Intelligent factory management method and system

Abstract

The invention relates to an intelligent factory management method and system, and relates to the field of factory intelligent management technology, and the method comprises the steps: 100, collecting execution parameters of execution equipment; 101, judging whether a fault exists or not in response to the execution parameters and a preset fault interval; step 102, when a fault exists, determining a fault parameter in combination with the execution parameter and a preset fault interval; step 103, determining a fault type according to the fault parameters; and step 104, generating and sending fault prompt information based on the fault type. The method and the device have the effects of improving the management efficiency of the factory on the execution equipment and accurately analyzing the cause of the fault when the fault occurs.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent factory management technology, and in particular to an intelligent factory management method and system. BACKGROUND

[0002] The control cabinet is a device that integrates data acquisition, communication interaction, intelligent analysis, remote monitoring and other digital capabilities, can realize precise control, state monitoring, fault warning and collaborative management of production equipment, and is used to connect field devices and factory management systems.

[0003] In the prior art, the current of the execution device is recorded in real time by the control cabinet, so that when the execution device fails, the fault data is fed back to the DCS through the control cabinet, and the DCS analyzes the fault cause to notify the relevant staff. Limited by the data exchange speed of the control cabinet and the DCS, the data fed back to the DCS by the control cabinet is limited.

[0004] When the execution device fails, the DCS cannot accurately determine the cause of the failure based on limited data, so the staff needs to arrive at the scene to determine the cause of the failure again, which reduces the management efficiency of the factory on the execution device. SUMMARY

[0005] In order to improve the management efficiency of the factory on the execution device and accurately analyze the cause of the failure, the present application provides an intelligent factory management method and system.

[0006] In a first aspect, the present application provides an intelligent factory management method, which adopts the following technical solution: An intelligent factory management method, comprising: Step 100: collecting execution parameters of an execution device; Step 101: determining whether there is a failure in response to the execution parameters and a preset failure interval; Step 102: when there is a failure, determining a failure parameter in combination with the execution parameters and the preset failure interval; Step 103: determining a failure type according to the failure parameter; Step 104: generating and sending a failure prompt information based on the failure type.

[0007] By adopting the above technical solution, the current and other parameters of the execution device are monitored in real time on the control cabinet, so that when a parameter of the execution device is abnormal, the cause of the failure of the execution device is analyzed in combination with the real-time parameters such as current, and then the analyzed failure cause is fed back to the DCS, thereby improving the accuracy of the failure cause analysis and improving the management efficiency of the factory on the execution device.

[0008] Optionally, it further comprises: Step 105: when there is a fault, the historical parameters are called based on the fault parameters, and the peak threshold is determined based on the fault parameters; Step 106: the peak point is determined in response to the historical parameters and the peak threshold; Step 107: the peak fluctuation curve is generated based on the peak point; Step 108: the fluctuation slope is read from the peak fluctuation curve, and the fluctuation threshold is determined according to the fault category; Step 109: when the fluctuation slope is less than the fluctuation threshold, the fault prompt information is generated and sent based on the fault category.

[0009] By adopting the above technical solution, when a parameter of an execution device is abnormal, the historical data of the abnormal parameter of the execution device is called to view the damage speed of the execution device, and when the damage speed is slow, the cause of the parameter abnormality is judged as normal mechanical wear.

[0010] Optionally, it further comprises: Step 110: when the fluctuation slope is less than the fluctuation threshold, the same type number is called according to the fault category; Step 111: the same type parameter is called based on the same type number; Step 112: the same type fluctuation point is determined in combination with the same type parameter and the peak threshold; Step 113: the same type slope is calculated based on the same type fluctuation point; Step 114: if the same type slope is consistent with the fluctuation slope, the fault duration is determined in response to the same type fluctuation point and the peak point; Step 115: the fault risk information is generated and sent in combination with the same type number and the fault duration.

[0011] By adopting the above technical solution, the service life of the same device under the same environment is similar, when the execution device fails due to mechanical wear, the same type parameter of the same type device with the same mechanical structure is checked, and when the change of the same type parameter is similar to that of the execution device, the duration of the same type device to failure is estimated, so as to early warn the staff.

[0012] Optionally, it further comprises a fault positioning method, and the fault positioning method comprises: Step 200: when the fluctuation slope is less than the fluctuation threshold, the fluctuation interval is read based on the peak point; Step 201: the interval mean value is calculated based on the fluctuation interval; Step 202: the interval difference value is calculated in combination with the fluctuation interval and the interval mean value, and the period threshold is determined based on the peak point; Step 203: if the interval difference value is not greater than the period threshold value, querying the closest mechanical period in response to the interval mean value; Step 204: matching a fault site based on the mechanical period; Step 205: updating the fault prompt information based on the fault site and fault parameter.

[0013] By adopting the above technical solution, when the damage speed of the execution equipment is slow, it is checked whether the parameter that occurs abnormally has periodic fluctuation, and when the parameter has periodic fluctuation, it is judged that the reason for the parameter abnormality is related to the mechanical activity of the execution equipment, so that the related mechanical site is notified to the worker when the fault information is fed back, and the accuracy of fault identification is improved.

[0014] Optionally, the fault positioning method further comprises: Step 206: if the interval difference value is greater than the period threshold value, determining an approximate interval and a deviation interval in combination with the fluctuation interval and the interval mean value; Step 207: calculating an approximate mean value according to the approximate interval, and calculating a deviation mean value according to the deviation interval; Step 208: calculating the quotient of the deviation mean value and the approximate mean value, and defining it as a mean value multiple; Step 209: calculating a multiple deviation according to the mean value multiple; Step 210: if the multiple deviation falls within a preset error interval, calculating an approximate difference value in combination with the approximate mean value and the approximate interval; Step 211: when the approximate difference value is not greater than the period threshold value, calculating a deviation difference value in combination with the deviation mean value and the deviation interval; Step 212: if the deviation difference value is not greater than the period threshold value, querying the closest mechanical period in response to the approximate mean value.

[0015] By adopting the above technical solution, when the reason for the parameter abnormality is related to the mechanical activity of the execution equipment, the fluctuation condition of the parameter is easily affected by the degree of the mechanical activity, so that the fluctuation condition of the parameter is smaller or even does not occur when the degree of the mechanical activity is low, and when the fluctuation time intervals of the parameter are inconsistent, it is compared whether there is a multiple relationship between the time intervals, so that it is judged that the parameter has periodic fluctuation when there is a multiple relationship.

[0016] Optionally, the fault positioning method further comprises: Step 213: when the fluctuation slope is not less than the fluctuation threshold value, determining a mechanical range based on the fault type; Step 214: planning an investigation route according to the mechanical range; Step 215: collect troubleshooting images according to the troubleshooting route, and match out a fault feature based on the fault category; Step 216: if the fault feature exists in the troubleshooting image, generate a fault demonstration animation combined with the fault feature and the fault category; Step 217: update the fault prompt information in response to the fault demonstration animation.

[0017] By adopting the above technical solution, when the execution equipment is damaged at a speed that is too fast, it is judged that the mechanical structure of the execution equipment has a sudden fault such as jamming and fracture, at this time, the image of the related mechanical structure is collected, so that the condition of the mechanical structure is recognized through visual technology, and the working personnel are shown through the demonstration animation, thereby improving the accuracy of fault identification.

[0018] Optionally, it also includes a data separation method, which includes: Step 300: when there is a fault, determine the interval length based on the fault parameter; Step 301: determine the interval number in response to the interval length and the peak fluctuation curve; Step 302: determine the high-frequency interval according to the interval number; Step 303: determine the high-frequency parameter and the basic parameter combined with the high-frequency interval and the peak fluctuation curve; Step 304: update the fluctuation slope based on the basic parameter, and update the fluctuation interval based on the high-frequency parameter.

[0019] By adopting the above technical solution, when the abnormality of the parameter is caused by multiple reasons, it is easy to cause the fluctuation of the parameter to be relatively complex, at this time, the parameter needs to be split to facilitate the analysis of the fault reason, since the abnormal parameters affected by the mechanical activity are affected by the degree of the mechanical activity, the fluctuation of the parameter is similar, therefore, the parameters with similar fluctuation degree and high-frequency appearance are regarded as high-frequency parameters affected by the mechanical activity, and the remaining parameters are regarded as basic parameters affected by the mechanical wear, thereby improving the accuracy of fault identification.

[0020] Optionally, the data separation method further includes: Step 305: if the interval difference is greater than the period threshold, calculate the adjacent interval according to the fluctuation interval; Step 306: calculate the adjacent multiple according to the adjacent interval and the fluctuation interval, and calculate the adjacent deviation in response to the adjacent multiple; Step 307: when the adjacent deviation falls within a preset error interval, determine an error parameter based on the adjacent interval; Step 308: define the error parameter as the basic parameter.

[0021] By adopting the technical scheme, the time intervals of adjacent high-frequency parameters are added, and the added time interval is compared with the time intervals of other high-frequency parameters, so that when the added time interval has a multiplication relationship with the time interval of any high-frequency parameter, that is, the added time interval meets the periodic relationship, the high-frequency parameter is determined as the basic parameter, thereby improving the accuracy of fault identification.

[0022] Optionally, the data separation method further comprises: Step 309: generating a fluctuation fitting curve based on the basic parameter; Step 310: determining a large parameter and a small parameter in combination with the basic parameter and the fluctuation fitting curve; Step 311: determining a correction parameter according to the large parameter and the high-frequency interval; Step 312: determining a correction slope in response to the small parameter and the correction parameter, and determining a small slope in response to the small parameter; Step 313: calculating the difference between the small slope and the correction slope, and defining it as a slope difference; Step 314: defining the large parameter as a composite parameter if the slope difference is less than a preset composite threshold; Step 315: updating the high-frequency parameter and the basic parameter according to the composite parameter.

[0023] By adopting the technical scheme, when the fluctuation degree of the basic parameter is too large, the fluctuation of the high-frequency parameter is subtracted from the basic parameter, and the subtraction result is used to determine the fluctuation change, so that when the fluctuation change meets the fluctuation affected by wear, it is determined that the basic parameter is affected by both wear and mechanical activity, thereby improving the accuracy of fault identification.

[0024] In a second aspect, the application provides an intelligent factory management system, which adopts the following technical scheme: An intelligent factory management system comprises: A collection module for collecting execution parameters and troubleshooting images; A storage for storing the program of any of the intelligent factory management methods; A processor, and the program in the storage can be loaded and executed by the processor.

[0025] By adopting the technical scheme, the current and other parameters of the execution device are monitored in real time on the control cabinet, so that when a parameter of the execution device is abnormal, the fault cause of the execution device is analyzed in combination with the real-time current and other parameters, and then the analyzed fault cause is fed back to the DCS, thereby improving the accuracy of fault cause analysis and the management efficiency of the factory on the execution device.

[0026] To sum up, the present application includes at least one of the following beneficial technical effects: 1. Real-time monitoring of current and other parameters of the execution equipment on the control cabinet, so that when a parameter of the execution equipment is abnormal, the cause of the failure of the execution equipment is analyzed in combination with the real-time parameters such as current, and then the analyzed failure cause is fed back to the DCS, thereby improving the accuracy of failure cause analysis and improving the management efficiency of the execution equipment in the factory. 2. When a parameter of the execution equipment is abnormal, the historical data of the abnormal parameter of the execution equipment is called to view the damage speed of the execution equipment, and when the damage speed is slow, the cause of the parameter abnormality is judged to be normal mechanical wear. 3. When the damage speed of the execution equipment is slow, it is checked whether the parameter that is abnormal has periodic fluctuation, and when the parameter has periodic fluctuation, it is judged that the cause of the parameter abnormality is related to the mechanical activity of the execution equipment, so that when the failure information is fed back, the relevant mechanical parts are informed to the workers, and the accuracy of failure identification is improved. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a flow chart of an intelligent factory management method; Figure 2 is a flow chart of a fault locating method; Figure 3 is a flow chart of a data separation method. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0029] Referring to Figure 1 An intelligent factory management method comprises: Step 100: collecting execution parameters of the execution equipment.

[0030] The execution parameters refer to various parameters when the execution equipment is running. For example, when the execution equipment is a motor, the execution parameters include voltage parameters, current parameters, rotation speed parameters, rotation direction parameters and stall parameters and other data of the motor. The execution parameters can be collected by the corresponding sensors integrated on the control cabinet. The collection method of the execution parameters is selected by the workers according to the actual situation, which is not described here.

[0031] Step 101: judging whether there is a failure in response to the execution parameters and a preset failure interval.

[0032] The fault interval refers to a range of execution parameters when the execution device is in normal operation. Each type of execution parameter has a corresponding fault interval. When the execution parameter does not fall within the corresponding fault interval, it is determined that the execution device has a fault. The fault interval can be set by the staff in advance, and will not be described here.

[0033] Step 102: When there is a fault, determine the fault parameter in combination with the execution parameter and the preset fault interval.

[0034] The existence of a fault represents a jump in the data of the execution parameter. At this time, the execution parameter needs to be further analyzed to determine the cause of the jump in the data. The fault parameter is the execution parameter that does not fall within the corresponding fault interval. The determination method of the fault parameter is common knowledge to those skilled in the art, and will not be described here.

[0035] Step 103: Determine the fault type according to the fault parameter.

[0036] The fault type refers to the cause of the fault of the execution device. The fault type can be determined by the inspection rules set by the staff in advance. For example, when the execution device is a motor, if the current parameter continuously exceeds 1.2 times the upper limit of the fault interval (for example, the upper limit of the fault interval is 10A, and 12A or more is monitored), it may be that the mechanical part is stuck (such as the pump impeller is stuck by foreign matter), the load is too large (such as the conveyor belt is overloaded), or the motor winding is short-circuited. The determination method of the fault type is common knowledge to those skilled in the art, and will not be described here.

[0037] Step 104: Generate and send a fault prompt information based on the fault type.

[0038] The fault prompt information refers to a data packet including the fault type and other related information of the execution device fault sent to the DCS. The generation method of the fault prompt information is common knowledge to those skilled in the art, and will not be described here.

[0039] The current and other parameters of the execution device are monitored in real time on the control cabinet. When an abnormal parameter of the execution device occurs, the cause of the fault of the execution device is analyzed in combination with the real-time current and other parameters. Then, the analyzed fault cause is fed back to the DCS, thereby improving the accuracy of the analysis of the fault cause and improving the management efficiency of the factory on the execution device.

[0040] An intelligent factory management method further comprises: Step 105: When there is a fault, retrieve the historical parameter based on the fault parameter, and determine the peak threshold value based on the fault parameter.

[0041] The historical parameter refers to historical data of the fault parameter. The control cabinet records the execution parameter in a pre-set database in real time. The corresponding historical parameter can be called from the database according to the fault parameter. The calling method of the historical parameter is common knowledge in the field, and will not be described here.

[0042] The peak threshold refers to the highest value of the fault parameter caused by environmental factors and the like when the execution device is in normal operation. The greater the value of the fault parameter in normal operation, the greater the fluctuation, and the greater the peak threshold. The peak threshold corresponding to the fault parameter can be queried from a peak relationship table. The peak relationship table refers to a table recording different fault parameters and their corresponding peak thresholds.

[0043] Step 106: determining a peak point in response to the historical parameter and the peak threshold.

[0044] The peak point refers to a data point in the historical parameter exceeding the peak threshold. The determination method of the peak point is common knowledge in the field, and will not be described here.

[0045] Step 107: generating a peak fluctuation curve based on the peak point.

[0046] The peak fluctuation curve refers to a curve for showing the fluctuation of the historical parameter. The difference between the data of the peak point and the peak threshold is calculated as a fluctuation difference. The fluctuation difference is arranged in time sequence and fitted to form a peak fluctuation curve. The generation method of the peak fluctuation curve is common knowledge in the field, and will not be described here.

[0047] Step 108: reading a fluctuation slope from the peak fluctuation curve, and determining a fluctuation threshold according to the fault type.

[0048] The fluctuation slope refers to the slope of the peak fluctuation curve, that is, data for showing the change of the fluctuation of the historical parameter. The greater the fluctuation slope, the faster the change of the fluctuation. The reading method of the fluctuation slope is common knowledge in the field, and will not be described here.

[0049] The fluctuation threshold refers to the maximum value of the fluctuation slope of the historical parameter when the fault of the fault type occurs, that is, the maximum value of the fluctuation change speed of the historical parameter. The fluctuation threshold corresponding to the fault type can be queried from a threshold relationship table. The threshold relationship table refers to a table recording different fault types and their corresponding fluctuation thresholds.

[0050] Step 109: generating and sending a fault prompt information based on the fault type when the fluctuation slope is less than the fluctuation threshold.

[0051] The fluctuation slope less than the fluctuation threshold represents that the fluctuation change speed of the historical parameter before the fault is consistent with the fault type, that is, there is a gradual deterioration process before the fault occurs. At this time, the cause of the parameter anomaly is wear caused by normal use.

[0052] The intelligent factory management method further comprises: Step 110: When the fluctuation slope is less than the fluctuation threshold, calling the same type number according to the fault type.

[0053] The same type number refers to the number of other equipment with the same mechanical structure as the fault part of the execution equipment, each number corresponds to one piece of equipment, and the mechanical structure where the fault occurs can be determined according to the fault type, and then the same type number corresponding to the mechanical structure is queried from the structure relationship table. The structure relationship table refers to a data table recording different same type numbers and corresponding mechanical structures.

[0054] Step 111: Calling the same type parameter based on the same type number.

[0055] The same type parameter is the execution parameter of the equipment of the same type number, and the same type parameter calling method is well known to those skilled in the art, which will not be described here.

[0056] Step 112: Determine the same type fluctuation point in combination with the same type parameter and the peak threshold.

[0057] The same type fluctuation point is a value point in the same type parameter whose value is higher than the peak threshold, and the determination method of the same type fluctuation point is well known to those skilled in the art, which will not be described here.

[0058] Step 113: Calculate the same type slope based on the same type fluctuation point.

[0059] The same type slope is the slope of the curve formed according to the same type fluctuation point, and the determination method of the same type slope is referred to the above steps 107 and 108, which will not be described here.

[0060] Step 114: If the same type slope is consistent with the fluctuation slope, determine the fault duration in response to the same type fluctuation point and the peak point.

[0061] The same type slope consistent with the fluctuation slope represents that the wear change of the same type equipment is consistent with the execution equipment, and the fault duration refers to the duration of the same type equipment from the fault predicted according to the wear process of the execution equipment. The highest point in the same type fluctuation point is called, and the duration required for the same type fluctuation point not to fall into the corresponding fault interval is calculated as the fault duration in combination with the fluctuation slope.

[0062] Step 115: Generate and send the fault risk information in combination with the same type number and the fault duration.

[0063] The fault risk information refers to the data packet of the same type equipment which is easy to fail including the same type number and the fault duration, etc. The generation method of the fault risk information is well known to those skilled in the art, which will not be described here.

[0064] The same device has similar service life in the same environment, when the executing device fails due to mechanical wear, the same parameters of the same device with the same mechanical structure are checked, and when the change of the same parameters is similar to the executing device, the time to failure of the same device is estimated, so as to early warn the worker.

[0065] Reference Figure 2 The fault positioning method comprises: Step 200: When the fluctuation slope is less than the fluctuation threshold, the fluctuation interval is read based on the peak point.

[0066] The fluctuation interval refers to the time interval between adjacent peak points, and the reading method of the fluctuation interval is known to those skilled in the art, which is not described here.

[0067] Step 201: The interval mean is calculated based on the fluctuation interval.

[0068] The interval mean refers to the average value of the fluctuation interval, and the overall situation of the fluctuation interval is shown by the interval mean. The calculation method of the interval mean is known to those skilled in the art, which is not described here.

[0069] Step 202: The interval difference is calculated in combination with the fluctuation interval and the interval mean, and the period threshold is determined based on the peak point.

[0070] The interval difference refers to the absolute value of the difference between the fluctuation interval and the interval mean. The deviation of each fluctuation interval is shown by the interval difference. The calculation method of the interval difference is known to those skilled in the art, which is not described here.

[0071] The period threshold refers to the maximum interval difference when the fluctuation interval is similar. The more the number of peak points, the smaller the period threshold. The number of peaks can be counted first, and then the period threshold corresponding to the number of peaks is queried from the period relationship table. The period relationship table refers to a data table recording different peak numbers and their corresponding period thresholds.

[0072] Step 203: If the interval difference is not greater than the period threshold, the closest mechanical period is queried based on the interval mean.

[0073] The interval difference not greater than the period threshold represents that the fluctuation interval is similar, i.e. the peak point appears periodically. The mechanical period refers to the activity period of the mechanical structure on the executing device, such as the rotation period of the motor or the switching period of the valve. The mechanical period corresponding to the interval mean can be queried from the mechanical relationship table. The mechanical relationship table refers to a data table recording different mechanical structures and their corresponding mechanical periods.

[0074] Step 204: The fault site is matched based on the mechanical period.

[0075] The fault position is a mechanical structure position corresponding to the mechanical cycle, and the mechanical structure corresponding to the mechanical cycle can be queried from the mechanical relationship table as the fault position.

[0076] Step 205: updating the fault prompt information based on the fault position and the fault parameter.

[0077] When the damage speed of the execution equipment is slow, it is checked whether the parameter that occurs abnormity has periodic fluctuation, and it is judged that the reason of the parameter abnormity is related to the mechanical activity of the execution equipment when the parameter has periodic fluctuation, so that the related mechanical position is informed to the worker when the fault information is fed back, and the accuracy of fault identification is improved.

[0078] The fault positioning method further comprises: Step 206: if the interval difference value is greater than the cycle threshold value, the approximate interval and the deviation interval are determined in combination with the fluctuation interval and the interval mean value.

[0079] The interval difference value greater than the cycle threshold value represents that the difference of the fluctuation interval is large, the approximate interval refers to the fluctuation interval less than the interval mean value, and the deviation interval refers to the fluctuation interval not less than the interval mean value. The determination method of the approximate interval and the deviation interval is the common knowledge of the person skilled in the art, and will not be repeated here.

[0080] Step 207: calculating the approximate mean value according to the approximate interval, and calculating the deviation mean value according to the deviation interval.

[0081] The approximate mean value refers to the average value of the approximate interval, and the deviation mean value refers to the average value of the deviation interval. The calculation method of the approximate mean value and the deviation mean value is the common knowledge of the person skilled in the art, and will not be repeated here.

[0082] Step 208: calculating the quotient of the deviation mean value and the approximate mean value, and defining it as the mean value ratio.

[0083] The mean value ratio refers to a numerical value for showing the difference between the deviation mean value and the approximate mean value. The calculation method of the mean value ratio is the common knowledge of the person skilled in the art, and will not be repeated here.

[0084] Step 209: calculating the ratio deviation according to the mean value ratio.

[0085] The ratio deviation refers to the difference between the mean value ratio and the nearest integer. For example, when the mean value ratio is 1.9, the ratio deviation is 0.1, and when the mean value ratio is 2.4, the ratio deviation is 0.4. The calculation method of the ratio deviation is the common knowledge of the person skilled in the art, and will not be repeated here.

[0086] Step 210: if the ratio deviation falls into a preset error interval, calculating the approximate difference value in combination with the approximate mean value and the approximate interval.

[0087] The error interval refers to the maximum ratio deviation when the data has an allowable error. The error interval can be set by the staff in advance, and will not be described here.

[0088] The ratio deviation falling within the error interval means that the deviation mean and the approximate mean are in a multiple relationship. The approximate difference refers to the absolute value of the difference between the approximate interval and the approximate mean. The approximate difference shows the deviation of each approximate interval. The calculation method of the approximate difference is known to those skilled in the art, and will not be described here.

[0089] Step 211: When the approximate difference is not greater than the period threshold, the deviation difference is calculated in combination with the deviation mean and the deviation interval.

[0090] The approximate difference not being greater than the period threshold means that the approximate intervals are close, i.e., the peak points corresponding to the approximate intervals occur periodically. The deviation difference refers to the absolute value of the difference between the deviation interval and the deviation mean. The deviation difference shows the deviation of each deviation interval. The calculation method of the deviation difference is known to those skilled in the art, and will not be described here.

[0091] Step 212: If the deviation difference is not greater than the period threshold, the closest mechanical period is queried in response to the approximate mean.

[0092] The deviation difference not being greater than the period threshold means that the deviation intervals are close, i.e., the peak points corresponding to the deviation intervals occur periodically. When the cause of the parameter anomaly is related to the mechanical activity of the execution device, the parameter fluctuation is easily affected by the degree of mechanical activity, so that when the degree of mechanical activity is low, the parameter fluctuation is small or even does not occur. When the time intervals of the parameter fluctuations are inconsistent, it is compared whether there is a ratio relationship between the time intervals, so that when the ratio relationship exists, it is determined that the parameter has periodic fluctuations.

[0093] The fault positioning method further comprises: Step 213: When the fluctuation slope is not less than the fluctuation threshold, the mechanical range is determined based on the fault category.

[0094] The fluctuation slope not being less than the fluctuation threshold means that the fluctuation speed of the historical parameters before the fault occurs is too fast, i.e., the execution parameter mutates, and the execution device has a sudden fault. The mechanical range refers to the outer range of the mechanical structure corresponding to the fault category. The mechanical range corresponding to the fault category can be queried from the outer relationship table. The outer relationship table refers to a data table recording different fault categories and the mechanical ranges corresponding thereto.

[0095] Step 214: The troubleshooting route is planned according to the mechanical range.

[0096] The troubleshooting route is a route for troubleshooting the mechanical range through the intelligent device. Generally, a camera preset on a two-dimensional linear guide rail on the top of a drone or a factory is selected as the intelligent device, and the determination method of the troubleshooting route is selected by the staff according to the actual situation, which is not described here.

[0097] Step 215: Collecting a troubleshooting image according to the troubleshooting route, and matching a fault feature based on the fault category.

[0098] The troubleshooting image is an external image of the mechanical structure of the executing device that fails according to the troubleshooting route of the intelligent device. The collection method of the troubleshooting image is selected by the staff according to the actual situation, which is not described here.

[0099] The fault feature refers to the object that easily causes the fault category of sudden failure. For example, when the fault category is motor jamming, the fault feature is the object such as cloth that easily causes motor jamming. The fault feature is related to the environment of the executing device. The fault feature can be selected by the staff according to the actual situation, and the fault feature corresponding to the fault category can be queried from the feature data table. The feature data table refers to a data table recording different fault categories and their corresponding fault features.

[0100] Step 216: If the troubleshooting image contains the fault feature, a fault demonstration animation is generated in combination with the fault feature and the fault category.

[0101] The presence of the fault feature in the troubleshooting image means that the object causing the fault can be found from the appearance of the executing device. The fault demonstration animation is a scene simulation image showing the fault feature causing the fault category. Different fault demonstration animations corresponding to the fault feature causing the fault category can be generated in advance and stored in an animation database, and the fault demonstration animation corresponding to the fault feature can be retrieved from the animation database when the fault feature is identified.

[0102] Step 217: Updating the fault prompt information in response to the fault demonstration animation.

[0103] When the executing device is damaged too quickly, it is determined that the mechanical structure of the executing device has sudden failures such as jamming and breaking. At this time, the image of the related mechanical structure is collected, so that the condition of the mechanical structure is identified through visual technology, and the staff is shown through the demonstration animation, thereby improving the accuracy of fault identification.

[0104] Referring to Figure 3 , the data separation method comprises: Step 300: When there is a fault, determining the interval length based on the fault parameter.

[0105] The interval length refers to a range for judging the distribution of the peak point, and the greater the numerical change of the fault parameter, the greater the interval length required. The interval length corresponding to the fault parameter can be queried from the interval relationship table, which is a data table recording different fault parameter types and their corresponding interval lengths.

[0106] Step 301: determining the interval number in response to the interval length and the peak fluctuation curve.

[0107] The interval number is the number of data in the peak fluctuation curve that differs from the value of the peak point by less than the interval length. The determination method of the interval number is known to those skilled in the art and will not be repeated here.

[0108] Step 302: determining the high-frequency interval according to the interval number.

[0109] The high-frequency interval refers to the data value interval corresponding to the maximum interval number, i.e., the range from (peak point-interval length) to (peak point+interval length), wherein the peak point corresponds to the interval number. The determination method of the high-frequency interval is known to those skilled in the art and will not be repeated here.

[0110] Step 303: determining the high-frequency parameter and the basic parameter in combination with the high-frequency interval and the peak fluctuation curve.

[0111] The high-frequency parameter is the peak point in the peak fluctuation curve that falls within the high-frequency interval, and the basic parameter is the peak point in the peak fluctuation curve that does not fall within the high-frequency interval. The determination method of the high-frequency parameter and the basic parameter is known to those skilled in the art and will not be repeated here.

[0112] Step 304: updating the fluctuation slope based on the basic parameter and updating the fluctuation interval based on the high-frequency parameter.

[0113] When the abnormality of the parameter is caused by multiple reasons, it is easy to cause the fluctuation of the parameter to be complex. At this time, the parameter needs to be split to facilitate the analysis of the fault reason. Since the abnormal parameters affected by mechanical activities are affected by the degree of mechanical activities, the fluctuation of the parameters is similar. Therefore, the parameters with similar fluctuation degrees and high-frequency parameters are considered to be affected by mechanical activities, and the remaining parameters are considered to be affected by mechanical wear, thereby improving the accuracy of fault identification.

[0114] The data separation method further comprises: Step 305: if the interval difference is greater than the cycle threshold, calculating the adjacent interval according to the fluctuation interval.

[0115] The adjacent interval refers to the sum of the current fluctuation interval and the adjacent fluctuation interval in front. The calculation method of the adjacent interval is known to those skilled in the art and will not be repeated here.

[0116] Step 306: calculating a neighbor ratio according to the neighbor interval and the fluctuation interval, and calculating a neighbor deviation in response to the neighbor ratio.

[0117] The neighbor ratio is used to show the numerical value of the difference between the neighbor interval and the fluctuation interval, and the calculation method of the neighbor ratio is known to those skilled in the art, which will not be repeated here.

[0118] The neighbor deviation refers to the difference between the neighbor ratio and the nearest integer, and the calculation method of the neighbor deviation is referred to step 209, which will not be repeated here.

[0119] Step 307: determining an error parameter based on the neighbor interval when the neighbor deviation falls within a preset error interval.

[0120] The neighbor deviation falling within the error interval means that the neighbor interval and the fluctuation interval are in a multiple relationship, and the error parameter is the peak point between the two fluctuation intervals corresponding to the neighbor interval. The determination method of the error parameter is known to those skilled in the art, which will not be repeated here.

[0121] Step 308: defining the error parameter as the basic parameter.

[0122] The time intervals of the neighbor high-frequency parameters are added, and the added time interval is compared with the time intervals of other high-frequency parameters, so that when the added time interval and the time interval of any high-frequency parameter have a multiple relationship, i.e., the added time interval meets the periodic relationship, the high-frequency parameter is determined as the basic parameter, thereby improving the accuracy of fault identification.

[0123] The data separation method further comprises: Step 309: generating a fluctuation fitting curve based on the basic parameter.

[0124] The fluctuation fitting curve is a curve fitted and generated according to the basic parameter, and the generation method of the fluctuation fitting curve is known to those skilled in the art, which will not be repeated here.

[0125] Step 310: determining a large parameter and a small parameter in combination with the basic parameter and the fluctuation fitting curve.

[0126] The large parameter refers to the basic parameter located above the fluctuation fitting curve, i.e., greater than the fluctuation fitting curve, and the small parameter refers to the basic parameter located below the fluctuation fitting curve, i.e., not greater than the fluctuation fitting curve. The determination method of the large parameter and the small parameter is known to those skilled in the art, which will not be repeated here.

[0127] Step 311: determining a correction parameter according to the large parameter and the high-frequency interval.

[0128] The correction parameter is a value obtained by subtracting the center of the high-frequency interval from the large parameter, and the center of the high-frequency interval represents a value point with the highest frequency, i.e., a value of the execution parameter affected by mechanical activity. The correction parameter represents the value of the execution parameter not affected by mechanical activity. The calculation method of the correction parameter is well known to those skilled in the art, and thus is not described herein.

[0129] Step 312: determining a correction slope in response to the small parameter and the correction parameter, and determining a small slope in response to the small parameter.

[0130] The correction slope is a slope value of a curve fitted according to the small parameter and the correction parameter, and the small slope is a slope value of a curve fitted according to the small parameter. The determination methods of the correction slope and the small slope are well known to those skilled in the art, and thus are not described herein.

[0131] Step 313: calculating a difference between the small slope and the correction slope, and defining the difference as a slope difference.

[0132] The slope difference is a value used to represent the difference between the small slope and the correction slope. The calculation method of the slope difference is well known to those skilled in the art, and thus is not described herein.

[0133] Step 314: if the slope difference is less than a preset composite threshold, defining the large parameter as a composite parameter.

[0134] The composite threshold is a value of the influence of the maximum fluctuation of the execution parameter on the slope when the execution device is simultaneously affected by mechanical activity and mechanical wear. The composite threshold can be preset by a worker, and thus is not described herein. The slope difference less than the composite threshold represents that the correction parameter is consistent with the change of the small parameter, i.e., the change of the large parameter excluding the influence of mechanical activity is consistent with the change of mechanical wear. The composite parameter is the large parameter corresponding to the correction parameter. The determination method of the composite parameter is well known to those skilled in the art, and thus is not described herein.

[0135] Step 315: updating the high-frequency parameter and the base parameter according to the composite parameter.

[0136] When the fluctuation degree of the base parameter is too large, the base parameter is subtracted by the fluctuation of the high-frequency parameter, and the fluctuation of the subtracted base parameter is determined, so that when the fluctuation is consistent with the fluctuation affected by wear, it is determined that the base parameter is simultaneously affected by mechanical wear and mechanical activity, and one base parameter is divided into a base parameter affected only by mechanical wear and a high-frequency parameter affected only by mechanical activity, thereby improving the accuracy of fault identification.

[0137] Based on the same inventive concept, an embodiment of the present application provides an intelligent factory management system, comprising: The collection module is configured to collect the execution parameter and the troubleshooting image. The memory is configured to store a program of any of the intelligent factory management methods. The processor is configured to load and execute the program in the memory.

[0138] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is taken as an example, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0139] The above is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments. Any technical solution falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall also be considered as the protection scope of the present application.

Claims

1. An intelligent factory management method characterized by, Comprising: Step 100: Collecting execution parameters of an execution device; Step 101: Determining whether there is a fault in response to the execution parameters and a preset fault interval; Step 102: When there is a fault, determining a fault parameter in combination with the execution parameters and the preset fault interval; Step 103: Determining a fault category according to the fault parameter; Step 104: Generating and sending fault prompt information based on the fault category.

2. The intelligent factory management method of claim 1, wherein, Further comprising: Step 105: When there is a fault, calling historical parameters based on the fault parameter, and determining a peak threshold value based on the fault parameter; Step 106: Determining a peak point in response to the historical parameters and the peak threshold value; Step 107: Generating a peak fluctuation curve based on the peak point; Step 108: Reading a fluctuation slope from the peak fluctuation curve, and determining a fluctuation threshold value according to the fault category; Step 109: When the fluctuation slope is less than the fluctuation threshold value, generating and sending fault prompt information based on the fault category.

3. The intelligent factory management method of claim 2, wherein, Further comprising: Step 110: When the fluctuation slope is less than the fluctuation threshold value, calling a same-category number according to the fault category; Step 111: Calling a same-category parameter based on the same-category number; Step 112: Determining a same-category fluctuation point in combination with the same-category parameter and the peak threshold value; Step 113: Calculating a same-category slope based on the same-category fluctuation point; Step 114: If the same-category slope is consistent with the fluctuation slope, determining a fault duration in response to the same-category fluctuation point and the peak point; Step 115: Generating and sending fault risk information in combination with the same-category number and the fault duration.

4. The intelligent factory management method of claim 3, wherein, Further comprising a fault positioning method, the fault positioning method comprising: Step 200: When the fluctuation slope is less than the fluctuation threshold value, reading a fluctuation interval based on the peak point; Step 201: Calculating an interval mean value based on the fluctuation interval; Step 202: Calculating an interval difference value in combination with the fluctuation interval and the interval mean value, and determining a period threshold value based on the peak point; Step 203: If the interval difference value is not greater than the period threshold value, querying a closest mechanical period in response to the interval mean value; Step 204: Matching a fault site based on the mechanical period; Step 205: Updating the fault prompt information based on the fault site and the fault parameter.

5. The intelligent factory management method of claim 4, wherein, The fault positioning method further comprises: Step 206: If the interval difference value is greater than the period threshold value, determining an approximate interval and a deviation interval in combination with the fluctuation interval and the interval mean value; Step 207: Calculating an approximate mean value according to the approximate interval, and calculating a deviation mean value according to the deviation interval; Step 208: Calculating a quotient of the deviation mean value and the approximate mean value, and defining it as a mean value multiplier; Step 209: Calculating a multiplier deviation according to the mean value multiplier; Step 210: If the multiplier deviation falls within a preset error interval, calculating an approximate difference value in combination with the approximate mean value and the approximate interval; Step 211: When the approximate difference value is not greater than the period threshold value, calculating a deviation difference value in combination with the deviation mean value and the deviation interval; Step 212: If the deviation difference value is not greater than the period threshold value, querying a closest mechanical period in response to the approximate mean value.

6. The intelligent factory management method of claim 5, wherein, The fault positioning method further comprises: Step 213: determining a mechanical range based on the fault category when the fluctuation slope is not less than the fluctuation threshold value; Step 214: planning an investigation route according to the mechanical range; Step 215: collecting investigation images according to the investigation route and matching fault features based on the fault category; Step 216: generating a fault demonstration animation in combination with the fault features and the fault category if the fault features exist in the investigation images; Step 217: updating the fault prompt information in response to the fault demonstration animation.

7. The intelligent factory management method of claim 6, wherein, The data separation method comprises: Step 300: determining an interval length based on the fault parameter when there is a fault; Step 301: determining an interval number in response to the interval length and the peak fluctuation curve; Step 302: determining a high-frequency interval according to the interval number; Step 303: determining a high-frequency parameter and a basic parameter in combination with the high-frequency interval and the peak fluctuation curve; Step 304: updating the fluctuation slope based on the basic parameter and updating the fluctuation interval based on the high-frequency parameter.

8. The intelligent factory management method of claim 7, wherein, The data separation method further comprises: Step 305: calculating adjacent intervals according to the fluctuation interval if the interval difference is greater than a period threshold value; Step 306: calculating an adjacent ratio according to the adjacent intervals and the fluctuation interval and calculating an adjacent deviation in response to the adjacent ratio; Step 307: determining an error parameter based on the adjacent interval when the adjacent deviation falls within a preset error interval; Step 308: defining the error parameter as the basic parameter.

9. The intelligent factory management method of claim 8, wherein, The data separation method further comprises: Step 309: generating a fluctuation fitting curve based on the basic parameter; Step 310: determining a larger parameter and a smaller parameter in combination with the basic parameter and the fluctuation fitting curve; Step 311: determining a correction parameter according to the larger parameter and the high-frequency interval; Step 312: determining a correction slope in response to the smaller parameter and the correction parameter and determining a smaller slope in response to the smaller parameter; Step 313: calculating a difference between the smaller slope and the correction slope and defining it as a slope difference; Step 314: defining the larger parameter as a composite parameter if the slope difference is less than a preset composite threshold value; Step 315: updating the high-frequency parameter and the basic parameter according to the composite parameter.

10. An intelligent factory management system, characterized by, The system comprises: a collection module for collecting execution parameters and investigation images; a memory for storing a program of an intelligent factory management method according to any one of claims 1 to 9; a processor, and the program in the memory can be loaded and executed by the processor.

Images