydrogen storage alloy is ensured.

Description

Technical Field

[0001] The present application belongs to the technical field of hydrogen energy storage and material preparation, and in particular, relates to a method, device, electronic equipment and medium for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys. Background Art

[0002] As a green energy, hydrogen energy has the advantages of high efficiency and zero carbon emissions, and has wide application potential in transportation, industrial manufacturing, and renewable energy storage. Solid hydrogen storage alloys have become an important way to store hydrogen due to their reversible hydrogen absorption and release properties. Compared with gaseous and liquid hydrogen storage technologies, solid hydrogen storage has higher safety and greater hydrogen storage density.

[0003] However, the batch preparation process of solid hydrogen storage alloys faces the challenge of poor performance consistency. Different batches of hydrogen storage alloys often show significant differences in key properties such as hydrogen absorption and desorption rate, hydrogen storage capacity and cycle life. This performance fluctuation affects the stability and reliability in large-scale applications. In particular, the poisoning effect of impurities (such as oxygen and water vapor) can significantly reduce the hydrogen storage capacity of hydrogen storage alloys and cause material failure. Impurities react with the surface of hydrogen storage alloys to form oxides or hydroxides, which hinder the absorption of hydrogen and reduce the hydrogen storage efficiency and cycle life of hydrogen storage alloys.

[0004] Existing technical methods mainly rely on experience and simple process control models, which are difficult to deal with complex multi-physics field coupling problems and process fluctuations in the production process, resulting in difficulty in ensuring consistency in large-scale preparation. In particular, in terms of anti-poisoning ability, existing technologies cannot ensure the stability and anti-impurity ability of hydrogen storage alloys in mass production, and there is a problem of inconsistent anti-poisoning performance in large-scale production. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a control method, device, electronic equipment and medium for the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys, ensuring the consistency of anti-poisoning performance indicators of batch production of hydrogen storage alloys.

[0006] In a first aspect, the present application provides a method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys, which is applied to a production system of hydrogen storage alloys, wherein the production system includes a plurality of production equipment for performing a preparation process of the hydrogen storage alloy, and the method includes:

[0007] Acquire a performance prediction model of the production system, wherein the performance prediction model is constructed based on a time trajectory of historical process parameters of the production equipment and an anti-poisoning performance index of the produced hydrogen storage alloy;

[0008] Based on the performance prediction model, an upper-level decision controller is constructed with the goal of minimizing the difference in the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipments;

[0009] Optimizing the time trajectory of the historical process parameters by the upper-level decision controller to obtain the optimal time trajectory of the process parameters of the preparation process;

[0010] Based on the optimal time trajectory, the plurality of production equipments are controlled by a lower-layer model predictive controller.

[0011] According to one embodiment of the present application, based on the performance prediction model, an upper-level decision controller is constructed with the goal of minimizing the difference in the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipment, including:

[0012] Based on the performance prediction model, constructing a uniformity function of the production system, wherein the uniformity function is used to characterize the difference in anti-poisoning performance indicators of the plurality of production equipment;

[0013] Based on the constraints of the process parameters, the uniformity function is minimized and the upper-level decision controller is constructed.

[0014] According to one embodiment of the present application, the uniformity function includes:

[0015]

[0016] Wherein, F is the homogeneity function, which is characterized by the variance of the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipments, N is the number of production equipments in the production system, and y i is the anti-poisoning performance index of the hydrogen storage alloy produced by the i-th production equipment, It is the average value of the anti-poisoning performance index of the hydrogen storage alloy produced by N production equipments.

[0017] According to one embodiment of the present application, the performance prediction model is:

[0018] y=f(X(t))

[0019] The time trajectory of the historical process parameters is:

[0020] X(t)=[T(t),P(t)] T

[0021] Among them, y is the anti-poisoning performance index of the hydrogen storage alloy produced by the production equipment, X(t) is the time trajectory of the process parameters of the production equipment, T(t) is the temperature trajectory of the production equipment, and P(t) is the pressure trajectory of the production equipment.

[0022] According to an embodiment of the present application, the step of finding the minimum value of the uniformity function based on the constraint conditions of the process parameters and constructing the upper-level decision controller includes:

[0023]

[0024]

[0025] Wherein, F is the homogeneity function, N is the number of production equipment in the production system, is the historical process parameters of the i-th production equipment, X i (t) is the time trajectory of the process parameters of the i-th production equipment at time t, is the historical process parameters of the j-th production equipment, X j (t) is the time trajectory of the process parameters of the j-th production equipment at time t, T i (t) is the temperature trajectory of the i-th production equipment at time t, T min is the lower limit of the temperature of the constraint condition, T max is the upper temperature limit of the constraint condition, P i (t) is the pressure trajectory of the i-th production equipment at time t, P min is the lower limit of the pressure of the constraint condition, P max is the upper limit of pressure of the constraint condition.

[0026] According to one embodiment of the present application, controlling the multiple production equipment through a lower-layer model predictive controller based on the optimal time trajectory includes:

[0027] The optimal time trajectory is analyzed by the lower-layer model predictive controller to determine the target process parameters of the multiple production equipment at each moment;

[0028] The lower-layer model predictive controller adjusts the equipment parameters of the plurality of production equipment according to the target process parameters.

[0029] According to one embodiment of the present application, after controlling the plurality of production devices through a lower-layer model predictive controller based on the optimal time trajectory, the method further includes:

[0030] Collecting real-time process parameters of the production system;

[0031] The optimal time trajectory is updated using the real-time process parameters.

[0032] In a second aspect, the present application provides a control device for the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys, which is applied to a production system of hydrogen storage alloys, wherein the production system includes a plurality of production equipment for executing a preparation process of the hydrogen storage alloy, and the device includes:

[0033] An acquisition module, used for acquiring a performance prediction model of the production system, wherein the performance prediction model is constructed based on a time trajectory of historical process parameters of the production equipment and an anti-poisoning performance index of the produced hydrogen storage alloy;

[0034] A first processing module is used to construct an upper-level decision controller based on the performance prediction model with the goal of minimizing the difference in anti-poisoning performance indicators of hydrogen storage alloys produced by the multiple production equipment;

[0035] A second processing module, configured to optimize the time trajectory of the historical process parameters through the upper-layer decision controller to obtain the optimal time trajectory of the process parameters of the preparation process;

[0036] The third processing module is used to control the multiple production equipment through a lower-level model predictive controller based on the optimal time trajectory.

[0037] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements a method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys as described in the first aspect above.

[0038] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys as described in the first aspect above.

[0039] In a fifth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement a method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys as described in the first aspect.

[0040] In a sixth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements a method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys as described in the first aspect above.

[0041] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application.

[0042] The present invention provides a method, device, electronic equipment and medium for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys, which have the following beneficial effects compared with the prior art:

[0043] (1) By optimizing the time trajectory of the process parameters through the upper-level decision controller constructed based on the performance prediction model, the optimal time trajectory with the lowest difference in the anti-poisoning performance index can be obtained. The lower-level model prediction controller controls each production equipment according to the optimal time trajectory, thereby improving the stability and impurity resistance of the hydrogen storage alloy in mass production, ensuring the consistency of the anti-poisoning performance index of the hydrogen storage alloy in mass production, and being able to improve the consistency of the anti-poisoning performance of the solid-state hydrogen storage alloy, ensuring the uniformity of the hydrogen storage alloy in production and long-term performance.

[0044] (2) The upper-level decision controller is responsible for optimizing the time trajectory of the global process parameters. Based on the historical process parameters such as temperature and pressure collected in real time, the time trajectory of key process parameters such as temperature and pressure is optimized over a long period of time to minimize the difference in anti-poisoning performance between different production equipment, obtain the optimal time trajectory of the process parameters, and provide the temperature and pressure reference values ​​to the lower-level model prediction controller to ensure the consistency of the anti-poisoning performance of hydrogen storage alloys in large-scale production.

[0045] (3) The MPC controller in the lower-level model predictive controller adjusts the heating power and vacuum pump rate of the production equipment to control the temperature and pressure, and performs fine real-time control in a short period of time. According to the optimal time trajectory of process parameters such as temperature and pressure generated by the upper-level decision controller, the controllable parameters such as temperature and pressure of each production equipment are adjusted in real time, and environmental changes and performance fluctuations of production equipment are compensated in real time. The state space model is used to predict the short-term changes of process parameters, so that the process parameters of each production equipment meet the optimal time trajectory, ensuring the stability of the anti-poisoning performance of the hydrogen storage alloy in production. The feedback mechanism can also be used to ensure the consistency of the anti-poisoning performance of the hydrogen storage alloy in mass production, thereby improving the long-term service life and stability of the hydrogen storage alloy, making it suitable for large-scale industrial applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0047] Figure 1 This is one of the flow diagrams of the method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys provided in the embodiment of the present application;

[0048] Figure 2 This is the second flow chart of the method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys provided in the embodiment of the present application;

[0049] Figure 3 This is the third flow chart of the method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys provided in the embodiment of the present application;

[0050] Figure 4 It is a schematic diagram of the structure of a control device for consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys provided in an embodiment of the present application;

[0051] Figure 5 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0053] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0054] In combination with the accompanying drawings, the method for controlling the consistency of anti-poisoning performance in the batch preparation of solid-state hydrogen storage alloys, the control device for controlling the consistency of anti-poisoning performance in the batch preparation of solid-state hydrogen storage alloys, the electronic device and the readable storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0055] Among them, the control method for consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys can be applied to the terminal, and can be specifically executed by hardware or software in the terminal.

[0056] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad).

[0057] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse and a joystick.

[0058] The embodiment of the present application provides a method for controlling the consistency of anti-poison performance in the batch preparation of solid-state hydrogen storage alloys. The executor of the method for controlling the consistency of anti-poison performance in the batch preparation of solid-state hydrogen storage alloys can be an electronic device or a functional module or functional entity in the electronic device that can implement the method for controlling the consistency of anti-poison performance in the batch preparation of solid-state hydrogen storage alloys. The electronic devices mentioned in the embodiment of the present application include but are not limited to mobile phones, tablet computers, computers, cameras and wearable devices. The following describes the method for controlling the consistency of anti-poison performance in the batch preparation of solid-state hydrogen storage alloys provided in the embodiment of the present application by taking the electronic device as an example of the executor.

[0059] The method is applied to a production system of a hydrogen storage alloy, wherein the production system includes a plurality of production equipments for performing a preparation process of the hydrogen storage alloy.

[0060] Among them, multiple production equipment is used for batch preparation of hydrogen storage alloys. At this time, it is necessary to ensure the consistency of the anti-poisoning performance during the preparation.

[0061] In the production system, each production equipment executes the same process line for preparing hydrogen storage alloys.

[0062] like Figure 1 As shown, the method for controlling the consistency of anti-poisoning performance in batch preparation of the solid hydrogen storage alloy includes: steps 110 to 130.

[0063] Step 110: obtaining a performance prediction model of the production system, wherein the performance prediction model is constructed based on a time trajectory of historical process parameters of the production equipment and an anti-poisoning performance index of the produced hydrogen storage alloy;

[0064] Among them, the time trajectory of historical process parameters is collected during the process of preparing hydrogen storage alloys using multiple production equipment, and may include information such as the temperature and pressure of the production equipment at multiple consecutive moments during the preparation process that affects the anti-poisoning performance of the hydrogen storage alloy.

[0065] The anti-poisoning performance index can be characterized as the hydrogen absorption and desorption cycle efficiency of the hydrogen storage alloy, which is collected from the hydrogen storage alloy produced by the production equipment.

[0066] It can be understood that the performance prediction model contains a mapping relationship between the time trajectory of historical process parameters and the anti-poisoning performance index, which can be used to predict the anti-poisoning performance index of hydrogen storage alloys produced by each production equipment.

[0067] For example, by inputting the process parameters into the performance prediction model, the predicted anti-toxic performance index output by the performance prediction model can be obtained.

[0068] In this step, a performance prediction model of the production system is constructed based on a data set consisting of historical process parameters of multiple production equipment and anti-poisoning performance indicators of the production.

[0069] Step 120: Based on the performance prediction model, an upper-level decision controller is constructed with the goal of minimizing the difference in anti-poisoning performance indicators of hydrogen storage alloys produced by the multiple production equipments.

[0070] Among them, the performance prediction model is used to calculate the anti-poisoning performance index corresponding to the process parameters according to the input process parameters.

[0071] In this step, the performance prediction model is used to predict the anti-poisoning performance index of the production equipment for producing hydrogen storage alloys, and an upper-level decision controller is constructed with the goal of minimizing the differences in the anti-poisoning performance indexes corresponding to multiple production equipment.

[0072] Step 130: Optimize the time trajectory of the historical process parameters through the upper-level decision controller to obtain the optimal time trajectory of the process parameters of the preparation process.

[0073] Among them, the optimal time trajectory is used to instruct each production equipment to produce hydrogen storage alloys, so as to improve the consistency of the anti-poisoning performance indicators of the hydrogen storage alloys produced by each production equipment.

[0074] In this step, the differences between the anti-poisoning performance indicators of hydrogen storage alloys produced by multiple production equipment are evaluated through the upper-level decision controller, and the time trajectory of the process parameters corresponding to the minimized differences is optimized through optimization algorithms such as genetic algorithms or particle swarm optimization to obtain the optimal time trajectory for the entire cycle of hydrogen storage alloy production.

[0075] Step 140: Based on the optimal time trajectory, control the multiple production equipment through a lower-level model predictive controller.

[0076] In this step, the lower-level model predictive controller controls the operating state of each production equipment in the production system according to the process parameters corresponding to each moment in the optimal time trajectory to affect the process parameters of the production equipment.

[0077] According to the control method for consistency of anti-poisoning performance in batch preparation of solid-state hydrogen storage alloys provided in the embodiment of the present application, the time trajectory of historical process parameters is optimized by an upper-level decision controller constructed based on the performance prediction model, so that an optimal time trajectory with the lowest difference in anti-poisoning performance indicators can be obtained. Each production equipment is controlled according to the optimal time trajectory through the lower-level model prediction controller, thereby improving the stability and impurity resistance of the hydrogen storage alloy in batch production, ensuring the consistency of the anti-poisoning performance indicators of the batch production of hydrogen storage alloys, being able to improve the consistency of the anti-poisoning performance of solid-state hydrogen storage alloys, and ensuring the uniformity of the hydrogen storage alloys in production and long-term performance.

[0078] In some embodiments, based on the performance prediction model, an upper-level decision controller is constructed with the goal of minimizing the difference in the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipment, including:

[0079] Based on the performance prediction model, constructing a uniformity function of the production system, wherein the uniformity function is used to characterize the difference in anti-poisoning performance indicators of the plurality of production equipment;

[0080] Based on the constraints of the process parameters, the uniformity function is minimized and the upper-level decision controller is constructed.

[0081] Among them, the homogeneity function is used to characterize the differences in the anti-poisoning performance indicators of multiple production equipment.

[0082] Process parameter constraints are an important mechanism to ensure that the production process of hydrogen storage alloys operates within a safe, efficient and quality-compliant range.

[0083] The goal of the upper decision controller is to minimize the uniformity function and optimize under given process parameter constraints, using genetic algorithms, particle swarm optimization (PSO) or other optimization algorithms to solve the optimization problem.

[0084] In this embodiment, an upper-level decision controller can be effectively constructed to achieve uniformity of the anti-poisoning performance of multiple devices in the production system, thereby improving overall production efficiency and product quality.

[0085] In some embodiments, the performance prediction model is:

[0086] y=f(X(t))

[0087] The time trajectory of the historical process parameters is:

[0088] X(t)=[T(t),P(t)] T

[0089] Among them, y is the anti-poisoning performance index of the hydrogen storage alloy produced by the production equipment, X(t) is the time trajectory of the historical process parameters of the production equipment, T(t) is the temperature trajectory of the production equipment, and P(t) is the pressure trajectory of the production equipment.

[0090] In this embodiment, during the preparation of the hydrogen storage alloy, the production system collects the key process parameters (temperature, pressure, etc.) of the production equipment through the process monitoring module. These process parameters have an important impact on the anti-poisoning performance of the hydrogen storage alloy. The process monitoring module will continuously record the changes in these process parameters and store them in association with the final anti-poisoning performance of the hydrogen storage alloy, forming a large number of time trajectories of historical process parameters.

[0091] The anti-poisoning performance is defined as the hydrogen absorption and desorption cycle efficiency y. Through machine learning methods, the historical process parameters are analyzed offline to establish the time trajectory of the hydrogen absorption and desorption cycle efficiency (anti-poisoning performance index) y and the historical process parameters (such as temperature, pressure, etc.) X(t) = [T(t), P(t)] T The nonlinear relationship between them is y=f(X(t)). Where T(t) and P(t) represent the temperature trajectory and pressure trajectory respectively. This model can predict the anti-poisoning performance of hydrogen storage alloys under different process conditions.

[0092] On this basis, the historical process parameters were analyzed using machine learning algorithms to train a nonlinear model between the time trajectory of process parameters such as temperature and pressure and the anti-poisoning performance. This nonlinear model was used as a performance prediction model and was defined as y=f(X(t)).

[0093] In this embodiment, the constructed performance prediction model can accurately predict the contribution of different process parameters in the production process to the anti-poisoning ability of the produced hydrogen storage alloy by learning the influence of the combination of different process parameters on the anti-poisoning performance index. The performance prediction model is the basis of process optimization and is used to predict the changes in the anti-poisoning performance of the hydrogen storage alloy under different process parameters.

[0094] In some embodiments, the uniformity function comprises:

[0095]

[0096] Wherein, F is the homogeneity function, which is characterized by the variance of the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipments, N is the number of production equipments in the production system, and y i is the anti-poisoning performance index of the hydrogen storage alloy produced by the i-th production equipment, It is the average value of the anti-poisoning performance index of the hydrogen storage alloy produced by N production equipments.

[0097] Among them, the smaller F is, the better the uniformity is.

[0098] In this embodiment, the goal of the upper-level decision controller is to ensure that the hydrogen storage alloy manufactured by each production equipment meets the uniformity requirements of the anti-poisoning performance on a macroscopic time scale of the entire production process of the hydrogen storage alloy.

[0099] In order to ensure the consistency of production, the upper decision controller aims to minimize the variance of the anti-poisoning performance index, namely:

[0100]

[0101] Among them, y i It is the anti-poisoning performance index of the hydrogen storage alloy produced by the i-th production equipment, that is, the hydrogen absorption and desorption cycle efficiency; It is the average value of the hydrogen absorption and desorption cycle efficiency of all production equipment producing hydrogen storage alloys. F represents the variance of the anti-poisoning performance of all production equipment producing hydrogen storage alloys. i (t) and P i (t) represent the temperature and pressure of production equipment i at time t, T max , T min , P max With P min Represents T i (t) and P i The upper and lower limits of (t).

[0102] During the optimization process, the upper decision controller adjusts the time trajectory X of the process parameters of each production equipment. i (t) to optimize y i uniformity, so that it is as close to 0 as possible.

[0103] The nonlinear model (performance prediction model) y=f(X(t)) established by machine learning can be used to calculate the time trajectory of the process parameters X i (t) and anti-poisoning performance y i Connect.

[0104] Therefore, the variance function can be rewritten as a function of the time trajectory of the process parameters:

[0105]

[0106] In the actual optimization problem, the time trajectory of the process parameters X i (t) The following constraints need to be met:

[0107] The temperature, pressure and other parameters of each production equipment must be within a certain operational range. The construction constraints are as follows:

[0108]

[0109] Among them, T i (t) and P i (t) represent the temperature trajectory and pressure trajectory of the i-th production equipment at time t, T max , T min , P max With P min Represents T i (t) and P i The upper and lower limits of (t).

[0110] In some embodiments, the step of finding the minimum value of the uniformity function based on the constraint conditions of the process parameters and constructing the upper-level decision controller includes:

[0111]

[0112] Wherein, F is the homogeneity function, N is the number of production equipment in the production system, is the time trajectory of the historical process parameters of the i-th production equipment, X i (t) is the optimal time trajectory of the process parameters of the i-th production equipment at time t, is the historical process parameters of the j-th production equipment, X j (t) is the optimal time trajectory of the process parameters of the j-th production equipment at time t, T i (t) is the temperature trajectory of the i-th production equipment at time t, T min is the lower limit of the temperature of the constraint condition, T max is the upper temperature limit of the constraint condition, P i (t) is the pressure trajectory of the i-th production equipment at time t, P min is the lower limit of the pressure of the constraint condition, P max is the upper limit of pressure of the constraint condition.

[0113] Based on the historical process parameters collected in real time during the preparation of hydrogen storage alloys The particle swarm optimization algorithm is used to solve the above optimization problem and calculate the optimal time trajectory X of the future process parameters of each production equipment. i (t) (such as temperature, pressure, etc.) to minimize the variance F of the anti-poisoning performance and ensure that the hydrogen storage alloys produced by all production equipment remain consistent in their anti-poisoning performance.

[0114] It should be noted that during the production process, the upper-level decision controller is responsible for global optimization and adopts long-cycle control to construct the optimal time trajectory of future process parameters based on the historical process parameters collected during the preparation process and the changes in anti-poisoning performance predicted by the performance prediction model.

[0115] In this embodiment, the controlling the multiple production equipment by the lower-layer model predictive controller based on the optimal time trajectory includes:

[0116] The optimal time trajectory is analyzed by the lower-layer model predictive controller to determine the target process parameters of the multiple production equipment at each moment;

[0117] The lower-layer model predictive controller adjusts the equipment parameters of the plurality of production equipment according to the target process parameters.

[0118] It can be understood that the lower-level model predictive controller is responsible for short-cycle real-time control of each production equipment at the micro time scale, ensuring the precise execution of the time trajectory of the process parameters.

[0119] The lower-level model predictive controller adjusts the process parameters based on the optimal time trajectory of the process parameters provided by the upper-level decision controller to cope with process fluctuations and external disturbances that may occur in a short period of time, and ensures that the actual operating parameters of each production equipment always follow the optimal time trajectory provided by the upper-level decision controller.

[0120] Specifically, the lower-level model predictive controller performs short-term real-time control on each production equipment within a hydrogen storage alloy preparation cycle to ensure that the optimal time trajectory of the process parameters is accurately executed. The state space expression of the lower-level model predictive controller is defined as follows:

[0121] X i (t+1)=AX i (t)+Bu i (t)+D (6)

[0122] Y i (t) = EX i (t) (7)

[0123] Among them, X i (t+1) is the optimal time trajectory of the process parameters of the i-th production equipment at time t+1, represented as a state vector, including the temperature trajectory T of the production equipment i at time t+1 i (t+1) and pressure trajectory P i (t+1), X i (t) is the optimal time trajectory of the process parameters of the i-th production equipment at time t, represented as a state vector, including the temperature trajectory T of the production equipment i at time t i (t) and pressure trajectory P i (t),u i (t) is the control input of the lower-level model predictive controller to the production equipment i at time t, including the heating power P i，h(t) and vacuum pump speed P i，v (t), Y i (t) is the controlled output, i.e., the temperature T measured at time t for the i-th production equipment. i (t) and pressure P i (t) composed of a vector, X i (t) = [T i (t), P i (t)] r , the coefficient matrices A, B, D and E of the state equation are:

[0124]

[0125] Where Δt is the time step; k T represents the attenuation coefficient of temperature, reflecting the heat loss; m is the mass of the object; c p is the specific heat capacity; k P is the pressure attenuation coefficient; b P is the vacuum pump efficiency constant; d T and d P are the disturbance terms of temperature and pressure respectively.

[0126] The lower-level model predictive controller tracks the optimal time trajectory of the process parameters given by the upper-level decision controller by minimizing the control error. The MPC tracking problem is described as follows:

[0127]

[0128] Among them, R Y (t+k) is the reference trajectory of temperature and pressure. Y represents the weight matrix, which represents the weighting coefficient of the output error of temperature and pressure and determines the priority of the tracking error; Y(t+k) and X(t+k) represent the state vector and output vector at time t+k respectively.

[0129] X max , X min 、u max and u min They are:

[0130]

[0131] in, and The heating power P is h (t) and vacuum pump speed P v The upper and lower limits of (t).

[0132] In this embodiment, the lower-level model predictive controller predicts short-term changes in process parameters through a state-space model, and dynamically adjusts key parameters such as temperature and pressure according to the optimal time trajectory, and compensates in real time for deviations caused by environmental changes or fluctuations in production equipment performance. Through this fine control, the production process is adjusted in real time. The lower-level model predictive controller can ensure the consistency of process parameters of each production equipment in batch production, minimize the impact of fluctuations in the production process on anti-poisoning performance, thereby overcoming the consistency problem in the prior art and improving the long-term reliability of hydrogen storage alloys.

[0133] In some embodiments, after controlling the plurality of production equipment by a lower-layer model predictive controller based on the optimal time trajectory, the method further includes:

[0134] Collecting real-time process parameters of the production system;

[0135] The optimal time trajectory is updated using the real-time process parameters.

[0136] In actual implementation, during the entire preparation process of hydrogen storage alloy, the process monitoring module continuously collects real-time process parameters and uses the data feedback mechanism to feed back the real-time process parameters to the upper-level decision controller to update the time trajectory of the process parameters. This feedback mechanism continuously optimizes the process parameters in future batches, thereby continuously improving the consistency of batch production.

[0137] The lower-level model predictive controller continuously adjusts according to the optimal time trajectory, while the upper-level decision controller uses real-time process parameters to perform periodic global feedback optimization, updates the optimal time trajectory, and provides more accurate process optimization solutions in future production batches.

[0138] In this embodiment, the process parameters collected in real time can be compared, and the time trajectory of the process parameters can be dynamically adjusted to ensure the consistency of the anti-poisoning performance.

[0139] like Figure 2 As shown, a two-layer controller is constructed based on a two-layer control framework, including an upper-layer decision controller and a lower-layer model predictive controller. Through the synergistic effect of the upper and lower control systems, the key process parameters of the hydrogen storage alloy in the production are collected, optimized and controlled in real time. The production system can achieve multi-scale control from macro to micro, which improves the consistency of the anti-poisoning performance of hydrogen storage alloys produced by different production equipment, improves the stability of batch production, and is suitable for large-scale industrial production.

[0140] The lower-level model predictive controller includes N model predictive control (MPC) controllers, and there is a one-to-one correspondence between the MPC controllers and the production equipment.

[0141] The upper decision controller optimizes the time trajectory of process parameters over a long period to reduce the deviation of process parameters between production equipment and ensure global consistency; the lower model predictive controller compensates for short-term disturbances through short-term real-time control to maintain accurate execution of process parameters. The combination of the two realizes multi-scale control from macro to micro, ensuring that the anti-poisoning performance of multiple batches of hydrogen storage alloys can meet the design requirements.

[0142] In this embodiment, by optimizing the nonlinear relationship between process parameters (temperature, pressure, etc.) and anti-poisoning performance and combining a double-layer control system, consistency of anti-poisoning performance in batch preparation of hydrogen storage alloys is achieved, thereby improving the long-term service life and performance stability of the material.

[0143] The upper-level decision controller is responsible for optimizing the time trajectory of global process parameters. Based on the historical process parameters such as temperature and pressure collected in real time, the time trajectory of key process parameters such as temperature and pressure is optimized over a long period of time to minimize the difference in anti-poisoning performance between different production equipment, obtain the optimal time trajectory of process parameters, and provide temperature and pressure reference values ​​to the lower-level model prediction controller to ensure the consistency of anti-poisoning performance of hydrogen storage alloys in large-scale production.

[0144] The MPC controller in the lower-level model predictive controller adjusts the heating power and vacuum pump rate of the production equipment to control the temperature and pressure, and performs fine real-time control in a short period of time. According to the optimal time trajectory of process parameters such as temperature and pressure generated by the upper-level decision controller, the temperature, pressure and other controllable parameters of each production equipment are adjusted dynamically in real time, and environmental changes and performance fluctuations of production equipment are compensated in real time. The short-term changes of process parameters are predicted through the state space model to ensure that the process parameters of each production equipment meet the optimal time trajectory, thereby ensuring the stability of the anti-poisoning performance of the hydrogen storage alloy in production. The feedback mechanism can also be used to ensure the consistency of the anti-poisoning performance of the hydrogen storage alloy in mass production, thereby improving the long-term service life and stability of the hydrogen storage alloy, making it suitable for large-scale industrial applications.

[0145] like Figure 3 As shown, the time trajectory of historical process parameters is collected during the preparation of hydrogen storage alloys by multiple production equipment, and the nonlinear relationship between process parameters and anti-poisoning performance is established using machine learning methods to build a performance prediction model;

[0146] Based on the performance prediction model, an upper-level decision controller is constructed with the goal of minimizing the difference in the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipments;

[0147] The differences among the anti-poisoning performance indicators of hydrogen storage alloys produced by multiple production equipment are evaluated through the upper-level decision controller. The time trajectory of the process parameters corresponding to the minimized differences is optimized through optimization algorithms such as genetic algorithms or particle swarm optimization to obtain the optimal time trajectory for the entire cycle of hydrogen storage alloy production.

[0148] The lower-level model predictive controller controls the operating state of each production equipment in the production system according to the process parameters corresponding to each moment in the optimal time trajectory, so as to affect the process parameters of the production equipment and achieve the synergy of global optimization and local fine control.

[0149] During the entire preparation process of hydrogen storage alloy, the process monitoring module continuously collects real-time process parameters and uses the data feedback mechanism to feed back the real-time process parameters to the upper-level decision controller to update the time trajectory of the process parameters. This feedback mechanism continuously optimizes the process parameters in future batches, thereby continuously improving the consistency of batch production.

[0150] In this embodiment, the anti-poisoning performance consistency of the solid-state hydrogen storage alloy in large-scale production is improved by optimizing the process parameters, especially the improvement of the hydrogen storage alloy's anti-poisoning ability to impurities such as oxygen (O2) and water vapor (H2O). By combining material engineering and automated control technology, advanced control strategies are used to ensure the performance stability and consistency of the hydrogen storage alloy in mass production. This is suitable for the mass production of hydrogen energy materials and improves their anti-poisoning performance.

[0151] The control method for consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys provided in the embodiment of the present application can be executed by a control device for consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys. In the embodiment of the present application, the control method for consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys is executed by a control device for consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys as an example to illustrate the control device for consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys provided in the embodiment of the present application.

[0152] The embodiment of the present application also provides a device for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys.

[0153] like Figure 4 As shown, the control device for the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys is applied to a production system of hydrogen storage alloys, wherein the production system includes a plurality of production equipment for executing a preparation process of hydrogen storage alloys, and the device includes: an acquisition module 410, a first processing module 420, a second processing module 430 and a third processing module 440.

[0154] An acquisition module 410 is used to acquire a performance prediction model of the production system, wherein the performance prediction model is constructed based on a time trajectory of historical process parameters of the production equipment and an anti-poisoning performance index of the produced hydrogen storage alloy;

[0155] A first processing module 420 is used to construct an upper-level decision controller based on the performance prediction model with the goal of minimizing the difference in the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipments;

[0156] A second processing module 430 is used to optimize the time trajectory of the historical process parameters through the upper-level decision controller to obtain the optimal time trajectory of the process parameters of the preparation process;

[0157] The third processing module 440 is used to control the multiple production equipment through a lower-level model predictive controller based on the optimal time trajectory.

[0158] According to the control device for consistency of anti-poisoning performance in batch preparation of solid-state hydrogen storage alloys provided in the embodiment of the present application, the time trajectory of historical process parameters is optimized by the upper-level decision controller constructed based on the performance prediction model, so that the optimal time trajectory with the lowest difference in anti-poisoning performance indicators can be obtained. Each production equipment is controlled according to the optimal time trajectory through the lower-level model prediction controller, thereby improving the stability and impurity resistance of the hydrogen storage alloy in batch production, ensuring the consistency of the anti-poisoning performance indicators of the batch production of hydrogen storage alloys, being able to improve the consistency of the anti-poisoning performance of solid-state hydrogen storage alloys, and ensuring the uniformity of hydrogen storage alloys in production and long-term performance.

[0159] The control device for the consistency of anti-poisoning performance in the batch preparation of solid hydrogen storage alloys in the embodiment of the present application can be an electronic device, or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices other than a terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, an ultra-mobile personal computer (UMPC), a netbook or a personal computer (PC), etc., which is not specifically limited in the embodiment of the present application.

[0160] The control device for the consistency of anti-poisoning performance in the batch preparation of solid hydrogen storage alloys in the embodiment of the present application can be a device with an operating system. The operating system can be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0161] The control device for consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys provided in the embodiment of the present application can realize the various processes implemented by the control method for consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys in the above-mentioned embodiment. To avoid repetition, they will not be described here.

[0162] In some embodiments, Figure 5 As shown, an embodiment of the present application also provides an electronic device 500, including a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the program is executed by the processor 501, each process of the embodiment of the control method for consistency of anti-poisoning performance in batch preparation of the above-mentioned solid hydrogen storage alloy is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0163] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0164] An embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the embodiment of the control method for consistency of anti-poisoning performance in batch preparation of the above-mentioned solid hydrogen storage alloy are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0165] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0166] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements a method for controlling the consistency of anti-poisoning performance in batch preparation of the above-mentioned solid hydrogen storage alloy.

[0167] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0168] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, which are coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the embodiment of the control method for consistency of anti-poisoning performance in batch preparation of the above-mentioned solid-state hydrogen storage alloy, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0169] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0170] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0171] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the control method for the consistency of anti-poisoning performance in the batch preparation of solid hydrogen storage alloys in each embodiment of the present application.

[0172] In the description of this application, "first feature" or "second feature" may include one or more of the features.

[0173] In the description of the present application, “plurality” means two or more.

[0174] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0175] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0176] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys, characterized in that: A production system for a hydrogen storage alloy, the production system comprising a plurality of production equipment for performing a preparation process of the hydrogen storage alloy, the method comprising: Acquire a performance prediction model of the production system, wherein the performance prediction model is constructed based on a time trajectory of historical process parameters of the production equipment and an anti-poisoning performance index of the produced hydrogen storage alloy; Based on the performance prediction model, an upper-level decision controller is constructed with the goal of minimizing the difference in the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipments; Optimizing the time trajectory of the historical process parameters by the upper-level decision controller to obtain the optimal time trajectory of the process parameters of the preparation process; Based on the optimal time trajectory, the plurality of production equipments are controlled by a lower-layer model predictive controller.

2. The method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloy according to claim 1, characterized in that: Based on the performance prediction model, an upper-level decision controller is constructed with the goal of minimizing the difference in the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipment, including: Based on the performance prediction model, constructing a uniformity function of the production system, wherein the uniformity function is used to characterize the difference in anti-poisoning performance indicators of the plurality of production equipment; Based on the constraints of the process parameters, the uniformity function is minimized and the upper-level decision controller is constructed.

3. The method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloy according to claim 2, characterized in that: The uniformity function comprises: Wherein, F is the homogeneity function, which is characterized by the variance of the anti-poisoning performance index of the hydrogen storage alloy produced by the multiple production equipments, N is the number of production equipments in the production system, and y i is the anti-poisoning performance index of the hydrogen storage alloy produced by the i-th production equipment, It is the average value of the anti-poisoning performance index of the hydrogen storage alloy produced by N production equipments.

4. The method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloy according to claim 3, characterized in that: The performance prediction model is: y=f(X(t)) The time trajectory of the historical process parameters is: X(t)=[T(t),P(t)] T Among them, y is the anti-poisoning performance index of the hydrogen storage alloy produced by the production equipment, X(t) is the time trajectory of the process parameters of the production equipment, T(t) is the temperature trajectory of the production equipment, and P(t) is the pressure trajectory of the production equipment.

5. The method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloy according to claim 2, characterized in that: The step of finding the minimum value of the uniformity function based on the constraint conditions of the process parameters and constructing the upper-level decision controller includes: Wherein, F is the homogeneity function, N is the number of production equipment in the production system, is the historical process parameters of the i-th production equipment, X i (t) is the time trajectory of the process parameters of the i-th production equipment at time t, is the historical process parameters of the j-th production equipment, X j (t) is the time trajectory of the process parameters of the j-th production equipment at time t, T i (t) is the temperature trajectory of the i-th production equipment at time t, T min is the lower limit of the temperature of the constraint condition, T max is the upper temperature limit of the constraint condition, P i (t) is the pressure trajectory of the i-th production equipment at time t, P min is the lower limit of the pressure of the constraint condition, P max is the upper limit of pressure of the constraint condition.

6. The method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys according to claim 1, characterized in that: The controlling of the plurality of production equipment by a lower-layer model predictive controller based on the optimal time trajectory includes: The optimal time trajectory is analyzed by the lower-layer model predictive controller to determine the target process parameters of the multiple production equipment at each moment; The lower-layer model predictive controller adjusts the equipment parameters of the plurality of production equipment according to the target process parameters.

7. The method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys according to any one of claims 1 to 6, characterized in that: After controlling the plurality of production devices by a lower-layer model predictive controller based on the optimal time trajectory, the method further includes: Collecting real-time process parameters of the production system; The optimal time trajectory is updated using the real-time process parameters.

8. A control device for the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys, characterized in that: A production system for a hydrogen storage alloy, the production system comprising a plurality of production equipment for performing a preparation process of the hydrogen storage alloy, the device comprising: An acquisition module, used for acquiring a performance prediction model of the production system, wherein the performance prediction model is constructed based on a time trajectory of historical process parameters of the production equipment and an anti-poisoning performance index of the produced hydrogen storage alloy; A first processing module is used to construct an upper-level decision controller based on the performance prediction model with the goal of minimizing the difference in anti-poisoning performance indicators of hydrogen storage alloys produced by the multiple production equipment; A second processing module, configured to optimize the time trajectory of the historical process parameters through the upper-layer decision controller to obtain the optimal time trajectory of the process parameters of the preparation process; The third processing module is used to control the multiple production equipment through a lower-level model predictive controller based on the optimal time trajectory.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, a method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method for controlling the consistency of anti-poisoning performance in batch preparation of solid hydrogen storage alloys as described in any one of claims 1 to 7.

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