Method and device for determining crimping process parameters and electronic equipment

Abstract

The invention discloses a method and device for determining crimping process parameters and electronic equipment. The method comprises the steps that a crimping request of target crimping equipment is received, and the crimping request carries target crimping parameters; in response to the crimping request, determining an initial process parameter according to the target crimping parameter; in the process that the target crimping equipment carries out crimping operation according to the initial process parameters, actual crimping parameters are determined; determining a parameter deviation determination mode according to the target crimping parameter and the actual crimping parameter; determining a parameter determination mode according to the target crimping parameter, the parameter deviation determination mode and a device model corresponding to the target crimping device; and determining a target process parameter according to the actual crimping parameter and the parameter determination mode so as to control the target crimping equipment to perform crimping operation according to the target process parameter. According to the invention, the technical problems that the crimping process parameters are difficult to accurately control and the high-precision requirement of the crimping operation cannot be met in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, to a method, an apparatus, and an electronic device for determining crimping process parameters. Background Art

[0002] Wire crimping is a key link in the construction of transmission lines. With the improvement of the voltage level and capacity of transmission lines, the requirements for wire crimping technology and quality are also increasing. At present, traditional wire crimping mainly relies on the experience and skills of operators, and it is difficult to accurately control the crimping force, resulting in the technical problem that it is difficult to meet the requirements of high precision and high efficiency in the crimping process.

[0003] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] Embodiments of the present invention provide a method, an apparatus, and an electronic device for determining crimping process parameters, so as to at least solve the technical problem that it is difficult to accurately control the crimping process parameters in related technologies and the high-precision requirements of crimping operations cannot be met.

[0005] According to one aspect of the embodiments of the present invention, a method for determining crimping process parameters is provided, including: receiving a crimping request of a target crimping device, where the crimping request carries target crimping parameters; in response to the crimping request, determining initial process parameters according to the target crimping parameters; during the process that the target crimping device performs a crimping operation according to the initial process parameters, determining actual crimping parameters; determining a parameter deviation determination method according to the target crimping parameters and the actual crimping parameters; determining a parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the device model corresponding to the target crimping device; and determining target process parameters according to the actual crimping parameters and the parameter determination method, so as to control the target crimping device to perform a crimping operation according to the target process parameters.

[0006] Optionally, before determining the parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the device model corresponding to the target crimping device, it further includes: determining a device function of the target crimping device, where the device function is used to represent the functional relationship between the input parameters and the output parameters of the target crimping device, the input parameters represent the crimping process parameters received by the target crimping device, and the output parameters represent the corresponding crimping parameters during the process that the target crimping device performs a crimping operation according to the input parameters; and determining the device model corresponding to the target crimping device according to the device function and the device physical parameters corresponding to the target crimping device.

[0007] Optionally, determining the parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the equipment model corresponding to the target crimping device includes: determining an initial parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the equipment model; determining an interference compensation method according to the target crimping parameters and the parameter deviation determination method; determining a jitter deviation compensation method according to the parameter deviation determination method and the equipment model; and determining the parameter determination method according to the initial parameter determination method, the jitter deviation compensation method, and the interference compensation method.

[0008] Optionally, determining the initial parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the equipment model includes: determining an initial coefficient matrix according to the target crimping parameters and the equipment model, where the initial coefficient matrix is used to represent the proportional relationship between process parameters and corresponding crimping parameters; and determining the initial parameter determination method according to the parameter deviation determination method and the initial coefficient matrix.

[0009] Optionally, determining the interference compensation method according to the target crimping parameters and the parameter deviation determination method includes: determining an initial interference compensation method according to the target crimping parameters and the parameter deviation determination method; determining interference compensation parameters corresponding to multiple interference items according to the initial interference compensation method; determining an interference coefficient matrix, where the interference coefficient matrix is used to represent the weight values corresponding to multiple interference compensation parameters in the interference compensation method; and determining the interference compensation method according to the multiple interference compensation parameters and the interference coefficient matrix.

[0010] Optionally, after determining the parameter determination method according to the initial parameter determination method, the jitter deviation compensation method, and the interference compensation method, it further includes: determining a verification function according to the parameter deviation determination method, the equipment model, and the parameter determination method; predicting a crimping result according to the verification function, where the crimping result is used to represent the probability value that the target crimping device obtains the target crimping parameters by performing a crimping operation according to the process parameters determined by the parameter determination method; and updating the parameter determination method according to the crimping result.

[0011] Optionally, determining the parameter deviation determination method according to the target crimping parameters and the actual crimping parameters includes: determining parameter items corresponding to the multiple target crimping parameters when the multiple target crimping parameters and the multiple actual crimping parameters are included; determining deviation values corresponding to the multiple parameter items according to the multiple target crimping parameters and the actual crimping parameters corresponding to the multiple target crimping parameters; and determining the parameter deviation determination method according to the multiple deviation values.

[0012] According to one aspect of an embodiment of the present invention, a device for determining crimping process parameters is provided, including: a receiving module, configured to receive a crimping request of a target crimping device, where the crimping request carries target crimping parameters; a response module, configured to, in response to the crimping request, determine initial process parameters according to the target crimping parameters; a first determination module, configured to determine actual crimping parameters during the process that the target crimping device performs a crimping operation according to the initial process parameters; a second determination module, configured to determine a parameter deviation determination method according to the target crimping parameters and the actual crimping parameters; a third determination module, configured to determine a parameter determination method according to the target crimping parameters, the parameter deviation determination method, and a device model corresponding to the target crimping device; and a fourth determination module, configured to determine target process parameters according to the actual crimping parameters and the parameter determination method, so as to control the target crimping device to perform a crimping operation according to the target process parameters.

[0013] According to one aspect of an embodiment of the present invention, an electronic device is provided, including: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the method for determining crimping process parameters according to any one of the above.

[0014] According to one aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for determining crimping process parameters according to any one of the above.

[0015] In an embodiment of the present invention, a crimping request of a target crimping device is received, where the crimping request carries target crimping parameters; in response to the crimping request, initial process parameters are determined according to the target crimping parameters; during the process that the target crimping device performs a crimping operation according to the initial process parameters, actual crimping parameters are determined; a parameter deviation determination method is determined according to the target crimping parameters and the actual crimping parameters; a parameter determination method is determined according to the target crimping parameters, the parameter deviation determination method, and a device model corresponding to the target crimping device; target process parameters are determined according to the actual crimping parameters and the parameter determination method, so as to control the target crimping device to perform a crimping operation according to the target process parameters. By determining a parameter determination method according to the target crimping parameters, the parameter deviation determination method, and a device model corresponding to the target crimping device, the purpose of determining target process parameters according to the actual crimping parameters and the parameter determination method, so as to control the target crimping device to perform a crimping operation according to the target process parameters is achieved. Since the parameter determination method can update the initial process parameters to target process parameters, the actual crimping parameters gradually approach the target crimping parameters, thereby solving the technical problem in the related art that it is difficult to accurately control crimping process parameters and the high-precision requirements of crimping operations cannot be met. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are provided to further understand the present invention and form a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0017] Figure 1 is a flowchart of a method for determining crimping process parameters according to an embodiment of the present invention;

[0018] Figure 2 is a structural diagram of a neural network provided by an alternative embodiment of the present invention;

[0019] Figure 3 is a flowchart of a sliding mode control strategy based on neural network compensation provided by an alternative embodiment of the present invention;

[0020] Figure 4 is a structural diagram of a crimping tool control system provided by an alternative embodiment of the present invention;

[0021] Figure 5 is an embedded flowchart of a crimping tool control system provided by an alternative embodiment of the present invention;

[0022] Figure 6 is a structural block diagram of a device for determining crimping process parameters according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0025] Example 1

[0026] According to an embodiment of the present invention, an embodiment of a method for determining crimping process parameters is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0027] Figure 1 is a flowchart of the method for determining crimping process parameters according to an embodiment of the present invention. As Figure 1 shown, the method includes the following steps:

[0028] Step S102, receive a crimping request from a target crimping device, where the crimping request carries target crimping parameters.

[0029] In step S102 provided in this application, a crimping request from a target crimping device is received.

[0030] Among them, the target crimping device is involved. The target crimping device refers to a specific tool or machine that performs crimping operations and can crimp wires, etc. to ensure the reliability and safety of electrical connections.

[0031] Among them, the crimping request is involved. The crimping request refers to an operation instruction sent to the target crimping device, instructing the device to perform a crimping operation. This request usually contains necessary parameters and information to ensure that the device can perform the operation correctly.

[0032] Among them, the target crimping parameters are involved. The target crimping parameters refer to the expected crimping parameters. For example, parameters such as crimping force, crimping speed, and crimping depth.

[0033] In this step, the crimping request is received. The crimping request carries specific crimping requirements, that is, target crimping parameters. This is the basic step of the crimping operation. By receiving the crimping request and target parameters, the operation of the crimping tool can be automatically adjusted without manual intervention, improving the automation level of the crimping process.

[0034] Step S104, in response to the crimping request, determine initial process parameters according to the target crimping parameters.

[0035] In step S104 provided in this application, the initial process parameters are determined.

[0036] Among them, the initial process parameters are involved. The initial process parameters refer to a set of process parameters calculated and set based on the target crimping parameters. For example, jaw displacement, speed, and crimping force, etc. These parameters are the control settings that the crimping device should adopt when starting work in order to meet the requirements of the target crimping parameters.

[0037] In this step, after receiving the crimping request, the target crimping parameters in the request are parsed, and based on these parameters, a set of initial process parameters are calculated and set. These initial process parameters will guide the operating state of the crimping equipment at the start of the operation, ensuring that the equipment can operate according to the predetermined crimping requirements.

[0038] Through this step, after the crimping request arrives, it can quickly respond and adjust the equipment parameters, reducing the waiting time of the equipment and improving the continuity and overall efficiency of the operation.

[0039] Step S106, during the process of the target crimping equipment performing the crimping operation according to the initial process parameters, determine the actual crimping parameters.

[0040] In step S106 provided by the present application, determine the actual crimping parameters.

[0041] Among them, the actual crimping parameters are involved. The actual crimping parameters refer to the parameter values that are monitored and fed back in real time by sensors during the operation of the crimping equipment. They reflect the specific execution situation of the crimping operation, such as parameters like the actual crimping force, actual crimping speed, and actual crimping depth.

[0042] In this step, when the crimping equipment starts to operate according to the initially set process parameters, the actual operating state of the equipment is monitored in real time, and data is collected through sensors to determine the actual crimping parameters. These parameters directly reflect the actual working condition of the equipment. Through this step, the actual crimping parameters are determined, and the difference between the actual operating state and the target state of the crimping equipment is understood in real time, providing a basis for subsequent adjustment and optimization of the crimping process.

[0043] Step S108, based on the target crimping parameters and the actual crimping parameters, determine the parameter deviation determination method.

[0044] In step S108 provided by the present application, the parameter deviation determination method is determined.

[0045] Among them, the parameter deviation determination method is involved. The parameter deviation determination method refers to the method of determining the difference between the target crimping parameters and the actual crimping parameters.

[0046] In this step, by comparing the target crimping parameters with the actual crimping parameters, the parameter deviation determination method is determined. The parameter deviation determination method is the key basis for the control system to adjust, and it can dynamically guide the equipment to adjust parameters such as the crimping force, position, and speed, ensuring that the crimping quality and effect meet the predetermined standards.

[0047] Step S110, based on the target crimping parameters, the parameter deviation determination method, and the equipment model corresponding to the target crimping equipment, determine the parameter determination method.

[0048] In step S110 provided in this application, a parameter determination method is determined.

[0049] Among them, a device model is involved. The device model refers to the mathematical model of the crimping device, which is used to describe the dynamic characteristics of the device, such as how the crimping device responds to control inputs, its behavior under disturbances, and changes in its own characteristics.

[0050] Among them, a parameter determination method is involved. The parameter determination method refers to the strategy for determining the actual parameters to be used based on considering the crimping device model and the parameter deviation determination method. It involves how to calculate and adjust the next control parameters according to the current deviation value, the response predicted by the device model, and the control target.

[0051] In this step, combining the target crimping parameters, the parameter deviation determination method, and the device model, a parameter determination method is designed. By combining the device model with the real-time deviation, the parameter determination method can accurately predict the response of the device under different conditions, thereby generating more accurate control signals to ensure that the crimping force, position control, etc. are closer to the target values.

[0052] Step S112: Determine the target process parameters according to the actual crimping parameters and the parameter determination method, so as to control the target crimping device to perform crimping operations according to the target process parameters.

[0053] In step S112 provided in this application, the target process parameters are determined.

[0054] Among them, the target process parameters are involved. The target process parameters refer to the target process parameters that need to be input during the crimping process in order to achieve the expected target crimping parameters.

[0055] In this step, according to the actual crimping parameters and the parameter determination method, the target process parameters are determined. Subsequently, the control input of the device is adjusted to the target process parameters to make the actual crimping parameters as close as possible to the target crimping parameters, thereby achieving high-quality crimping operations. By continuously adjusting the target process parameters, the key parameters such as the crimping force and the jaw position during the crimping process are precisely controlled, improving the efficiency of the crimping operation and ensuring the stability and consistency of the quality of the crimping process.

[0056] It should be noted that during the crimping process, the parameter determination method is a continuously iterative process. The system will continuously optimize the control strategy according to the parameter deviations monitored in real time to ensure that the crimping operation can reach the best state under various conditions.

[0057] Through the above steps S102 - S112, it is possible to receive a crimping request from a target crimping device, where the crimping request carries target crimping parameters; in response to the crimping request, determine initial process parameters according to the target crimping parameters; during the process of the target crimping device performing a crimping operation according to the initial process parameters, determine actual crimping parameters; according to the target crimping parameters and the actual crimping parameters, determine a parameter deviation determination method; according to the target crimping parameters, the parameter deviation determination method, and the device model corresponding to the target crimping device, determine a parameter determination method; according to the actual crimping parameters and the parameter determination method, determine target process parameters to control the target crimping device to perform a crimping operation according to the target process parameters. By determining the parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the device model corresponding to the target crimping device, the purpose of determining the target process parameters according to the actual crimping parameters and the parameter determination method to control the target crimping device to perform a crimping operation according to the target process parameters is achieved. Since the parameter determination method can update the initial process parameters to target process parameters, making the actual crimping parameters continuously approach the target crimping parameters, the technical problem in the related art that it is difficult to precisely control crimping process parameters and cannot meet the high-precision requirements of crimping operations is solved.

[0058] As an optional embodiment, before determining the parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the device model corresponding to the target crimping device, it further includes: determining the device function of the target crimping device, where the device function is used to represent the functional relationship between the input parameters and output parameters of the target crimping device, the input parameters represent the crimping process parameters received by the target crimping device, and the output parameters represent the corresponding crimping parameters during the process of the target crimping device performing a crimping operation according to the input parameters; according to the device function and the device physical parameters corresponding to the target crimping device, determine the device model corresponding to the target crimping device.

[0059] In this embodiment, the specific steps for determining the device model corresponding to the target crimping device are described.

[0060] Among them, the device function is involved. The device function refers to a mathematical expression that describes the relationship between the device input and output. It reflects the response characteristics of the device under various inputs and is the basis for establishing the device model.

[0061] Among them, the device physical parameters are involved. The device physical parameters refer to the physical properties and parameters of the device itself, such as the pressure coefficient, the response time of the actuator, the accuracy of the sensor, and the mechanical characteristics of the device. The device physical parameters determine the physical behavior of the device and are an important basis for establishing the device function and the device model.

[0062] In this step, first establish a device function to describe the relationship between device inputs and actual outputs, and then construct a device model by combining the physical parameters of the device. By constructing the device model, it is possible to more accurately predict the behavior of the device under different control inputs, which helps to design more effective control strategies, ensure that the crimping operation meets expectations, and provide a theoretical basis for subsequent precise control strategies. At the same time, through the accurate description of the device function and the physical parameters of the device, the device model can better adapt to changes in device characteristics, such as wear and aging, making the control system more adaptable and robust.

[0063] As an alternative embodiment, according to the target crimping parameters, the parameter deviation determination method, and the device model corresponding to the target crimping device, determine the parameter determination method, including: determining the initial parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the device model; determining the disturbance compensation method according to the target crimping parameters and the parameter deviation determination method; determining the jitter deviation compensation method according to the parameter deviation determination method and the device model; and determining the parameter determination method according to the initial parameter determination method, the jitter deviation compensation method, and the disturbance compensation method.

[0064] In this embodiment, the specific steps of determining the parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the device model corresponding to the target crimping device are described.

[0065] Among them, the initial parameter determination method is involved. The initial parameter determination method refers to the parameter determination strategy initially determined based on the device model and the target crimping parameters, which can provide a starting point for determining the target process parameters.

[0066] Among them, the disturbance compensation method is involved. The disturbance compensation method refers to the compensation strategy determined to eliminate unknown disturbances in the crimping control process.

[0067] Among them, the jitter deviation compensation method is involved. The jitter deviation compensation method refers to the strategy of reducing and eliminating high-frequency vibrations or jitters that may occur during the crimping operation to improve the crimping quality and the stability of the device.

[0068] In this step, first determine the initial parameter determination method, then determine the disturbance compensation method that can eliminate unknown disturbances and the jitter deviation compensation method that suppresses high-frequency vibrations and jitters. According to the disturbance compensation method and the jitter deviation compensation method, improve the initial parameter determination method, and finally determine the parameter determination method. Through this step, the robustness of the finally determined control strategy against uncertain factors and external disturbances is enhanced, enabling the system to remain stable in a complex and changing working environment.

[0069] It should be noted that when determining the interference compensation method, a neural network compensation mechanism can be designed based on the analysis of external interference, and in an online learning and approximation manner, the influence of interference on the crimping process can be reduced; when determining the jitter deviation compensation method, the high-frequency vibration that the device may generate when approaching the sliding surface can be considered, and a hyperbolic tangent function or other smoothing function can be designed to replace the traditional sign function to reduce the jitter phenomenon in the control process.

[0070] As an optional embodiment, according to the target crimping parameters, the parameter deviation determination method, and the device model, determine the initial parameter determination method, including: according to the target crimping parameters and the device model, determine the initial coefficient matrix, where the initial coefficient matrix is used to represent the proportional relationship between the process parameters and the corresponding crimping parameters; according to the parameter deviation determination method and the initial coefficient matrix, determine the initial parameter determination method.

[0071] In this embodiment, the specific steps of determining the initial parameter determination method according to the target crimping parameters, the parameter deviation determination method, and the device model are described.

[0072] Among them, the initial coefficient matrix is involved. The initial coefficient matrix refers to a matrix composed of a series of proportional coefficients, which reflect the linear or non-linear relationship between the crimping parameters and the crimping process parameters.

[0073] In this step, first, according to the target crimping parameters and the device model, establish an initial coefficient matrix to quantify the influence of the control process parameters on the crimping parameters, and then use the initial coefficient matrix and the deviation determination method to determine the initial parameter determination method to initially set the control parameters, laying a foundation for subsequent dynamic adjustment.

[0074] Through this step, by determining the appropriate initial coefficient matrix, the control system can quickly respond to the crimping requirements, quickly adjust the control input by calculating the offset from the target parameters, and shorten the time for the crimping operation to reach the stable state.

[0075] As an optional embodiment, according to the target crimping parameters and the parameter deviation determination method, determine the interference compensation method, including: according to the target crimping parameters and the parameter deviation determination method, determine the initial interference compensation method; according to the initial interference compensation method, determine the interference compensation parameters corresponding to multiple interference items respectively; determine the interference coefficient matrix, where the interference coefficient matrix is used to represent the weight values corresponding to multiple interference compensation parameters in the interference compensation method respectively; according to multiple interference compensation parameters and the interference coefficient matrix, determine the interference compensation method.

[0076] In this embodiment, the specific steps of determining the interference compensation method according to the target crimping parameters and the parameter deviation determination method are described.

[0077] Among them, the initial interference compensation method is involved. The initial interference compensation method refers to the strategy for the control system to first attempt to compensate for external interference. Based on the equipment model and target crimping parameters, it provides a starting point for subsequent interference compensation.

[0078] Among them, the interference term is involved. The interference term refers to various external factors that may be encountered during the crimping process, such as changes in material hardness, fluctuations in environmental temperature, equipment aging, etc. These factors will affect the crimping parameters and need to be compensated through control strategies.

[0079] Among them, the interference compensation parameter is involved. The interference compensation parameter refers to the control parameter introduced to compensate for a specific interference term.

[0080] Among them, the interference coefficient matrix is involved. The interference coefficient matrix refers to a mathematical matrix used to quantify the contribution degree of multiple interference compensation parameters to the total compensation effect. The weight of each interference compensation parameter in its matrix reflects its importance in eliminating specific interference.

[0081] In this step, first, an initial interference compensation method is determined through the target crimping parameters and the parameter deviation determination method, which provides a theoretical framework and an initial strategy for subsequent interference compensation. Then, all possible interference terms during the crimping process need to be identified, and for each interference term, the corresponding interference compensation parameter is determined. Next, by analyzing the severity of the impact of each interference term on the crimping process, the interference coefficient matrix is determined. Each weight in this matrix reflects the relative importance of the corresponding interference compensation parameter in eliminating interference. Finally, by combining multiple interference compensation parameters with the interference coefficient matrix, a comprehensive interference compensation method is determined to ensure that the crimping parameters can still approach the target value in the presence of external interference, thereby improving the quality and stability of the crimping operation. Through this step, the interference compensation method can effectively cope with the uncertainties and external interferences during the crimping process, enabling the control system to remain stable and efficient in a complex and changing environment.

[0082] As an optional embodiment, after determining the parameter determination method based on the initial parameter determination method, the jitter deviation compensation method, and the interference compensation method, it further includes: determining a verification function according to the parameter deviation determination method, the equipment model, and the parameter determination method; predicting the crimping result according to the verification function, where the crimping result is used to represent the probability value of obtaining the target crimping parameters when the target crimping equipment performs a crimping operation according to the process parameters determined by the parameter determination method; and updating the parameter determination method according to the crimping result.

[0083] In this embodiment, the specific steps for verifying the parameter determination method are described.

[0084] Among them, a verification function is involved. The verification function refers to a function used to evaluate the accuracy and reliability of the parameter determination method, and can simulate and predict the process parameters determined according to the parameter deviation determination method, and control the performance achieved by the crimping equipment.

[0085] Among them, the crimping result is involved. The crimping result refers to the matching degree or deviation degree between the actual crimping parameters and the target crimping parameters after controlling the crimping equipment with the process parameters determined according to the parameter deviation determination method, and usually uses a probability value to represent the possibility that the equipment reaches the set crimping standard.

[0086] In this step, after determining the parameter determination method, it is also necessary to construct a verification function. This function is based on the equipment model and the parameter deviation determination method and is used to predict the result of the crimping operation. The predicted crimping result is a probability value, which reflects the possibility that the equipment reaches the set target crimping parameters under the current parameter determination method. If the predicted crimping result has a large deviation from the expected target, the parameter determination method will be updated and optimized according to the feedback of the crimping result to improve the quality and stability of the crimping operation. Through this step, the verification function predicts the crimping result, pre-evaluates the effectiveness of the control strategy, and adjusts the parameter determination method in a timely manner, so as to improve the accuracy of the crimping parameters and ensure the crimping quality.

[0087] As an optional embodiment, determining the parameter deviation determination method according to the target crimping parameters and the actual crimping parameters includes: when there are multiple target crimping parameters and multiple actual crimping parameters, determining the parameter items corresponding to each of the multiple target crimping parameters; determining the deviation values corresponding to each of the multiple parameter items according to the multiple target crimping parameters and the actual crimping parameters corresponding to each of the multiple target crimping parameters; and determining the parameter deviation determination method according to the multiple deviation values.

[0088] In this embodiment, the specific steps of determining the parameter deviation determination method according to the target crimping parameters and the actual crimping parameters are described.

[0089] Among them, the parameter item is involved. The parameter item refers to each specific parameter in the target crimping parameters and the actual crimping parameters, such as the crimping force, the crimping position, etc. Each parameter item has its independent control and monitoring requirements.

[0090] Among them, the deviation value is involved. The deviation value refers to the difference between the actual crimping parameter and the target crimping parameter, which is used to quantify the error in the control process and is the basic data for adjusting the control strategy to achieve the expected target.

[0091] In this step, when there are multiple target crimping parameters and multiple actual crimping parameters, first identify all the parameter items in the target crimping parameters, such as crimping force, crimping position, etc. Then, for each target crimping parameter item, compare it with the actual crimping parameter and calculate the deviation value between them. Finally, based on all the deviation values, design and optimize a parameter deviation determination method, which will guide the control system on how to adjust the input parameters to minimize the deviation of each parameter item and ensure that the crimping operation meets the set quality standards.

[0092] Through this step, it is possible to simultaneously monitor and adjust multiple crimping parameters, respond in a timely manner to changes in the equipment state, minimize the deviation between the target parameters and the actual parameters, reduce operation errors, ensure that all key indicators reach or approach the target values, improve the crimping quality and production efficiency, and enhance the comprehensiveness and accuracy of the crimping process.

[0093] Based on the above embodiments and optional embodiments, an optional implementation manner is provided, which is specifically described below.

[0094] Wire crimping is a key link in the construction of transmission lines. With the increase in the voltage level and capacity of transmission lines, the requirements for wire crimping technology and quality have also increased, and extremely high control precision requirements are imposed on the crimping force and the position of the jaws. However, currently, traditional wire crimping mainly relies on the experience and skills of the operator. It is difficult to precisely control the crimping force, which may lead to insecure crimping or damage to the wire, and it is difficult to meet the control requirements for high precision and high efficiency.

[0095] Hydraulic lifting mechanisms are widely used inside crimping tools. Due to the limited internal space of the hydraulic lifting mechanism, it is difficult to install energy absorption devices such as accumulators and pressure reducing valves. This results in large fluctuations in fluid pressure during the operation of the system, affecting the stability and precision of the system. Traditional control methods have weak capabilities in dealing with the non-linear characteristics and internal and external disturbances of the system, and it is difficult to precisely control the position of the jaws and the crimping force. Although some improved control strategies have improved the control precision, they often rely on accurate model descriptions. In actual applications, the working environment and load of the crimping tool change frequently, and different types of cables and materials have different requirements for the crimping process. This makes it very difficult to describe an accurate model. Therefore, existing control strategies often have difficulty ensuring system stability and crimping quality under changing working conditions.

[0096] In addition, existing control methods often fail to consider the dynamic uncertainties and external disturbances during the crimping process, which leads to significant fluctuations in crimping quality and system stability. As a robust control method, sliding mode control can effectively address system uncertainties and external disturbances. However, traditional sliding mode control methods still have some problems. High-frequency chattering may occur when the system approaches the sliding mode surface, which can cause unstable control of the crimping tool and even damage to mechanical components.

[0097] In view of this, an optional embodiment of the present invention provides a sliding mode control method based on neural network compensation for the automatic control of a wire crimping system. The optional embodiment of the present invention includes: First, construct a dynamic model of the crimping tool to obtain the current crimping force and jaw position deviation; Secondly, design a sliding mode control surface and a sliding mode control law based on the above dynamic model, and compensate for the dynamic uncertainties and external disturbances of the system through a neural network; Finally, apply the control input to the crimping tool driver to precisely control the crimping process. The optional embodiment of the present invention combines the robustness of sliding mode control and the adaptive ability of neural networks, and compensates for the dynamic uncertainties and external disturbances of the crimping tool in real time through a neural network, solves the chattering problem in traditional sliding mode control methods, and effectively improves the control accuracy of the crimping force and jaw position. The application of this method in the crimping tool control system can automatically adjust control parameters according to different materials and cable specifications, improve the stability of the system and the crimping quality, and has significant technical advantages.

[0098] The following details the specific steps of the sliding mode control method based on neural network compensation provided in the optional embodiment of the present invention.

[0099] S1. Receive a crimping request from the target crimping device, where the crimping request carries target crimping parameters.

[0100] S2. In response to the crimping request, determine the initial process parameters based on the target crimping parameters.

[0101] S3. During the crimping operation of the target crimping device according to the initial process parameters, determine the actual crimping parameters.

[0102] S4. Determine the parameter deviation determination method based on the target crimping parameters and the actual crimping parameters.

[0103] S5. Determine the parameter determination method based on the target crimping parameters, the parameter deviation determination method, and the device model corresponding to the target crimping device.

[0104] A1. Determine the device model corresponding to the target crimping device.

[0105] Build the basic dynamic model of the crimping tool, including input variables, i.e., the driving force; state variables (same as the above process parameters), i.e., the displacement, velocity, and crimping force of the jaws; and output variables (same as the above crimping parameters), i.e., the actual crimping force and the jaw position.

[0106] The dynamic equation of the crimping tool (same as the above equipment model) is simplified to:

[0107]

[0108] where x is the displacement of the jaws of the crimping tool, u is the driving force control input, N is the inertia matrix, B is the damping matrix, K is the stiffness matrix, and D is the disturbance term.

[0109] A2. Determine the sliding mode surface.

[0110] According to the dynamic state equation designed in step A1, define the fast terminal sliding mode surface. The control objective of the wire crimping system is to make the error between the desired displacement and the actual displacement zero, so as to achieve the precise tracking of the desired path by the wire crimping system. The sliding mode surface is designed as:

[0111]

[0112] where e = x d -x is the state error (same as the above parameter deviation determination method), and x d is the desired displacement vector (same as the above target crimping parameters), Z is the designed positive definite matrix; v represents the sliding mode function, which is used to guide the system state to tend to the desired trajectory.

[0113] A3. Determine the disturbance compensation method.

[0114] Design a radial basis function (RBF) neural network (same as the above disturbance compensation method) to approximate the unknown dynamic parameters of the system.

[0115] Design the nonlinear unknown part in the online estimation control law of the RBF neural network. Figure 2 is the structure diagram of the neural network provided by the optional implementation manner of the present invention. As Figure 2 shown, the adopted RBF neural network structure belongs to a feedforward neural network, including an input layer, a hidden layer, and an output layer. Its basic principle is to divide the input space into multiple non-overlapping subspaces, and each subspace is described by one RBF, so as to effectively extract the nonlinear features in the data and realize the modeling of the nonlinear input-output relationship through the connection between the input layer, the hidden layer, and the output layer, thus solving complex tasks. Input the state information r of the system, and use the online learning algorithm to adjust the network parameters to approximate the time-varying term in the dynamic model of the wire crimping system, and output the dynamic compensation value u NN .

[0116] Among them, the input of the RBF neural network is:

[0117]

[0118] The selected Gaussian function (the same as the above initial interference compensation method) is:

[0119]

[0120] where c i is the center vector of the Gaussian function; v i is the normalization constant; h i is the output of the hidden node of the neural network. The output of the RBF neural network ( the same as the above interference compensation parameters) is:

[0121]

[0122] Determine the weight coefficients of the neural network (w, the same as the above interference compensation matrix):

[0123] w T = [w k1 w k2 … w kn T

[0124] where, w k1 … w kn are the weight coefficients corresponding to multiple neural network layers respectively.

[0125] Next, by adjusting the weights online, compensate for the system uncertainty and disturbance torque, realize the adaptive weight update, and determine the interference compensation method as:

[0126]

[0127] In the ideal case, when the weight coefficient approaches the optimal weight coefficient, the weight coefficient will stop changing, and the output of the RBF neural network approaches the uncertainty term, which is the system uncertainty approximated by the RBF neural network.

[0128] Introduce the hyperbolic tangent function tanh() to replace the traditional sign function sgn(). The hyperbolic tangent function (the same as the above jitter deviation compensation method) is:

[0129]

[0130] where, κ is the coefficient in the hyperbolic tangent function.

[0131] The properties of the hyperbolic tangent function are:

[0132] ​

[0133] Using the hyperbolic tangent function for control law design can reduce the impact of high-frequency vibration on the actuator.

[0134] Figure 3 It is the flowchart of the sliding mode control strategy based on neural network compensation provided by an alternative embodiment of the present invention. As Figure 3 shown, the obtained sliding mode control law (in the same parameter determination method as above) is:

[0135] u = u eq + u r + u NN

[0136] where u eq is the equivalent control law component (in the same initial parameter determination method as above), u r is the reaching control law component (in the same chattering deviation compensation method as above), u NN is the neural network dynamic compensation value (in the same disturbance compensation method as above).

[0137] Substituting it in, the sliding mode control law is:

[0138]

[0139] where, K v is the control gain matrix (in the same initial coefficient matrix as above); is the hyperbolic tangent function, used to reduce the chattering phenomenon; δ N is the parameter in the controller.

[0140] A4. Verify the parameter determination method.

[0141] Prove the system stability of the above control law:

[0142] Define the Lyapunov function (Lyapunov function, V, in the same verification function as above) as:

[0143]

[0144] Analyze its derivative which is:

[0145]

[0146] From it can be seen that the designed controller can operate stably.

[0147] S6. Determine the target process parameters according to the actual crimping parameters and the parameter determination method, so as to control the target crimping equipment to perform crimping operations according to the target process parameters.

[0148] In an alternative embodiment of the present invention, the main technical performance indicators and controller parameters used are: the controller parameter γ = diag[6, 6], Z = 51, κ = 0.01, K v = 151, δ N = 12.

[0149] In addition, an alternative embodiment of the present invention also provides a control system that applies a sliding mode control strategy based on neural network compensation to a crimping tool. Figure 4 Figure 354 is a structural diagram of the crimping tool control system provided by an alternative embodiment of the present invention. As Figure 4 shown, the control system includes a crimping tool, a state sensor, a wireless module, a core controller module, a global positioning (GPS) positioning module, and a host computer. The crimping tool is composed of components such as an actuator, a crimping die, and a rotating shaft, and is used to perform the crimping operation of the wire; the state sensor is used to measure the jaw position and the crimping force in real time, and provides real-time pressure data for the system; the core controller includes a sliding mode control module and a neural network module, which are responsible for receiving and processing the sensor detection signals and commanding other modules to work together; the GPS positioning module is responsible for providing the accurate geographical location information of the crimping operation, and the mobile data network (4G) wireless module is responsible for transmitting the processed data to the host computer; the host computer receives the data transmitted by the wireless module, and performs further analysis, storage, and management, and provides a user interface for the operator to monitor and review. The state sensor, the wireless transmission module, and the positioning module are respectively connected to the core processor, and the wireless module is connected to the host computer.

[0150] The crimping tool is composed of components such as an actuator, a crimping die, and a rotating shaft. The actuator is used to drive the jaws of the crimping tool to close and adjust the magnitude of the crimping force according to the control input.

[0151] The state sensor can measure the jaw position and the crimping force in real time, obtain the position of the jaws of the crimping tool and the applied force through a displacement sensor and a pressure sensor respectively, provide real-time pressure and position data for the system, and transmit the data to the core controller module.

[0152] The controller is responsible for receiving the real-time data provided by the sensor, processing it, and generating appropriate control signals. The sliding mode control module calculates the error and generates control signals to control the crimping tool to perform precise crimping operations. The neural network module is updated according to the real-time data and the empirical compensation model to optimize the control signals.

[0153] Furthermore, an adaptive neural network and a sliding mode controller are designed to meet the requirements of controlling the crimping force and jaw position during the crimping process. The sliding mode control module designs a sliding surface and a control law according to the control algorithm, and adjusts the actuator action by outputting a control signal to ensure the precise control of the crimping force and jaw position. The neural network module uses an RBF neural network to approximate the uncertainties or unknown dynamic characteristics of the system, thereby reducing the degree of dependence of the sliding mode control on the system model and at the same time reducing the influence of the chattering phenomenon. The neural network module automatically adjusts the control strategy according to the jaw position and pressure data to cope with different types of cable and material characteristics.

[0154] The 4G data transmission module transmits the processed data to the remote host computer, including sensor data, control instructions, and operation status information. Through the wireless module, the system can achieve remote monitoring and data synchronization.

[0155] The GPS positioning module provides accurate geographical location information for the crimping operation, helps record and transmit the location information of the current crimping operation, and ensures the accuracy and traceability of each crimping operation.

[0156] Furthermore, the communication between the GPS positioning module and the core processor is directly connected by an asynchronous transceiver (serial port UART); the controller is connected to the wireless module, and the data is sent to the wireless module in a transparent transmission form.

[0157] Furthermore, after the wireless module joins the corresponding 4G network, when the wireless module is triggered, the stored data is uploaded to the host computer cloud service platform through the standard protocol to realize remote monitoring and management of the data. Furthermore, the host computer can monitor the operation status of the crimping tool in real time, and provide feedback to the operator through data analysis to help optimize the operation process. The host computer provides an interface for the operator to interact with the control system, allowing setting of target crimping parameters, viewing of historical records, and real-time adjustment of control strategies.

[0158] Figure 5 is the embedded flowchart of the crimping tool control system provided by the optional embodiment of the present invention. As Figure 5 shown, after the system completes the initialization of the hardware components and communication protocols, it starts data acquisition, including collecting sensor data such as displacement, pressure, and position information, and processing these data. When the system receives an instruction, it will execute the host computer instruction received from the cloud service and perform corresponding control operations. The data collected by the system will be uploaded to the cloud server for subsequent analysis and monitoring. If there is a problem during the crimping process, the device may trigger an alarm (such as a buzzer warning). The system enters a waiting state after the alarm until the user takes action to clear the alarm.

[0159] Through the above optional embodiments, at least the following beneficial effects can be achieved:

[0160] (1) The sliding mode control strategy based on neural network compensation can adjust the crimping force and position of the crimping tool jaws in real time, ensuring the uniformity and consistency of the crimping force and position during the crimping process, effectively reducing the cable connection failure caused by over-crimping or under-crimping, and improving the crimping quality;

[0161] (2) The online learning ability of the neural network enables the control system to adaptively adjust according to the characteristics of different cable materials and sizes, ensuring that the crimping quality remains stable under various working conditions. This adaptability enables the system to handle different materials and specifications in complex and variable production environments, avoiding the uncertainties and errors brought by manual adjustment;

[0162] (3) Effectively improve the accuracy, robustness and intelligent level of the equipment, so that the position control accuracy of the crimping tool reaches 0.1 mm, and the pressure control accuracy reaches 0.5% FS.

[0163] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0164] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of the various embodiments of the present invention.

[0165] Embodiment 2

[0166] According to an embodiment of the present invention, there is also provided a device for implementing the method for determining the above-mentioned crimping process parameters. Figure 6 It is a structural block diagram of the device for determining the crimping process parameters according to an embodiment of the present invention, as Figure 6 shown. The device includes: a receiving module 602, a response module 604, a first determination module 606, a second determination module 608, a third determination module 610, and a fourth determination module 612. The device will be described in detail below.

[0167] A receiving module 602, configured to receive a crimping request of a target crimping device, where the crimping request carries target crimping parameters; a response module 604, connected to the receiving module 602, configured to, in response to the crimping request, determine initial process parameters according to the target crimping parameters; a first determination module 606, connected to the response module 604, configured to determine actual crimping parameters during the process that the target crimping device performs a crimping operation according to the initial process parameters; a second determination module 608, connected to the first determination module 606, configured to determine a parameter deviation determination method according to the target crimping parameters and the actual crimping parameters; a third determination module 610, connected to the second determination module 608, configured to determine a parameter determination method according to the target crimping parameters, the parameter deviation determination method, and a device model corresponding to the target crimping device; a fourth determination module 612, connected to the third determination module 610, configured to determine target process parameters according to the actual crimping parameters and the parameter determination method, so as to control the target crimping device to perform a crimping operation according to the target process parameters.

[0168] It should be noted here that the above receiving module 602, response module 604, first determination module 606, second determination module 608, third determination module 610, and fourth determination module 612 correspond to steps S102 to S112 in the method for determining crimping process parameters. The examples and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the content disclosed in the above Embodiment 1.

[0169] Embodiment 3

[0170] According to another aspect of the embodiments of the present invention, an electronic device is further provided, including: a processor; a memory for storing instructions executable by the processor, where the processor is configured to execute the instructions to implement the method for determining crimping process parameters in any one of the above.

[0171] Embodiment 4

[0172] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is further provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the method for determining crimping process parameters in any one of the above.

[0173] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0174] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0175] In several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0176] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0178] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs, etc., which can store program codes.

[0179] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for determining crimping process parameters, characterized in that: include: Receiving a crimping request from a target crimping device, wherein the crimping request carries a target crimping parameter; In response to the crimping request, determining initial process parameters according to the target crimping parameters; Determining actual crimping parameters during the crimping operation performed by the target crimping device according to the initial process parameters; Determining a parameter deviation determination method according to the target crimping parameter and the actual crimping parameter; Determine a parameter determination method according to the target crimping parameter, the parameter deviation determination method and a device model corresponding to the target crimping device; According to the actual crimping parameters and the parameter determination method, target process parameters are determined to control the target crimping equipment to perform crimping operations according to the target process parameters.

2. The method according to claim 1, characterized in that Before determining the parameter determination method according to the target crimping parameter, the parameter deviation determination method and the device model corresponding to the target crimping device, the method further includes: Determine a device function of the target crimping device, wherein the device function is used to represent a functional relationship between an input parameter and an output parameter of the target crimping device, the input parameter represents a crimping process parameter received by the target crimping device, and the output parameter represents a corresponding crimping parameter of the target crimping device during a crimping operation according to the input parameter; According to the device function and the device physical parameters corresponding to the target crimping device, a device model corresponding to the target crimping device is determined.

3. The method according to claim 1, characterized in that The method of determining the parameter according to the target crimping parameter, the parameter deviation determination method and the device model corresponding to the target crimping device includes: Determining an initial parameter determination method according to the target crimping parameter, the parameter deviation determination method and the equipment model; Determining an interference compensation method according to the target crimping parameter and the parameter deviation determination method; Determining a jitter deviation compensation method according to the parameter deviation determination method and the device model; The parameter determination method is determined according to the initial parameter determination method, the jitter deviation compensation method and the interference compensation method.

4. The method according to claim 3, characterized in that The method of determining the initial parameter according to the target crimping parameter, the parameter deviation determination method and the equipment model includes: Determining an initial coefficient matrix according to the target crimping parameters and the equipment model, wherein the initial coefficient matrix is ​​used to represent a proportional relationship between process parameters and corresponding crimping parameters; The initial parameter determination method is determined according to the parameter deviation determination method and the initial coefficient matrix.

5. The method according to claim 3, characterized in that: The method of determining the interference compensation method according to the target crimping parameter and the parameter deviation determination method includes: Determining an initial interference compensation method according to the target crimping parameter and the parameter deviation determination method; Determining interference compensation parameters corresponding to a plurality of interference items respectively according to the initial interference compensation method; Determine an interference coefficient matrix, wherein the interference coefficient matrix is ​​used to represent weight values ​​corresponding to a plurality of interference compensation parameters in the interference compensation method; The interference compensation method is determined according to the multiple interference compensation parameters and the interference coefficient matrix.

6. The method according to claim 3, characterized in that After determining the parameter determination method according to the initial parameter determination method, the jitter deviation compensation method and the interference compensation method, the method further includes: Determine a verification function according to the parameter deviation determination method, the device model and the parameter determination method; Predicting a crimping result according to the verification function, wherein the crimping result is used to indicate a probability value of a target crimping parameter obtained by the target crimping device performing a crimping operation according to the process parameters determined by the parameter determination method; The parameter determination method is updated according to the crimping result.

7. The method according to any one of claims 1 to 6, characterized in that: The method of determining the parameter deviation according to the target crimping parameter and the actual crimping parameter includes: In the case where the target crimping parameters include multiple ones and the actual crimping parameters include multiple ones, determining parameter items corresponding to the multiple target crimping parameters respectively; Determine deviation values ​​corresponding to a plurality of parameter items respectively according to a plurality of target crimping parameters and actual crimping parameters respectively corresponding to the plurality of target crimping parameters; The parameter deviation determination method is determined according to the multiple deviation values.

8. A device for determining crimping process parameters, characterized in that: include: A receiving module, used for receiving a crimping request from a target crimping device, wherein the crimping request carries a target crimping parameter; A response module, configured to respond to the crimping request and determine initial process parameters according to the target crimping parameters; A first determination module is used to determine actual crimping parameters during the crimping operation performed by the target crimping device according to the initial process parameters; A second determination module is used to determine a parameter deviation determination method according to the target crimping parameter and the actual crimping parameter; A third determination module is used to determine a parameter determination method according to the target crimping parameter, the parameter deviation determination method and a device model corresponding to the target crimping device; The fourth determination module is used to determine the target process parameters according to the actual crimping parameters and the parameter determination method, so as to control the target crimping equipment to perform the crimping operation according to the target process parameters.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method for determining the crimping process parameters as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for determining the crimping process parameters as claimed in any one of claims 1 to 7.

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