Industrial sewing machine equipment maintenance method and device, equipment and storage medium

Abstract

The invention discloses an industrial sewing machine equipment maintenance method, device and equipment and a storage medium, is applied to an intelligent management platform of industrial sewing machine equipment, and relates to the technical field of garment manufacturing. Triggering fault early warning corresponding to the fault sewing machine equipment based on the analysis result; determining a skill matching degree based on the degree of familiarity of each maintainer to the faulty sewing machine equipment and a historical repair success rate, and determining a preset order sending algorithm based on a fault weight corresponding to the faulty sewing machine equipment, the skill matching degree corresponding to each maintainer, the distance between each maintainer and the faulty sewing machine equipment and the fault weight; and determining a target maintainer corresponding to the faulty sewing machine equipment based on the fault early warning by using a preset order sending algorithm, and notifying the target maintainer to maintain the faulty sewing machine equipment. And equipment fault discovery and maintenance delay can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of clothing manufacturing, and in particular to an industrial sewing machine equipment maintenance method, device, equipment and storage medium. Background Art

[0002] In the apparel manufacturing industry, efficient and stable equipment operation and rapid and effective maintenance response are key factors in maintaining and improving production capacity. However, current apparel factories face numerous pressing challenges in equipment management and repair call processing. From an equipment management perspective, existing condition monitoring methods are relatively crude, relying primarily on manual inspections, which results in significant delays in fault detection. Furthermore, while traditional data acquisition and supervisory control systems (DSCS) can collect equipment data, they operate independently and lack effective integration with maintenance processes. This results in lengthy fault location processing times, often requiring 30 minutes or more, significantly impacting repair efficiency. Regarding repair call processing, existing notification methods have significant flaws. On-screen notifications in the MES (Manufacturing and Equivalent Equipment) system become ineffective when the maintenance personnel leave the site.

[0003] As can be seen from the above, how to avoid delays in equipment failure detection and maintenance is an urgent problem that needs to be solved. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an industrial sewing machine equipment maintenance method, device, equipment and storage medium, which can avoid equipment fault detection and repair delays. The specific solution is as follows:

[0005] In a first aspect, the present application provides an industrial sewing machine equipment maintenance method, which is applied to an intelligent management platform for industrial sewing machine equipment, comprising:

[0006] collecting operating data of each of the industrial sewing machine devices based on a preset multi-source sensor, analyzing the operating data using a data acquisition and monitoring control system, and triggering a fault warning corresponding to the faulty sewing machine device based on the analysis result;

[0007] Determine the fault weight corresponding to the faulty sewing machine device, and determine a preset dispatching algorithm based on the skill matching degree corresponding to each maintenance worker, the distance between each maintenance worker and the faulty sewing machine device, and the fault weight; the skill matching degree is determined based on the professional adaptability and historical repair success rate of each maintenance worker; the professional adaptability is the familiarity of each maintenance worker with the faulty sewing machine device;

[0008] Based on the fault warning and using the preset dispatching algorithm, a target maintenance worker corresponding to the faulty sewing machine device is determined, and the target maintenance worker is notified to repair the faulty sewing machine device.

[0009] Optionally, the collecting of operating data of each of the industrial sewing machine devices based on a preset multi-source sensor, analyzing the operating data using a data acquisition and monitoring control system, and triggering a fault warning corresponding to the faulty sewing machine device based on the analysis result, includes:

[0010] Build preset multi-source sensors based on vibration sensors, current sensors, and temperature sensors;

[0011] collecting operating data of each of the industrial sewing machines using the preset multi-source sensor, and transmitting the operating data to a data acquisition and monitoring control system so that the data acquisition and monitoring control system analyzes the operating data and determines abnormal signals in the operating data based on the obtained analysis results;

[0012] A fault warning corresponding to the faulty sewing machine device is triggered based on the abnormal signal.

[0013] Optionally, triggering a fault warning corresponding to the faulty sewing machine device based on the abnormal signal includes:

[0014] If the abnormal signal indicates that the increase in the amplitude of the main shaft of the industrial sewing machine equipment is greater than a preset increase threshold, a preset warning rule is used to trigger a fault warning of a corresponding level.

[0015] Optionally, determining the fault weight corresponding to the faulty sewing machine device includes:

[0016] Determine the target safety level coefficient based on the fault nature corresponding to the fault warning, and determine the production line weight coefficient based on the impact of the faulty sewing machine equipment on the entire production line;

[0017] Determine the impact range coefficient using the number of faulty devices corresponding to the faulty sewing machine device, the total number of devices on the production line, and the production line weight coefficient;

[0018] A fault time coefficient is determined based on the fault duration corresponding to the faulty sewing machine device, and a fault weight corresponding to the faulty sewing machine device is determined using the target safety level coefficient, the impact range coefficient, and the fault time coefficient.

[0019] Optionally, determining a target safety level coefficient based on the fault property corresponding to the fault warning includes:

[0020] If the fault nature corresponding to the fault warning is a fault involving personal safety, the first value is determined as the target safety level coefficient;

[0021] If the fault nature corresponding to the fault warning is a fault that poses a risk of damage to the faulty sewing machine equipment, the second value is determined as the target safety level coefficient;

[0022] If the fault nature corresponding to the fault warning is a fault that only affects production, the third value is determined as the target safety level coefficient;

[0023] The first value is greater than the second value; the second value is greater than the third value.

[0024] Optionally, the preset dispatching algorithm is determined based on the skill matching degree of each maintenance worker, the distance between each maintenance worker and the faulty sewing machine device, and the fault weight, including:

[0025] Determine the professional suitability based on the familiarity of each maintenance worker with the faulty sewing machine equipment, and determine the historical repair success rate using the number of successful repairs and the total number of repair orders received by each maintenance worker within a preset time period;

[0026] Determining a maintenance time efficiency coefficient based on the actual maintenance time and the standard maintenance time, and determining a skill matching degree using the professional suitability, the historical repair success rate, and the maintenance time efficiency coefficient;

[0027] The preset dispatching algorithm is determined by using the skill matching degree, the fault weight, and the distance between each of the maintenance personnel and the faulty sewing machine equipment.

[0028] Optionally, notifying the target maintenance person to repair the faulty sewing machine device includes:

[0029] Based on the preset maintenance manual, the target maintenance personnel is notified to repair the faulty sewing machine equipment. After the maintenance operation is completed, the professional adaptability, the historical repair success rate and the maintenance time efficiency coefficient in the skill matching degree corresponding to the target maintenance personnel are updated based on the maintenance operation to obtain an updated skill matching degree.

[0030] In a second aspect, the present application provides an industrial sewing machine equipment maintenance device, which is applied to an intelligent management platform for industrial sewing machine equipment, including:

[0031] a data analysis module for collecting operating data of each of the industrial sewing machines based on a preset multi-source sensor, analyzing the operating data using a data acquisition and monitoring control system, and triggering a fault warning corresponding to the faulty sewing machine based on the analysis results;

[0032] an algorithm determination module, configured to determine a fault weight corresponding to the faulty sewing machine device, and determine a preset dispatching algorithm based on the skill matching degree corresponding to each maintenance worker, the distance between each maintenance worker and the faulty sewing machine device, and the fault weight; the skill matching degree is a skill matching degree determined based on the professional adaptability and historical repair success rate of each maintenance worker; the professional adaptability is the familiarity of each maintenance worker with the faulty sewing machine device;

[0033] An equipment maintenance module is used to determine the target maintenance personnel corresponding to the faulty sewing machine equipment based on the fault warning and using the preset dispatching algorithm, and to notify the target maintenance personnel to repair the faulty sewing machine equipment.

[0034] In a third aspect, the present application provides an electronic device, comprising:

[0035] Memory, used to store computer programs;

[0036] The processor is used to execute the computer program to implement the aforementioned industrial sewing machine equipment maintenance method.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program implements the aforementioned industrial sewing machine equipment maintenance method when executed by a processor.

[0038] In the present application, the operating data of each of the industrial sewing machine devices is collected based on a preset multi-source sensor, the operating data is analyzed by using a data acquisition and monitoring control system, and a fault warning corresponding to the faulty sewing machine device is triggered based on the analysis result; the fault weight corresponding to the faulty sewing machine device is determined, and a preset dispatching algorithm is determined based on the skill matching degree corresponding to each maintenance worker, the distance between each maintenance worker and the faulty sewing machine device, and the fault weight; the skill matching degree is the skill matching degree determined based on the professional adaptability and historical repair success rate of each maintenance worker; the professional adaptability is the familiarity of each maintenance worker with the faulty sewing machine device; based on the fault warning and using the preset dispatching algorithm, the target maintenance worker corresponding to the faulty sewing machine device is determined, and the target maintenance worker is notified to repair the faulty sewing machine device.

[0039] As can be seen from the above, the present application is based on preset multi-source sensors and uses a data acquisition and monitoring control system to collect and analyze the operating data of each of the industrial sewing machine equipment, which can greatly improve the timeliness and accuracy of fault discovery, reduce fault discovery delays and time-consuming fault location; then, based on the fault weight corresponding to the fault sewing machine equipment, the professional adaptability of each maintenance worker, and the distance between each maintenance worker and the faulty sewing machine equipment, a preset dispatching algorithm is determined, and the target maintenance worker is determined by the preset dispatching algorithm. The dispatching method that comprehensively considers multiple factors can more scientifically evaluate the maintenance worker's ability and efficiency in handling the fault, ensuring that the maintenance task is assigned to the most suitable maintenance worker. In this way, based on accurate fault warning and scientific dispatching decisions, the target maintenance worker can be quickly notified to repair the faulty sewing machine equipment, avoiding maintenance delays caused by factors such as untimely information transmission and unreasonable dispatching, and improving the maintenance response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0041] Figure 1 This is a flow chart of a method for repairing an industrial sewing machine disclosed in this application;

[0042] Figure 2 A flowchart of a specific industrial sewing machine equipment maintenance method disclosed in this application;

[0043] Figure 3 This is a schematic structural diagram of an industrial sewing machine equipment maintenance device disclosed in this application;

[0044] Figure 4 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0046] At present, garment factories are facing many urgent problems in equipment management and maintenance calls. From the perspective of equipment management, the existing status monitoring method mainly relies on manual inspections, which results in significant delays in fault detection. Moreover, the traditional data acquisition and monitoring control system operates independently and cannot be effectively connected with the maintenance process, resulting in a long time-consuming fault location, which greatly affects the maintenance efficiency. To this end, the present application provides an industrial sewing machine equipment maintenance method, which is based on accurate fault warning and scientific dispatching decisions, and can quickly notify the target maintenance personnel to repair the faulty sewing machine equipment, avoiding maintenance delays caused by factors such as untimely information transmission and unreasonable dispatching, and improving the maintenance response speed.

[0047] See also Figure 1 As shown, an embodiment of the present invention discloses an industrial sewing machine equipment maintenance method, which is applied to an intelligent management platform for industrial sewing machine equipment, comprising:

[0048] Step S11: collecting operating data of each of the industrial sewing machine devices based on a preset multi-source sensor, analyzing the operating data using a data acquisition and monitoring control system, and triggering a fault warning corresponding to the faulty sewing machine device based on the analysis result.

[0049] In this embodiment, a preset multi-source sensor is used to collect operating data from each industrial sewing machine device, and the operating data is transmitted to a data acquisition and monitoring control system (i.e., a SCADA system) so that the data acquisition and monitoring control system analyzes the operating data and triggers a fault warning corresponding to the faulty sewing machine device based on the analysis results. Specifically, the method of collecting operating data from each industrial sewing machine device based on the preset multi-source sensor, analyzing the operating data using the data acquisition and monitoring control system, and triggering a fault warning corresponding to the faulty sewing machine device based on the analysis results includes: constructing a preset multi-source sensor based on a vibration sensor, a current sensor, and a temperature sensor; collecting operating data from each industrial sewing machine device using the preset multi-source sensor, and transmitting the operating data to the data acquisition and monitoring control system so that the data acquisition and monitoring control system analyzes the operating data and determines an abnormal signal in the operating data based on the obtained analysis results; and triggering a fault warning corresponding to the faulty sewing machine device based on the abnormal signal.

[0050] In a specific embodiment, if the preset multi-source sensor detects that the amplitude of the main shaft of the industrial sewing machine equipment suddenly increases by more than 30%, a second-level warning is triggered, and a shutdown may occur in the next hour. Specifically, the fault warning corresponding to the faulty sewing machine equipment is triggered based on the abnormal signal, including: if the abnormal signal indicates that the increase in the amplitude of the main shaft of the industrial sewing machine equipment is greater than a preset increase threshold, then the preset warning rule is used to trigger a fault warning of the corresponding level. It is worth mentioning that the preset increase threshold can be adjusted according to actual conditions and is not specifically limited here.

[0051] Step S12: determine the fault weight corresponding to the faulty sewing machine equipment, and determine a preset dispatching algorithm based on the skill matching degree corresponding to each maintenance worker, the distance between each maintenance worker and the faulty sewing machine equipment, and the fault weight; the skill matching degree is the skill matching degree determined based on the professional adaptability and historical repair success rate of each maintenance worker; the professional adaptability is the familiarity of each maintenance worker with the faulty sewing machine equipment.

[0052] In this embodiment, after the fault warning is triggered, the fault warning is pushed to the preset call dispatch system so that the preset call dispatch system determines the target maintenance personnel based on the preset dispatch algorithm and pushes the relevant maintenance tasks to the mobile handheld terminal. The determination process of the preset dispatch algorithm is as follows: first, the target safety level coefficient is determined based on the nature of the fault corresponding to the fault warning. Specifically, the target safety level coefficient is determined based on the nature of the fault corresponding to the fault warning, including: if the nature of the fault corresponding to the fault warning is a fault involving personal safety, then the first value is determined as the target safety level coefficient; if the nature of the fault corresponding to the fault warning is a fault that poses a risk of damage to the faulty sewing machine equipment, then the second value is determined as the target safety level coefficient; if the nature of the fault corresponding to the fault warning is a fault that only affects production, then the third value is determined as the target safety level coefficient; wherein, the first value is greater than the second value; and the second value is greater than the third value.

[0053] It is understood that after determining the target safety level coefficient, the production line weight coefficient is determined using the degree of impact of the faulty sewing machine on the entire production line, and the impact range coefficient is determined based on the number of faulty devices corresponding to the faulty sewing machine, the total number of devices on the production line, and the production line weight coefficient. The formula corresponding to the impact range coefficient is as follows:

[0054] ;

[0055] in, is the influence range coefficient; After obtaining the impact range coefficient, the fault time coefficient is determined based on the fault duration corresponding to the faulty sewing machine device. The formula corresponding to the fault time coefficient is as follows:

[0056] ;

[0057] in, is the fault time coefficient; is the fault duration.

[0058] Furthermore, after obtaining the fault time-effect coefficient, the target safety level coefficient, the impact range coefficient, and the fault time-effect coefficient are used to determine the fault weight corresponding to the faulty sewing machine device. The formula corresponding to the fault weight is as follows:

[0059] ;

[0060] in, is the fault weight; 、 、 are weight coefficients corresponding to the safety level coefficient, the impact range coefficient, and the fault time coefficient, respectively, and can be adjusted according to actual conditions; is the influence range coefficient; is the fault time coefficient; is the safety level coefficient, and the corresponding values ​​of the safety level coefficient are as follows:

[0061] ;

[0062] The values ​​of the safety level coefficients and the corresponding fault conditions may be adjusted accordingly based on actual conditions.

[0063] Specifically, determining the fault weight corresponding to the faulty sewing machine equipment includes: determining a target safety level coefficient based on the fault nature corresponding to the fault warning, and determining a production line weight coefficient using the degree of influence of the faulty sewing machine equipment on the overall production line; determining an influence range coefficient using the number of faulty devices corresponding to the faulty sewing machine equipment, the total number of devices on the production line, and the production line weight coefficient; determining a fault timeliness coefficient based on the fault duration corresponding to the faulty sewing machine equipment, and determining the fault weight corresponding to the faulty sewing machine equipment using the target safety level coefficient, the influence range coefficient, and the fault timeliness coefficient.

[0064] In this embodiment, after obtaining the fault weight, the professional suitability is determined based on the familiarity of each maintenance worker with the faulty sewing machine. The professional suitability is determined as follows:

[0065] ;

[0066] The professional compatibility value and the maintenance personnel's familiarity with the faulty sewing machine can be adjusted accordingly based on actual conditions. After obtaining the professional compatibility, the historical repair success rate is determined using the number of successful repairs performed by each maintenance personnel and the total number of repair orders received within a preset time period. In one specific embodiment, the historical repair success rate is determined based on the number of successful repairs performed by each maintenance personnel and the total number of repair orders received in the past 90 days. The formula corresponding to the historical repair success rate is as follows:

[0067] ;

[0068] After obtaining the historical repair success rate, the maintenance time efficiency coefficient is determined based on the actual maintenance time and the standard maintenance time. The formula corresponding to the maintenance time efficiency coefficient is as follows:

[0069] ;

[0070] After obtaining the maintenance time efficiency coefficient, the skill matching degree is determined using the professional adaptability, the historical repair success rate, and the maintenance time efficiency coefficient. The formula corresponding to the skill matching degree is as follows:

[0071] ;

[0072] in, is the skill matching degree; suitability for the profession; is the historical repair success rate; is the maintenance time efficiency coefficient; 、 、 The weight coefficients corresponding to the professional adaptability, the historical repair success rate and the maintenance timeliness coefficient can be adjusted according to actual conditions.

[0073] Furthermore, after obtaining the skill matching degree, a preset dispatching algorithm is determined based on the skill matching degree, the fault weight, and the distance between each of the maintenance personnel and the faulty sewing machine device; the formula corresponding to the preset dispatching algorithm is as follows:

[0074] ;

[0075] in, is the fault weight; is the skill matching degree; The distance between each maintenance worker and the faulty sewing machine device; 、 、 They are global coefficients for adjusting the fault weight, the skill matching degree and the distance respectively.

[0076] Specifically, the preset dispatching algorithm is determined based on the skill matching degree corresponding to each maintenance worker, the distance between each maintenance worker and the faulty sewing machine equipment, and the fault weight, including: determining the professional adaptability based on the familiarity of each maintenance worker with the faulty sewing machine equipment, and determining the historical repair success rate using the number of successful repairs and the total number of repair orders received by each maintenance worker within a preset time period; determining the maintenance time efficiency coefficient based on the actual maintenance time and the standard maintenance time, and determining the skill matching degree using the professional adaptability, the historical repair success rate, and the maintenance time efficiency coefficient; determining the preset dispatching algorithm using the skill matching degree, the fault weight, and the distance between each maintenance worker and the faulty sewing machine equipment.

[0077] Step S13: determining a target maintenance worker corresponding to the faulty sewing machine device based on the fault warning and using the preset dispatching algorithm, and notifying the target maintenance worker to repair the faulty sewing machine device.

[0078] In this embodiment, a preset dispatching algorithm is used to determine a target repairman corresponding to the faulty sewing machine device. Based on a preset maintenance manual, the target repairman is notified to repair the faulty sewing machine device. After the maintenance operation is completed, the professional adaptability, the historical repair success rate, and the maintenance time efficiency coefficient in the skill matching degree corresponding to the target repairman are updated based on the maintenance operation to obtain an updated skill matching degree. When processing the next faulty sewing machine device, the preset dispatching algorithm is updated using the updated skill matching degree to determine a more suitable target repairman. Specifically, notifying the target repairman to repair the faulty sewing machine device includes: based on a preset maintenance manual, notifying the target repairman to repair the faulty sewing machine device, and after the maintenance operation is completed, updating the professional adaptability, the historical repair success rate, and the maintenance time efficiency coefficient in the skill matching degree corresponding to the target repairman based on the maintenance operation to obtain an updated skill matching degree.

[0079] It is worth mentioning that although the preset dispatching algorithm can guarantee a certain degree of fairness and rationality in task allocation, there may be problems such as low work enthusiasm and lack of competitive awareness among maintenance personnel. In this case, a grabbing order mode can be adopted and a grabbing order ranking list can be added. Specifically, when the equipment failure warning is triggered, the system will publish the maintenance task information to the maintenance personnel's special tools in real time. The maintenance personnel can choose whether to accept the task based on their own skill level, work saturation, location and other factors. For example, maintenance personnel A currently has less work on hand and is more familiar with the fault type corresponding to the faulty sewing machine equipment. He can quickly grab orders and get maintenance opportunities. In order to further motivate maintenance personnel to actively grab orders and complete maintenance tasks efficiently, a real-time ranking list can be generated based on indicators such as the number of orders grabbed by maintenance personnel, maintenance completion time, and repair success rate. The ranking list can be counted and displayed according to different periods such as day, week, and month. This can create a competitive atmosphere, stimulate the maintenance personnel's desire to win, and prompt them to continuously improve their maintenance skills and work efficiency.

[0080] As can be seen from the above, the present application is based on preset multi-source sensors and uses a data acquisition and monitoring control system to collect and analyze the operating data of each of the industrial sewing machine equipment, which can greatly improve the timeliness and accuracy of fault discovery, reduce fault discovery delays and time-consuming fault location; then, based on the fault weight corresponding to the fault sewing machine equipment, the professional adaptability of each maintenance worker, and the distance between each maintenance worker and the faulty sewing machine equipment, a preset dispatching algorithm is determined, and the target maintenance worker is determined by the preset dispatching algorithm. The dispatching method that comprehensively considers multiple factors can more scientifically evaluate the maintenance worker's ability and efficiency in handling the fault, ensuring that the maintenance task is assigned to the most suitable maintenance worker. In this way, based on accurate fault warning and scientific dispatching decisions, the target maintenance worker can be quickly notified to repair the faulty sewing machine equipment, avoiding maintenance delays caused by factors such as untimely information transmission and unreasonable dispatching, and improving the maintenance response speed.

[0081] It can be seen from the above embodiments that the present application adds a call for mechanic repair and an intelligent scheduling algorithm (i.e., a preset dispatching algorithm) to the original MES operation panel to improve the maintenance efficiency of industrial sewing machine equipment. Therefore, the process of adding a call for mechanic repair and an intelligent scheduling algorithm to the original MES operation panel is described.

[0082] See also Figure 2 As shown, the embodiment of the present invention discloses a specific industrial sewing machine equipment maintenance method, which is applied to an intelligent management platform for industrial sewing machine equipment, including:

[0083] In this embodiment, the intelligent management platform for industrial sewing machines receives operational data from each of the industrial sewing machines, where the operational data is collected based on a preset multi-source sensor; the preset multi-source sensor is a multi-source sensor constructed based on a vibration sensor, a current sensor, and a temperature sensor. The equipment status monitoring module analyzes the operational data using a data acquisition and monitoring control system and, based on the analysis results, triggers a fault warning corresponding to the faulty sewing machine. The maintenance call dispatch module, after triggering the fault warning, pushes the fault warning to a preset call dispatch system, which then determines a target maintenance person based on a preset dispatch algorithm and pushes the relevant maintenance task to the target maintenance person's mobile handheld terminal.

[0084] Specifically, the target safety level coefficient is determined based on the nature of the fault corresponding to the fault warning, the production line weight coefficient is determined using the degree of influence of the faulty sewing machine equipment on the overall production line, the influence range coefficient is determined based on the number of faulty equipment corresponding to the faulty sewing machine equipment, the total number of equipment on the production line, and the production line weight coefficient, and then the fault timeliness coefficient is determined based on the fault duration corresponding to the faulty sewing machine equipment, and the fault weight corresponding to the faulty sewing machine equipment is determined using the target safety level coefficient, the influence range coefficient, and the fault timeliness coefficient; the professional adaptability is determined based on the familiarity of each maintenance technician with the faulty sewing machine equipment, and the historical repair success rate is determined using the number of successful repairs by each maintenance technician and the total number of repair orders received within a preset time period.

[0085] Then, a maintenance time efficiency coefficient is determined based on the actual maintenance time and the standard maintenance time, and a preset dispatching algorithm is determined based on the skill matching degree, the fault weight, and the distance between each maintenance worker and the faulty sewing machine. Furthermore, after obtaining the preset dispatching algorithm, the preset dispatching algorithm is used to determine a target maintenance worker corresponding to the faulty sewing machine, and the maintenance task corresponding to the faulty sewing machine is sent to the mobile handheld terminal corresponding to the target maintenance worker, so that the target maintenance worker is notified to repair the faulty sewing machine. Alternatively, a loudspeaker can be used to broadcast the message, so that the target maintenance worker can quickly repair the faulty sewing machine.

[0086] As can be seen from the above, this application uses a pre-set multi-source sensor and a data acquisition and monitoring control system to achieve fault warnings for faulty sewing machine equipment. Then, a pre-set dispatching algorithm is determined based on the fault weight corresponding to the faulty sewing machine equipment, the professional suitability of each repairman, and the distance between each repairman and the faulty sewing machine equipment. This algorithm is used to determine the target repairman. In this way, by comprehensively considering multiple factors when dispatching, repair tasks can be intelligently matched to the nearest and optimal repairman, greatly improving repair efficiency.

[0087] Accordingly, see Figure 3 As shown, the present application also provides an industrial sewing machine equipment maintenance management device, which is applied to an intelligent management platform for industrial sewing machine equipment, including:

[0088] a data analysis module 11 for collecting operating data of each of the industrial sewing machines based on a preset multi-source sensor, analyzing the operating data using a data acquisition and monitoring control system, and triggering a fault warning corresponding to the faulty sewing machine based on the analysis results;

[0089] an algorithm determination module 12 for determining a fault weight corresponding to the faulty sewing machine device, and determining a preset dispatching algorithm based on the skill matching degree corresponding to each maintenance worker, the distance between each maintenance worker and the faulty sewing machine device, and the fault weight; the skill matching degree is determined based on the professional suitability and historical repair success rate of each maintenance worker; the professional suitability is the familiarity of each maintenance worker with the faulty sewing machine device;

[0090] The equipment maintenance module 13 is used to determine the target maintenance personnel corresponding to the faulty sewing machine equipment based on the fault warning and using the preset dispatching algorithm, and notify the target maintenance personnel to repair the faulty sewing machine equipment.

[0091] As can be seen from the above, the present application is based on preset multi-source sensors and uses a data acquisition and monitoring control system to collect and analyze the operating data of each of the industrial sewing machine equipment, which can greatly improve the timeliness and accuracy of fault discovery, reduce fault discovery delays and time-consuming fault location; then, based on the fault weight corresponding to the fault sewing machine equipment, the professional adaptability of each maintenance worker, and the distance between each maintenance worker and the faulty sewing machine equipment, a preset dispatching algorithm is determined, and the target maintenance worker is determined by the preset dispatching algorithm. The dispatching method that comprehensively considers multiple factors can more scientifically evaluate the maintenance worker's ability and efficiency in handling the fault, ensuring that the maintenance task is assigned to the most suitable maintenance worker. In this way, based on accurate fault warning and scientific dispatching decisions, the target maintenance worker can be quickly notified to repair the faulty sewing machine equipment, avoiding maintenance delays caused by factors such as untimely information transmission and unreasonable dispatching, and improving the maintenance response speed.

[0092] In some specific implementations, the data analysis module 11 may specifically include:

[0093] A sensor construction unit, configured to construct a preset multi-source sensor based on a vibration sensor, a current sensor, and a temperature sensor;

[0094] an abnormal signal determining unit, configured to collect operating data of each of the industrial sewing machine devices using the preset multi-source sensor, and transmit the operating data to a data acquisition and monitoring control system, so that the data acquisition and monitoring control system analyzes the operating data and determines abnormal signals in the operating data based on the obtained analysis results;

[0095] A fault warning triggering unit is used to trigger a fault warning corresponding to the faulty sewing machine device based on the abnormal signal.

[0096] In some specific implementations, the data analysis module 11 may specifically include:

[0097] The increase degree comparison unit is used to trigger a fault warning of a corresponding level using a preset warning rule if the abnormal signal indicates that the increase degree of the main shaft amplitude of the industrial sewing machine equipment is greater than a preset increase threshold.

[0098] In some specific implementations, the algorithm determination module 12 may specifically include:

[0099] a weight coefficient determination unit, configured to determine a target safety level coefficient based on the nature of the fault corresponding to the fault warning, and to determine a production line weight coefficient based on the degree of influence of the faulty sewing machine equipment on the entire production line;

[0100] a range coefficient determining unit, configured to determine an impact range coefficient using the number of faulty devices corresponding to the faulty sewing machine device, the total number of devices on the production line, and the production line weight coefficient;

[0101] The weight determination unit is used to determine a fault time coefficient based on the fault duration corresponding to the faulty sewing machine device, and to determine a fault weight corresponding to the faulty sewing machine device using the target safety level coefficient, the impact range coefficient and the fault time coefficient.

[0102] In some specific implementations, the algorithm determination module 12 may specifically include:

[0103] a first safety factor determination unit, configured to determine a first value as a target safety level coefficient if the nature of the fault corresponding to the fault warning is a fault involving personal safety;

[0104] a second safety factor determining unit, configured to determine a second value as a target safety level coefficient if the nature of the fault corresponding to the fault warning is a fault that poses a risk of damage to the faulty sewing machine device;

[0105] The third safety factor determination unit is configured to determine a third value as a target safety level coefficient if the nature of the fault corresponding to the fault warning is a fault that only affects production.

[0106] In some specific implementations, the algorithm determination module 12 may specifically include:

[0107] a success rate determination unit, configured to determine the professional suitability of each of the maintenance personnel based on their familiarity with the faulty sewing machine equipment, and determine a historical repair success rate using the number of successful repairs performed by each of the maintenance personnel and the total number of repair orders received within a preset time period;

[0108] a matching degree determination unit, configured to determine a maintenance time efficiency coefficient based on the actual maintenance time and the standard maintenance time, and determine a skill matching degree using the professional suitability, the historical repair success rate, and the maintenance time efficiency coefficient;

[0109] The dispatch algorithm determination unit is used to determine a preset dispatch algorithm by using the skill matching degree, the fault weight and the distance between each of the maintenance personnel and the faulty sewing machine device.

[0110] In some specific implementations, the equipment maintenance module 13 may specifically include:

[0111] The time efficiency coefficient updating unit is used to notify the target maintenance personnel to repair the faulty sewing machine equipment based on a preset maintenance manual, and after the maintenance operation is completed, update the professional adaptability, the historical repair success rate and the maintenance time efficiency coefficient in the skill matching degree corresponding to the target maintenance personnel based on the maintenance operation to obtain an updated skill matching degree.

[0112] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 4 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram should not be considered as any limitation on the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the industrial sewing machine equipment maintenance method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0113] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0114] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0115] The operating system 221 is used to manage and control the hardware devices on the electronic device 20 and the computer program 222, and can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the industrial sewing machine equipment maintenance method executed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0116] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when executed by a processor, the computer program implements the aforementioned industrial sewing machine equipment maintenance method. The specific steps of this method can be referred to the corresponding contents disclosed in the aforementioned embodiments and will not be repeated here.

[0117] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0118] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0119] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0120] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0121] The above is a detailed introduction to the technical solution provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for repairing an industrial sewing machine, characterized in that: Intelligent management platform for industrial sewing machines, including: collecting operating data of each of the industrial sewing machine devices based on a preset multi-source sensor, analyzing the operating data using a data acquisition and monitoring control system, and triggering a fault warning corresponding to the faulty sewing machine device based on the analysis result; Determine the fault weight corresponding to the faulty sewing machine device, and determine a preset dispatching algorithm based on the skill matching degree corresponding to each maintenance worker, the distance between each maintenance worker and the faulty sewing machine device, and the fault weight; the skill matching degree is determined based on the professional adaptability and historical repair success rate of each maintenance worker; the professional adaptability is the familiarity of each maintenance worker with the faulty sewing machine device; Based on the fault warning and using the preset dispatching algorithm, a target maintenance worker corresponding to the faulty sewing machine device is determined, and the target maintenance worker is notified to repair the faulty sewing machine device.

2. The industrial sewing machine equipment maintenance method according to claim 1, characterized in that: The method of collecting the operating data of each industrial sewing machine device based on a preset multi-source sensor, analyzing the operating data using a data acquisition and monitoring control system, and triggering a fault warning corresponding to the faulty sewing machine device based on the analysis result includes: Build preset multi-source sensors based on vibration sensors, current sensors, and temperature sensors; collecting operating data of each of the industrial sewing machines using the preset multi-source sensor, and transmitting the operating data to a data acquisition and monitoring control system so that the data acquisition and monitoring control system analyzes the operating data and determines abnormal signals in the operating data based on the obtained analysis results; A fault warning corresponding to the faulty sewing machine device is triggered based on the abnormal signal.

3. The industrial sewing machine equipment maintenance method according to claim 2, characterized in that: The triggering of a fault warning corresponding to the faulty sewing machine device based on the abnormal signal includes: If the abnormal signal indicates that the increase in the amplitude of the main shaft of the industrial sewing machine equipment is greater than a preset increase threshold, a preset warning rule is used to trigger a fault warning of a corresponding level.

4. The industrial sewing machine equipment maintenance method according to claim 1, characterized in that: The determining of the fault weight corresponding to the faulty sewing machine device includes: Determine the target safety level coefficient based on the fault nature corresponding to the fault warning, and determine the production line weight coefficient based on the impact of the faulty sewing machine equipment on the entire production line; Determine the impact range coefficient using the number of faulty devices corresponding to the faulty sewing machine device, the total number of devices on the production line, and the production line weight coefficient; A fault time coefficient is determined based on the fault duration corresponding to the faulty sewing machine device, and a fault weight corresponding to the faulty sewing machine device is determined using the target safety level coefficient, the impact range coefficient, and the fault time coefficient.

5. The industrial sewing machine equipment maintenance method according to claim 4, characterized in that: The determining of the target safety level coefficient based on the fault property corresponding to the fault warning includes: If the fault nature corresponding to the fault warning is a fault involving personal safety, the first value is determined as the target safety level coefficient; If the fault nature corresponding to the fault warning is a fault that poses a risk of damage to the faulty sewing machine equipment, the second value is determined as the target safety level coefficient; If the fault nature corresponding to the fault warning is a fault that only affects production, the third value is determined as the target safety level coefficient; The first value is greater than the second value; the second value is greater than the third value.

6. The industrial sewing machine equipment maintenance method according to claim 1, characterized in that: The preset dispatching algorithm is determined based on the skill matching degree of each maintenance worker, the distance between each maintenance worker and the faulty sewing machine device, and the fault weight, including: Determine the professional suitability based on the familiarity of each maintenance worker with the faulty sewing machine equipment, and determine the historical repair success rate using the number of successful repairs and the total number of repair orders received by each maintenance worker within a preset time period; Determining a maintenance time efficiency coefficient based on the actual maintenance time and the standard maintenance time, and determining a skill matching degree using the professional suitability, the historical repair success rate, and the maintenance time efficiency coefficient; The preset dispatching algorithm is determined by using the skill matching degree, the fault weight, and the distance between each of the maintenance personnel and the faulty sewing machine device.

7. The industrial sewing machine equipment maintenance method according to claim 6, characterized in that: The notifying the target maintenance person to repair the faulty sewing machine device includes: Based on the preset maintenance manual, the target maintenance personnel is notified to repair the faulty sewing machine equipment. After the maintenance operation is completed, the professional adaptability, the historical repair success rate and the maintenance time efficiency coefficient in the skill matching degree corresponding to the target maintenance personnel are updated based on the maintenance operation to obtain an updated skill matching degree.

8. An industrial sewing machine equipment maintenance management device, characterized in that: Intelligent management platform for industrial sewing machines, including: a data analysis module for collecting operating data of each of the industrial sewing machines based on a preset multi-source sensor, analyzing the operating data using a data acquisition and monitoring control system, and triggering a fault warning corresponding to the faulty sewing machine based on the analysis results; an algorithm determination module, configured to determine a fault weight corresponding to the faulty sewing machine device, and determine a preset dispatching algorithm based on the skill matching degree corresponding to each maintenance worker, the distance between each maintenance worker and the faulty sewing machine device, and the fault weight; the skill matching degree is a skill matching degree determined based on the professional adaptability and historical repair success rate of each maintenance worker; the professional adaptability is the familiarity of each maintenance worker with the faulty sewing machine device; An equipment maintenance module is used to determine the target maintenance personnel corresponding to the faulty sewing machine equipment based on the fault warning and using the preset dispatching algorithm, and to notify the target maintenance personnel to repair the faulty sewing machine equipment.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the industrial sewing machine equipment maintenance method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store a computer program, wherein when the computer program is executed by a processor, it implements the industrial sewing machine equipment maintenance method according to any one of claims 1 to 7.

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