Wire extrusion control method and system based on sensing feedback

Abstract

The invention discloses a wire extrusion control method and system based on sensing feedback, and the method comprises the steps: obtaining the sensing information of a plurality of regions of target wire extrusion equipment through a sensor when the target wire extrusion equipment works; calculating link sensing information corresponding to a plurality of working links of the target wire extrusion equipment according to the plurality of pieces of area sensing information; determining a working defect condition corresponding to the target wire extrusion equipment according to the link sensing information and a preset corresponding relation between sensing information and defects; determining a control instruction of at least one equipment part of the target wire extrusion equipment according to the working defect condition and a corresponding relation between a preset condition and a control parameter strategy; the control instruction is used for controlling the corresponding equipment part to reduce or solve the working defect condition. Therefore, the working condition of the wire extrusion equipment can be comprehensively monitored in real time based on the sensing information, and the defects can be corrected in time, so that the utilization rate and the extrusion quality of consumables by the wire extrusion equipment are improved, and greener and more environment-friendly waste recovery work is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a wire extrusion control method and system based on sensor feedback. Background Art

[0002] With the promotion of the concept of green economy, more and more technical research and development work has begun to focus on how to improve the reuse rate of waste materials. Among them, 3D printing technology has begun to think about how to reheat recycled materials to achieve new printing work. For example, some companies or institutions have introduced 3D printing filament extrusion technology to process a large amount of waste printing consumables such as plastics or nylon for reuse. However, the filament extrusion printing equipment of the prior art generally only performs mechanical and simple waste treatment according to the designed mechanical structure and processing flow, and does not effectively use the sensor information obtained by the sensor to judge the working status of the filament extrusion process in real time and adjust the working parameters accordingly. Therefore, the filament extrusion technology of the prior art cannot achieve more efficient and stable filament extrusion, and its corresponding processing efficiency of waste materials is also limited, and it cannot meet the requirements of greener and more environmentally friendly work. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a wire extrusion control method and system based on sensor feedback, which can monitor the working condition of the wire extrusion equipment in real time and comprehensively based on the sensor information and correct defects in time, so as to improve the utilization rate of consumables and the extrusion quality of the wire extrusion equipment and realize greener and more environmentally friendly waste recycling.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a wire extrusion control method based on sensor feedback, the method comprising: When the target filament extrusion device is working, acquiring multiple area sensing information of the target filament extrusion device through sensors; Calculating link sensing information corresponding to multiple working links of the target wire extrusion equipment according to the multiple regional sensing information; Determine the working defect situation corresponding to the target wire extrusion equipment according to the link sensing information and the corresponding relationship between the preset sensing information and the defect; According to the working defect situation, and the correspondence between the preset situation and the control parameter strategy, a control instruction of at least one device component of the target filament extrusion device is determined; the control instruction is used to control the corresponding device component to reduce or solve the working defect situation.

[0005] As an optional embodiment, in the first aspect of the present invention, the regional sensing information is the sensing information obtained by the sensor equipment arranged in the different component areas of the target wire extrusion equipment; the component areas are the drive motor area, the silo area, the discharge port area, the gear cycloid area or the wire winding area; the sensing information includes one or more combinations of image information, sound information, temperature information and infrared ranging information.

[0006] As an optional embodiment, in the first aspect of the present invention, calculating the link sensing information corresponding to the multiple working links of the target filament extrusion device according to the multiple regional sensing information includes: For each of the regional sensing information, determining the component area and information type corresponding to the regional sensing information; the information type is image information, sound information, temperature information or infrared ranging information; According to the component area and the information type, determining a link prediction model corresponding to the sensor information of the area from a plurality of candidate prediction models; Inputting the regional sensor information into the link prediction model to obtain the working link corresponding to the regional sensor information; All the regional sensing information corresponding to each working link of the target filament extrusion equipment is determined as the link sensing information corresponding to the working link.

[0007] As an optional implementation, in the first aspect of the present invention, determining the link prediction model corresponding to the sensor information of the region from multiple candidate prediction models according to the component region and the information type includes: For each candidate prediction model, determine all component region annotations in the training data set corresponding to the prediction model to obtain a training component annotation set; Calculating a first similarity parameter between the training component annotation set and the component region; Determine the data types of all input data in the historical prediction records corresponding to the prediction model, and obtain a historical data type set; Calculating a second similarity parameter between the historical data type set and the information type; Calculating the product of the first similarity parameter and the second similarity parameter to obtain a model priority parameter corresponding to the prediction model; The prediction model with the highest model priority parameter is determined as the link prediction model corresponding to the regional sensor information; the link prediction model is obtained by training a training data set including regional sensor information of multiple training components and corresponding work link annotations.

[0008] As an optional embodiment, in the first aspect of the present invention, the working link is a consumable heating link, a consumable extrusion link, an extrusion molding link, a gear winding link or a wire winding link.

[0009] As an optional embodiment, in the first aspect of the present invention, determining the working defect corresponding to the target wire extrusion device according to the link sensor information and the corresponding relationship between the preset sensor information and the defect includes: For each of the working links, the link sensor information corresponding to the working link is input into the defect prediction neural network corresponding to the working link to obtain a plurality of predicted defects corresponding to the working link; the defect prediction neural network is trained by a training data set including a plurality of training link sensor information of the working links and corresponding working defect annotations; For any two working links that are adjacent to each other in a preset working link sequence, the intersection of all the predicted defects corresponding to the two working links is calculated to obtain the intersection defect set corresponding to the two working links; the working link sequence is, in order, a heating consumables link, an extrusion consumables link, an extrusion molding link, a gear winding link, and a wire winding link; Determine the set of influencing defects corresponding to the two working links according to a preset correspondence library of continuous links and influencing defects; Calculate the intersection of the intersection defect set and the influencing defect set to obtain the screened defects corresponding to the two working links; The screened defects corresponding to all the working links are determined as the working defect conditions corresponding to the target wire extrusion equipment.

[0010] As an optional embodiment, in the first aspect of the present invention, the working defect includes a combination of one or more of motor defects, heating temperature defects, insufficient consumable extrusion defects, excessive consumable impurities defects, consumable blockage defects, gear failure defects, gear disc line speed defects and wire winding speed defects.

[0011] As an optional embodiment, in the first aspect of the present invention, determining the control instruction of at least one device component of the target filament extrusion device according to the working defect situation and the correspondence between the preset situation and the control parameter strategy includes: According to a preset correspondence between working defect conditions and equipment components, determining at least one defective equipment component and a corresponding working adjustment parameter corresponding to the working defect condition; generating a control instruction corresponding to the defective equipment component and including the working adjustment parameter; and / or, For each defect in the working defect situation, determining the associated equipment component corresponding to the defect according to the preset corresponding relationship between the working defect situation and the equipment component; The objective function is set to minimize the number of control instructions in the control instruction combination; Setting restrictions includes: The union of the possibly affected equipment components corresponding to all the control instructions in the control instruction combination includes all the associated equipment components corresponding to the working defect; the possibly affected equipment components are predicted by inputting the control instructions into a trained component impact prediction neural network; The working defect situation is included in the union of defect resolution effects corresponding to all control instructions in the control instruction combination; the defect resolution effect is obtained according to the corresponding relationship between the control effect corresponding to the control instruction and the preset control effect and defect resolution effect; the control effect is obtained by inputting the control instruction into a trained control effect prediction algorithm model; Based on a dynamic programming algorithm, an iterative calculation is performed according to the objective function and the constraint conditions to obtain an optimal control instruction combination; the control instruction combination includes control instructions for at least one device component of the target wire extrusion device; the control instructions include one or more of a motor restart instruction, a motor acceleration instruction, an extrusion head control instruction, a heating control instruction, an extrusion component control instruction, a gear control instruction, and a winding motor control instruction.

[0012] A second aspect of an embodiment of the present invention discloses a wire extrusion control system based on sensor feedback, the system comprising: An acquisition module, used for acquiring multiple area sensing information of the target filament extrusion device through a sensor when the target filament extrusion device is working; A calculation module, used for calculating link sensor information corresponding to multiple working links of the target wire extrusion equipment according to the multiple regional sensor information; A determination module, used to determine the working defect corresponding to the target wire extrusion equipment according to the link sensing information and the corresponding relationship between the preset sensing information and the defect; A control module is used to determine the control instructions of at least one device component of the target filament extrusion device according to the working defect situation and the correspondence between the preset situation and the control parameter strategy; the control instructions are used to control the corresponding device component to reduce or solve the working defect situation.

[0013] As an optional embodiment, in the second aspect of the present invention, the regional sensing information is the sensing information obtained by the sensor equipment arranged in the different component areas of the target wire extrusion equipment; the component areas are the drive motor area, the silo area, the discharge port area, the gear cycloid area or the wire winding area; the sensing information includes one or more combinations of image information, sound information, temperature information and infrared ranging information.

[0014] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the calculation module calculates the link sensor information corresponding to the multiple working links of the target filament extrusion device according to the multiple regional sensor information includes: For each of the regional sensing information, determining the component area and information type corresponding to the regional sensing information; the information type is image information, sound information, temperature information or infrared ranging information; According to the component area and the information type, determining a link prediction model corresponding to the sensor information of the area from a plurality of candidate prediction models; Inputting the regional sensor information into the link prediction model to obtain the working link corresponding to the regional sensor information; All the regional sensing information corresponding to each working link of the target filament extrusion equipment is determined as the link sensing information corresponding to the working link.

[0015] As an optional implementation, in the second aspect of the present invention, the specific manner in which the calculation module determines the link prediction model corresponding to the sensor information of the region from multiple candidate prediction models according to the component region and the information type includes: For each candidate prediction model, determine all component region annotations in the training data set corresponding to the prediction model to obtain a training component annotation set; Calculating a first similarity parameter between the training component annotation set and the component region; Determine the data types of all input data in the historical prediction records corresponding to the prediction model, and obtain a historical data type set; Calculating a second similarity parameter between the historical data type set and the information type; Calculating the product of the first similarity parameter and the second similarity parameter to obtain a model priority parameter corresponding to the prediction model; The prediction model with the highest model priority parameter is determined as the link prediction model corresponding to the regional sensor information; the link prediction model is obtained by training a training data set including regional sensor information of multiple training components and corresponding work link annotations.

[0016] As an optional embodiment, in the second aspect of the present invention, the working link is a consumable heating link, a consumable extrusion link, an extrusion molding link, a gear winding link or a wire winding link.

[0017] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the working defect corresponding to the target wire extrusion device according to the link sensor information and the corresponding relationship between the preset sensor information and the defect includes: For each of the working links, the link sensor information corresponding to the working link is input into the defect prediction neural network corresponding to the working link to obtain a plurality of predicted defects corresponding to the working link; the defect prediction neural network is trained by a training data set including a plurality of training link sensor information of the working links and corresponding working defect annotations; For any two working links that are adjacent to each other in a preset working link sequence, the intersection of all the predicted defects corresponding to the two working links is calculated to obtain the intersection defect set corresponding to the two working links; the working link sequence is, in order, a heating consumables link, an extrusion consumables link, an extrusion molding link, a gear winding link, and a wire winding link; Determine the set of influencing defects corresponding to the two working links according to a preset correspondence library of continuous links and influencing defects; Calculate the intersection of the intersection defect set and the influencing defect set to obtain the screened defects corresponding to the two working links; The screened defects corresponding to all the working links are determined as the working defect conditions corresponding to the target wire extrusion equipment.

[0018] As an optional embodiment, in the second aspect of the present invention, the working defect includes a combination of one or more of motor defects, heating temperature defects, insufficient consumable extrusion defects, excessive consumable impurities defects, consumable blockage defects, gear failure defects, gear disk line speed defects and wire winding speed defects.

[0019] As an optional embodiment, in the second aspect of the present invention, the control module determines the specific manner of the control instruction of at least one device component of the target filament extrusion device according to the working defect situation and the correspondence between the preset situation and the control parameter strategy, including: According to a preset correspondence between working defect conditions and equipment components, determining at least one defective equipment component and a corresponding working adjustment parameter corresponding to the working defect condition; generating a control instruction corresponding to the defective equipment component and including the working adjustment parameter; and / or, For each defect in the working defect situation, determining the associated equipment component corresponding to the defect according to the preset corresponding relationship between the working defect situation and the equipment component; The objective function is set to minimize the number of control instructions in the control instruction combination; Setting restrictions includes: The union of the possibly affected equipment components corresponding to all the control instructions in the control instruction combination includes all the associated equipment components corresponding to the working defect; the possibly affected equipment components are predicted by inputting the control instructions into a trained component impact prediction neural network; The working defect situation is included in the union of defect resolution effects corresponding to all control instructions in the control instruction combination; the defect resolution effect is obtained according to the corresponding relationship between the control effect corresponding to the control instruction and the preset control effect and defect resolution effect; the control effect is obtained by inputting the control instruction into a trained control effect prediction algorithm model; Based on a dynamic programming algorithm, an iterative calculation is performed according to the objective function and the constraint conditions to obtain an optimal control instruction combination; the control instruction combination includes control instructions for at least one device component of the target wire extrusion device; the control instructions include one or more of a motor restart instruction, a motor acceleration instruction, an extrusion head control instruction, a heating control instruction, an extrusion component control instruction, a gear control instruction, and a winding motor control instruction.

[0020] The third aspect of the present invention discloses another wire extrusion control system based on sensor feedback, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the wire extrusion control method based on sensor feedback disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the wire extrusion control method based on sensor feedback disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can calculate link sensor information corresponding to multiple working links based on multiple area sensor information of the target wire extrusion equipment, and then determine the corresponding working defect situation of the target wire extrusion equipment according to the link sensor information and the corresponding relationship between the preset sensor information and the defect, and based on this, determine the control instructions of at least one equipment component of the target wire extrusion equipment to reduce or solve the working defect situation, so that the working condition of the wire extrusion equipment can be monitored in real time and comprehensively based on the sensor information and the defects can be corrected in time, so as to improve the utilization rate and extrusion quality of consumables of the wire extrusion equipment and realize more green and environmentally friendly waste recycling work. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a wire extrusion control method based on sensor feedback disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of a wire extrusion control system based on sensor feedback disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another wire extrusion control system based on sensor feedback disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a wire extrusion control method and system based on sensor feedback, which can calculate link sensor information corresponding to multiple working links based on multiple regional sensor information of target wire extrusion equipment, and then determine the corresponding working defect of the target wire extrusion equipment according to the link sensor information and the corresponding relationship between the preset sensor information and the defect, and determine the control instructions of at least one device component of the target wire extrusion equipment based on this to reduce or solve the working defect, so that the working condition of the wire extrusion equipment can be monitored in real time and comprehensively based on the sensor information and the defects can be corrected in time, so as to improve the utilization rate and extrusion quality of the wire extrusion equipment for consumables, and realize more green and environmentally friendly waste recycling. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 : is a flow chart of a wire extrusion control method based on sensor feedback disclosed in an embodiment of the present invention. Figure 1 The described wire extrusion control method based on sensor feedback can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the wire extrusion control method based on sensor feedback may include the following operations: 101. When the target filament extrusion device is working, multiple area sensing information of the target filament extrusion device is obtained through sensors.

[0032] 102. Calculate link sensor information corresponding to multiple working links of the target wire extrusion equipment based on multiple area sensor information. 103. According to the link sensor information and the corresponding relationship between the preset sensor information and the defect, the corresponding working defect situation of the target wire extrusion equipment is determined. 104. Determine a control instruction for at least one device component of the target filament extrusion device according to the working defect situation and the correspondence between the preset situation and the control parameter strategy.

[0033] Optionally, the control instructions are used to control corresponding equipment components to reduce or resolve working defects.

[0034] In a specific embodiment, the control scheme of the present invention can be applied to a 3D printing device with a filament extrusion printing function, such as an FDM printing device, whose structure can be found in the Chinese invention patent publication number CN108790174A, or in the structure of the original desktop filament extruder model MK2.5 sold by ARTME 3D. The control scheme of the present invention should be regarded as an improvement on this type of filament extrusion equipment, that is, other devices with similar structures should also be regarded as included in the protection scope of the present invention.

[0035] It can be seen that the above-mentioned embodiment of the invention can calculate the link sensor information corresponding to multiple working links based on the multiple area sensor information of the target wire extrusion equipment, and then determine the working defect situation corresponding to the target wire extrusion equipment according to the link sensor information and the corresponding relationship between the preset sensor information and the defect, and based on this, determine the control instructions of at least one equipment component of the target wire extrusion equipment to reduce or solve the working defect situation, so that the working condition of the wire extrusion equipment can be monitored in real time and comprehensively based on the sensor information and the defects can be corrected in time, so as to improve the utilization rate and extrusion quality of consumables of the wire extrusion equipment and realize greener and more environmentally friendly waste recycling.

[0036] As an optional embodiment, in the above steps, the regional sensing information is sensing information acquired by sensor devices arranged in different component areas of the target filament extrusion device.

[0037] Specifically, the component area is a drive motor area, a silo area, a discharge port area, a gear cycloid area or a wire winding area.

[0038] Optionally, the sensing information includes a combination of one or more of image information, sound information, temperature information and infrared ranging information.

[0039] It can be seen that through the above optional embodiments, the content of the regional sensor information is limited to comprehensively characterize the working condition characteristics of the equipment component area, facilitate subsequent defect prediction and instruction determination, and assist in realizing real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on the sensor information and timely correction of defects, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment and achieve greener and more environmentally friendly waste recycling.

[0040] As an optional embodiment, in the above steps, calculating link sensing information corresponding to multiple working links of the target wire extrusion equipment according to multiple area sensing information includes: For each area sensor information, determine the component area and information type corresponding to the area sensor information; optionally, the information type is image information, sound information, temperature information or infrared ranging information; According to the component area and the information type, a link prediction model corresponding to the sensor information in the area is determined from multiple candidate prediction models; Inputting the regional sensor information into the link prediction model to obtain the working link corresponding to the regional sensor information; All the regional sensing information corresponding to each working link of the target filament extrusion equipment is determined as the link sensing information corresponding to the working link.

[0041] It can be seen that through the above optional embodiments, it is possible to screen out a suitable prediction model based on the area and type of the sensor information to accurately predict the working link corresponding to the regional sensor information, so as to facilitate subsequent defect determination, and assist in realizing real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on the sensor information and correct defects in time, so as to improve the utilization rate of consumables and the extrusion quality of the wire extrusion equipment, and realize greener and more environmentally friendly waste recycling.

[0042] As an optional embodiment, in the above step, determining the link prediction model corresponding to the sensor information of the region from multiple candidate prediction models according to the component region and the information type includes: For each candidate prediction model, determine all component region annotations in the training data set corresponding to the prediction model to obtain a training component annotation set; Calculating a first similarity parameter between the training component annotation set and the component region; Determine the data types of all input data in the historical prediction records corresponding to the prediction model, and obtain a historical data type set; Calculating a second similarity parameter between the historical data type set and the information type; Calculating the product of the first similarity parameter and the second similarity parameter to obtain a model priority parameter corresponding to the prediction model; The prediction model with the highest model priority parameter is determined as the link prediction model corresponding to the regional sensor information; the link prediction model is obtained by training a training data set including regional sensor information of multiple training components and corresponding work link annotations.

[0043] It can be seen that through the above-mentioned optional embodiments, the most reasonable and accurate link prediction model corresponding to the regional sensor information can be determined based on the similarity calculation of the model training data and the historical prediction records, which is convenient for subsequent link determination and defect prediction, and assists in realizing real-time and comprehensive monitoring of the working conditions of the wire extrusion equipment based on the sensor information and timely correction of defects, so as to improve the utilization rate of consumables and the extrusion quality of the wire extrusion equipment, and realize greener and more environmentally friendly waste recycling.

[0044] As an optional embodiment, in the above steps, the working links are heating consumables, extruding consumables, extrusion molding, gear winding or wire winding.

[0045] It can be seen that through the above optional embodiments, the types of work links are defined to accurately characterize the division of labor in the work process, and to facilitate subsequent defect prediction and instruction determination, thereby assisting in realizing real-time and comprehensive monitoring of the working conditions of the wire extrusion equipment based on sensor information and correcting defects in a timely manner, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment and achieve greener and more environmentally friendly waste recycling.

[0046] As an optional embodiment, in the above steps, determining the working defect corresponding to the target wire extrusion device according to the link sensor information and the preset correspondence between the sensor information and the defect includes: For each working link, the link sensor information corresponding to the working link is input into the defect prediction neural network corresponding to the working link to obtain a plurality of predicted defects corresponding to the working link; optionally, the defect prediction neural network is trained by a training data set including a plurality of training link sensor information of the working link and corresponding working defect annotations; For any two working links that are adjacent to each other in a preset working link sequence, the intersection of all predicted defects corresponding to the two working links is calculated to obtain the intersection defect set corresponding to the two working links; optionally, the working link sequence is the heating consumables link, the extrusion consumables link, the extrusion molding link, the gear winding link and the wire winding link; According to the preset correspondence library between continuous links and influencing defects, determine the influencing defect set corresponding to the two working links; Calculate the intersection of the intersection defect set and the influencing defect set to obtain the screened defects corresponding to the two work links; The screened defects corresponding to all working links are determined as the working defect conditions corresponding to the target wire extrusion equipment.

[0047] It can be seen that through the above-mentioned optional embodiments, based on the intersection of defects between continuous links and the screening of the corresponding relationship between preset links and defects, a more accurate working defect situation corresponding to the target wire extrusion equipment can be determined, which is convenient for subsequent instruction generation and control, and assists in realizing real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on sensor information and timely correction of defects, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment and realize greener and more environmentally friendly waste recycling.

[0048] As an optional embodiment, in the above steps, the working defects include one or more combinations of motor defects, heating temperature defects, insufficient consumable extrusion defects, excessive consumable impurities defects, consumable blockage defects, gear failure defects, gear disk line speed defects and wire winding speed defects.

[0049] It can be seen that through the above optional embodiments, the content of the working defect situation is limited to comprehensively and accurately characterize the possible defects of the wire extrusion equipment, facilitate the subsequent precise determination of control instructions for access correction, and assist in realizing real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on sensor information and timely correction of defects, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment, and realize greener and more environmentally friendly waste recycling.

[0050] As an optional embodiment, in the above steps, determining the control instruction of at least one device component of the target filament extrusion device according to the working defect situation and the correspondence between the preset situation and the control parameter strategy includes: According to a preset correspondence between working defect conditions and equipment components, determining at least one defective equipment component corresponding to the working defect condition and a corresponding working adjustment parameter; Generate control instructions corresponding to defective equipment components including working adjustment parameters; and / or, For each defect in the working defect situation, determine the associated equipment component corresponding to the defect according to the preset corresponding relationship between the working defect situation and the equipment component; The objective function is set to minimize the number of control instructions in the control instruction combination; Setting restrictions includes: The union of the possibly affected equipment components corresponding to all control instructions in the control instruction combination includes all associated equipment components corresponding to the working defect conditions; optionally, the possibly affected equipment components are predicted by inputting the control instructions into a trained component impact prediction neural network; The union of defect resolution effects corresponding to all control instructions in the control instruction combination includes the working defect situation; optionally, the defect resolution effect is obtained according to the corresponding relationship between the control effect corresponding to the control instruction and the preset control effect and the defect resolution effect; the control effect is obtained by inputting the control instruction into a trained control effect prediction algorithm model; Based on the dynamic programming algorithm, iterative calculation is performed according to the objective function and constraints to obtain the optimal control instruction combination; optionally, the control instruction combination includes control instructions for at least one device component of the target wire extrusion device; the control instructions include one or more of motor restart instructions, motor acceleration instructions, extrusion head control instructions, heating control instructions, extrusion component control instructions, gear control instructions and winding motor control instructions.

[0051] It can be seen that through the above optional embodiments, the specific scheme for determining the control instructions is limited, among which the scheme directly determined according to the corresponding relationship can directly generate the corresponding control instructions when there are fewer defects, and the scheme based on the dynamic programming algorithm can obviously achieve the effect of minimum instruction control while ensuring defect correction, so as to improve the control efficiency, realize real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on sensor information and correct defects in time, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment, and realize greener and more environmentally friendly waste recycling.

[0052] Embodiment 2 See also Figure 2 , Figure 2 : is a schematic diagram of a wire extrusion control system based on sensor feedback disclosed in an embodiment of the present invention. Figure 2 The described wire extrusion control system based on sensor feedback can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the wire extrusion control system based on sensor feedback may include: The acquisition module 201 is used to acquire multiple area sensing information of the target filament extrusion device through sensors when the target filament extrusion device is working.

[0053] The calculation module 202 is used to calculate the link sensor information corresponding to the multiple working links of the target wire extrusion equipment according to the multiple area sensor information. The determination module 203 is used to determine the working defect situation corresponding to the target wire extrusion equipment according to the link sensor information and the corresponding relationship between the preset sensor information and the defect. The control module 204 is used to determine the control instruction of at least one device component of the target filament extrusion device according to the working defect situation and the corresponding relationship between the preset situation and the control parameter strategy.

[0054] Optionally, the control instructions are used to control corresponding equipment components to reduce or resolve working defects.

[0055] It can be seen that the above-mentioned embodiment of the invention can calculate the link sensor information corresponding to multiple working links based on the multiple area sensor information of the target wire extrusion equipment, and then determine the working defect situation corresponding to the target wire extrusion equipment according to the link sensor information and the corresponding relationship between the preset sensor information and the defect, and based on this, determine the control instructions of at least one equipment component of the target wire extrusion equipment to reduce or solve the working defect situation, so that the working condition of the wire extrusion equipment can be monitored in real time and comprehensively based on the sensor information and the defects can be corrected in time, so as to improve the utilization rate and extrusion quality of consumables of the wire extrusion equipment and realize greener and more environmentally friendly waste recycling.

[0056] As an optional embodiment, the regional sensing information is sensing information obtained by sensor equipment arranged in different component areas of the target wire extrusion equipment; the component area is the drive motor area, the silo area, the discharge port area, the gear cycloid area or the wire winding area; the sensing information includes a combination of one or more of image information, sound information, temperature information and infrared ranging information.

[0057] It can be seen that through the above optional embodiments, the content of the regional sensor information is limited to comprehensively characterize the working condition characteristics of the equipment component area, facilitate subsequent defect prediction and instruction determination, and assist in realizing real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on the sensor information and timely correction of defects, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment and achieve greener and more environmentally friendly waste recycling.

[0058] As an optional embodiment, the specific manner in which the calculation module calculates link sensing information corresponding to multiple working links of the target wire extrusion device according to multiple area sensing information includes: For each area sensor information, determine the component area and information type corresponding to the area sensor information; optionally, the information type is image information, sound information, temperature information or infrared ranging information; According to the component area and the information type, a link prediction model corresponding to the sensor information in the area is determined from multiple candidate prediction models; Inputting the regional sensor information into the link prediction model to obtain the working link corresponding to the regional sensor information; All the regional sensing information corresponding to each working link of the target filament extrusion equipment is determined as the link sensing information corresponding to the working link.

[0059] It can be seen that through the above optional embodiments, it is possible to screen out a suitable prediction model based on the area and type of the sensor information to accurately predict the working link corresponding to the regional sensor information, so as to facilitate subsequent defect determination, and assist in realizing real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on the sensor information and correct defects in time, so as to improve the utilization rate of consumables and the extrusion quality of the wire extrusion equipment, and realize greener and more environmentally friendly waste recycling.

[0060] As an optional embodiment, the specific manner in which the calculation module determines the link prediction model corresponding to the sensor information of the region from multiple candidate prediction models according to the component region and the information type includes: For each candidate prediction model, determine all component region annotations in the training data set corresponding to the prediction model to obtain a training component annotation set; Calculating a first similarity parameter between the training component annotation set and the component region; Determine the data types of all input data in the historical prediction records corresponding to the prediction model, and obtain a historical data type set; Calculating a second similarity parameter between the historical data type set and the information type; Calculating the product of the first similarity parameter and the second similarity parameter to obtain a model priority parameter corresponding to the prediction model; The prediction model with the highest model priority parameter is determined as the link prediction model corresponding to the regional sensor information; the link prediction model is obtained by training a training data set including regional sensor information of multiple training components and corresponding work link annotations.

[0061] It can be seen that through the above-mentioned optional embodiments, the most reasonable and accurate link prediction model corresponding to the regional sensor information can be determined based on the similarity calculation of the model training data and the historical prediction records, which is convenient for subsequent link determination and defect prediction, and assists in realizing real-time and comprehensive monitoring of the working conditions of the wire extrusion equipment based on the sensor information and timely correction of defects, so as to improve the utilization rate of consumables and the extrusion quality of the wire extrusion equipment, and realize greener and more environmentally friendly waste recycling.

[0062] As an optional embodiment, the working link is a consumable heating link, a consumable extrusion link, an extrusion molding link, a gear winding link or a wire winding link.

[0063] It can be seen that through the above optional embodiments, the types of work links are defined to accurately characterize the division of labor in the work process, and to facilitate subsequent defect prediction and instruction determination, thereby assisting in realizing real-time and comprehensive monitoring of the working conditions of the wire extrusion equipment based on sensor information and correcting defects in a timely manner, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment and achieve greener and more environmentally friendly waste recycling.

[0064] As an optional embodiment, the specific manner in which the determination module determines the working defect corresponding to the target wire extrusion device according to the link sensor information and the preset correspondence between the sensor information and the defect includes: For each working link, the link sensor information corresponding to the working link is input into the defect prediction neural network corresponding to the working link to obtain a plurality of predicted defects corresponding to the working link; optionally, the defect prediction neural network is trained by a training data set including a plurality of training link sensor information of the working link and corresponding working defect annotations; For any two working links that are adjacent to each other in a preset working link sequence, the intersection of all predicted defects corresponding to the two working links is calculated to obtain the intersection defect set corresponding to the two working links; optionally, the working link sequence is the heating consumables link, the extrusion consumables link, the extrusion molding link, the gear winding link and the wire winding link; According to the preset correspondence library between continuous links and influencing defects, determine the influencing defect sets corresponding to the two working links; Calculate the intersection of the intersection defect set and the influencing defect set to obtain the screened defects corresponding to the two work links; The screened defects corresponding to all working links are determined as the working defect conditions corresponding to the target wire extrusion equipment.

[0065] It can be seen that through the above-mentioned optional embodiments, based on the intersection of defects between continuous links and the screening of the corresponding relationship between preset links and defects, a more accurate working defect situation corresponding to the target wire extrusion equipment can be determined, which is convenient for subsequent instruction generation and control, and assists in realizing real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on sensor information and timely correction of defects, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment and realize greener and more environmentally friendly waste recycling.

[0066] As an optional embodiment, the working defect includes one or more combinations of motor defects, heating temperature defects, insufficient consumable extrusion defects, excessive consumable impurities defects, consumable blockage defects, gear failure defects, gear disc line speed defects and wire winding speed defects.

[0067] It can be seen that through the above optional embodiments, the content of the working defect situation is limited to comprehensively and accurately characterize the possible defects of the wire extrusion equipment, facilitate the subsequent precise determination of control instructions for access correction, and assist in realizing real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on sensor information and timely correction of defects, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment, and realize greener and more environmentally friendly waste recycling.

[0068] As an optional embodiment, the control module determines the specific manner of the control instruction of at least one device component of the target filament extrusion device according to the working defect situation and the correspondence between the preset situation and the control parameter strategy, including: According to a preset correspondence between working defect conditions and equipment components, determining at least one defective equipment component corresponding to the working defect condition and a corresponding working adjustment parameter; Generate control instructions corresponding to defective equipment components including working adjustment parameters; and / or, For each defect in the working defect situation, determine the associated equipment component corresponding to the defect according to the preset corresponding relationship between the working defect situation and the equipment component; The objective function is set to minimize the number of control instructions in the control instruction combination; Setting restrictions includes: The union of the possibly affected equipment components corresponding to all control instructions in the control instruction combination includes all associated equipment components corresponding to the working defect conditions; optionally, the possibly affected equipment components are predicted by inputting the control instructions into a trained component impact prediction neural network; The union of defect resolution effects corresponding to all control instructions in the control instruction combination includes the working defect situation; optionally, the defect resolution effect is obtained according to the corresponding relationship between the control effect corresponding to the control instruction and the preset control effect and the defect resolution effect; the control effect is obtained by inputting the control instruction into a trained control effect prediction algorithm model; Based on the dynamic programming algorithm, iterative calculation is performed according to the objective function and constraints to obtain the optimal control instruction combination; optionally, the control instruction combination includes control instructions for at least one device component of the target wire extrusion device; the control instructions include one or more of motor restart instructions, motor acceleration instructions, extrusion head control instructions, heating control instructions, extrusion component control instructions, gear control instructions and winding motor control instructions.

[0069] It can be seen that through the above optional embodiments, the specific scheme for determining the control instructions is limited, among which the scheme directly determined according to the corresponding relationship can directly generate the corresponding control instructions when there are fewer defects, and the scheme based on the dynamic programming algorithm can obviously achieve the effect of minimum instruction control while ensuring defect correction, so as to improve the control efficiency, realize real-time and comprehensive monitoring of the working condition of the wire extrusion equipment based on sensor information and correct defects in time, so as to improve the utilization rate of consumables and extrusion quality of the wire extrusion equipment, and realize greener and more environmentally friendly waste recycling.

[0070] Embodiment 3 See also Figure 3 , Figure 3This is another wire extrusion control system based on sensor feedback disclosed in an embodiment of the present invention. Figure 3 The described wire extrusion control system based on sensor feedback is applied in a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the wire extrusion control system based on sensor feedback may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the wire extrusion control method based on sensor feedback described in the first embodiment.

[0071] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the wire extrusion control method based on sensor feedback described in the first embodiment.

[0072] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the wire extrusion control method based on sensor feedback described in the first embodiment.

[0073] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0075] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0076] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0077] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0081] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0082] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0083] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0084] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0085] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0086] Finally, it should be noted that the sensor feedback-based wire extrusion control method and system disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wire extrusion control method based on sensor feedback, characterized in that: The method comprises: When the target filament extrusion device is working, acquiring multiple area sensing information of the target filament extrusion device through sensors; Calculating link sensing information corresponding to multiple working links of the target wire extrusion equipment according to the multiple regional sensing information; Determine the working defect situation corresponding to the target wire extrusion equipment according to the link sensing information and the corresponding relationship between the preset sensing information and the defect; According to the working defect situation, and the correspondence between the preset situation and the control parameter strategy, a control instruction of at least one device component of the target filament extrusion device is determined; the control instruction is used to control the corresponding device component to reduce or solve the working defect situation.

2. The wire extrusion control method based on sensor feedback according to claim 1, characterized in that: The regional sensing information is sensing information obtained by sensor equipment arranged in different component areas of the target wire extrusion equipment; the component areas are the drive motor area, the silo area, the discharge port area, the gear cycloid area or the wire winding area; the sensing information includes a combination of one or more of image information, sound information, temperature information and infrared ranging information.

3. The wire extrusion control method based on sensor feedback according to claim 2, characterized in that: The step of calculating link sensing information corresponding to multiple working links of the target wire extrusion device according to the multiple area sensing information includes: For each of the regional sensing information, determining the component area and information type corresponding to the regional sensing information; the information type is image information, sound information, temperature information or infrared ranging information; According to the component area and the information type, determining a link prediction model corresponding to the sensor information of the area from a plurality of candidate prediction models; Inputting the regional sensor information into the link prediction model to obtain the working link corresponding to the regional sensor information; All the regional sensing information corresponding to each working link of the target filament extrusion equipment is determined as the link sensing information corresponding to the working link.

4. The wire extrusion control method based on sensor feedback according to claim 3, characterized in that: The step of determining the link prediction model corresponding to the sensor information of the region from a plurality of candidate prediction models according to the component region and the information type includes: For each candidate prediction model, determine all component region annotations in the training data set corresponding to the prediction model to obtain a training component annotation set; Calculating a first similarity parameter between the training component annotation set and the component region; Determine the data types of all input data in the historical prediction records corresponding to the prediction model, and obtain a historical data type set; Calculating a second similarity parameter between the historical data type set and the information type; Calculating the product of the first similarity parameter and the second similarity parameter to obtain a model priority parameter corresponding to the prediction model; The prediction model with the highest model priority parameter is determined as the link prediction model corresponding to the regional sensor information; the link prediction model is obtained by training a training data set including regional sensor information of multiple training components and corresponding work link annotations.

5. The wire extrusion control method based on sensor feedback according to claim 1, characterized in that: The working links are heating consumables link, extruding consumables link, extrusion molding link, gear winding link or wire winding link.

6. The wire extrusion control method based on sensor feedback according to claim 5, characterized in that: The step of determining the working defect of the target wire extrusion device according to the link sensor information and the corresponding relationship between the preset sensor information and the defect includes: For each of the working links, the link sensor information corresponding to the working link is input into the defect prediction neural network corresponding to the working link to obtain a plurality of predicted defects corresponding to the working link; the defect prediction neural network is trained by a training data set including a plurality of training link sensor information of the working links and corresponding working defect annotations; For any two working links that are adjacent to each other in a preset working link sequence, the intersection of all the predicted defects corresponding to the two working links is calculated to obtain the intersection defect set corresponding to the two working links; the working link sequence is, in order, a heating consumables link, an extrusion consumables link, an extrusion molding link, a gear winding link, and a wire winding link; Determine the set of influencing defects corresponding to the two working links according to a preset correspondence library of continuous links and influencing defects; Calculate the intersection of the intersection defect set and the influencing defect set to obtain the screened defects corresponding to the two working links; The screened defects corresponding to all the working links are determined as the working defect conditions corresponding to the target wire extrusion equipment.

7. The wire extrusion control method based on sensor feedback according to claim 1, characterized in that: The working defects include one or more combinations of motor defects, heating temperature defects, insufficient consumable extrusion defects, excessive consumable impurities defects, consumable blockage defects, gear failure defects, gear disc linear speed defects and wire winding speed defects.

8. The wire extrusion control method based on sensor feedback according to claim 7, characterized in that: Determining the control instruction of at least one device component of the target filament extrusion device according to the working defect situation and the correspondence between the preset situation and the control parameter strategy includes: According to a preset correspondence between working defect conditions and equipment components, determining at least one defective equipment component and a corresponding working adjustment parameter corresponding to the working defect condition; generating a control instruction corresponding to the defective equipment component and including the working adjustment parameter; and / or, For each defect in the working defect situation, determining the associated equipment component corresponding to the defect according to the preset corresponding relationship between the working defect situation and the equipment component; The objective function is set to minimize the number of control instructions in the control instruction combination; Setting restrictions includes: The union of the possibly affected equipment components corresponding to all the control instructions in the control instruction combination includes all the associated equipment components corresponding to the working defect; the possibly affected equipment components are predicted by inputting the control instructions into a trained component impact prediction neural network; The working defect situation is included in the union of defect resolution effects corresponding to all control instructions in the control instruction combination; the defect resolution effect is obtained according to the corresponding relationship between the control effect corresponding to the control instruction and the preset control effect and defect resolution effect; the control effect is obtained by inputting the control instruction into a trained control effect prediction algorithm model; Based on a dynamic programming algorithm, an iterative calculation is performed according to the objective function and the constraint conditions to obtain an optimal control instruction combination; the control instruction combination includes control instructions for at least one device component of the target wire extrusion device; the control instructions include one or more of a motor restart instruction, a motor acceleration instruction, an extrusion head control instruction, a heating control instruction, an extrusion component control instruction, a gear control instruction, and a winding motor control instruction.

9. A wire extrusion control system based on sensor feedback, characterized in that: The system comprises: An acquisition module, used for acquiring multiple area sensing information of the target filament extrusion device through a sensor when the target filament extrusion device is working; A calculation module, used for calculating link sensor information corresponding to multiple working links of the target wire extrusion equipment according to the multiple regional sensor information; A determination module, used to determine the working defect corresponding to the target wire extrusion equipment according to the link sensing information and the corresponding relationship between the preset sensing information and the defect; A control module is used to determine the control instructions of at least one device component of the target filament extrusion device according to the working defect situation and the correspondence between the preset situation and the control parameter strategy; the control instructions are used to control the corresponding device component to reduce or solve the working defect situation.

10. A wire extrusion control system based on sensor feedback, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the wire extrusion control method based on sensor feedback as described in any one of claims 1-8.

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