Textile material blending method and system based on Internet of Things

Abstract

The invention is suitable for the technical field of material transportation, and particularly relates to a textile material allocation method and system based on the Internet of Things, and the method comprises the steps: constructing a production line local area network; obtaining material margin data from the Internet of Things equipment according to a preset time interval, and constructing a material margin coordinate based on the material margin data; function fitting is carried out based on the material allowance coordinates corresponding to the Internet of Things devices, a material allowance prediction function is obtained, and a material distribution scheme in a preset time period is generated; and carrying out material consumption abnormity diagnosis on the production line based on the real-time material allowance prediction function, judging whether the production line has material consumption abnormity or not, and generating an abnormity judgment result. According to the invention, material scheduling of each production line is realized based on the monitoring result, and whether the production line is abnormal or not is diagnosed according to the material consumption condition of each production line, so that the stability of material transportation is ensured, shutdown caused by abnormal material transportation is avoided, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of material transportation, and in particular relates to a textile material allocation method and system based on the Internet of Things. Background Art

[0002] Textile material allocation refers to the precise planning and management of the procurement, inventory, transportation and use of raw materials such as yarn, dyes and accessories during the textile production process to ensure a smooth and efficient production process. With the help of modern information technology, especially the application of the Internet of Things (IoT), real-time monitoring and automated management of materials from suppliers to production lines and even finished product warehouses can be achieved, optimizing resource allocation, reducing waste, lowering costs, and increasing the speed and flexibility of responding to market changes, thereby enhancing the competitiveness of enterprises.

[0003] In the current textile material transportation process, the material consumption rates on different production lines are different. The material transportation cannot automatically control the material delivery volume to each production line, which easily leads to problems of material accumulation or material shortage. Summary of the invention

[0004] The purpose of the present invention is to provide a textile material allocation method based on the Internet of Things, aiming to solve the problem that material consumption rates on different production lines are different, and the material transportation cannot automatically control the material delivery amount to each production line, which easily leads to material accumulation or material shortage.

[0005] The present invention is implemented in this way: a textile material mixing method based on the Internet of Things, the method comprising:

[0006] Constructing a production line local area network, wherein the production line local area network includes multiple IoT devices, each of which is used to monitor the material balance on the corresponding production line;

[0007] Obtain material remainder data from the IoT device at preset time intervals, and construct material remainder coordinates based on the material remainder data;

[0008] Function fitting is performed based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and a material allocation plan within a preset time period is generated based on the material remainder prediction function;

[0009] Based on the real-time material surplus prediction function, the production line is diagnosed with abnormal material consumption, and it is determined whether there is abnormal material consumption in the production line, and an abnormality determination result is generated.

[0010] Preferably, the step of acquiring material remainder data from the IoT device at a preset time interval and constructing material remainder coordinates based on the material remainder data includes:

[0011] Poll each IoT device at a preset time interval to obtain the material balance data corresponding to each IoT device;

[0012] Read material balance data and dynamically adjust the time interval for polling IoT devices based on the material balance data;

[0013] The time information and the corresponding material remainder in the material remainder data are extracted to construct a material remainder coordinate, wherein the abscissa of the material remainder coordinate is the time of data collection, and the ordinate is the current material remainder.

[0014] Preferably, the step of performing function fitting based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and generating a material allocation plan within a preset time period based on the material remainder prediction function specifically includes:

[0015] Retrieve all material remainder coordinates belonging to the same IoT device, import the material remainder coordinates into the function fitting software, and obtain multiple material fitting functions;

[0016] The accuracy of the material remainder coordinates and the material fitting function is checked by the least square method, and the function with the highest fitting accuracy is selected as the material remainder prediction function;

[0017] Import the preset time value into the material remainder prediction function to obtain the material remainder prediction value, and generate the material allocation plan based on the material remainder prediction value.

[0018] Preferably, the step of diagnosing abnormal material consumption of the production line based on the real-time material surplus prediction function, determining whether the production line has abnormal material consumption, and generating an abnormality determination result specifically includes:

[0019] Construct a two-dimensional coordinate system and construct a material remainder assessment curve based on the material remainder prediction function;

[0020] Each time a set of material surplus assessment curves is selected, and the material consumption range interval is constructed based on all other material surplus assessment curves;

[0021] The length of the selected material surplus assessment curve within the material consumption range and the total length of the material surplus assessment curve are counted, and an abnormality determination result is generated based on the ratio of the curve lengths.

[0022] Preferably, the ratio of the curve length is the ratio of the curve length to the total length of the material balance assessment curve. When the ratio is higher than a preset value, it is determined that abnormal material consumption exists.

[0023] Another object of the present invention is to provide a textile material preparation system based on the Internet of Things, the system comprising:

[0024] A local area network construction module is used to construct a production line local area network, wherein the production line local area network includes multiple Internet of Things devices, each of which is used to monitor the material balance on the corresponding production line;

[0025] A surplus coordinate construction module is used to obtain material surplus data from the IoT device at a preset time interval and construct material surplus coordinates based on the material surplus data;

[0026] An allocation plan building module is used to perform function fitting based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and generate an allocation plan for materials within a preset time period based on the material remainder prediction function;

[0027] The abnormality determination module is used to diagnose the abnormal material consumption of the production line based on the real-time material surplus prediction function, determine whether there is abnormal material consumption on the production line, and generate abnormality determination results.

[0028] Preferably, the residual coordinate construction module includes:

[0029] A data acquisition unit, used to poll each IoT device at a preset time interval to obtain material balance data corresponding to each IoT device;

[0030] A time interval adjustment unit, used for reading material balance data and dynamically adjusting the time interval for polling the IoT device based on the material balance data;

[0031] The coordinate construction unit is used to extract the time information and the corresponding material remainder in the material remainder data, and construct the material remainder coordinates, wherein the horizontal coordinate of the material remainder coordinates is the time of data collection, and the vertical coordinate is the current material remainder.

[0032] Preferably, the allocation scheme building module includes:

[0033] The function fitting unit is used to retrieve the material remainder coordinates of all materials belonging to the same IoT device, import the material remainder coordinates into the function fitting software, and obtain multiple material fitting functions;

[0034] The accuracy checking unit is used to check the accuracy of the material remainder coordinates and the material fitting function by the least square method, and select the function with the highest fitting accuracy as the material remainder prediction function;

[0035] The allocation plan generating unit is used to import a preset time value into the material remainder prediction function, obtain a material remainder prediction value, and generate a material allocation plan according to the material remainder prediction value.

[0036] Preferably, the abnormality determination module includes:

[0037] A curve construction unit, used to construct a two-dimensional coordinate system and to construct a material remainder assessment curve based on a material remainder prediction function;

[0038] A consumption range construction unit is used to select a set of material surplus assessment curves each time and construct a material consumption range interval based on all other material surplus assessment curves;

[0039] The abnormality determination unit is used to count the curve length of the selected material surplus assessment curve within the material consumption range and the total length of the material surplus assessment curve, and generate an abnormality determination result based on the ratio of the curve length.

[0040] Preferably, the ratio of the curve length is the ratio of the curve length to the total length of the material balance assessment curve. When the ratio is higher than a preset value, it is determined that abnormal material consumption exists.

[0041] The invention provides a textile material allocation method based on the Internet of Things. The method monitors the material surplus on each production line through the Internet of Things equipment, realizes material scheduling for each production line based on the monitoring result, and diagnoses whether there is any abnormality in the production line according to the material consumption of each production line, thereby ensuring the stability of material transportation, avoiding downtime caused by abnormal material transportation, and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of a textile material mixing method based on the Internet of Things provided by an embodiment of the present invention;

[0043] Figure 2 A flowchart of the steps of obtaining material remainder data from an Internet of Things device at a preset time interval and constructing material remainder coordinates based on the material remainder data provided by an embodiment of the present invention;

[0044] Figure 3 A flowchart of the steps of performing function fitting based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and generating a material allocation plan within a preset time period based on the material remainder prediction function, provided in an embodiment of the present invention;

[0045] Figure 4 A flowchart of the steps of diagnosing abnormal material consumption of a production line based on a real-time material surplus prediction function, determining whether the production line has abnormal material consumption, and generating abnormality determination results provided by an embodiment of the present invention;

[0046] Figure 5 An architecture diagram of a textile material allocation system based on the Internet of Things provided by an embodiment of the present invention;

[0047] Figure 6 An architecture diagram of a residual coordinate construction module provided by an embodiment of the present invention;

[0048] Figure 7 An architecture diagram of a distribution scheme building module provided by an embodiment of the present invention;

[0049] Figure 8 An architectural diagram of an abnormality determination module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] like Figure 1 As shown, it is a flow chart of a textile material mixing method based on the Internet of Things provided by an embodiment of the present invention, and the method includes:

[0052] S100, constructing a production line local area network, wherein the production line local area network includes multiple Internet of Things devices, and each Internet of Things device is used to monitor the material balance on the corresponding production line.

[0053] In this step, a production line local area network is constructed, and IoT devices are set up on each production line. The IoT device can be a weighing device, which is used to measure the weight of the material. If the material is liquid, the IoT device can be a liquid level sensor. The IoT devices are connected through a wireless network, and data connections are established between IoT devices through a gateway. Specifically, data connections can also be established through wireless communication methods such as Bluetooth. The production line local area network contains a data processing center, and all data collected by the IoT devices are uploaded to the data processing center.

[0054] S200, obtaining material remainder data from the IoT device at a preset time interval, and constructing material remainder coordinates based on the material remainder data.

[0055] In this step, material remainder data is obtained from the IoT device at preset time intervals, which may be one minute. Specifically, the preset time interval may be determined based on the consumption rate of the production line. Each IoT device detects the material on the production line to obtain material remainder data. The IoT device sends the material remainder data to the data processing center and constructs material remainder coordinates. The horizontal axis of the material remainder coordinates is the time value of material remainder collection, and the vertical axis is the collected material remainder.

[0056] S300, performing function fitting based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and generating a material allocation plan within a preset time period based on the material remainder prediction function.

[0057] In this step, function fitting is performed based on the material remainder coordinates corresponding to each IoT device. When collecting data, data collection is performed once at a certain interval. Therefore, each IoT device will correspond to multiple material remainder coordinates. The material remainder coordinates are imported into the function simulation software to obtain the material remainder prediction function. The material remainder prediction function can be used to predict the material consumption on each production line in a short period of time, and the material allocation plan can be generated according to the material remainder of each production line in the future, thereby ensuring the stability of the material remainder of each production line.

[0058] S400, diagnose abnormal material consumption on the production line based on the real-time material surplus prediction function, determine whether the production line has abnormal material consumption, and generate an abnormality determination result.

[0059] In this step, the material consumption anomaly diagnosis of the production line is performed based on the real-time material balance prediction function. During the production process of the production line, since the equipment may have anomalies, if the production line is not detected, it can only shut down when an abnormality or failure occurs in the production line, affecting the production efficiency of the equipment. In the present invention, the corresponding curve is constructed by the material balance prediction function, and the material consumption level of the production line is evaluated according to the positional relationship between the curves to generate an abnormality judgment result. In the abnormality judgment result, it is determined whether there is an abnormality in the current production line. If there is an abnormality, the corresponding management personnel are notified to handle it, thereby avoiding shutdown maintenance and affecting production efficiency.

[0060] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of obtaining material remainder data from the IoT device at a preset time interval and constructing material remainder coordinates based on the material remainder data includes:

[0061] S201, polling each IoT device at a preset time interval to obtain material balance data corresponding to each IoT device.

[0062] In this step, each IoT device is polled at a preset time interval, which may be one minute, i.e., data is collected once every minute. The preset time interval is determined based on the nature of the production line and historical material consumption. When collecting data and transmitting material balance data, the material balance data is encrypted for transmission.

[0063] S202, reading material remaining data, and dynamically adjusting the time interval for polling the IoT device based on the material remaining data.

[0064] In this step, the material balance data is read. When the material balance data is encrypted, the data processing center first decrypts the material balance data, and determines the corresponding time interval based on the current material balance based on the preset material balance-time interval mapping table. When the material balance is less, the time interval for polling the IoT device is shorter, which is used to grasp the material balance of the IoT device in real time.

[0065] S203, extracting the time information and the corresponding material remainder in the material remainder data, and constructing a material remainder coordinate, wherein the abscissa of the material remainder coordinate is the time of data collection, and the ordinate is the current material remainder.

[0066] In this step, the time information in the material remainder data and the corresponding material remainder are extracted to construct the material remainder coordinates, and the material remainder coordinates are used to record the material remainder data, wherein the time of collecting the material remainder is used as the horizontal coordinate, and the collected material remainder is used as the vertical coordinate.

[0067] like Figure 3 As shown, as a preferred embodiment of the present invention, the steps of performing function fitting based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and generating a material allocation plan within a preset time period based on the material remainder prediction function specifically include:

[0068] S301, retrieve all material remainder coordinates belonging to the same IoT device, import the material remainder coordinates into function fitting software, and obtain multiple material fitting functions.

[0069] In this step, all material remaining coordinates belonging to the same IoT device are retrieved, such as IoT device A. Starting from time B, data collection is performed every one minute to obtain five sets of material remaining coordinates (T i , M i ), import all material remainder coordinates into function fitting software, which may be matlab, to obtain material fitting function. There are multiple types of material fitting functions.

[0070] S302, performing accuracy verification on the material remainder coordinates and the material fitting function by using the least square method, and selecting the function with the highest fitting accuracy as the material remainder prediction function.

[0071] In this step, the accuracy of the material remainder coordinates and the material fitting function is checked by the least squares method. The material fitting function is marked in a two-dimensional coordinate system, and the coordinates of each material remainder are marked in a two-dimensional coordinate system. The sum of the squares of the vertical distances from all points to the fitting curve is calculated, and the material fitting function with the smallest sum of squares is selected as the material remainder prediction function.

[0072] S303, importing a preset time value into the material remainder prediction function to obtain a material remainder prediction value, and generating a material allocation plan according to the material remainder prediction value.

[0073] In this step, the preset time value is imported into the material remainder prediction function. Each time value imported into the material remainder prediction function can generate a material remainder prediction value. For example, for five IoT devices, at the future time H, the material remainder prediction values ​​of each IoT device are G1, G2, G3, G4 and G5 respectively. The material consumption rate of each production line during the monitoring time is calculated, and the material exhaustion time is calculated based on the material remainder prediction value and the material consumption rate, which are t1, t2, t3, t4 and t5 respectively. Then, in the material allocation plan, the proportion of materials allocated to each production line is (1-t1): (1-t2): (1-t3): (1-t4): (1-t5).

[0074] like Figure 4 As shown, as a preferred embodiment of the present invention, the steps of diagnosing abnormal material consumption of the production line based on the real-time material surplus prediction function, determining whether the production line has abnormal material consumption, and generating abnormality determination results specifically include:

[0075] S401, constructing a two-dimensional coordinate system, and constructing a material remainder assessment curve based on a material remainder prediction function.

[0076] In this step, a two-dimensional coordinate system is constructed. On a production line, generally a single production line has an abnormality. In order to identify the abnormal production line, a corresponding material change curve is generated in the two-dimensional coordinate system according to the material remainder prediction function, which is the material remainder evaluation curve. If there are multiple groups of IoT devices, there will be multiple groups of material remainder evaluation curves.

[0077] S402, each time a group of material surplus assessment curves is selected, and a material consumption range interval is constructed based on all other material surplus assessment curves.

[0078] In this step, a group of material remainder evaluation curves is selected each time. For example, if there are five IoT devices, one IoT device is selected each time, and the corresponding material remainder evaluation function is selected. According to the maximum and minimum values ​​of the remaining four material remainder evaluation curves at each time value, the maximum and minimum values ​​of material consumption within the preset time period are determined based on the above maximum and minimum values, thereby obtaining a group of maximum edge lines and minimum edge lines in the two-dimensional coordinate system, and the area between the maximum edge line and the minimum edge line is the material consumption range.

[0079] S403, counting the length of the selected material surplus assessment curve within the material consumption range and the total length of the material surplus assessment curve, and generating an abnormality determination result based on the ratio of the curve lengths.

[0080] In this step, the curve length of the selected material surplus assessment curve within the material consumption range and the total length of the material surplus assessment curve are counted. If the selected material surplus assessment curve is within the material consumption range, it means that the material surplus of the production line in this time period is likely to be normal. The longer the line length within the material consumption range, the lower the probability of abnormality of the production line. The ratio of the curve length is the ratio of the curve length to the total length of the material surplus assessment curve. When the ratio is higher than the preset value, it is determined that material consumption abnormality exists.

[0081] like Figure 5 As shown, a textile material allocation system based on the Internet of Things is provided in an embodiment of the present invention, and the system includes:

[0082] The local area network construction module 100 is used to construct a production line local area network, and the production line local area network includes multiple Internet of Things devices, each of which is used to monitor the material balance on the corresponding production line.

[0083] In this system, the local area network construction module 100 constructs a production line local area network, and an Internet of Things device is set on each production line. The Internet of Things device can be a weighing device, which is used to measure the weight of the material. If the material is liquid, the Internet of Things device can be a liquid level sensor. The Internet of Things devices are connected through a wireless network, and data connections are established between the Internet of Things devices through a gateway. Specifically, data connections can also be established through wireless communication methods such as Bluetooth. The production line local area network contains a data processing center, and all data collected by the Internet of Things devices are uploaded to the data processing center.

[0084] The remaining quantity coordinate construction module 200 is used to obtain material remaining quantity data from the Internet of Things device at a preset time interval and construct material remaining quantity coordinates based on the material remaining quantity data.

[0085] In this system, the remainder coordinate construction module 200 obtains material remainder data from the IoT device at a preset time interval, and the time interval can be one minute. Specifically, the preset time interval can be determined according to the consumption speed of the production line. Each IoT device detects the material on the production line to obtain the material remainder data. The IoT device sends the material remainder data to the data processing center and constructs the material remainder coordinates. The horizontal coordinate of the material remainder coordinate is the time value of the material remainder collection, and the vertical coordinate is the collected material remainder.

[0086] The allocation plan construction module 300 is used to perform function fitting based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and generate an allocation plan for materials within a preset time period based on the material remainder prediction function.

[0087] In this system, the allocation plan construction module 300 performs function fitting based on the material remainder coordinates corresponding to each IoT device. When collecting data, data collection is performed once at a certain interval. Therefore, each IoT device will correspond to multiple material remainder coordinates. The material remainder coordinates are imported into the function simulation software to obtain the material remainder prediction function. The material remainder prediction function can be used to predict the material consumption on each production line in a short period of time, and the material allocation plan is generated according to the material remainder on each production line in the future, thereby ensuring the stability of the material remainder of each production line.

[0088] The abnormality determination module 400 is used to diagnose abnormal material consumption of the production line based on the real-time material surplus prediction function, determine whether there is abnormal material consumption in the production line, and generate an abnormality determination result.

[0089] In this system, the abnormality judgment module 400 diagnoses the abnormal material consumption of the production line based on the real-time material balance prediction function. During the production process of the production line, since the equipment may have abnormalities, if the production line is not detected, it can only shut down when the production line has abnormalities or failures, affecting the production efficiency of the equipment. In the present invention, the corresponding curve is constructed through the material balance prediction function, and the material consumption level of the production line is evaluated according to the positional relationship between the curves to generate an abnormality judgment result. In the abnormality judgment result, it is determined whether there is an abnormality in the current production line. If there is an abnormality, the corresponding management personnel are notified to handle it, thereby avoiding shutdown maintenance and affecting production efficiency.

[0090] like Figure 6 As shown, as a preferred embodiment of the present invention, the residual coordinate construction module 200 includes:

[0091] The data acquisition unit 201 is used to poll each IoT device at a preset time interval to obtain the material balance data corresponding to each IoT device.

[0092] In this module, the data acquisition unit 201 polls each IoT device at a preset time interval. The preset time interval may be one minute, that is, data collection is performed once every minute. The preset time interval is determined according to the nature of the production line and the historical material consumption. When data collection is performed and when material balance data is transmitted, the material balance data is encrypted for transmission.

[0093] The time interval adjustment unit 202 is used to read the material remaining data and dynamically adjust the time interval for polling the Internet of Things devices based on the material remaining data.

[0094] In this module, the time interval adjustment unit 202 reads the material balance data. When the material balance data is encrypted, the data processing center first decrypts the material balance data, and determines the corresponding time interval based on the preset material balance-time interval mapping table and the current material balance. When the material balance is less, the time interval for polling the IoT device is shorter, so as to grasp the material balance of the IoT device in real time.

[0095] The coordinate construction unit 203 is used to extract the time information and the corresponding material remainder in the material remainder data, and construct the material remainder coordinates, wherein the abscissa of the material remainder coordinates is the time of data collection, and the ordinate is the current material remainder.

[0096] In this module, the coordinate construction unit 203 extracts the time information in the material remainder data and the corresponding material remainder to construct the material remainder coordinates, and the material remainder coordinates are used to record the material remainder data, wherein the time of collecting the material remainder is used as the horizontal coordinate, and the collected material remainder is used as the vertical coordinate.

[0097] like Figure 7 As shown, as a preferred embodiment of the present invention, the allocation scheme building module 300 includes:

[0098] The function fitting unit 301 is used to retrieve the material remainder coordinates of all materials belonging to the same IoT device, import the material remainder coordinates into the function fitting software, and obtain multiple material fitting functions.

[0099] In this module, the function fitting unit 301 retrieves all material remaining coordinates belonging to the same IoT device, such as IoT device A, and collects data once every one minute from time B to obtain five sets of material remaining coordinates (T i , M i ), import all material remainder coordinates into function fitting software, which may be matlab, to obtain material fitting function. There are multiple types of material fitting functions.

[0100] The accuracy checking unit 302 is used to perform accuracy checking on the material remainder coordinates and the material fitting function by using the least square method, and select the function with the highest fitting accuracy as the material remainder prediction function.

[0101] In this module, the accuracy verification unit 302 performs accuracy verification on the material remainder coordinates and the material fitting function by the least squares method, marks the material fitting function in a two-dimensional coordinate system, and marks each material remainder coordinate in a two-dimensional coordinate system, calculates the sum of the squares of the vertical distances from all points to the fitting curve, and selects a material fitting function with the smallest sum of squares as the material remainder prediction function.

[0102] The allocation plan generating unit 303 is used to import a preset time value into the material remainder prediction function, obtain a material remainder prediction value, and generate a material allocation plan according to the material remainder prediction value.

[0103] In this module, the allocation plan generation unit 303 imports a preset time value into the material remainder prediction function. Each time value imported into the material remainder prediction function can generate a material remainder prediction value. For example, for five IoT devices, at the future time H, the material remainder prediction values ​​of each IoT device are G1, G2, G3, G4 and G5 respectively, and the material consumption rate of each production line within the monitoring time is calculated. The material exhaustion time is calculated based on the material remainder prediction value and the material consumption rate, which are t1, t2, t3, t4 and t5 respectively. Then, in the material allocation plan, the ratio of materials allocated to each production line is (1-t1): (1-t2): (1-t3): (1-t4): (1-t5).

[0104] like Figure 8 As shown, as a preferred embodiment of the present invention, the abnormality determination module 400 includes:

[0105] The curve construction unit 401 is used to construct a two-dimensional coordinate system and to construct a material remainder assessment curve based on the material remainder prediction function.

[0106] In this module, the curve construction unit 401 constructs a two-dimensional coordinate system. On a production line, generally a single production line has an abnormality. In order to identify the abnormal production line, a corresponding material change curve is generated in the two-dimensional coordinate system according to the material remainder prediction function, which is the material remainder evaluation curve. If there are multiple groups of Internet of Things devices, there are corresponding multiple groups of material remainder evaluation curves.

[0107] The consumption range construction unit 402 is used to select a group of material surplus assessment curves each time and construct a material consumption range interval based on all other material surplus assessment curves.

[0108] In this module, the consumption range construction unit 402 selects a group of material remainder evaluation curves each time. If there are five IoT devices, one IoT device is selected each time, and the corresponding material remainder evaluation function is selected. According to the maximum and minimum values ​​of the remaining four material remainder evaluation curves at each time value, the maximum and minimum values ​​of material consumption within the preset time period are determined based on the above maximum and minimum values, thereby obtaining a group of maximum edge lines and minimum edge lines in the two-dimensional coordinate system, and the material consumption range interval is between the maximum edge line and the minimum edge line.

[0109] The abnormality determination unit 403 is used to count the curve length of the selected material surplus assessment curve within the material consumption range and the total length of the material surplus assessment curve, and generate an abnormality determination result based on the ratio of the curve length.

[0110] In this module, the abnormality judgment unit 403 counts the curve length of the selected material surplus assessment curve within the material consumption range and the total length of the material surplus assessment curve. If the selected material surplus assessment curve is within the material consumption range, it means that the material surplus of the production line in this time period is likely to be in a normal state. The longer the line length within the material consumption range, the lower the abnormality probability of the production line. The ratio of the curve length is the ratio of the curve length to the total length of the material surplus assessment curve. When the ratio is higher than the preset value, it is determined that there is a material consumption abnormality.

[0111] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A textile material preparation method based on the Internet of Things, characterized in that: The method comprises: Constructing a production line local area network, wherein the production line local area network includes multiple IoT devices, each of which is used to monitor the material balance on the corresponding production line; Obtain material remainder data from the IoT device at preset time intervals, and construct material remainder coordinates based on the material remainder data; Function fitting is performed based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and a material allocation plan within a preset time period is generated based on the material remainder prediction function; Based on the real-time material surplus prediction function, the production line is diagnosed with abnormal material consumption, and it is determined whether there is abnormal material consumption in the production line, and an abnormality determination result is generated.

2. The method for preparing textile materials based on the Internet of Things according to claim 1, characterized in that: The step of obtaining material remainder data from the IoT device at a preset time interval and constructing material remainder coordinates based on the material remainder data includes: Poll each IoT device at a preset time interval to obtain the material balance data corresponding to each IoT device; Read material balance data and dynamically adjust the time interval for polling IoT devices based on the material balance data; The time information and the corresponding material remainder in the material remainder data are extracted to construct a material remainder coordinate, wherein the abscissa of the material remainder coordinate is the time of data collection, and the ordinate is the current material remainder.

3. The method for preparing textile materials based on the Internet of Things according to claim 1, characterized in that: The steps of performing function fitting based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and generating a material allocation plan within a preset time period based on the material remainder prediction function specifically include: Retrieve all material remainder coordinates belonging to the same IoT device, import the material remainder coordinates into the function fitting software, and obtain multiple material fitting functions; The accuracy of the material remainder coordinates and the material fitting function is checked by the least square method, and the function with the highest fitting accuracy is selected as the material remainder prediction function; Import the preset time value into the material remainder prediction function to obtain the material remainder prediction value, and generate the material allocation plan based on the material remainder prediction value.

4. The method for preparing textile materials based on the Internet of Things according to claim 1, characterized in that: The step of diagnosing abnormal material consumption of the production line based on the real-time material surplus prediction function, determining whether the production line has abnormal material consumption, and generating an abnormality determination result specifically includes: Construct a two-dimensional coordinate system and construct a material remainder assessment curve based on the material remainder prediction function; Each time a set of material surplus assessment curves is selected, and the material consumption range interval is constructed based on all other material surplus assessment curves; The length of the selected material surplus assessment curve within the material consumption range and the total length of the material surplus assessment curve are counted, and an abnormality determination result is generated based on the ratio of the curve lengths.

5. The method for preparing textile materials based on the Internet of Things according to claim 4, characterized in that: The ratio of the curve length is the ratio of the curve length to the total length of the material balance assessment curve. When the ratio is higher than a preset value, it is determined that there is abnormal material consumption.

6. A textile material preparation system based on the Internet of Things, characterized in that: The system comprises: A local area network construction module is used to construct a production line local area network, wherein the production line local area network includes multiple Internet of Things devices, each of which is used to monitor the material balance on the corresponding production line; A surplus coordinate construction module is used to obtain material surplus data from the IoT device at a preset time interval and construct material surplus coordinates based on the material surplus data; An allocation plan building module is used to perform function fitting based on the material remainder coordinates corresponding to each IoT device to obtain a material remainder prediction function, and generate an allocation plan for materials within a preset time period based on the material remainder prediction function; The abnormality determination module is used to diagnose the abnormal material consumption of the production line based on the real-time material surplus prediction function, determine whether there is abnormal material consumption on the production line, and generate an abnormality determination result.

7. The textile material preparation system based on the Internet of Things according to claim 6 is characterized in that: The residual coordinate construction module includes: A data acquisition unit, used to poll each IoT device at a preset time interval to obtain material balance data corresponding to each IoT device; A time interval adjustment unit, used for reading material balance data and dynamically adjusting the time interval for polling the IoT device based on the material balance data; The coordinate construction unit is used to extract the time information and the corresponding material remainder in the material remainder data, and construct the material remainder coordinates, wherein the horizontal coordinate of the material remainder coordinates is the time of data collection, and the vertical coordinate is the current material remainder.

8. The textile material preparation system based on the Internet of Things according to claim 6 is characterized in that: The allocation scheme building module includes: The function fitting unit is used to retrieve the material remainder coordinates of all materials belonging to the same IoT device, import the material remainder coordinates into the function fitting software, and obtain multiple material fitting functions; The accuracy checking unit is used to check the accuracy of the material remainder coordinates and the material fitting function by the least square method, and select the function with the highest fitting accuracy as the material remainder prediction function; The allocation plan generating unit is used to import a preset time value into the material remainder prediction function, obtain a material remainder prediction value, and generate a material allocation plan according to the material remainder prediction value.

9. The textile material preparation system based on the Internet of Things according to claim 6, characterized in that: The abnormality determination module comprises: A curve construction unit, used to construct a two-dimensional coordinate system and to construct a material remainder assessment curve based on a material remainder prediction function; A consumption range construction unit is used to select a set of material surplus assessment curves each time and construct a material consumption range interval based on all other material surplus assessment curves; The abnormality determination unit is used to count the curve length of the selected material surplus assessment curve within the material consumption range and the total length of the material surplus assessment curve, and generate an abnormality determination result based on the ratio of the curve length.

10. The textile material preparation system based on the Internet of Things according to claim 7, characterized in that: The ratio of the curve length is the ratio of the curve length to the total length of the material balance assessment curve. When the ratio is higher than a preset value, it is determined that there is abnormal material consumption.

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