Electromagnetic induction welding control method and device, control equipment, medium and program product

Abstract

The invention relates to an electromagnetic induction welding control method and device, control equipment, a computer readable storage medium and a computer program product. The method comprises the steps that in the process that electromagnetic induction welding equipment conducts electromagnetic induction welding on a cable, state parameters of the cable and working condition parameters of the electromagnetic induction welding equipment are obtained, the state parameters and the working condition parameters are input into a control prediction model, and a control parameter sequence output by the control prediction model is obtained. By adopting the method, the stability of electromagnetic induction welding can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control, and in particular, to an electromagnetic induction welding control method and device, a control equipment, a computer readable storage medium and a computer program product. BACKGROUND

[0002] With the continuous development of power technology and the continuous improvement of manufacturing technology, the manufacturing and connection technology of cables also develops continuously. In large infrastructure projects, high-quality connection of cable conductors is a key link to ensure the stability of engineering construction. Traditional welding technologies mainly include gas shielded welding, electric arc welding and flame brazing. These methods rely on open flame or high-temperature electric arc as heat source, which not only has low energy utilization rate, but also is difficult to maintain stable welding power under high current and low voltage conditions.

[0003] In the prior art, electromagnetic induction heating welding technology can also be used for welding. Electromagnetic induction welding technology is widely used in the automatic welding of metal pipes, connecting pieces and cable terminals due to its fast heating speed, high thermal efficiency and strong local temperature control capability. However, electromagnetic induction welding technology is greatly disturbed by external environment and has the problem of insufficient stability. SUMMARY

[0004] Therefore, it is necessary to provide an electromagnetic induction welding control method, device, control equipment, computer readable storage medium and computer program product capable of improving the stability of electromagnetic clean welding to solve the above technical problems.

[0005] In a first aspect, the present application provides an electromagnetic induction welding control method, comprising: obtaining state parameters of a cable and working condition parameters of an electromagnetic induction welding equipment during electromagnetic induction welding of the cable by the electromagnetic induction welding equipment, the working condition parameters comprising operating parameters and / or environmental parameters of the electromagnetic induction welding equipment; inputting the state parameters and the working condition parameters into a control prediction model to obtain a control parameter sequence output by the control prediction model, the control parameter sequence being used for electromagnetic induction welding of the cable by the electromagnetic induction welding equipment within a target time period in the case of network interruption.

[0006] In one of the embodiments, inputting the state parameters and the working condition parameters into the control prediction model to obtain the control parameter sequence output by the control prediction model comprises: inputting the state parameters and the working condition parameters into the control prediction model to determine a plurality of initial control parameters of the electromagnetic induction welding equipment by the control prediction model; and performing prediction processing by the control prediction model according to the initial control parameters, the operating parameters and the environmental parameters to obtain the control parameter sequence.

[0007] In one of the embodiments, the state parameters and the working condition parameters are input into the control prediction model, and a plurality of initial control parameters of the electromagnetic induction welding device are determined by the control prediction model, including: inputting the state parameters and the working condition parameters into the control prediction model, and determining welding heat parameters by the control prediction model according to the state parameters and the environment parameters; determining initial power parameters and heating time parameters by the control prediction model according to the welding heat parameters and the state parameters; determining initial induction frequency, initial current and initial duty cycle as initial control parameters by the control prediction model according to the initial power parameters and the heating time parameters; wherein the state parameters include at least one of cable material parameters, cross-sectional area, overlap length and insulation layer material parameters.

[0008] In one of the embodiments, the control parameter sequence is obtained by the control prediction model according to the initial control parameters, the running parameters and the environment parameters, including: determining a heat dissipation coefficient by the control prediction model according to the environment parameters; obtaining a plurality of control parameters corresponding to the target time period for each initial control parameter by the control prediction model according to the running parameters and the heat dissipation coefficient; and generating the control parameter sequence by the control prediction model according to each initial control parameter and each control parameter.

[0009] In one of the embodiments, the control parameter sequence is obtained by the control prediction model according to the initial control parameters, the running parameters and the environment parameters, including: determining a heat dissipation coefficient by the control prediction model according to the environment parameters; obtaining a plurality of control parameters corresponding to the target time period for each initial control parameter by the control prediction model according to the running parameters and the heat dissipation coefficient; and generating the control parameter sequence by the control prediction model according to each initial control parameter and each control parameter.

[0010] In one of the embodiments, the method further includes: in the case of detecting network interruption of the electromagnetic induction welding device, sending the control parameters included in the control parameter sequence to the electromagnetic induction welding device at a preset frequency through the local area network between the electromagnetic induction welding device.

[0011] In the second aspect, the application further provides an electromagnetic induction welding control device, including: a parameter acquisition module, configured to acquire state parameters of a cable and working condition parameters of an electromagnetic induction welding device in a process of electromagnetic induction welding of the cable by the electromagnetic induction welding device, the working condition parameters including running parameters and / or environment parameters of the electromagnetic induction welding device; and a control parameter prediction module, configured to input the state parameters and the working condition parameters into a control prediction model, and obtain a control parameter sequence output by the control prediction model, the control parameter sequence being used for electromagnetic induction welding of the electromagnetic induction welding device in a target time period in the case of network interruption.

[0012] In a third aspect, the present application also provides a control device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in the first aspect when executing the computer program.

[0013] In a fourth aspect, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method in the first aspect when executed by a processor.

[0014] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, and the computer program implements the steps of the method in the first aspect when executed by a processor.

[0015] The electromagnetic induction welding control method, device, control device, computer readable storage medium and computer program product described above, in the process of electromagnetic induction welding of the cable by the electromagnetic induction welding device, the state data of the cable and the operation parameter and / or environmental parameter of the electromagnetic induction welding device are acquired, the state parameter, the operation parameter and / or the environmental parameter are input into the control prediction model, the control parameter sequence is predicted through the online control prediction model, the control parameter sequence output by the control prediction model is obtained, and the control parameter sequence can be used to enable the electromagnetic induction device to perform electromagnetic induction welding within a target time period in the case of network interruption, so that uninterrupted device control is realized in the case of Internet interruption, and the stability of electromagnetic induction welding is improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 An application environment diagram of the electromagnetic induction welding control method in an embodiment;

[0018] Figure 2 A flowchart of the electromagnetic induction welding control method in an embodiment;

[0019] Figure 3 A flowchart of step 202 in an embodiment;

[0020] Figure 4 A flowchart of step 301 in an embodiment;

[0021] Figure 5 A flowchart of step 403 in an embodiment;

[0022] Figure 6 a flowchart of step 502 in one embodiment;

[0023] Figure 7 a flowchart of the step of sending control parameters in one embodiment;

[0024] Figure 8 a flowchart of the electromagnetic induction welding control method in another embodiment;

[0025] Figure 9 a block diagram of the electromagnetic induction welding control device in one embodiment;

[0026] Figure 10 an internal structure diagram of the control device in one embodiment. DETAILED DESCRIPTION

[0027] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0028] It should be noted that the terms "first", "second", etc. used in the present application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "include" and "have" and any variations thereof used in the present application are intended to cover non-exclusive inclusion. The term "a plurality of" used in the present application refers to two or more. The term "and / or" used in the present application refers to one of the options or any combination of multiple options.

[0029] The electromagnetic induction welding control method provided by the embodiments of the present application can be applied in an application environment as shown in the figure. Figure 1 The application environment at least includes an electromagnetic induction welding device 101, a control device 102, an uninterruptible power supply (UPS) 103 and a control prediction model 104.

[0030] The electromagnetic induction welding device 101 is used for electromagnetic induction welding of a cable; during the welding process, under the condition of internet connection, electromagnetic induction control is performed according to the instruction of an external online device, under the condition of internet interruption, a control parameter sequence of the control device 102 is received to perform electromagnetic induction welding; at the same time, under the condition of internet interruption, power supplied by the uninterruptible power supply 103 is received to perform electromagnetic induction welding.

[0031] The control device 102 is configured to acquire the state parameters of the cable and the working condition parameters of the electromagnetic induction welding device 101 during the electromagnetic induction welding of the cable by the electromagnetic induction welding device 101, and input the state parameters and the working condition parameters into the control prediction model 104 to obtain a control parameter sequence output by the control prediction model 104. The control device 102 can be a server, which can be a standalone server or a server cluster or a distributed system composed of multiple physical servers. In addition, the control device 102 can also be a terminal device, which can be various personal computers, notebook computers, tablet computers, smart phones, Internet of Things devices and other devices with communication capabilities.

[0032] The uninterruptible power supply 103 can be a power supply connected with the electromagnetic induction welding device 101 and the control device 102, and can provide power to the electromagnetic induction welding device 101 and the control device 102 when detecting that the electromagnetic induction welding device 101 and the control device 102 are powered off.

[0033] The control prediction model 104 is configured to receive the state parameters of the cable and the working condition parameters of the electromagnetic induction welding device 101 sent by the control device 102, and perform prediction processing based on the state parameters and the working condition parameters to obtain a control parameter sequence in a target time period, and output the control parameter sequence to the control device 102. The control prediction model 104 can be deployed in an external device, which can be a server, which can be a standalone physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0034] The electromagnetic induction welding device 101, the control device 102 and the uninterruptible power supply 103 can be connected through a local area network, which can be established through a network cable. The control device 102 and the control prediction model 104 can communicate through the Internet.

[0035] In actual scenarios, high-quality connection of cable conductors is a key link to ensure stable operation of the system in power construction, rail transit, emergency maintenance and large infrastructure projects. Traditional welding methods mainly include gas shielded welding, electric arc welding and flame brazing, etc. These methods rely on open flame or high-temperature electric arc as heat source, which not only has low energy utilization rate, but also is difficult to maintain stable welding power under high current and low voltage conditions. Especially when welding inside the cable joint or in the insulation covering area, open flame welding is easy to cause carbonization or local ablation of the insulation layer, thereby causing joint breakdown or long-term insulation performance degradation. Therefore, how to realize open flame-free, uniform and controllable conductor welding in limited space has become the focus of industry research. With the development of electromagnetic induction heating technology, induction welding is widely used in automatic welding scenarios of metal pipes, connecting pieces and cable terminals due to its fast heating speed, high thermal efficiency and strong local temperature control capability. Such devices generate eddy current heat in the metal through high-frequency alternating magnetic field, making the conductor itself heat up to complete melting and welding without direct contact or open flame. However, existing induction welding equipment is designed for factory environment and relies on stable AC power supply. Its power control system mainly uses fixed frequency and current closed loop, which is difficult to adapt to outdoor use conditions with large power supply fluctuations and frequent environmental disturbances. When the power supply voltage drops or there is intermittent power failure, the welding point temperature drops suddenly, which is easy to produce structural defects such as "incomplete welding" and "slag inclusion". In addition, in strong wind, low temperature or high humidity environment, changes in heat dissipation conditions will make the welding temperature control system respond slowly, leading to uneven heating or overheating problems.

[0036] To solve these problems, temperature feedback control and current compensation mechanism can be used to maintain the welding point temperature. For example, the induction welding temperature closed-loop control system based on infrared temperature feedback maintains the surface temperature constant by adjusting the current; fuzzy control or adaptive gain method can also be introduced to automatically correct the heating power under current fluctuation. Although these methods can effectively improve the welding stability in laboratory environment, they still need stable power input as a prerequisite and cannot cope with outdoor power interruption or energy switching scenarios. For cable welding in high-altitude, field or emergency conditions, such systems still have significant limitations.

[0037] In addition, the safety control strategy of existing induction welding equipment mainly focuses on over-temperature alarm or delay shutdown, without establishing a temperature grading control mechanism for protection of the cable insulation layer. In actual application, when the surface temperature of the welding point is too high, even if the controller shuts down in time, heat will still be conducted to the insulation layer through the conductor, causing it to exceed the temperature limit in a short time and causing hidden damage. Such hidden damage is easy to cause insulation breakdown and short circuit accidents in subsequent long-term operation.

[0038] In summary, the prior art has made some progress in welding efficiency, local temperature control and automatic control, but still has significant shortcomings. First, the existing induction welding device depends heavily on network stability and cannot work continuously under outdoor network interruption conditions. Second, there is a lack of control algorithm that can predict and compensate for power changes, resulting in temperature regulation response lag. Third, the insulation layer safety protection mechanism is imperfect and cannot achieve targeted protection and heat distribution balance control.

[0039] To this end, the application obtains the state parameters of the cable and the working condition parameters of the electromagnetic induction welding equipment through an additional control device during the electromagnetic induction welding process of the electromagnetic induction welding equipment. The control parameter sequence is generated in real time by the online control prediction model according to the state parameters and the working condition parameters, and the control parameter sequence is stored in the control device. When the network interruption of the electromagnetic induction welding equipment is detected, the electromagnetic induction equipment continues to perform electromagnetic induction welding through the control parameter sequence, thereby improving the stability of the electromagnetic induction welding.

[0040] In an exemplary embodiment, as shown in Figure 2 , an electromagnetic induction welding control method is provided. The method is applied to the control device in Figure 1 for illustration, which includes the following steps 201 to 202.

[0041] Step 201, during the electromagnetic induction welding process of the electromagnetic induction welding equipment on the cable, the state parameters of the cable and the working condition parameters of the electromagnetic induction welding equipment are obtained.

[0042] In the present application, the electromagnetic induction equipment refers to the equipment that performs metal welding through the principle of electromagnetic induction heating. The electromagnetic induction equipment can include a control unit that can communicate and interact with external devices through the Internet. The control unit can obtain control parameters sent by the external device through the Internet to cooperate with the external device to perform dynamic electromagnetic induction welding according to real-time equipment state control. The control unit can also interact with the control device to receive the control parameter sequence sent by the control device to realize dynamic electromagnetic induction welding under network interruption. The working condition parameters of the electromagnetic induction welding equipment can include operating parameters and / or environmental parameters of the electromagnetic induction welding equipment. The operating parameters can include at least one of the output current, output power and electromagnetic induction intensity of the electromagnetic induction welding equipment, and the environmental parameters can include at least one of the ambient temperature, cable temperature and wind speed. It should be noted that the operating parameters can be collected by the electromagnetic induction welding equipment and sent to the control device, and the environmental parameters can be collected by the external sensing device of the control device.

[0043] In the present application, the state parameters of the cable are data used to represent the state of the welded cable, and the state parameters can include dynamic parameters and static parameters. The dynamic parameters can include at least one of the cable temperature, the cable center temperature, the insulation layer temperature, and the equivalent impedance. The static parameters can include at least one of the cable material parameters, the cross-sectional area, the overlap length, and the insulation layer material parameters of the cable. The cable material parameters can be the material and / or the electrical conductivity of the cable. The insulation layer material parameters can be the material, the thickness, and / or the thermal conductivity of the insulation layer.

[0044] In the implementation process, the state parameters of the cable and the working condition parameters of the electromagnetic induction welding device are obtained simultaneously in the process of electromagnetic induction welding of the cable by the electromagnetic induction welding device, and in the case that the network of the electromagnetic induction welding device is online, i.e., the case that the internet of the control device is online.

[0045] In the present application, the electromagnetic induction welding device itself has the function of communicating with external devices to adjust its own control parameters in the process of electromagnetic induction welding. In the case of connecting to the internet, the electromagnetic induction welding device can interact with external devices to output control, so as to avoid damage to the cable and the insulation layer. However, the network and power are extremely unstable when welding outdoors. In the case of network interruption, the electromagnetic induction welding device loses the function of dynamic control, which easily causes unstable welding. Therefore, the control device dynamically predicts the control parameter sequence by real-time acquisition of the state parameters of the cable and the working condition parameters of the electromagnetic induction welding device, so that the electromagnetic induction welding device still has the function of dynamic control in the case of network interruption through the control parameter sequence, thereby improving the stability of welding.

[0046] In addition, the electromagnetic induction device can also perform insulation protection and thermal management in the process of electromagnetic induction welding of the electromagnetic induction welding device. The electromagnetic induction device can execute a heating strategy according to the temperature control stages of “fast rise, constant temperature nucleation, heat preservation densification, and slow cooling”. In the fast rise stage, a high current is used to rapidly heat to the target temperature interval. In the constant temperature nucleation stage, a fine-tuning current is used to maintain temperature stability to prevent surface overheating. In the heat preservation stage, the weld is kept dense, the temperature distribution is monitored, and the frequency is adjusted to achieve uniform heating. In the slow cooling stage, the current is reduced and the cooling flow is increased to make the temperature drop smoothly. If the surface temperature approaches the insulation safety threshold, the system automatically switches to pulse heating mode with a duty cycle between 30% and 50%, and a small amplitude frequency reduction is used to increase the heat penetration thickness to prevent damage to the insulation layer. The segmented temperature rise refers to fast rise→constant temperature nucleation→heat preservation densification→slow cooling. surf Approaching the threshold refers to switching to pulse heating (duty cycle 30-50%) and small amplitude frequency reduction to increase penetration for uniform heating. Cooling linkage refers to when T surf When the temperature is too high or the temperature difference gradient is large, the ejection / air cooling flow is increased, I is reduced first, and then f is reduced slightly to ensure that the weld formation is not damaged. Forced interlocking refers to Tsurf Maximum safe temperature T allowed by the insulation layer ins_max Standby; Z rapid flow reduction (decoupling / short circuit); coil / cooling over-temperature reduction or shutdown; outdoor disturbance robustness refers to sudden wind speed increase → predictor increases heat dissipation term and energy compensation in MPC; rain / water humidity anomaly → automatic conversion to pulse mode and derating.

[0047] In addition, the electromagnetic induction welding device can also be linked with the cooling system for adjustment. During the adjustment process, when the electromagnetic induction welding device detects that the surface temperature of the welding point is too high or the internal and external temperature difference is too large, the controller increases the ejector airflow or water cooling flow, preferentially reduces the current output, and then adjusts the frequency according to the change of heat distribution, so as to keep the weld forming quality unchanged. When the environmental wind speed suddenly increases or the cooling temperature difference is abnormal, the system dynamically adjusts the heat transfer coefficient and corrects the energy distribution to ensure temperature stability. The temperature and power data after cooling adjustment are fed back to the control model in real time for prediction update in the next cycle; or, forced safety interlocking and fault protection can also be performed, the electromagnetic induction welding device can monitor the temperature, impedance and power state throughout the process, and when the surface temperature of the welding point exceeds the upper limit of the insulation layer safety, it immediately stops heating and keeps cooling; when the impedance suddenly changes or the phase is abnormal, it quickly reduces the current and retests if short circuit or decoupling occurs, and if the anomaly persists, it stops and alarms; when the temperature of the coil or cooling circuit reaches the limit value, the current and frequency are gradually reduced according to the pre-set derating curve to prevent overheating of the device. After each interlocking condition is removed, a safety delay is reset to re-enter a stable operating state.

[0048] Through the sequential execution and data feedback of the above steps, this method realizes complete closed-loop control from energy estimation, real-time monitoring, prediction control to power-off takeover and insulation protection. The output of each step is used as the input of the next step to form a continuously optimized adaptive control chain, so that the welding device can still realize stable, continuous and safe welding operation under outdoor low-voltage high-current power supply conditions.

[0049] In the process of insulation protection and thermal management of electromagnetic induction equipment, the electromagnetic induction equipment can run according to the temperature control track of "fast rise-constant temperature nucleation-holding densification-slow cooling" throughout the welding cycle. In the fast rise stage, the surface temperature of the welding point near area is rapidly increased to 70%-80% of the target temperature by matching the initial value of the frequency with a higher current. The current rise adopts a limited slope method, typically not more than 2% of the current reference change per millisecond, and the temperature rise rate is evaluated every 10 milliseconds. If the temperature rises too fast, causing the surface-internal temperature difference to increase, the current reference is immediately reduced and the frequency is slightly reduced by 1-3 kHz to increase the heat penetration thickness, avoiding the surface overheating first. In the constant temperature nucleation stage, the target temperature platform is maintained, and the temperature closed loop is realized by current fine tuning, and the frequency is only used for small amplitude coupling optimization; when the temperature fluctuation exceeds the allowed bandwidth, the duty cycle is first reduced, then the current is adjusted, and finally the frequency is slightly adjusted to ensure stable weld nucleation. In the holding densification stage, a small temperature slope and stable axial pressure are maintained to suppress pores; if the detection of exhaust volume reduction or impedance phase change prompts that the melting zone is being densified, the current frequency is kept unchanged, and the duty cycle is slightly pulsed to improve the heating. In the slow cooling stage, the current is reduced and the cooling flow is increased while maintaining clamping, the frequency returns to the set value that is more conducive to surface heat conduction to uniformly reduce the temperature, and if the temperature gradient is still greater than the threshold during slow cooling, a short duty cycle is inserted to maintain internal heat and avoid stress caused by sudden cooling of the outer layer.

[0050] When the surface temperature of the welding point approaches the safety threshold of the insulation layer, the controller switches from continuous heating to pulse heating mode, with the duty cycle preferably taking 30%-50% and linearly scaling with temperature error; when switching, the frequency is slightly reduced to increase the effective heating depth, and then the temperature rise and temperature drop are evaluated in each pulse interval, if the net temperature rise is still too high, the duty cycle is further reduced, then the current is reduced, and if necessary, the pulse interval is lengthened. The entry and exit of pulse mode use S-curve smooth transition to prevent thermal shock; when the temperature falls below the safety threshold and remains stable for a period of time, it can return to continuous heating or pulse with a higher duty cycle.

[0051] The linkage of cooling system and thermal management is driven by dual criteria of temperature and temperature gradient: once the surface temperature exceeds the upper limit of the target bandwidth, or the surface-internal temperature difference exceeds the allowed threshold, immediately increase the flow of the ejector airway and the water cooling flow; during the cooling increase, the power side reduces the capacity according to the priority of "current first, frequency second", to ensure that the weld penetration and formation are not damaged by large frequency changes. If the temperature is still higher than the target bandwidth after cooling adjustment, the duty cycle is reduced for a short time to pull back the temperature, and then restored to the original setting with small steps. To avoid overcooling leading to external cooling and internal heating, the cooling flow gain is set with an upper limit, and the minimum allowed value is set for the cooling outlet temperature, below which the duty cycle is shortened instead of continuing to increase the cooling.

[0052] Forced interlocks are in effect as the highest priority safety measures: power output is stopped immediately and kept cooling when the surface temperature of the welding spot reaches or exceeds the upper limit of insulation safety; when an abnormal step or phase mutation of equivalent impedance suggests decoupling or short circuit, the current is quickly reduced and retested, and if the abnormality persists, the machine is stopped and the fault is locked; when the temperature of the coil or cooling circuit reaches the device limit, the current and duty cycle are reduced with a fixed derating curve, while the cooling is increased, and if the temperature does not fall, the machine is stopped.

[0053] For robustness to outdoor disturbances, the system continuously reads the wind speed, ambient temperature and humidity; when a gust of wind occurs, the weight of the heat dissipation term and energy compensation in prediction and optimization is increased, the current is temporarily increased and the frequency is slightly reduced, and after the gust subsides, it returns to the original plan with a limited slope; when rain or humidity anomalies are detected, resulting in a sudden increase in surface heat exchange, automatically switch to pulse mode and overall derate, while limiting the up and down swing of the frequency to reduce the coupling uncertainty under water film conditions. The above adjustments are all subject to the criterion of prioritizing weld quality, prioritizing this weld closure and density, and then considering energy optimization; the calculation of the relationship between power and insulation layer thickness can refer to the formula where P is the active power of the coil, is the effective value of the coil current, is the resistance part of the equivalent impedance, used to reduce the current first when derating to reduce active heating as quickly as possible; the correction relationship of wind speed or cooling flow to heat transfer coefficient is as follows: where h is the convective heat transfer coefficient, h0 is the static base value, v is the wind speed, and k is an empirical constant.

[0054] Through the coordinated execution of the above temperature control segmentation, cooling linkage, forced interlocks and disturbance robustness strategies, the system realizes stable penetration and forming quality under the premise of ensuring insulation safety; all transitions are smoothed with a limited slope and S-curve to avoid thermal shock, and are consistent with the aforementioned rolling prediction and UPS takeover mechanism, so that the thermal state before and after power interruption is controllable and welding is uninterrupted.

[0055] Step 202, inputting state parameters and working condition parameters into the control prediction model to obtain a control parameter sequence output by the control prediction model.

[0056] In the implementation process, the control device inputs the state parameters and the working condition parameters into the control prediction model to obtain a control parameter sequence output by the control prediction model in the target time period. In the execution process, the control prediction model can be an online model, and the control device calls a data input interface of the control prediction model through the Internet, inputs the state parameters and the working condition parameters into the control prediction model, and obtains the control parameter sequence output by the control prediction model returned by the interface call; in addition, the control prediction model can also be an offline model deployed in the control device, and the control device directly inputs the state parameters and the working condition parameters into the control prediction model to obtain the control parameter sequence. In the case where the control prediction model is an offline model, the control device can continue to execute steps 201 to 202 in the case of network interruption until the welding of the cable is completed.

[0057] In the present application, the control parameter sequence includes control parameters corresponding to multiple time points, and the control parameter sequence is used to enable the electromagnetic induction welding device to perform electromagnetic induction welding in the target time period in the case of network interruption; wherein the control parameters can include an induction frequency f, an induction current I and a duty cycle D, and the control parameter sequence can be represented as: {f, I, D}.

[0058] Further, the control device can also control the uninterruptible power supply (UPS) to perform power adaptation, and the control device can synchronously input the input power parameters of the electromagnetic induction welding device into the UPS through the local area network; in addition, the UPS also independently acquires the input power parameters of the electromagnetic induction welding device for adaptation, that is, to perform dual-machine hot backup and seamless takeover control.

[0059] In the process of double-machine hot backup and seamless takeover control of the UPS, the UPS and the main power source keep frequency synchronization and phase locking, real-time receive the predicted segment with signature issued by the main controller and store it, the UPS detects the bus voltage, phase synchronization state and watchdog signal, once the voltage drops, out of step or communication timeout, the UPS takes over the power output in a very short time, continues to execute the frequency, current and duty cycle curve according to the latest predicted segment, ensures the welding process without interruption. During the takeover, the UPS dynamically adjusts the output duty cycle according to its power upper limit and remaining energy, and smoothly switches back to the main power supply after the main power recovery according to the power droop principle. This step realizes continuous welding in the case of outdoor power failure. Among them, double-machine hot backup refers to the UPS keeping the same frequency sampling and phase locking with the main power source; receiving the "6h predicted segment" signed by the main control every second (for example, 1s resolution or coarser 100ms element in the future 6h + linear interpolation in the segment); the takeover criterion is that any of the following triggers: Vbus drop, contactor switch quantity, out of step phase, watchdog timeout, then the UPS switches to the "latest effective prediction schedule" within 1-2 control periods; flexible following refers to the smooth S-curve transition to the target {f, I, D} at the beginning of the takeover, and local amplitude limiting (UPS own rating / transient constraints priority) is performed; reset back refers to the main power recovery and stability ≥ Tsafe, the control right is returned to the main power supply, and the UPS returns to hot standby.

[0060] During the execution of the UPS, the UPS and the main power source can run in parallel in the hot standby state under normal power supply, the sampling clock and the inverter carrier frequency are kept consistent, and the error between the reference phase on the UPS side and the voltage / current fundamental phase on the coil side is maintained within the allowable range through the phase-locked loop. The main control generates and issues a "six-hour prediction segment" with signature and CRC check once every second, the segment contains time stamp and {f(t), I(t), D(t)} sequence, the resolution is defaulted to 1s, while the optional refinement of 100ms level element is attached, the UPS locally saves two circular buffers: one is "verified next 2min execution interval", the other is "subsequent reference interval". When the new segment passes the signature and CRC check and the time stamp is continuous, it is overwritten and moved backward; if the check fails or the time stamp is broken, the last version of the execution interval is retained until a valid segment is received. The phase locking requires that wherein, is the difference between the UPS reference phase and the coil fundamental phase, is the allowed phase error upper limit, used to ensure that the current command will not introduce impact due to phase mutation during takeover. The takeover criterion uses multi-signal joint triggering, and any condition is true to enter the UPS execution state: bus voltage drops below the threshold, contactor state indicates disconnection, phase out of step, main control watchdog timeout, etc. Its logic can refer to formula (1):

[0061] Formula (1);

[0062] wherein, is a takeover trigger flag is a bus voltage, is a voltage lower limit, is a contactor state quantity, is a watchdog timeout event. After triggering, switch to the last “validated prediction schedule” within 1-2 control cycles and execute from the {f, I, D} corresponding to the current timestamp of the table.

[0063] To avoid the impact of takeover transient, the UPS smoothly transitions from the current output to the prediction target in a transition time with a S-curve, the frequency, current, and duty cycle all adopt the same normalized quintic polynomial trajectory, see equation (2):

[0064] equation (2);

[0065] wherein, , is the actual value of the takeover transient, is the prediction target value, t0 is the takeover start time, t r is the transition time. If there is a risk of rating / transient constraint on the UPS side during the transition process, the trajectory is limited, and the limiting formula is: ; wherein, is the interval clipping, , given by the real-time capability boundaries of the UPS power, inverter current, and device temperature. The parallel energy constraint adopts the integral of the prediction segment energy and the UPS remaining energy, if it is over limit, then execute the degraded trajectory, see equation (3):

[0066] equation (3);

[0067] wherein, is the UPS DC input power, is the available remaining energy of the UPS, is the reserved safety energy. The priority rule when over limit is to reduce the duty cycle and current and extend the holding / cooling time, and to maintain the weld integrity priority.

[0068] During the UPS execution state, new segments are still received every 1s beat and signed and CRC checked; if the new segment is available, it covers the future 2min execution interval, realizing rolling correction. If the temperature or device temperature is close to the upper limit, the current and duty cycle are locally derated without changing the frequency trajectory; when the surface temperature approaches the safety upper limit of the insulation layer, the current is preferentially reduced, followed by the frequency, and if necessary, pulse duty is inserted to suppress surface overheating. The corresponding relationship is shown in equation (4):

[0069] Equation (4);

[0070] wherein, is the equivalent resistance part, is the insulation layer depth, and is the conductor resistivity and permeability. By first reducing I and then slightly adjusting f, the surface heat load is constrained without sacrificing the penetration depth.

[0071] After the main power is restored, the back-switching criterion is entered, the bus voltage and phase are stable and last for no less than the safety time, and the main power declares the takeover ability to meet the power droop condition. The back-switching adopts a power droop hybrid control, and the power ratio is smoothly transferred from the UPS to the main power, as shown in Equation (5):

[0072] Equation (5);

[0073] wherein, is the power ratio borne by the UPS, is the end-of-takeover instantaneous ratio, is the current total power demand. When drops to zero, the UPS returns to the hot standby and restores the phase locking and cache updating. During the entire back-switching process, the amplitude clipping under the temperature and capacity constraints is still performed, and if any of them is out of range, the back-switching is immediately suspended and kept in the UPS execution state until safety.

[0074] To ensure robustness, the communication and storage layer adopts a timestamp alignment and double buffering strategy: only the "subsequent reference interval" is replaced each time, and only when the start of the new segment covers the end of the current execution interval and the signature and CRC check pass, the reference interval is pushed to the new "verified execution interval". If the communication jitter causes the segment to be missing, the current execution interval is maintained until it naturally expires, and then the local degradation curve (low duty cycle warm-up and slow cooling) is entered to protect the weld quality. Each of the above steps is operated under a temperature hard constraint, and the temperature hard constraint is , the coil and support temperature cannot exceed the respective upper limit; if the hard constraint is triggered, the power is reduced or the machine is stopped unconditionally, and after the temperature falls and the cooling capacity is restored, it is decided whether to continue to execute the prediction table or to enter the safety termination process according to the energy and power conditions.

[0075] The electromagnetic induction welding control method of the present application has a real-time closed-loop control structure. The outer control loop takes the surface temperature T surf of the weld as the control target, and the inner control loop takes the coil current I rmsThe effective value is the adjustment object. During operation, the system will real-time fine-tune the working frequency f according to the change of the heating coupling state, so as to keep the induction efficiency within the optimal range. At the same time, the control algorithm will continuously monitor the working state of the battery, power supply, coil and cooling system, and automatically limit the output power or frequency when these elements approach the safety boundary, so as to ensure that the system can still operate stably under limited conditions.

[0076] The prediction link adopts a long-period rolling calculation method. The control system recalculates the induction heating operation plan for the next six hours at a frequency of once per second, including adjustment parameters such as working frequency f(t), output current I(t) and duty cycle D(t) of heating and cooling period, so as to form a continuous prediction operation schedule. The schedule is updated over time and written to the buffer area of the uninterruptible power supply UPS in real time. When the main power is normal, the main power executes control; once power grid outage or voltage anomaly is detected, the uninterruptible power supply immediately takes over power output according to the last prediction schedule that has passed verification and signature, and continuously executes according to the heating rhythm and power distribution in it, so as to realize seamless connection and stable transition of the welding process.

[0077] In the above-mentioned electromagnetic induction welding control method, during the process of electromagnetic induction welding of the electromagnetic induction welding equipment on the cable, the state data of the cable and the operating parameters and / or environmental parameters of the electromagnetic induction welding equipment are obtained, the state parameters, operating parameters and / or environmental parameters are input into the control prediction model, the control parameter sequence is predicted through the online control prediction model, and the control parameter sequence output by the control prediction model is obtained. The control parameter sequence can be used to perform electromagnetic induction welding in a target time period in the case of network interruption, realize uninterrupted equipment control in the case of Internet interruption, and improve the stability of electromagnetic induction welding.

[0078] Based on the above-mentioned one exemplary embodiment, the following is provided in one or more exemplary embodiments. An electromagnetic induction welding control method is provided, which is applied to Figure 1 the control equipment in the above-mentioned embodiment, and specifically includes the following contents.

[0079] In the process of data acquisition, part of the data can be acquired first, and another part of the data can be obtained through data inference based on the acquired data, so as to input the above-mentioned two data into the control prediction model; the current I rms , the coil equivalent impedance Z=R+jX, the temperature T surf of the welding point near zone, the coil / support temperature, the cooling flow / temperature, the bus voltage V bus , the battery SoC / temperature / internal resistance, the wind speed / environmental temperature, the coupling efficiency η, the surface-internal temperature difference ΔT and the heat dissipation coefficient h (adapted to wind speed / posture) are inferred.

[0080] In the process of data acquisition, the control device can collect key electrical parameters, thermal parameters and environmental parameters during the operation of the welding device, and dynamically correct the welding power and heating state based on these data. The system deploys multiple sensing nodes distributed at the power supply end, the coil end, the support surface, the cooling circuit and the environmental measurement point, to ensure synchronous acquisition of various signals and data closed loop; among them, the electrical parameters include the coil current effective value, the equivalent impedance and the bus voltage, etc., which are mainly used to judge the electromagnetic coupling state and the load change. The square average value of the coil current effective value is calculated in real time as the input basis of the instantaneous heat power; the equivalent impedance is determined by the phase difference between the sampled voltage and current, so as to judge the impedance change trend of the heating coil. Its power can be obtained by formula: wherein, is the coil active power, is the coil current effective value, is the equivalent resistance. The power is used to estimate the actual heat absorbed by the conductor; the thermal parameter part focuses on monitoring the surface temperature of the near zone of the welding point, the coil and the support, and calculating the temperature rise rate. The system arranges a thermocouple array on the outer surface of the support for real-time judgment of the uniformity of temperature distribution, and corrects the current and frequency output according to the temperature gradient to prevent local overheating from causing damage to the insulation layer; the cooling and environmental parameters include the cooling liquid flow, the inlet and outlet temperature difference, the environmental temperature and the wind speed. The system calculates the heat exchange capacity of the cooling circuit through the flow meter and the temperature difference, so as to dynamically adjust the cooling water circulating pump speed. Its cooling power can be represented by the formula: wherein, is the cooling heat power, is the cooling medium mass flow, is the specific heat capacity of the medium, and are the inlet and outlet temperatures of the cooling circuit respectively; the battery module outputs the state of charge (SoC), the internal resistance and the temperature information in real time as the energy constraint basis of the power supply controller. The system updates the battery charge by Coulomb counting method, and measures the internal resistance at the current step to predict the supply capacity decay trend.

[0081] In the data inference process, the control device can calculate the coupling efficiency η of the coil to the welding zone according to the measured power and thermal response; combined with the surface temperature change rate and the cooling heat flow, the actual heat absorption power of the welding zone is calculated, and then the difference ΔT between the internal temperature and the surface temperature is calculated to evaluate whether the internal weld is fully melted. The heat dissipation coefficient h is adaptively calculated by the wind speed and the attitude parameters, which is used to correct the thermal model.

[0082] Through the above data acquisition and inference, the controller can update the energy balance state of the system in each sampling period, dynamically correct the welding current and frequency, and synchronize the predicted six-hour operation curve to the UPS control unit, to realize smooth connection and continuous heating control in the case of power failure.

[0083] In the process of prediction, the control prediction model can be controlled to determine the initial control parameters first, and then predict the corresponding control parameters of the initial control parameters to obtain the control parameter sequence; in an optional embodiment provided by the application, as shown in Figure 3 As shown in FIG. 2, step 202 includes steps 301 to 302:

[0084] Step 301, inputting the state parameters and the working condition parameters into the control prediction model, and determining the multiple initial control parameters of the electromagnetic induction welding equipment through the control prediction model.

[0085] In the implementation process, the control device inputs the state parameters and the working condition parameters into the control prediction model, and the control prediction model performs prediction based on the state parameters and the working condition parameters to obtain the multiple initial control parameters of the electromagnetic welding equipment.

[0086] In the execution process, the control prediction model can perform prediction through the first neural network layer, and can perform parameter mapping on the state parameters and the working condition parameters to obtain the multiple initial control parameters as the mapping result. In addition, the control prediction model can also perform prediction through a thermal model, and one or more functions and / or algorithms can be encapsulated in the thermal model. The control prediction model performs thermal model solving according to the state parameters and the working condition parameters to obtain the multiple initial control parameters.

[0087] In the process of thermal model solving, the control prediction model can give the power / time initial value through “required heat = mass x specific heat x temperature rise + latent heat of phase change + heat loss”, and then give the f initial value using the relationship between the insulation layer thickness and the material and the frequency. The I initial value is inversely solved from P≈I²Re(Z).

[0088] Step 302, performing prediction processing on each initial control parameter, the running parameter and the environmental parameter through the control prediction model to obtain the control parameter sequence.

[0089] In the implementation process, after obtaining the initial control parameters, the control prediction model performs prediction processing on each initial control parameter, the running parameter and the environmental parameter to obtain the control parameter sequence. Further, the control parameter sequence can be output to the control device.

[0090] In the process of prediction, the control prediction model can perform parameter mapping through the second neural network layer to obtain a control parameter sequence corresponding to the target time period for each initial control parameter; or the control prediction model can also perform optimization function solving according to the optimization condition, each initial control parameter, the operation parameter and the environment parameter to obtain the control parameter sequence.

[0091] In the process of generating the control parameter sequence, the control prediction model can take {the effective value of the coil current Irms, the working frequency f of the induction heating system, the equivalent impedance Z between the coil and the welded conductor, the environment temperature Ta, the environment wind speed or cooling air flow speed v, and the real-time temperature Tsurf of the welding point or the conductor surface} as independent variables to fit the gain and time lag of the 'active heat input per unit time → Tsurf response'; the parameters are recursively updated every second, and {f(t), I(t), D(t)} of 6-hour rolling prediction is output, and a confidence interval is given.

[0092] In an optional embodiment provided in the application, the initial control parameter is obtained through the control prediction model, and the subsequent control parameter is obtained through the control prediction model according to the initial control parameter, so that continuous and real-time control parameter generation is realized, and the reliability of the control parameter sequence is improved.

[0093] In the process of predicting the initial control parameter, the control prediction model can obtain the initial control parameter through the thermal model solving method; in an optional embodiment provided in the application, as shown in Figure 4 Step 301 includes steps 401 to 403.

[0094] Step 401 inputs the state parameter and the working condition parameter into the control prediction model, and determines the welding heat parameter through the control prediction model according to the state parameter and the environment parameter.

[0095] In the implementation process, the control device inputs the state parameter and the working condition parameter into the control prediction model, and the control prediction model performs feedforward calculation according to the state parameter and the environment parameter to obtain the total heat required for welding, which is taken as the welding heat parameter.

[0096] In the implementation process, the control prediction model can read the information of the conductor material, the cross-sectional area, the overlap length included in the state parameter, and the environment temperature and the wind speed included in the working condition parameter, estimate the total heat required for the welding point area, and the heat is determined by the conductor mass, the specific heat, the target temperature rise, the latent heat of phase change and the heat loss caused by convection and radiation, and can be expressed as: wherein, is the conductor mass, is the expected temperature rise, is the equivalent latent heat of the welding melting section, is the heat loss caused by convection and radiation.

[0097] Step 402: Determine the initial power parameters and heating time parameters by controlling the prediction model based on the welding heat parameters and state parameters.

[0098] During implementation, the control prediction model uses specific functions to solve the thermal model based on the welding heat parameters and the state parameters of the electromagnetic induction welding equipment, and obtains the initial power parameters and heating time parameters.

[0099] During the process, based on the relationship that heat equals power multiplied by time, the initial power parameters and heating time parameters can be obtained from the welding heat parameters; among them, the initial power parameters can be preset.

[0100] Step 403: The initial induction frequency, initial current, and initial duty cycle are determined as initial control parameters by the control prediction model based on the initial power parameters and heating time parameters.

[0101] During implementation, the control prediction model solves the thermal model based on the initial power parameters and heating time parameters to obtain the initial induction frequency. Then, the initial current value is calculated using the initial induction frequency, initial power parameters, and material resistivity, and the initial duty cycle is selected. The initial induction frequency, initial current, and initial duty cycle are used as the initial control parameters.

[0102] During execution, the control prediction model inversely solves for the initial value of the induced frequency based on the initial power parameters, heating time parameters, and the relationship between the insulation layer thickness and the material resistivity and permeability. , and by Find the initial value of the current. .

[0103] Furthermore, the control prediction model can also infer the mapping relationship between the temperature change rate and the input power based on the correspondence between the solder joint heating rate and the input power, which can be used as a subsequent correction term for the model. The correspondence can be expressed as: .

[0104] One optional implementation provided in this application calculates the initial control parameters by solving a thermal model, which improves the accuracy and reliability of the initial control parameters. At the same time, by solving the thermal model, the training of a neural network model is avoided, thereby improving the efficiency of use.

[0105] In the process of generating control parameter sequences using a control prediction model, the control prediction model can correct the parameters of the thermal model based on environmental parameters through model calibration. The corrected parameters and operating parameters are then used for prediction processing to obtain the control parameter sequence. One optional implementation provided in this application is as follows: Figure 5 As shown, step 403 includes steps 501 to 503:

[0106] Step 501, determining the heat dissipation coefficient according to the environmental parameters by the control prediction model.

[0107] In the implementation process, the control prediction model is fitted based on the environmental parameters and / or the operation parameters to obtain the heat dissipation coefficient of the thermal model.

[0108] In the implementation process, the control prediction model can adaptively fit the gain and time delay between the active heat input per unit time and the temperature response according to the coil current , the induction frequency , the equivalent impedance , the environmental temperature , the wind speed v and the solder joint surface temperature , and form an online ARX model. When the wind speed changes or the conductor contact state changes, the model can automatically update the parameters within a few seconds to realize real-time tracking of the heating dynamic characteristics.

[0109] Step 502, performing prediction processing according to the operation parameters and the heat dissipation coefficient by the control prediction model to obtain a plurality of control parameters corresponding to the target time period for each initial control parameter.

[0110] In the implementation process, the control prediction model performs prediction processing based on the operation parameters and the heat dissipation coefficient to obtain a plurality of control parameters corresponding to the target time period for each initial control parameter.

[0111] In the prediction process, the control prediction model can perform parameter mapping through a convolution layer to obtain a plurality of control parameters corresponding to the target time period for each initial control parameter; or the control prediction model can also obtain a plurality of control parameters corresponding to the target time period for each initial control parameter through function solving.

[0112] Step 503, generating a control parameter sequence according to each initial control parameter and each control parameter by the control prediction model.

[0113] In the implementation process, the control prediction model performs parameter combination based on each initial control parameter and each control parameter to obtain a control parameter sequence of the target time period; optionally, the target time period is 6h.

[0114] An optional embodiment provided by the present application improves the reliability and accuracy of the control parameter sequence by predicting the control parameter sequence according to multiple dimensions of data such as environmental parameters and operation parameters through the control prediction model, thereby improving the stability of the electromagnetic induction welding.

[0115] In actual scenarios, the environmental parameters are constantly changing, and the control parameter sequence can be obtained by adjusting the control parameters based on the real-time environmental parameters through an optimization function. An optional embodiment provided by the present application includes step 601:

[0116] In step 601, the control prediction model solves the optimization function according to the operating parameters, the heat dissipation coefficient and the optimization condition to obtain the control parameters.

[0117] In the present application, the optimization function is a function for representing the constraint projection algorithm of model predictive control (MPC), which can perform collaborative optimization of energy and temperature for a future prediction time domain, and the target is to minimize the square of the deviation of the spot surface temperature, the total energy consumption and the system oscillation caused by frequent switching; the optimization condition is related to the temperature of the cable and the power of the electromagnetic induction welding equipment.

[0118] In the implementation process, the control prediction model solves the optimization function according to the operating parameters, the heat dissipation coefficient and the optimization condition to obtain the control parameters; in the execution process, the control prediction model can substitute the operating parameters and the heat dissipation coefficient into formula (6):

[0119] Formula (6);

[0120] Wherein, is the expected temperature trajectory, is the actual input power, , , are the frequency, current and duty cycle change amounts respectively, , , are weight coefficients for balancing temperature stability, energy efficiency and control smoothness. The optimization is solved in a cycle refreshed once per second, so that the control output meets multiple hard constraints: the spot temperature cannot exceed the upper limit of the insulation layer safety , the coil and support temperature cannot exceed the design limit, the power voltage and current remain within the rated range, and the UPS power and energy budget are not exceeded.

[0121] The technical logic of the present application is illustrated by an example. For example, in a copper conductor welding process, the initial calculation obtains a power of about a1 kW, a frequency of about b1 kHz, and a current of about c1 A. When the environmental wind speed suddenly increases from d1 m / s to d2 m / s, the predictor detects that the temperature rise rate decreases in real time, the model updates the heat dissipation coefficient, and the controller automatically increases the current by about n% and slightly reduces the frequency to b2 kHz, so that the welding point temperature is maintained in the target interval and does not exceed e ℃. In this process, the MPC rewrites the new six-hour prediction curve into the UPS cache, and when the main power supply is unexpectedly disconnected, the UPS can continue to drive the coil based on the latest updated prediction data to achieve seamless welding without interruption. Through the combination of "physical feedforward + data correction + constraint prediction", the welding system can stably maintain the melting efficiency and safe temperature control in long-term continuous operation, and realize self-adaptive continuous operation under outdoor low-voltage high-current conditions.

[0122] In an optional embodiment provided by the present application, the adjustment of the control parameters is performed by combining the optimization function and the optimization condition, so that the control parameter sequence can be updated in real time as the environmental parameters are updated, thereby improving the reliability of the control parameter sequence and further improving the reliability and stability of the electromagnetic induction welding.

[0123] In actual scenarios, the storage space of the electromagnetic induction welding equipment may be small. In the case of detecting network interruption of the electromagnetic induction welding equipment, the control device can send the control parameters in the control parameter sequence to the electromagnetic induction welding equipment in a segmented manner. As shown in Figure 7 the method further includes step 701:

[0124] Step 701: In the case of detecting network interruption of the electromagnetic induction welding equipment, the control parameters included in the control parameter sequence are sent to the electromagnetic induction welding equipment through the local area network between the control device and the electromagnetic induction welding equipment according to a preset frequency.

[0125] In the implementation process, in the case of detecting network interruption of the electromagnetic induction welding equipment, the control parameters included in the control parameter sequence are sent to the electromagnetic induction welding equipment through the local area network between the control device and the electromagnetic induction welding equipment according to a preset time frequency and data amount; and the electromagnetic induction welding equipment performs electromagnetic induction welding according to the received control parameters.

[0126] In an optional embodiment provided by the present application, the segmented data sending manner avoids the risk of downtime of the electromagnetic induction welding equipment due to insufficient memory, and also avoids the problem of performing electromagnetic induction welding according to offline control parameters after network connection, thereby improving the reliability of the electromagnetic induction welding.

[0127] Further, the data structure of the data acquired by the control device in the application can be: running snapshot (refreshed every 1s): timestamp, state of charge SoC of the battery, system bus voltage V bus rms surf coil i i i i

[0128] In addition, the control flow of the control device can be: acquisition -> state estimation: update coupling efficiency η, convective heat transfer coefficient h, time lag; feedforward calculation: get {initial operating frequency f0, initial current value I0, initial heating time or time step t0}; outer loop temperature control PID gives power correction ΔP; inner loop current loop gives I ref ; frequency fine-tuning keeps target phase; predictor: generate 6 hours {f, I, D} with the latest parameters and working conditions and pass through MPC constraints; release: write to local execution buffer and UPS buffer (with signature / CRC); monitoring: interlocking / guard dog / abnormality detection; trigger degradation or shutdown if necessary.

[0129] In addition, the key limiting of the control device can be: power upper limit = battery / UPS bus voltage multiplied by maximum allowed current multiplied by power factor and inverter efficiency; frequency boundary is given by the matching relationship of insulation layer thickness and conductor radius; temperature boundary = long-term allowed temperature of insulation minus safety margin; predicted energy budget ≤ UPS remaining energy minus safety reserve.

[0130] In addition, the abnormality and degradation control of the control device can be: prediction mismatch (measured deviation > highest bandwidth and exceeds critical duration threshold T crit ): shorten the prediction window to 1 hour and increase the refresh rate to 0.2s; sensor disconnection: enable "power-time" conservative curve and tighter temperature limiting; insufficient cooling: automatically extend the holding / annealing section and reduce the duty cycle; UPS energy emergency: shorten the subsequent holding according to the priority of weld seam integrity, and give priority to guarantee the closure of this welding forming.

[0131] ​​​​​​​​The following lists the beneficial technical effects of the present application: First, based on real-time monitoring and hybrid predictive control, the welding power and temperature control are more stable. By continuously collecting signals such as current, impedance, temperature and wind speed, and using recursive algorithm to correct the power-temperature response model, the system can adjust the frequency and current in real time when the environment is disturbed or the load changes, maintain the constant temperature of the welding point, and significantly improve the temperature control accuracy of induction welding. Compared with the traditional fixed power or single PID control method, this method can predict the temperature change trend, avoid overshoot or delay, and ensure the uniform heat distribution of the welding area.

[0132] Second, the UPS takeover mechanism brings the effect of power failure and continues welding. The system generates a six-hour power and frequency prediction curve through rolling and synchronizes it to the UPS, so that when the main power is unexpectedly disconnected, the UPS can take over in milliseconds and continue to output according to the predicted value. This design solves the problem of incomplete fusion of welds due to power failure in existing outdoor welding, ensuring the continuity of the welding process and the quality of fusion, which is a function that traditional battery-powered systems cannot achieve.

[0133] Third, the insulation protection and segmented thermal management strategy improves the welding safety and weld density. Through the segmented control logic of "fast rise-constant temperature nucleation-heat preservation density-slow cooling", the system can accurately control the heating rate and heat distribution at different stages; when the surface temperature is close to the insulation safety threshold, it automatically switches to pulse heating and reduces the frequency slightly, achieving internal full fusion while protecting the outer insulation from ablation. Compared with traditional induction welding, this strategy significantly reduces the risk of insulation damage and improves the consistency of the welding points.

[0134] Fourth, the linkage control of the cooling and heating systems improves the thermal stability of the device. The system dynamically adjusts the flow of air cooling and water cooling based on the temperature and temperature difference of the welding point, and preferentially balances the heat input and heat dissipation output by reducing the current and fine-tuning the frequency, so as to maintain uniform temperature distribution and avoid pores or uneven organization caused by local overheating. This adaptive cooling method is superior to the traditional fixed air volume or delayed cooling structure, and can achieve stable welding in high temperature and strong wind environment.

[0135] Fifth, the model predictive control (MPC) realizes the balance of energy consumption and welding quality under multiple constraint optimizations. The system minimizes energy consumption and frequency and current switching times while ensuring temperature, power and device safety limits, making the entire heating process smooth and efficient. Compared with simple temperature tracking or fixed parameter control methods, this optimization mechanism can automatically adjust the weight according to the welding stage and the remaining energy of the UPS, achieving the dual effect of energy saving and extended endurance.

[0136] Sixth, the outdoor disturbance robustness mechanism significantly enhances the system reliability. When the wind speed suddenly increases or the humidity is abnormal, the prediction model immediately corrects the heat dissipation parameters, and the controller stabilizes the heat field distribution through pulse heating and reduced output, so that the welding quality is not affected by the climate. Compared with existing portable welding equipment without adaptive ability, the scheme can maintain continuous and stable welding state in complex environment.

[0137] In an embodiment, referring to Figure 8 , a flowchart of an electromagnetic induction welding control method provided by the embodiment of the application is shown, which can be applied to Figure 1 , a control device. As shown in Figure 8 , the electromagnetic induction welding control method can include the following steps:

[0138] Step 801, during the electromagnetic induction welding of the cable by the electromagnetic induction welding device, the state parameters of the cable and the working condition parameters of the electromagnetic induction welding device are obtained.

[0139] Step 802, the state parameters and the working condition parameters are input into the control prediction model to obtain the control parameter sequence output by the control prediction model.

[0140] Among them, the control prediction model is an online thermal model.

[0141] Step 803, in the case of detecting the network interruption of the electromagnetic induction welding device, the control parameters included in the control parameter sequence are sent to the electromagnetic induction welding device according to the preset frequency through the local area network between the electromagnetic induction welding device and the electromagnetic induction welding device.

[0142] Among them, the local area network is connected through a network cable.

[0143] For step 802, the control prediction model can determine the welding heat parameters according to the state parameters and the environmental parameters, determine the initial power parameters and the heating time parameters according to the welding heat parameters and the state parameters, and determine the initial induction frequency, the initial current and the initial duty cycle as the initial control parameters according to the initial power parameters and the heating time parameters; further, the optimization function can be solved according to the running parameters, the heat dissipation coefficient and the optimization conditions to obtain a plurality of control parameters corresponding to each initial control parameter and a target time period, and then a control parameter sequence is constructed based on each initial control parameter and the plurality of control parameters.

[0144] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time but can be executed at different times, and the execution of the steps or stages is not necessarily sequential but can be performed alternately or alternately with at least some of the other steps or steps or stages in other steps. It can be understood that the steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by the combination are within the scope of protection of the present application.

[0145] Based on the same inventive concept, the embodiments of the present application also provide an electromagnetic induction welding control device for implementing the above-mentioned electromagnetic induction welding control method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more electromagnetic induction welding control device embodiments provided below can refer to the limitations of the electromagnetic induction welding control method in the above text, which will not be repeated here.

[0146] In one exemplary embodiment, as shown in Figure 9 An electromagnetic induction welding control device is provided, comprising: a parameter acquisition module 901 and a control parameter prediction module 902, wherein: the parameter acquisition module 901 is configured to acquire state parameters of the cable and working condition parameters of the electromagnetic induction welding equipment during the electromagnetic induction welding of the cable by the electromagnetic induction welding equipment, the working condition parameters including operating parameters and / or environmental parameters of the electromagnetic induction welding equipment; the control parameter prediction module 902 is configured to input the state parameters and the working condition parameters into a control prediction model to obtain a control parameter sequence output by the control prediction model, the control parameter sequence being used for the electromagnetic induction welding of the electromagnetic induction welding equipment within a target time period in the case of network interruption.

[0147] In one embodiment, the control parameter prediction module 902 includes an initial control parameter prediction unit and a control parameter sequence prediction unit, wherein: the initial control parameter prediction unit is configured to input the state parameters and the working condition parameters into the control prediction model to determine a plurality of initial control parameters of the electromagnetic induction welding equipment through the control prediction model; and the control parameter sequence prediction unit is configured to perform prediction processing according to the initial control parameters, the operating parameters and the environmental parameters through the control prediction model to obtain the control parameter sequence.

[0148] In one of the embodiments, the initial control parameter prediction unit comprises a welding heat determination unit, a power parameter determination unit and an initial control parameter determination unit, wherein: the welding heat determination unit is configured to input the state parameters and the working condition parameters into the control prediction model, and determine the welding heat parameters according to the state parameters and the environment parameters through the control prediction model; the power parameter determination unit is configured to determine the initial power parameters and the heating time parameters according to the welding heat parameters and the state parameters through the control prediction model; and the initial control parameter determination unit is configured to determine the initial inductive frequency, the initial current and the initial duty cycle as the initial control parameters according to the initial power parameters and the heating time parameters through the control prediction model.

[0149] In one of the embodiments, the control parameter sequence prediction unit comprises a heat dissipation coefficient determination unit, a control parameter prediction unit and a control sequence generation unit, wherein: the heat dissipation coefficient determination unit is configured to determine the heat dissipation coefficient according to the environment parameters through the control prediction model; the control parameter prediction unit is configured to perform prediction processing according to the operation parameters and the heat dissipation coefficient through the control prediction model, so as to obtain a plurality of control parameters corresponding to the target time period for each initial control parameter; and the control sequence generation unit is configured to generate the control parameter sequence according to each initial control parameter and each control parameter through the control prediction model.

[0150] In one of the embodiments, the control parameter prediction unit comprises an optimization prediction unit, and the optimization prediction unit is configured to solve the optimization function according to the operation parameters, the heat dissipation coefficient and the optimization condition through the control prediction model, so as to obtain each control parameter, wherein the optimization condition is related to the temperature of the cable and the power of the electromagnetic induction welding device.

[0151] In one of the embodiments, the device further comprises a parameter sending module, which is configured to send the control parameters included in the control parameter sequence to the electromagnetic induction welding device through the local area network between the electromagnetic induction welding device according to a preset frequency in the case that the electromagnetic induction welding device network is detected to be interrupted.

[0152] The above-mentioned various modules in the electromagnetic induction welding control device can be realized by software, hardware and combinations thereof in whole or in part. The above-mentioned various modules can be embedded in or independent of the processor in the control device in the form of hardware, or can be stored in the memory in the control device in the form of software, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned various modules.

[0153] In one of the embodiments, a control device is provided, which can be a server, and the internal structure diagram thereof can be as shown in Figure 10The control device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the control device is used to provide computing and control capabilities. The memory of the control device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the control device is used to store electromagnetic induction welding control data. The input / output interface of the control device is used to exchange information between the processor and external devices. The communication interface of the control device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement an electromagnetic induction welding control method.

[0154] Those skilled in the art can understand that, Figure 10 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the control device to which the scheme of the present application is applied. The specific control device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0155] In one exemplary embodiment, a control device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the following steps: during the process of electromagnetic induction welding of a cable by an electromagnetic induction welding device, obtaining a state parameter of the cable and a working condition parameter of the electromagnetic induction welding device, the working condition parameter including an operating parameter and / or an environmental parameter of the electromagnetic induction welding device; inputting the state parameter and the working condition parameter into a control prediction model to obtain a control parameter sequence output by the control prediction model, the control parameter sequence being used for electromagnetic induction welding of the electromagnetic induction welding device within a target time period in the case of network interruption.

[0156] In one embodiment, the processor executing the computer program further implements the following steps: inputting the state parameter and the working condition parameter into the control prediction model to determine a plurality of initial control parameters of the electromagnetic induction welding device through the control prediction model; and performing prediction processing according to each initial control parameter, the operating parameter and the environmental parameter through the control prediction model to obtain the control parameter sequence.

[0157] In one embodiment, the processor, when executing the computer program, further implements the following steps: inputting the state parameters and the working condition parameters into the control prediction model, determining the welding heat parameters according to the state parameters and the environment parameters through the control prediction model, determining the initial power parameters and the heating time parameters according to the welding heat parameters and the state parameters through the control prediction model, and determining the initial inductive frequency, the initial current and the initial duty cycle as the initial control parameters according to the initial power parameters and the heating time parameters through the control prediction model; wherein the state parameters include at least one of the cable material parameters, the cross-sectional area, the overlap length and the insulation layer material parameters.

[0158] In one embodiment, the processor, when executing the computer program, further implements the following steps: determining the heat dissipation coefficient according to the environment parameters through the control prediction model, performing the prediction processing according to the operating parameters and the heat dissipation coefficient through the control prediction model to obtain a plurality of control parameters corresponding to the target time period for each initial control parameter, and generating the control parameter sequence according to each initial control parameter and each control parameter through the control prediction model.

[0159] In one embodiment, the processor, when executing the computer program, further implements the following steps: solving the optimization function according to the operating parameters, the heat dissipation coefficient and the optimization conditions through the control prediction model to obtain each control parameter, wherein the optimization conditions are related to the temperature of the cable and the power of the electromagnetic induction welding device.

[0160] In one embodiment, the processor, when executing the computer program, further implements the following steps: in the case of detecting the network interruption of the electromagnetic induction welding device, sending the control parameters included in the control parameter sequence to the electromagnetic induction welding device according to the preset frequency through the local area network between the electromagnetic induction welding device and the cable.

[0161] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program, when executed by a processor, implements the following steps: in the process of electromagnetic induction welding of the cable by the electromagnetic induction welding device, obtaining the state parameters of the cable and the working condition parameters of the electromagnetic induction welding device, wherein the working condition parameters include the operating parameters and / or the environment parameters of the electromagnetic induction welding device; inputting the state parameters and the working condition parameters into the control prediction model to obtain the control parameter sequence output by the control prediction model, wherein the control parameter sequence is used for the electromagnetic induction welding of the electromagnetic induction welding device within the target time period in the case of network interruption.

[0162] In one embodiment, the processor, when executing the computer program, further implements the following steps: inputting the state parameters and the working condition parameters into the control prediction model, determining a plurality of initial control parameters of the electromagnetic induction welding device through the control prediction model, and performing the prediction processing according to each initial control parameter, the operating parameters and the environment parameters through the control prediction model to obtain the control parameter sequence.

[0163] In one embodiment, the processor, when executing the computer program, further implements the following steps: inputting the state parameters and the working condition parameters into the control prediction model, determining the welding heat parameters by the control prediction model according to the state parameters and the environment parameters, determining the initial power parameters and the heating time parameters by the control prediction model according to the welding heat parameters and the state parameters, and determining the initial inductive frequency, the initial current and the initial duty cycle as the initial control parameters by the control prediction model according to the initial power parameters and the heating time parameters; wherein the state parameters include at least one of the cable material parameters, the cross-sectional area, the overlap length and the insulation layer material parameters.

[0164] In one embodiment, the processor, when executing the computer program, further implements the following steps: determining the heat dissipation coefficient by the control prediction model according to the environment parameters, performing the prediction processing by the control prediction model according to the operating parameters and the heat dissipation coefficient to obtain a plurality of control parameters corresponding to the target time period for each initial control parameter, and generating the control parameter sequence by the control prediction model according to each initial control parameter and each control parameter.

[0165] In one embodiment, the processor, when executing the computer program, further implements the following steps: solving the optimization function by the control prediction model according to the operating parameters, the heat dissipation coefficient and the optimization conditions to obtain each control parameter, wherein the optimization conditions are related to the temperature of the cable and the power of the electromagnetic induction welding device.

[0166] In one embodiment, the processor, when executing the computer program, further implements the following steps: in the case of detecting the network interruption of the electromagnetic induction welding device, sending the control parameters included in the control parameter sequence to the electromagnetic induction welding device according to the preset frequency through the local area network between the electromagnetic induction welding device and the cable.

[0167] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the following steps: in the process of electromagnetic induction welding of a cable by an electromagnetic induction welding device, obtaining state parameters of the cable and working condition parameters of the electromagnetic induction welding device, wherein the working condition parameters include operating parameters and / or environment parameters of the electromagnetic induction welding device; inputting the state parameters and the working condition parameters into a control prediction model to obtain a control parameter sequence output by the control prediction model, wherein the control parameter sequence is used for electromagnetic induction welding of the cable by the electromagnetic induction welding device within a target time period in the case of network interruption.

[0168] In one embodiment, the processor, when executing the computer program, further implements the following steps: inputting the state parameters and the working condition parameters into the control prediction model, determining a plurality of initial control parameters of the electromagnetic induction welding device by the control prediction model, and performing the prediction processing by the control prediction model according to each initial control parameter, the operating parameters and the environment parameters to obtain the control parameter sequence.

[0169] In one embodiment, the processor, when executing the computer program, also implements the following steps: inputting the state parameters and the working condition parameters into the control prediction model, determining the welding heat parameters according to the state parameters and the environment parameters through the control prediction model; determining the initial power parameters and the heating time parameters according to the welding heat parameters and the state parameters through the control prediction model; determining the initial inductive frequency, the initial current and the initial duty cycle as the initial control parameters according to the initial power parameters and the heating time parameters through the control prediction model; wherein the state parameters include at least one of the cable material parameters, the cross-sectional area, the overlap length and the insulation layer material parameters.

[0170] In one embodiment, the processor, when executing the computer program, also implements the following steps: determining the heat dissipation coefficient according to the environment parameters through the control prediction model; performing the prediction processing according to the running parameters and the heat dissipation coefficient through the control prediction model to obtain a plurality of control parameters corresponding to the target time period for each initial control parameter; and generating the control parameter sequence according to each initial control parameter and each control parameter through the control prediction model.

[0171] In one embodiment, the processor, when executing the computer program, also implements the following steps: solving the optimization function according to the running parameters, the heat dissipation coefficient and the optimization conditions through the control prediction model to obtain each control parameter, wherein the optimization conditions are related to the temperature of the cable and the power of the electromagnetic induction welding equipment.

[0172] In one embodiment, the processor, when executing the computer program, also implements the following steps: in the case of detecting the network interruption of the electromagnetic induction welding equipment, sending the control parameters included in the control parameter sequence to the electromagnetic induction welding equipment according to the preset frequency through the local area network between the electromagnetic induction welding equipment and the server.

[0173] It should be noted that the data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data need to comply with relevant regulations.

[0174] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0175] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0176] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. An electromagnetic induction welding control method, characterized in that, The method includes: During the electromagnetic induction welding process of the cable by the electromagnetic induction welding equipment, the state parameters of the cable and the operating parameters of the electromagnetic induction welding equipment are acquired. The operating parameters include the operating parameters of the electromagnetic induction welding equipment and / or environmental parameters. The state parameters and operating condition parameters are input into the control prediction model to obtain the control parameter sequence output by the control prediction model. The control parameter sequence is used by the electromagnetic induction welding equipment to perform electromagnetic induction welding within a target time period when the network is interrupted.

2. The method according to claim 1, characterized in that, The step of inputting the state parameters and the operating condition parameters into the control prediction model to obtain the control parameter sequence output by the control prediction model includes: The state parameters and the operating condition parameters are input into the control prediction model, and multiple initial control parameters of the electromagnetic induction welding equipment are determined by the control prediction model. The control parameter sequence is obtained by performing prediction processing based on the initial control parameters, the operating parameters, and the environmental parameters through the control prediction model.

3. The method according to claim 2, characterized in that, The process involves inputting the state parameters and operating condition parameters into the control prediction model, and determining multiple initial control parameters for the electromagnetic induction welding equipment through the control prediction model, including: The state parameters and the operating condition parameters are input into the control prediction model, and the control prediction model determines the welding heat parameters based on the state parameters and the environmental parameters. The initial power parameters and heating time parameters are determined by the control prediction model based on the welding heat parameters and the state parameters. The control prediction model determines the initial induction frequency, initial current, and initial duty cycle as the initial control parameters based on the initial power parameters and the heating time parameters. The state parameters include at least one of the following: cable material parameters, cross-sectional area, overlap length, and insulation layer material parameters.

4. The method according to claim 2, characterized in that, The process of obtaining the control parameter sequence by performing prediction processing based on the initial control parameters, the operating parameters, and the environmental parameters through the control prediction model includes: The heat dissipation coefficient is determined based on the environmental parameters using the control prediction model. The control prediction model performs prediction processing based on the operating parameters and the heat dissipation coefficient to obtain multiple control parameters corresponding to each initial control parameter and the target time period. The control parameter sequence is generated by the control prediction model based on the initial control parameters and the control parameters.

5. The method according to claim 4, characterized in that, The process involves using the control prediction model to perform prediction based on the operating parameters and the heat dissipation coefficient, resulting in multiple control parameters corresponding to each initial control parameter and the target time period, including: The control prediction model solves the optimization function based on the operating parameters, the heat dissipation coefficient, and the optimization conditions to obtain the control parameters. The optimization conditions are related to the temperature of the cable and the power of the electromagnetic induction welding equipment.

6. The method according to claim 1, characterized in that, The method further includes: In the event of a network interruption detected in the electromagnetic induction welding equipment, the control parameters included in the control parameter sequence are sent to the electromagnetic induction welding equipment via the local area network between the two devices at a preset frequency.

7. An electromagnetic induction welding control device, characterized in that, The device includes: The parameter acquisition module is used to acquire the state parameters of the cable and the operating parameters of the electromagnetic induction welding equipment during the electromagnetic induction welding process of the electromagnetic induction welding equipment. The operating parameters include the operating parameters of the electromagnetic induction welding equipment and / or environmental parameters. The control parameter prediction module is used to input the state parameters and the operating condition parameters into the control prediction model to obtain the control parameter sequence output by the control prediction model. The control parameter sequence is used by the electromagnetic induction welding equipment to perform electromagnetic induction welding within a target time period when the network is interrupted.

8. A control 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 6.

9. 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 6.

10. A computer program product, comprising a computer program, 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 6.

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