An operation monitoring method and system based on LED silicone light strip extruder

Abstract

The invention relates to the technical field of state monitoring, and specifically discloses an operation monitoring method and system based on an LED silicone light strip extruder. Within a preset period, extrusion state data of the extruder is obtained, and an extrusion state behavior value of the extruder within the preset period is obtained based on the extrusion state data; the extruder state is divided according to the extrusion state behavior value to obtain an extruder state signal; based on an extruder operation state difference signal, the extruder is modularly divided to obtain operation area units after the extruder is divided, and a monitoring abnormal value of each operation area unit is obtained; based on the monitoring abnormal value of the operation area unit, the operation area unit is linked to obtain a monitoring reminder value of the operation area unit, and directional monitoring of the extruder operation area unit is completed based on the monitoring reminder value. The invention realizes intelligent monitoring of the operation maintenance of the extruder by monitoring the state data of the extruder during operation, and according to the monitoring result, the operation maintenance of the extruder is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of state monitoring, and in particular to an operation monitoring method and system based on an LED silicone light strip extruder. Background Art

[0002] The LED silicone light strip extruder melts silicone at high temperature, then uses a screw to push the molten silicone into the extruder head, and forms a silicone light strip of a certain shape through extrusion. Its working principle mainly relies on the motor to drive the screw to rotate, and send solid silicone particles into the extruder. After high-temperature melting, it forms a viscous flow of silicone, and then passes through the mold of the extruder head to form a silicone light strip of the desired shape.

[0003] LED silicone light strip extruder has many advantages, such as high production efficiency, standard extrusion size, stable product quality, etc.

[0004] Based on this, the present application proposes an operation monitoring method and system based on an LED silicone light strip extruder, which aims to monitor the status data of the extruder during operation, so as to realize the monitoring of the operating status of the extruder, improve the operating safety of the extruder, improve the reliability of the operation and maintenance of the extruder, ensure the long-term storage and traceability of the extruder operating data, and realize intelligent monitoring of the operation and maintenance of the extruder according to the monitoring results. Summary of the invention

[0005] The object of the present invention is to provide an operation monitoring method and system based on an LED silicone light strip extruder. Under the premise that the extrusion speed of the extruder is affected by the screw speed and the heating temperature during the extrusion time, on the basis of obtaining the monitoring abnormal value of each operating area unit, the adjacent area unit directly adjacent to the target area unit is processed to obtain the adjacent monitoring abnormal value, and then the built-in analytical value of the target area unit is obtained by the use time of a single target component in the target area unit body, the use frequency of the overall target component and the number of target components. By processing the built-in analytical value and the regional linkage monitoring abnormal value, the abnormal degree of the target area unit is identified by the internal state of the target area unit and the related state of the adjacent area unit, and further the maintenance position intelligent monitoring of the target position during the operation of the extruder is completed to solve the above-mentioned background problems.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for monitoring the operation of an LED silicone light strip extruder comprises the following steps:

[0008] Acquire extrusion state data of the extruder within a preset period, and obtain an extrusion state behavior value of the extruder within the preset period based on the extrusion state data;

[0009] The extruder state is divided according to the extrusion state behavior value to obtain the extruder state signal;

[0010] Among them, the extruder status signal includes an excellent extruder operation status signal, a good extruder operation status signal and a poor extruder operation status signal;

[0011] Based on the difference signal of the extruder operation state, the extruder is modularized to obtain the divided operation area units of the extruder, and the monitoring abnormal value of each operation area unit is obtained;

[0012] Based on the monitoring abnormal value of the operating area unit, the operating area unit is linked to obtain the monitoring reminder value of the operating area unit, and the directional monitoring of the extruder operating area unit is completed based on the monitoring reminder value.

[0013] As a further solution of the present invention: the extrusion state data of the extruder includes an abnormal analysis value of the screw speed and an abnormal analysis value of the heating temperature;

[0014] The screw speed abnormal analysis value is recorded as Z Z ;

[0015] The heating temperature abnormal analysis value is recorded as Zw;

[0016] The screw speed abnormal analysis value Z Z Heating temperature abnormal analysis value Z W Processing, through the formula Calculate the extrusion state behavior value Z of the extruder within the preset cycle i , where Z P It is the abnormal analysis value of extrusion speed.

[0017] As a further solution of the present invention: the limit value of the extrusion state behavior value is preset to Z i 1 and Z i 2, where Z i 1<Z i 2;

[0018] When Z i <Z i When 1, it means that the extruder is in excellent operating condition overall, and a signal of excellent extruder operating condition is obtained;

[0019] When Z i 1≤Z i <Z i 2, it means that the extruder is in good operating condition overall, and a signal that the extruder is in good operating condition is obtained;

[0020] When Z i ≥Z i2, it indicates that the overall operating state of the extruder is poor, and a poor operating state signal of the extruder is obtained.

[0021] As a further solution of the present invention: the process of obtaining the extrusion speed abnormal analysis value is:

[0022] In the preset period, an extrusion speed curve of time-extrusion speed is constructed;

[0023] According to the rated range of the extrusion speed during the operation of the extruder, the extrusion speed curve is processed to obtain an extrusion speed unstable area and an extrusion speed stable area;

[0024] The time period of the extrusion velocity unstable region is divided into a number of time unstable subunits;

[0025] Divide the time period of the extrusion speed stabilization region into a number of time stabilization subunits;

[0026] The ratio of the time period corresponding to the unstable region of the extrusion speed to the time period corresponding to the stable region of the extrusion speed is calculated to obtain the unstable time ratio;

[0027] The maximum extrusion speed and the minimum extrusion speed are calculated by difference to obtain the vibration frequency deviation value, and the vibration frequency deviation value is calculated by ratio with the extrusion speed standard value to obtain the extrusion speed deviation ratio;

[0028] The abnormal analysis value of the extrusion speed is obtained by multiplying the proportion of the unstable time length with the extrusion speed deviation ratio.

[0029] As a further solution of the present invention: the process of obtaining the abnormal analysis value of the screw speed is:

[0030] The unstable deviation value of the screw speed of the unstable time subunit and the stable deviation value of the screw speed of the stable time subunit are processed to obtain the screw speed deviation ratio;

[0031] The subunits in the time unstable subunit and the time stable subunit whose screw speed deviation value is greater than or equal to the screw speed deviation threshold are recorded as screw speed abnormal units;

[0032] The ratio of the number of screw speed abnormal units to the total number of time units is calculated to obtain the screw speed abnormal unit number ratio;

[0033] The screw speed deviation ratio is multiplied by the screw speed abnormal unit number ratio to obtain the screw speed abnormal analysis value;

[0034] The process of obtaining the abnormal analysis value of heating temperature is as follows:

[0035] Obtain the heating temperature value at the middle moment of each time unstable subunit and process it to obtain the heating temperature deviation ratio;

[0036] The subunit whose heating temperature value at the intermediate moment between the time unstable subunit and the time stable subunit is greater than or equal to the temperature threshold is recorded as a temperature abnormal subunit;

[0037] The ratio of the number of temperature anomaly sub-units to the total number of time units is calculated to obtain the temperature anomaly unit ratio;

[0038] The heating temperature deviation ratio is multiplied by the temperature anomaly unit number ratio to obtain the heating temperature anomaly analysis value.

[0039] As a further solution of the present invention: obtaining target components to be monitored in each operating area unit and monitoring items to be monitored for each target component;

[0040] If the real-time monitoring data of the monitoring items of the target component is outside the preset data requirements of the monitoring items and exceeds the preset time, the target component will be recorded as a non-qualified component;

[0041] Obtain the continuous abnormal time ratio and monitoring deviation base number of the same monitoring item in the non-conforming component;

[0042] The non-fixed monitoring value F of each monitoring item of the non-qualified component is obtained by multiplying the continuous abnormal time ratio of the same monitoring item of the non-qualified component by the monitoring deviation base. k , k represents the number of monitoring items of non-conforming components, k=1, 2, ..., n;

[0043] The non-fixed monitoring values ​​of the monitoring items in the same non-conforming component are processed by the formula Calculate the monitoring interference value Fa of the non-qualified component j , where j is the number of non-conforming components, j=1, 2, ..., m.

[0044] As a further solution of the present invention: the monitoring interference values ​​of all non-qualified components in the same operating area unit are processed, and the formula Calculate the monitoring change value F of the operating area unit b , where F q is the non-conforming ratio of regional unit components;

[0045] The area unit component non-conforming ratio is the ratio of the number of non-conforming components to the number of target components.

[0046] As a further solution of the present invention: any operating area unit is recorded as a target area unit, and an operating area unit directly adjacent to the target area unit is recorded as a side area unit;

[0047] The monitoring abnormal value of all adjacent regional units is summed and averaged to obtain the adjacent monitoring abnormal value;

[0048] The monitoring change value of the target area unit is processed with the monitoring change value of the adjacent unit to obtain the regional linkage monitoring change value, which is recorded as FB i ;

[0049] Get the built-in analytical value of the target area unit and record it as E i ;

[0050] The regional linkage monitoring abnormal value FB i The built-in analytical value E of the target area unit i Processing, through the formula Calculate and obtain the monitoring reminder value FE of the operating area unit;

[0051] Arrange each operating area unit in the extruder in descending order according to the monitoring reminder value, and record the operating area unit corresponding to the largest monitoring reminder value as the priority maintenance area.

[0052] As a further solution of the present invention: the process of acquiring the built-in analytical value of the target area unit is:

[0053] Obtain the rated use time ratio of each target component in the target area unit, and record the maximum value of the rated use time ratio of the target component in the target area unit as E1;

[0054] comparing the rated usage time ratio of the target component with a rated usage time ratio threshold of the target component;

[0055] If the rated usage time ratio of the target component is greater than or equal to the rated usage time ratio threshold of the target component, the corresponding target component is recorded as a time analysis component;

[0056] Calculate the ratio of the number of time-analyzed components to the number of target components to obtain the time-analyzed ratio of the target component, which is recorded as E2;

[0057] By formula Calculate the built-in analytical value E of the target area unit i， Wherein, E3 is the number of target components in the target area unit.

[0058] As a further solution of the present invention: a blockchain-based extruder operation status monitoring system, comprising:

[0059] A state analysis module, which is used to obtain the extrusion state data of the extruder within a preset period, obtain the extrusion state behavior value of the extruder within the preset period based on the extrusion state data, and upload the extrusion state behavior value to the cloud management and control platform;

[0060] The behavior classification module receives the extrusion state behavior value transmitted by the cloud control platform, and the behavior classification module classifies the extruder state according to the extrusion state behavior value to obtain the extruder state signal, and uploads the extruder state signal to the cloud control platform;

[0061] Among them, the extruder status signal includes an excellent extruder operation status signal, a good extruder operation status signal and a poor extruder operation status signal;

[0062] The regional identification module receives the extruder status information transmitted by the cloud management and control platform. Based on the difference signal of the extruder operation status, the regional identification module divides the extruder into modular units to obtain the divided operation area units of the extruder, obtains the monitoring abnormal value of each operation area unit, and uploads it to the cloud management and control platform;

[0063] The linkage positioning module receives the monitoring abnormality value transmitted by the cloud management and control platform. The linkage positioning module performs linkage processing on the operating area unit based on the monitoring abnormality value of the operating area unit, obtains the monitoring reminder value of the operating area unit, and completes the directional monitoring of the extruder operating area unit based on the monitoring reminder value.

[0064] Beneficial effects of the present invention:

[0065] (1) When the extruder is running unstably, the extrusion speed of the extruder will fluctuate. During the operation of the extruder, the present invention collects the real-time extrusion speed of the extruder within a preset period to obtain the unstable extrusion speed area and the stable extrusion speed area of ​​the extruder during the operation, and obtains the abnormal screw speed analysis value and the abnormal heating temperature analysis value according to the change values ​​of the noise and heat in the unstable extrusion speed area and the stable extrusion speed area during the operation of the extruder. The extrusion state behavior value of the extruder within the preset period is obtained by processing the extrusion speed, noise and heat during the operation of the extruder, and the extruder state is identified according to the extrusion state behavior value of the extruder within the preset period, with high accuracy;

[0066] (2) When the extruder is in a poor operating state, the present invention divides the extruder into different operating area units by modularization, processes the monitoring items of the target components in the operating area units, divides the target components into non-qualified components, and obtains the non-qualified ratio of the area unit components according to the number of non-qualified components (the larger the ratio, the more unstable target components there are in the operating area unit, and the worse the extruder state is). Then, according to the degree of deviation of the monitoring items of the non-qualified components, the non-qualified components are identified to obtain the monitoring interference value (the larger the ratio, the greater the deviation of each monitoring item in the non-qualified components, and the worse the extruder state is). Then, the monitoring interference values ​​of all non-qualified components in the same operating area unit are processed to obtain the monitoring abnormality value, thereby completing the state evaluation of the operating area unit.

[0067] (3) The present invention processes the adjacent regional units directly adjacent to the target regional units on the basis of obtaining the monitoring abnormality value of each operating regional unit to obtain the adjacent monitoring abnormality value, and then obtains the built-in analytical value of the target regional unit by analyzing the usage time of a single target component in the target regional unit body, the usage frequency of the overall target components and the number of target components. By processing the built-in analytical value and the regional linkage monitoring abnormality value, the abnormality degree of the target regional unit is identified by analyzing the internal state of the target regional unit and the related states of the adjacent regional units, and further completing the intelligent monitoring of the maintenance position of the target position during the operation of the extruder. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The present invention will be further described below in conjunction with the accompanying drawings.

[0069] Figure 1 It is a flow chart of an operation monitoring method based on an LED silicone light strip extruder according to an embodiment of the present invention;

[0070] Figure 2 It is a flow chart of the division of the operation status of an extruder in an operation monitoring method based on an LED silicone light strip extruder according to an embodiment of the present invention;

[0071] Figure 3 It is a flowchart of a blockchain-based extruder operation status monitoring system in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] Example 1

[0074] See also Figure 1 As shown, the present invention is an operation monitoring method based on an LED silicone light strip extruder, comprising the following steps:

[0075] Acquire extrusion state data of the extruder within a preset period, and obtain an extrusion state behavior value of the extruder within the preset period based on the extrusion state data;

[0076] The extruder state is divided according to the extrusion state behavior value to obtain the extruder state signal;

[0077] Among them, the extruder status signal includes an excellent extruder operation status signal, a good extruder operation status signal and a poor extruder operation status signal;

[0078] Based on the difference signal of the extruder operation state, the extruder is modularized to obtain the divided operation area units of the extruder, and the monitoring abnormal value of each operation area unit is obtained;

[0079] Based on the monitoring abnormal value of the operating area unit, the operating area unit is linked to obtain the monitoring reminder value of the operating area unit, and the directional monitoring of the extruder operating area unit is completed based on the monitoring reminder value.

[0080] Among them, the extrusion state data of the extruder includes the abnormal analysis value of the screw speed and the abnormal analysis value of the heating temperature;

[0081] The acquisition of the extrusion state data of the extruder is based on monitoring the extrusion speed of the extruder, and the screw speed and heating temperature during the extrusion time are identified according to the extrusion speed of the extruder;

[0082] It should be noted that: why the screw speed and heating temperature are identified? During the extrusion process, the plastic is conveyed and plasticized by the rotation of the screw. Therefore, the screw speed directly determines the conveying speed and plasticizing efficiency of the plastic in the extruder. When the screw speed increases, the conveying speed and plasticizing efficiency of the plastic will also increase accordingly, resulting in an increase in the extrusion speed. Conversely, when the screw speed decreases, the extrusion speed will also slow down accordingly.

[0083] During the extrusion process, the plastic needs to be heated to a certain temperature in order to be plasticized and extruded smoothly. The faster the heating speed, the shorter the time it takes for the plastic to reach the plasticizing temperature, so that the extrusion process can be started faster. However, it should be noted that heating too fast may also cause the plastic to overheat or degrade, affecting the extrusion quality. Therefore, in actual operation, the heating speed needs to be reasonably controlled to ensure that the plastic is plasticized and extruded at the appropriate temperature.

[0084] Exemplary:

[0085] Divide the preset cycle into a number of time nodes of equal time length, and establish a plane coordinate system with the running time of the extruder as the X-axis and the extrusion speed corresponding to the running of the extruder as the Y-axis;

[0086] Obtain each time node and corresponding extrusion speed when the extruder is running within a preset cycle, and construct an extrusion speed curve in a plane coordinate system according to the obtained time node-extrusion speed;

[0087] Obtaining a rated extrusion speed range of the extruder during operation, recording the rated upper limit of the extrusion speed as an upper critical extrusion speed, and recording the rated lower limit of the extrusion speed as a lower critical extrusion speed;

[0088] Draw a horizontal line parallel to the X-axis in the plane coordinate system for the upper critical extrusion speed, which is recorded as the upper limit of the extrusion speed;

[0089] Draw a horizontal line parallel to the X-axis in the plane coordinate system for the lower critical extrusion speed, which is recorded as the lower limit of the extrusion speed;

[0090] Analyze the extrusion speed curve, the upper limit of the extrusion speed, and the lower limit of the extrusion speed;

[0091] The area where the extrusion speed curve is located above the upper limit of the extrusion speed or below the lower limit of the extrusion speed is recorded as the extrusion speed unstable area;

[0092] The area of ​​the extrusion speed curve between the upper extrusion speed limit and the lower extrusion speed limit is recorded as the extrusion speed stable area;

[0093] Obtaining the time period corresponding to the unstable region of the extrusion speed on the extrusion speed curve, and dividing the time period of the unstable region of the extrusion speed into a plurality of time unstable sub-units;

[0094] The time period corresponding to the extrusion speed stabilization region on the extrusion speed curve is obtained, and the time period of the extrusion speed stabilization region is divided into a plurality of time stabilization sub-units.

[0095] The process of obtaining the abnormal analysis value of screw speed is as follows:

[0096] Obtain the screw speed deviation value in each time unstable subunit, sum and average the screw speed deviation values ​​in all time unstable subunits, and obtain the screw speed unstable deviation value of the time unstable subunit;

[0097] Obtaining the screw speed deviation value in each time stabilization subunit, summing up and averaging the screw speed deviation values ​​in all time stabilization subunits, and obtaining the screw speed stabilization deviation value of the time stabilization subunit;

[0098] The screw speed deviation abnormal value is obtained by performing a difference calculation between the unstable deviation value of the time unstable subunit and the screw speed stable deviation value of the time stable subunit, and the screw speed deviation ratio is obtained by performing a ratio calculation between the screw speed deviation abnormal value and the screw speed stable deviation value.

[0099] The subunits in which the screw speed deviation value in the time unstable subunit and the time stable subunit is greater than or equal to the screw speed deviation threshold are recorded as screw speed abnormal units, the number of screw speed abnormal units is obtained, and the ratio of the number of screw speed abnormal units to the total number of time units is calculated to obtain the screw speed abnormal unit number ratio;

[0100] Among them, the total number of time units is the sum of the number of time unstable subunits and time stable subunits;

[0101] The screw speed deviation ratio is multiplied by the screw speed abnormal unit number ratio to obtain the screw speed abnormality analysis value of the extruder.

[0102] The process of obtaining the abnormal analysis value of heating temperature is as follows:

[0103] Obtain the heating temperature value at the middle moment of each time unstable subunit, sum and average the heating temperature values ​​at the middle moment of all time unstable subunits, and obtain the unstable heating temperature of the time unstable subunit;

[0104] Obtain the heating temperature value at the middle moment of each time-stabilized subunit, sum and average the heating temperature values ​​at the middle moments of all time-stabilized subunits, and obtain the stabilization heating temperature of the time-stabilized subunit;

[0105] The unstable heating temperature of the unstable time subunit is calculated by difference with the stable heating temperature of the stable time subunit to obtain the extrusion temperature difference, and the extrusion temperature difference is calculated by ratio with the stable heating temperature to obtain the heating temperature deviation ratio;

[0106] The subunits whose heating temperature value at the intermediate moment between the time unstable subunit and the time stable subunit is greater than or equal to the preset heating temperature threshold are recorded as temperature abnormal subunits, the number of temperature abnormal subunits is obtained, and the ratio of the number of temperature abnormal subunits to the total number of time units is calculated to obtain the temperature abnormal unit number ratio;

[0107] The heating temperature deviation ratio is multiplied by the temperature anomaly unit number ratio to obtain the heating temperature anomaly analysis value.

[0108] The screw speed abnormal analysis value is recorded as Z Z ;

[0109] The heating temperature abnormality analysis value is recorded as Z W ;

[0110] The screw speed abnormal analysis value Z Z Heating temperature abnormal analysis value Z W Processing, through the formula Calculate the extrusion state behavior value Z of the extruder within the preset cycle i , where Z P It is the abnormal analysis value of extrusion speed;

[0111] Among them, the process of obtaining the extrusion speed abnormal analysis value is:

[0112] The unstable duration is obtained by summing up the time periods corresponding to the unstable region of the extrusion speed;

[0113] The time period corresponding to the extrusion speed stabilization area is summed to obtain the stabilization time;

[0114] Calculate the ratio of the unstable duration to the combined stable duration to obtain the proportion of the unstable duration;

[0115] The maximum value and the minimum value of the extrusion speed in the unstable region of the extrusion speed are obtained, and the ratio of the maximum value of the extrusion speed in the unstable region of the extrusion speed to the standard value of the extrusion speed is calculated to obtain a first extrusion speed deviation ratio;

[0116] The minimum value of the extrusion speed in the unstable extrusion speed region is calculated by ratio with the standard value of the extrusion speed to obtain a second extrusion speed deviation ratio;

[0117] The first extrusion speed deviation ratio and the second extrusion speed deviation ratio are summed and averaged to obtain the extrusion speed deviation ratio;

[0118] Among them, the standard value of extrusion speed is the middle value of the rated range of extrusion speed;

[0119] The abnormal extrusion speed analysis value is obtained by multiplying the unstable time ratio and the extrusion speed deviation ratio, and is recorded as Z P .

[0120] Example 2

[0121] Based on the extrusion state behavior value of the extruder within the preset cycle, the operating state of the extruder is divided, and the state division process is as follows:

[0122] See also Figure 2 , the preset limit value of the extrusion state behavior value is Z i 1 and Z i 2, where Z i 1<Z i 2;

[0123] Among them, the limit value Z of the extrusion state behavior value i 1 and Z i 2 is an empirical value, obtained based on experience:

[0124] In the actual process of obtaining, there are many groups of screw speed abnormal analysis values, heating temperature abnormal analysis values ​​and extrusion speed abnormal analysis values. Many groups of screw speed abnormal analysis values, heating temperature abnormal analysis values ​​and extrusion speed abnormal analysis values ​​are processed to obtain the corresponding groups of extrusion state behavior values. The staff identifies the extruder operation state level according to so many groups of extrusion state behavior values, thereby obtaining a corresponding relationship between an extrusion state behavior value and an extruder operation state level, and then derives and divides the extrusion state behavior value threshold according to the extruder operation state of the extrusion state behavior value, thereby obtaining the limit value Z of the extrusion state behavior value. i 1 and Z i 2. By comparing the limit values ​​of the extrusion state behavior values, the identification of the extruder operation state level corresponding to the extrusion state behavior value is completed;

[0125] When Z i <Z i When 1, it means that the extruder is in excellent operating condition overall, and a signal of excellent extruder operating condition is obtained;

[0126] When Z i 1≤Z i <Z i 2, it means that the extruder is in good operating condition overall, and a signal that the extruder is in good operating condition is obtained;

[0127] When Z i ≥Z i 2, it means that the overall operation state of the extruder is poor, and a signal of poor extruder operation state is obtained;

[0128] By dividing the operating status of the extruder into different levels, it is convenient for the staff to accurately control the operating status of the extruder and realize the real-time and timely maintenance of the extruder during operation.

[0129] Example 3

[0130] Based on the difference signal of the operation state of the extruder, the extruder is modularized to obtain the operation area units after the extruder is divided, and different operation area units are processed;

[0131] Wherein, the operating area unit of the extruder includes but is not limited to a feeding zone, a heating zone, an extrusion zone and a cooling zone;

[0132] Specific:

[0133] Obtain the target components to be monitored in each operating area unit, and obtain the monitoring items to be monitored for each target component within a preset period;

[0134] Among them, the monitoring items required to be monitored for the target component include but are not limited to one or more;

[0135] Acquire the real-time monitoring data of the corresponding monitoring items of the target component of the operation area unit during operation, and retrieve the preset data requirements of the corresponding monitoring items. If the real-time monitoring data of the monitoring items of the target component are outside the preset data requirements of the monitoring items and exceed the preset time, the target component is recorded as a non-qualified component;

[0136] Obtaining the number of non-conforming components and the number of target components in the operating area unit, calculating the ratio of the number of non-conforming components to the number of target components, and obtaining the non-conforming ratio of the area unit components;

[0137] Obtain the continuous unstable time corresponding to the real-time monitoring data of the same monitoring item in the non-qualified component being outside the preset data requirements of the monitoring item, calculate the ratio of the continuous unstable time to the total operation time of the non-qualified component in the preset period, and obtain the continuous abnormal time ratio;

[0138] Exemplary:

[0139] When the monitoring item is the real-time temperature of the extruder, the time period when the real-time temperature of the extruder is greater than the real-time temperature threshold is recorded as the abnormal time period, and all abnormal time periods are summed to obtain the continuous unstable time (temperature). The continuous unstable time is calculated by the ratio of the total operation time of the non-qualified parts in the preset cycle to obtain the continuous abnormal time ratio (temperature), which represents the proportion of the total continuous time of the temperature abnormality;

[0140] Obtain the maximum monitoring deviation value of the real-time monitoring data of the same monitoring item of the non-qualified component compared with the preset data of the corresponding monitoring item, and calculate the product of the monitoring deviation value and the corresponding preset deviation coefficient to obtain the monitoring deviation base;

[0141] Among them, the values ​​of the preset deviation coefficients are all greater than zero, and are pre-entered by the staff and stored in the processor. The larger the value of the preset deviation coefficient, the greater the impact of the deviation of the corresponding monitoring item on the abnormal operation of the non-qualified component;

[0142] Exemplary:

[0143] When the monitoring item is the real-time temperature of the extruder, the maximum value of the real-time temperature of the extruder during operation is calculated by the difference with the real-time temperature threshold to obtain the monitoring deviation value (temperature), and the monitoring deviation value is multiplied by the corresponding preset deviation coefficient to obtain the monitoring deviation base (temperature), which represents the proportion of the temperature deviation degree;

[0144] The non-fixed monitoring value F of each monitoring item of the non-qualified component is obtained by multiplying the continuous abnormal time ratio of the same monitoring item of the non-qualified component by the monitoring deviation base. k , k represents the number of monitoring items of non-conforming components, k=1, 2, ..., n;

[0145] The non-fixed monitoring values ​​of the monitoring items in the same non-conforming component are processed by the formula Calculate the monitoring interference value Fa of the non-qualified component j , where j is the number of non-conforming parts, j=1, 2, ..., m;

[0146] The monitoring interference values ​​of all non-qualified components in the same operating area unit are processed by the formula Calculate the monitoring change value F of the operating area unit b , where F qis the non-conforming ratio of regional unit components;

[0147] It should be noted that the principles for modularizing the extruder include but are not limited to division according to functional areas and physical areas.

[0148] Example 4

[0149] Based on the monitoring abnormal value of each operating area unit, any operating area unit is recorded as a target area unit, and the operating area unit directly adjacent to the target area unit is recorded as a side area unit;

[0150] The monitoring abnormal value of all adjacent regional units is summed and averaged to obtain the adjacent monitoring abnormal value;

[0151] Process the monitoring abnormal value of the target area unit and the monitoring abnormal value of the adjacent unit to obtain the regional linkage monitoring abnormal value;

[0152] Example:

[0153] The monitoring change value of the target area unit is recorded as Fb1;

[0154] The side monitoring abnormal value corresponding to the side area unit is recorded as Fb2;

[0155] By Formula FB i =Fb1*d1+Fb2*d2 to get the regional linkage monitoring abnormal value FB i , where d1 and d2 represent the weights of the monitoring change value of the target area unit and the monitoring change value of the adjacent unit, d1+d2=1, and 0<d2<d1;

[0156] Obtain the rated usage time ratio of each target component in the target area unit, and obtain the time difference between the start time of use of the target component and the current time, and record it as the used time of the component;

[0157] Calculate the ratio of the used time of the component to the rated time of the target component to obtain the rated use time ratio of the target component;

[0158] Obtaining the maximum value of the rated use time ratio of the target component in the target area unit, recorded as E1;

[0159] Among them, the rated duration of the target component is an empirical value, which is set by the staff based on experience or by the manufacturer of the target component;

[0160] comparing the rated usage time ratio of the target component with a rated usage time ratio threshold of the target component;

[0161] If the rated usage time ratio of the target component is greater than or equal to the rated usage time ratio threshold of the target component, the corresponding target component is recorded as a time analysis component;

[0162] Obtain the number of time-analysis components, calculate the ratio of the number of time-analysis components to the number of target components, and obtain the time-analysis ratio of the target component, which is recorded as E2;

[0163] By formula Calculate the built-in analytical value E of the target area unit i , where E3 is the number of target components in the target area unit;

[0164] The regional linkage monitoring abnormal value FB i The built-in analytical value E of the target area unit i Processing, through the formula The monitoring reminder value FE of the operating area unit is calculated, where: is the preset scale factor, Greater than 0;

[0165] in, The acquisition process is:

[0166] There are m groups of historical data, each of which includes the regional linkage monitoring abnormal value FB i , the built-in analytical value E of the target area unit i and the monitoring reminder value FE of the operating area unit;

[0167] Use a linear model to fit m groups of historical data, bring the prepared historical data into the selected fitting model for fitting, and get the mean of the fitting coefficients as the preset proportional coefficient .

[0168] Each operating area unit in the extruder is arranged in descending order according to the monitoring reminder value, and the operating area unit corresponding to the largest monitoring reminder value is recorded as the priority maintenance area, thereby completing the intelligent monitoring and identification of maintenance during the operation of the extruder.

[0169] Example 5

[0170] See also Figure 3 As shown, the present invention is a blockchain-based extruder operation status monitoring system, comprising:

[0171] A state analysis module, which is used to obtain the extrusion state data of the extruder within a preset period, obtain the extrusion state behavior value of the extruder within the preset period based on the extrusion state data, and upload the extrusion state behavior value to the cloud management and control platform;

[0172] The behavior classification module receives the extrusion state behavior value transmitted by the cloud control platform, and the behavior classification module classifies the extruder state according to the extrusion state behavior value to obtain the extruder state signal, and uploads the extruder state signal to the cloud control platform;

[0173] Among them, the extruder status signal includes an excellent extruder operation status signal, a good extruder operation status signal and a poor extruder operation status signal;

[0174] The regional identification module receives the extruder status information transmitted by the cloud management and control platform. Based on the difference signal of the extruder operation status, the regional identification module divides the extruder into modular units to obtain the divided operation area units of the extruder, obtains the monitoring abnormal value of each operation area unit, and uploads it to the cloud management and control platform;

[0175] The linkage positioning module receives the monitoring abnormality value transmitted by the cloud management and control platform. The linkage positioning module performs linkage processing on the operating area unit based on the monitoring abnormality value of the operating area unit, obtains the monitoring reminder value of the operating area unit, and completes the directional monitoring of the extruder operating area unit based on the monitoring reminder value.

[0176] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for monitoring the operation of an LED silicone light strip extruder, characterized in that: The following steps are involved: Acquire extrusion state data of the extruder within a preset period, and obtain an extrusion state behavior value of the extruder within the preset period based on the extrusion state data; The extrusion state data of the extruder includes the abnormal analysis value of the screw speed and the abnormal analysis value of the heating temperature; The extruder state is divided according to the extrusion state behavior value to obtain the extruder state signal; Among them, the extruder status signal includes an excellent extruder operation status signal, a good extruder operation status signal and a poor extruder operation status signal; Based on the difference signal of the extruder operation state, the extruder is modularized to obtain the divided operation area units of the extruder, and the monitoring abnormal value of each operation area unit is obtained; Based on the monitoring abnormal value of the operating area unit, the operating area unit is linked to obtain the monitoring reminder value of the operating area unit, and the directional monitoring of the extruder operating area unit is completed based on the monitoring reminder value; Obtain the target components to be monitored in each operating area unit and the monitoring items to be monitored for each target component; If the real-time monitoring data of the monitoring items of the target component is outside the preset data requirements of the monitoring items and exceeds the preset time, the target component will be recorded as a non-qualified component; Obtain the continuous abnormal time ratio and monitoring deviation base number of the same monitoring item in the non-conforming component; The non-fixed monitoring value F of each monitoring item of the non-qualified component is obtained by multiplying the continuous abnormal time ratio of the same monitoring item of the non-qualified component by the monitoring deviation base. k , k represents the number of monitoring items of non-conforming components, k=1, 2, ..., n; The non-fixed monitoring values ​​of the monitoring items in the same non-conforming component are processed by the formula Calculate the monitoring interference value Fa of the non-qualified component j , where j is the number of non-conforming parts, j=1, 2, ..., m; The monitoring interference values ​​of all non-qualified components in the same operating area unit are processed by the formula Calculate the monitoring change value F of the operating area unit b , where F q is the non-conforming ratio of regional unit components; The non-conforming ratio of regional unit components is the ratio of the number of non-conforming components to the number of target components; Any operating area unit is recorded as a target area unit, and the operating area unit directly adjacent to the target area unit is recorded as a side area unit; The monitoring abnormal value of all adjacent regional units is summed and averaged to obtain the adjacent monitoring abnormal value; The monitoring change value of the target area unit is processed with the monitoring change value of the adjacent unit to obtain the regional linkage monitoring change value, which is recorded as FB i ; Get the built-in analytical value of the target area unit and record it as E i ; The regional linkage monitoring abnormal value FB i The built-in analytical value E of the target area unit i Processing, through the formula Calculate and obtain the monitoring reminder value FE of the operating area unit; Arrange each operating area unit in the extruder in descending order according to the monitoring reminder value, and record the operating area unit corresponding to the largest monitoring reminder value as the priority maintenance area; The process of obtaining the built-in analytical value of the target area unit is: Obtain the rated use time ratio of each target component in the target area unit, and record the maximum value of the rated use time ratio of the target component in the target area unit as E1; comparing the rated usage time ratio of the target component with a rated usage time ratio threshold of the target component; If the rated usage time ratio of the target component is greater than or equal to the rated usage time ratio threshold of the target component, the corresponding target component is recorded as a time analysis component; Calculate the ratio of the number of time-analyzed components to the number of target components to obtain the time-analyzed ratio of the target component, which is recorded as E2; By formula Calculate the built-in analytical value E of the target area unit i , where E3 is the number of target components in the target area unit.

2. The operation monitoring method based on the LED silicone light strip extruder according to claim 1 is characterized in that: Processing the extrusion state data of the extruder including the abnormal analysis value of the screw speed and the abnormal analysis value of the heating temperature; The screw speed abnormal analysis value is recorded as Z Z ; The heating temperature abnormality analysis value is recorded as Z W ; The screw speed abnormal analysis value Z Z Heating temperature abnormal analysis value Z W Processing, through the formula Calculate the extrusion state behavior value Z of the extruder within the preset cycle i , where Z P It is the abnormal analysis value of extrusion speed.

3. The operation monitoring method based on the LED silicone light strip extruder according to claim 2 is characterized in that: The limit value of the preset extrusion state behavior value is Z i 1 and Z i 2, where Z i 1<Z i 2; When Z i <Z i When 1, it means that the extruder is in excellent operating condition overall, and a signal of excellent extruder operating condition is obtained; When Z i 1≤Z i <Z i 2, it means that the extruder is in good operating condition overall, and a signal that the extruder is in good operating condition is obtained; When Z i ≥Z i 2, it indicates that the overall operating state of the extruder is poor, and a poor operating state signal of the extruder is obtained.

4. The operation monitoring method based on the LED silicone light strip extruder according to claim 2 is characterized in that: The process of obtaining the abnormal analysis value of extrusion speed is as follows: In the preset period, an extrusion speed curve of time-extrusion speed is constructed; According to the rated range of the extrusion speed during the operation of the extruder, the extrusion speed curve is processed to obtain an extrusion speed unstable area and an extrusion speed stable area; The time period of the extrusion velocity unstable region is divided into a number of time unstable subunits; Divide the time period of the extrusion speed stabilization region into a number of time stabilization subunits; The ratio of the time period corresponding to the unstable region of the extrusion speed to the time period corresponding to the stable region of the extrusion speed is calculated to obtain the unstable time ratio; The maximum extrusion speed and the minimum extrusion speed are calculated by difference to obtain the vibration frequency deviation value, and the vibration frequency deviation value is calculated by ratio with the extrusion speed standard value to obtain the extrusion speed deviation ratio; The abnormal analysis value of the extrusion speed is obtained by multiplying the proportion of the unstable time length with the extrusion speed deviation ratio.

5. The operation monitoring method based on the LED silicone light strip extruder according to claim 4 is characterized in that: The process of obtaining the abnormal analysis value of screw speed is as follows: The unstable deviation value of the screw speed of the unstable time subunit and the stable deviation value of the screw speed of the stable time subunit are processed to obtain the screw speed deviation ratio; The subunits in the time unstable subunit and the time stable subunit whose screw speed deviation value is greater than or equal to the screw speed deviation threshold are recorded as screw speed abnormal units; The ratio of the number of screw speed abnormal units to the total number of time units is calculated to obtain the screw speed abnormal unit number ratio; The screw speed deviation ratio is multiplied by the screw speed abnormal unit number ratio to obtain the screw speed abnormal analysis value; The process of obtaining the abnormal analysis value of heating temperature is as follows: Obtain the heating temperature value at the middle moment of each time unstable subunit and process it to obtain the heating temperature deviation ratio; The subunit whose heating temperature value at the intermediate moment between the time unstable subunit and the time stable subunit is greater than or equal to the temperature threshold is recorded as a temperature abnormal subunit; The ratio of the number of temperature anomaly sub-units to the total number of time units is calculated to obtain the temperature anomaly unit ratio; The heating temperature deviation ratio is multiplied by the temperature anomaly unit number ratio to obtain the heating temperature anomaly analysis value.

6. An operation monitoring system based on an LED silicone light strip extruder, used to implement the operation monitoring method based on an LED silicone light strip extruder as described in any one of claims 1 to 5, characterized in that: include: A state analysis module, which is used to obtain the extrusion state data of the extruder within a preset period, obtain the extrusion state behavior value of the extruder within the preset period based on the extrusion state data, and upload the extrusion state behavior value to the cloud management and control platform; The behavior classification module receives the extrusion state behavior value transmitted by the cloud control platform, and the behavior classification module classifies the extruder state according to the extrusion state behavior value to obtain the extruder state signal, and uploads the extruder state signal to the cloud control platform; Among them, the extruder status signal includes an excellent extruder operation status signal, a good extruder operation status signal and a poor extruder operation status signal; The regional identification module receives the extruder status information transmitted by the cloud management and control platform. Based on the difference signal of the extruder operation status, the regional identification module divides the extruder into modular units to obtain the divided operation area units of the extruder, obtains the monitoring abnormal value of each operation area unit, and uploads it to the cloud management and control platform; The linkage positioning module receives the monitoring abnormality value transmitted by the cloud management and control platform. The linkage positioning module performs linkage processing on the operating area unit based on the monitoring abnormality value of the operating area unit, obtains the monitoring reminder value of the operating area unit, and completes the directional monitoring of the extruder operating area unit based on the monitoring reminder value.

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