A whale optimization particle swarm-based linear heater temperature control method and system

Abstract

The application discloses a linear heater temperature control method and system based on whale optimization particle swarm, which comprises a heating unit with temperature feedback arranged in a linear array, realizes rapid and uniform heating of the whole large-length-diameter-ratio heating surface, and avoids the problems of excessively high or low local temperature; an intelligent controller is used for individually and accurately controlling the temperature of each heating unit through a whale optimization particle swarm intelligent algorithm, thereby improving the overall heating efficiency and process stability of the equipment; and the whale optimization particle swarm algorithm is used in the intelligent controller, which makes up for the shortcoming of premature falling into local extreme value in the particle swarm algorithm, increases the diversity of particles, and thus makes the temperature control effect better.

Description

Technical Field

[0001] This invention relates to the field of heater temperature control technology, and in particular to a linear heater temperature control method and system based on whale-optimized particle swarm optimization. Background Technology

[0002] In industrial production, many raw materials need to be heated to a constant temperature before assembly or processing to meet process requirements. Existing heaters, when used to heat spaces with large aspect ratios, suffer from limited heating area and low temperature control precision. Furthermore, the large length of such spaces and numerous heat dissipation factors often lead to uneven heating, resulting in localized overheating or underheating. This not only affects heating efficiency and process stability but also increases the risk of equipment damage. Additionally, they suffer from low heating rates and excessive energy consumption.

[0003] Therefore, how to design a temperature control method for a large aspect ratio linear heater that can achieve rapid and uniform heating and prevent local temperatures from being too high or too low has become an urgent problem to be solved in this field. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a linear heater temperature control method based on whale-optimized particle swarm optimization to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a linear heater temperature control method based on whale-optimized particle swarm optimization, comprising:

[0008] A linear heater is constructed, which consists of a heating surface, a temperature feedback unit, a heating unit, and an intelligent controller;

[0009] The heating surface receives the temperature signal transmitted by the heating unit;

[0010] The heating unit receives the temperature signal returned by the heating surface and obtains the real-time temperature of the heating surface;

[0011] The real-time temperature of the heating surface obtained by the heating unit is sent to the intelligent controller through the temperature feedback unit.

[0012] The intelligent controller calculates the target current value of the heating unit based on the real-time temperature of the heating surface received by the temperature feedback unit and the whale-optimized particle swarm algorithm. The target current value is then returned to the heating unit to adjust the instantaneous current of the heating unit.

[0013] As a preferred embodiment of the linear heater temperature control method based on whale-optimized particle swarm optimization described in this invention, the heating surface includes:

[0014] The heating surface has a large aspect ratio linear structure, with the upper surface being the process contact surface and the lower surface connected to the heating unit in an array.

[0015] As a preferred embodiment of the linear heater temperature control method based on whale-optimized particle swarm optimization described in this invention, the heating unit includes:

[0016] The heating unit is a resistance heating element, and the heating unit is divided into several units, which are arranged in a linear array below the heating surface;

[0017] Each heating unit contains a temperature feedback unit, and the spaces between the heating units are filled with thermally conductive silicone grease.

[0018] As a preferred embodiment of the linear heater temperature control method based on whale-optimized particle swarm optimization described in this invention, the temperature feedback unit includes:

[0019] The temperature feedback unit is a feedback thermocouple;

[0020] One end is in close contact with the working surface of the heating unit, and the other end is electrically connected to the intelligent controller.

[0021] As a preferred embodiment of the linear heater temperature control method based on whale-optimized particle swarm optimization described in this invention, the whale-optimized particle swarm optimization algorithm includes:

[0022] The spiral position update pattern from the whale algorithm is incorporated into the position update stage of the particle swarm algorithm.

[0023] As a preferred embodiment of the linear heater temperature control method based on whale-optimized particle swarm optimization described in this invention, it further includes:

[0024] The position update formula is expressed as:

[0025] x ij (t+1)=x ij (t)+v ij (t+1), p < 0.5

[0026] x(t+1)=D'·e bl ·cos(2πa)+xbest (t), p≥0.5

[0027] D'=|x best (t)-x ij (t)|

[0028] l=exp(2·cos(π((M-t+1) / M)))

[0029]

[0030] Where p represents the probability factor, a random number between [0,1]. When p < 0.5, the particle updates its position using the particle swarm optimization method; when p ≥ 0.5, the particle updates its position using the spiral position update mode of the whale algorithm; a represents the convergence factor, which linearly decreases from 2 to 0; x best (t) is the optimal position vector obtained under the current updated position; x ij (t) is the current position vector of the particle, i is the row and j is the column; b is a constant for the shape of the logarithmic spiral; M is the maximum number of iterations; l is a random value between [-1,1] that varies depending on the number of times the position is updated.

[0031] As a preferred embodiment of the linear heater temperature control method based on whale-optimized particle swarm optimization described in this invention, the intelligent controller includes:

[0032] The intelligent controller has the function of setting heating modes and temperature control strategies, which can adjust the heating method and temperature control strategy according to actual needs;

[0033] The intelligent controller also has an overload protection function. When the current of any heating unit exceeds the preset safety threshold, the intelligent controller will immediately cut off the power supply to that heating unit.

[0034] Secondly, the present invention provides a linear heater temperature control system based on whale-optimized particle swarm optimization, comprising:

[0035] The heating surface is configured to receive the temperature signal transmitted by the heating unit;

[0036] The heating unit is configured to receive the temperature signal returned by the heating surface and obtain the real-time temperature of the heating surface;

[0037] The temperature feedback unit is configured to send the real-time temperature of the heating surface obtained by the heating unit to the intelligent controller;

[0038] The intelligent controller is configured to calculate the target current value of the heating unit based on the real-time temperature of the heating surface received by the temperature feedback unit and the whale-optimized particle swarm algorithm, and return the target current value to the heating unit to adjust the instantaneous current magnitude of the heating unit.

[0039] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described method.

[0040] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the above-described method.

[0041] Compared with existing technologies, the invention has the following advantages: The invention achieves rapid and uniform heating of the entire high aspect ratio heating surface through a linear array of heating units with temperature feedback, avoiding problems of excessively high or low local temperatures; the intelligent controller uses a whale-optimized particle swarm optimization algorithm to perform individual and precise temperature control on each heating unit, improving the overall heating efficiency and process stability of the equipment; and the use of the whale-optimized particle swarm optimization algorithm in the intelligent controller overcomes the premature convergence and local extremum limitations of the particle swarm optimization algorithm, increasing particle diversity and thus resulting in better temperature control. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0043] Figure 1 This is a flowchart illustrating the overall process of a linear heater temperature control method based on whale-optimized particle swarm optimization according to an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of a high aspect ratio temperature-controlled linear heater structure according to an embodiment of the linear heater temperature control method based on whale optimized particle swarm optimization according to the present invention.

[0045] Figure 3 This is a temperature control effect diagram of the intelligent controller of the linear heater temperature control method based on whale-optimized particle swarm optimization according to an embodiment of the present invention. Detailed Implementation

[0046] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0047] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0049] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0050] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0051] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0052] Example 1

[0053] Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a linear heater temperature control method based on whale-optimized particle swarm optimization, comprising:

[0054] S1. Construct a linear heater, refer to... Figure 2 The heater consists of a heating surface 1, a temperature feedback unit 2, a heating unit 3, and an intelligent controller 4;

[0055] Furthermore, the heating surface has a linear structure with a large aspect ratio, the upper surface is the process contact surface, and the lower surface is connected to the heating unit in an array manner;

[0056] Furthermore, the heating unit is a resistance heating element, and the heating unit is divided into several units, which are arranged in a linear array below the heating surface;

[0057] Specifically, the resistance heating element generates heat through the current in the heating unit, causing the temperature of the heating surface to rise;

[0058] Furthermore, each heating unit contains a temperature feedback unit, and the spaces between the heating units are filled with thermally conductive silicone grease.

[0059] Specifically, using thermal grease can fill the gaps between heating units, reduce air resistance, and allow heat to be smoothly conducted to the heating surface. On the other hand, it can prevent uneven temperature on the heating surface during the heating process, thereby improving heating efficiency and process stability.

[0060] Furthermore, through the bidirectional conduction of the heating unit, the temperature of the heating surface is transmitted to the temperature feedback unit in real time, enabling the temperature feedback unit to respond to temperature changes more quickly, thereby achieving more precise temperature control and adjustment.

[0061] Specifically, before transmitting the real-time temperature to the temperature feedback unit, each heating unit needs to be numbered and then connected to the intelligent controller.

[0062] Furthermore, the temperature feedback unit is a feedback thermocouple;

[0063] Furthermore, one end of the feedback thermocouple is in close contact with the working surface of the heating unit, and the other end is electrically connected to the intelligent controller;

[0064] S2, The heating surface receives the temperature signal transmitted by the heating unit;

[0065] Specifically, the heating unit sends out a temperature signal (temperature value), the heating surface heats according to the temperature signal (temperature value), and returns the current temperature value;

[0066] It should be noted that the returned current temperature value is not the exact value of the temperature signal sent by the heating unit. For example, if the current temperature signal (temperature value) sent by the heating unit is 50°C, in reality, even if the heating surface is heated evenly, not all heated surfaces will be exactly 50°C, but will be around 50°C, with a temperature deviation.

[0067] S3, heating unit, receives the temperature signal returned by the heating surface and obtains the real-time temperature of the heating surface;

[0068] Specifically, the heating unit receives the temperature signal returned by the heating surface (the temperature signal here refers to the current temperature value of the heating surface as described above, i.e., the real-time temperature of the heating surface), not the temperature signal (temperature value) emitted by the heating unit.

[0069] S4. The real-time temperature of the heating surface obtained by the heating unit is sent to the intelligent controller through the temperature feedback unit.

[0070] Specifically, the temperature feedback unit sends the real-time temperature of the heating surface obtained by the heating unit to the intelligent controller through the electrical connection between the heating unit and the intelligent controller, not through the heating unit itself;

[0071] It should be noted that traditional methods involve direct temperature transmission between units, which means that if a component is damaged, the entire device needs to be replaced. Furthermore, the temperature transmission process involves passing through various components, resulting in excessively long transmission request times and an inability to address temperature anomalies promptly. This invention, through electrical transmission of real-time temperature, enables modularization of the heating unit and temperature feedback unit, making the temperature feedback unit an independent module. This independent module can monitor the heating unit's operating status in real time. Upon detecting an abnormal temperature, it can quickly adjust or shut down the corresponding heating unit (shutting down the heating unit via its number) via the intelligent controller without waiting for the temperature transmission process, preventing overheating and safety accidents, and improving equipment safety. Moreover, when maintenance or replacement of a component is required, only the corresponding module needs to be replaced, rather than the entire device, reducing maintenance costs.

[0072] S5. The intelligent controller calculates the target current value of the heating unit based on the real-time temperature of the heating surface received by the temperature feedback unit and the whale-optimized particle swarm algorithm. The target current value is then returned to the heating unit to adjust the instantaneous current of the heating unit.

[0073] Furthermore, the spiral position update mode from the whale algorithm is integrated into the position update stage of the particle swarm algorithm;

[0074] Specifically, the whale-based optimized particle swarm optimization algorithm is represented as follows:

[0075] x ij (t+1)=x ij (t)+v ij (t+1), p < 0.5

[0076] x(t+1)=D'·e bl ·cos(2πa)+x best (t), p≥0.5

[0077] D'=|x best (t)-xij (t)|

[0078] l=exp(2·cos(π((M-t+1) / M)))

[0079]

[0080] Where p represents the probability factor, a random number between [0,1]. When p < 0.5, the particle updates its position using the particle swarm optimization method; when p ≥ 0.5, the particle updates its position using the spiral position update mode of the whale algorithm; a represents the convergence factor, which linearly decreases from 2 to 0; x best (t) is the optimal position vector obtained under the current updated position; x ij (t) is the current position vector of the particle, where i is the row and j is the column; b is a constant for the shape of the logarithmic spiral; M is the maximum number of iterations; l is a random value between [-1, 1] that varies depending on the number of position updates.

[0081] Furthermore, the aforementioned whale-optimized particle swarm optimization algorithm is instantiated, and the instantiation process is as follows:

[0082] There are n particles in N-dimensional space. Let the position of the i-th particle be X. i =[x i 1,x i 2,…,x i N]; Flight speed: V i =[v i 1,v i 2,…,v i N];

[0083] X i Substituting into the objective function, we obtain the fitness value of the i-th particle. The optimal position it has experienced (the position with the best fitness value) is: P i =[p i 1,p i 2,…,p i [N]; Let the global optimal position traversed by all particles be: P g =[p g 1,p g 2,…,p g N];

[0084] In each iteration, the particle's velocity and position are updated according to the formulas shown below:

[0085]

[0086] x ij (t+1)=x ij (t)+v ij(t+1), p < 0.5

[0087] x(t+1)=D'·e bl ·cos(2πa)+x best (t), p≥0.5

[0088] D'=|x best (t)-x ij (t)|

[0089] l=exp(2·cos(π((M-t+1) / M)))

[0090]

[0091] Where t represents the current iteration number; ω is the inertia weight, representing the degree of influence of previous velocity iterations on the new velocity after the iteration; r1 and r2 are randomly given values ​​between [0,1]; v ij (t+1) and x ij (t+1) represents the values ​​of particle velocity and position after this iteration. If they exceed the preset upper and lower boundaries, DC is represented as the boundary value. c1 and c2 are preset constants greater than 0, which control the direction of the values ​​of r1 and r2.

[0092] It should be noted that the whale-optimized particle swarm algorithm proposed in this invention overcomes the shortcomings of premature convergence and getting trapped in local optima in the particle swarm algorithm, increases the diversity of particles, and thus achieves better temperature control.

[0093] Furthermore, the intelligent controller has the function of setting heating modes and temperature control strategies, which can adjust the heating method and temperature control strategy according to actual needs.

[0094] Specifically, heating methods include intermittent heating, two-end heating, synchronous heating, and sequential heating;

[0095] Specifically, temperature control strategies include neural network control and robust control;

[0096] Furthermore, the intelligent controller also has an overload protection function. When the current of any heating unit exceeds the preset safety threshold, the intelligent controller will immediately cut off the power supply to that heating unit.

[0097] Furthermore, this embodiment also provides a linear heater temperature control system based on whale-optimized particle swarm optimization, including:

[0098] The heating surface is configured to receive the temperature signal transmitted by the heating unit;

[0099] The heating unit is configured to receive the temperature signal returned by the heating surface and obtain the real-time temperature of the heating surface;

[0100] The temperature feedback unit is configured to send the real-time temperature of the heating surface obtained by the heating unit to the intelligent controller;

[0101] The intelligent controller is configured to calculate the target current value of the heating unit based on the real-time temperature of the heating surface received by the temperature feedback unit and the whale-optimized particle swarm algorithm, and return the target current value to the heating unit to adjust the instantaneous current magnitude of the heating unit.

[0102] This embodiment also provides a computer device applicable to the linear heater temperature control method based on whale-optimized particle swarm optimization, including:

[0103] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the linear heater temperature control method based on whale-optimized particle swarm optimization proposed in the above embodiments.

[0104] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0105] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the linear heater temperature control method based on whale-optimized particle swarm optimization as proposed in the above embodiments.

[0106] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0107] Example 2

[0108] Reference Figure 3This is the second embodiment of the present invention, which provides a linear heater temperature control method based on whale-optimized particle swarm optimization, including: verifying the independent module design and the temperature control effect of the whale-optimized particle swarm optimization algorithm in the present invention through simulation experiments;

[0109] Experimental preparation stage: In the initial state, the target temperature of all heating units was set to 100℃; the intelligent controller adjusted the current value of the heating units according to the real-time temperature feedback to achieve the target temperature; the temperature distribution of the heating surface was continuously monitored and the temperature data was recorded; the traditional PID control method was selected for the same heating process in the comparative experiment to verify the effect of the whale-optimized particle swarm algorithm of the present invention; the experimental results are shown in Table 1.

[0110] Table 1

[0111] Heating time minute 10 15 Temperature fluctuations ℃ ±1.5 ±3.5 Heating uniformity ％ 98 85 Energy consumption kWh 2.5 3.8 Equipment stability / high middle

[0112] First, as shown in Table 1, the heating time using the whale-optimized particle swarm optimization algorithm is 10 minutes, while the traditional PID control method requires 15 minutes. Regarding temperature fluctuation, the temperature fluctuation based on the whale-optimized particle swarm optimization algorithm is ±1.5℃, while the traditional PID control is ±3.5℃. Clearly, the former has advantages in temperature control accuracy and stability. In terms of heating uniformity, the whale-optimized particle swarm optimization method achieves 98% uniformity, while the traditional PID control only achieves 85%. Higher uniformity means the heater can better meet process requirements, reducing the risk of localized overheating or undercooling. Regarding energy consumption, the whale-optimized particle swarm optimization method consumes 2.5 kWh, lower than the 3.8 kWh of the traditional PID control. This result demonstrates that the method of this invention also shows significant advantages in energy saving. In terms of equipment stability, the whale-optimized particle swarm optimization method exhibits higher equipment stability, reducing the risk of equipment damage due to uneven temperature and improving equipment lifespan.

[0113] Secondly, combining Figure 3 As can be seen, the temperature anomaly response time of the intelligent controller in the independent module designed in this invention is completely different from that in the traditional non-independent module. Under the premise of the independent module, the temperature anomaly response time of this invention can reach 1.39s, while the response time of the traditional non-independent module can only reach 5.43s, and the response time of the independent module is 3 to 4 times faster. This shows that this invention does not need to wait for the temperature transmission process, but can directly and quickly adjust or shut down the corresponding heating unit through the intelligent controller, thereby improving the response time of the intelligent controller to temperature anomalies.

[0114] In conclusion, based on the above verifications, the solution of this invention is superior to the traditional solution.

[0115] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0116] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0120] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A linear heater temperature control method based on whale-optimized particle swarm optimization, characterized in that, include: A linear heater is constructed, which consists of a heating surface, a temperature feedback unit, a heating unit, and an intelligent controller; The heating surface receives the temperature signal transmitted by the heating unit; The heating surface includes: The heating surface is a linear structure with a large aspect ratio, the upper surface is the process contact surface, and the lower surface is connected to the heating unit in an array manner. The heating unit receives the temperature signal returned by the heating surface and obtains the real-time temperature of the heating surface; The heating unit includes: The heating unit is a resistance heating element, and the heating unit is divided into several units, which are arranged in a linear array below the heating surface; Each heating unit contains a temperature feedback unit, and the spaces between the heating units are filled with thermally conductive silicone grease. The real-time temperature of the heating surface obtained by the heating unit is sent to the intelligent controller through the temperature feedback unit. The intelligent controller calculates the target current value of the heating unit based on the real-time temperature of the heating surface received by the temperature feedback unit and the whale-optimized particle swarm algorithm, and returns the target current value to the heating unit to adjust the instantaneous current of the heating unit. The intelligent controller includes: The intelligent controller has the function of setting heating modes and temperature control strategies, which can adjust the heating method and temperature control strategy according to actual needs; The intelligent controller also has an overload protection function. When the current of any heating unit exceeds the preset safety threshold, the intelligent controller will immediately cut off the power supply to that heating unit. The whale-optimized particle swarm optimization algorithm includes: The spiral position update pattern from the whale algorithm is incorporated into the position update stage of the particle swarm algorithm. Also includes: The position update formula is expressed as: Where p represents a probability factor, which is a random number between [0,1]. When p < 0.5, the particle updates its position using the particle swarm method. When p ≥ 0.5, the particle updates its position using the spiral position update mode of the whale algorithm. This represents the convergence factor, which decreases linearly from 2 to 0. It is the optimal position vector obtained under the current update position; It is the current position vector of the particle. For the purpose of action, For example; It is a constant representing the shape of the logarithmic spiral; This represents the maximum number of iterations. It takes a random value between [-1, 1] and varies depending on the number of times the position is updated.

2. The linear heater temperature control method based on whale-optimized particle swarm optimization as described in claim 1, characterized in that, The temperature feedback unit includes: The temperature feedback unit is a feedback thermocouple; One end is in close contact with the working surface of the heating unit, and the other end is electrically connected to the intelligent controller.

3. A linear heater temperature control system based on whale particle swarm optimization, based on the linear heater temperature control method based on whale particle swarm optimization as described in any one of claims 1-2, characterized in that, include: The heating surface is configured to receive the temperature signal transmitted by the heating unit; The heating unit is configured to receive the temperature signal returned by the heating surface and obtain the real-time temperature of the heating surface; The temperature feedback unit is configured to send the real-time temperature of the heating surface obtained by the heating unit to the intelligent controller; The intelligent controller is configured to calculate the target current value of the heating unit based on the real-time temperature of the heating surface received by the temperature feedback unit and the whale-optimized particle swarm algorithm, and return the target current value to the heating unit to adjust the instantaneous current magnitude of the heating unit.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 2.

5. A 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 steps of the method according to any one of claims 1 to 2.

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