Frame pipe grain-sized dicing impurity rate inspection device

Abstract

The invention relates to a rack pipe grain-sized dicing impurity rate inspection device, and belongs to the technical field of industrial automation control. The device comprises rack pipe grain-sized random sampling equipment, rack pipe grain-sized data acquisition equipment, model operation equipment, impurity rate model analysis equipment, impurity rate test result equipment and impurity rate early warning equipment, by means of the device, the accuracy and fairness of rack pipe pelletizing impurity rate inspection can be improved for enterprises, the influence of human factors is reduced, and the problem of low-quality risk possibly generated in the scrap steel rack pipe pelletizing impurity rate inspection process in the iron and steel industry can be effectively controlled.

Description

Technical Field

[0001] The invention belongs to the technical field of industrial automation control, and in particular relates to a device for inspecting impurity content of rack pipe pellets. Background Art

[0002] Rack pipe pelletizing material is a special material among scrap steel for steel enterprises. Compared with the conventional scrap steel material inspection, although there are fewer warehousing orders, the warehousing volume is large. Conventionally, there will be more than 3 trucks for each warehousing. Therefore, the impurity content inspection of this material is a key link in the management of steel enterprises. The rationality, timeliness, fairness and integrity of the inspection are related to the production quality, cost control and supply chain stability of the enterprise.

[0003] However, many steel companies do have some problems in the inspection of impurities in rack tube pelletizing materials, especially over-reliance on human eyes and human experience to determine the impurity rate. First of all, this practice is not only inefficient, but also easily affected by the subjective factors of quality inspectors. Different quality inspectors may give different impurity rates for the same batch of rack tube pelletizing materials, resulting in multiple trucks of rack tube pelletizing materials entering the factory being settled with either low prices for high-quality products or high prices for low-quality products. This subjectivity not only affects the fairness between them, but may also cause disputes between suppliers and steel companies; secondly, there is also the possibility of alliances between quality inspectors and suppliers, which increases the integrity risk of the quality inspection process. Some quality inspectors may be tempted by the interests of suppliers and relax the inspection standards for impurity rates, causing companies to pay unnecessary fees or even suffer economic losses. This behavior not only damages the interests of steel companies, but may also undermine the fairness and stability of the entire supply chain.

[0004] Therefore, how to overcome the shortcomings of the existing technology is an urgent problem to be solved in the field of industrial automation control technology. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and provide a device for testing the impurity content of rack pipe pellets, so as to achieve the fairness of the inspection of the impurity content of scrap steel rack pipe pellets in the steel industry, ensure that on-site quality inspectors are not corrupted by suppliers, and improve operational efficiency and cost control capabilities.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows: A device for testing the impurity content of rack pipe pellets, including a random sampling device for rack pipe pellets, a data collection device for rack pipe pellets, a model operation device, an impurity content model analysis device, an impurity content test result device and an impurity content early warning device; The rack tube pelletizing random sampling device is used to collect the weight and corresponding license plate number of the samples taken from each vehicle of the same batch of rack tube pelletizing materials; the rack tube pelletizing random sampling device also has a built-in random sampling algorithm, which randomly selects the sampling sample of a vehicle and marks the selected sample with a random sampling ID according to the rule. The random sampling ID is encoded as the unique identification of the random selection, and then sent to the rack tube pelletizing data collection device together with the weight of the previously taken sample and the corresponding license plate number; The rack tube pelletizing data collection device is connected to the rack tube pelletizing random sampling device, and is used to receive data from the rack tube pelletizing random sampling device, and transmit the received data to the model operation device; The model operation equipment has a built-in impurity rate model for rack tube pelletizing; The model running device is connected to the rack tube pelletizing data collection device and is used to calculate the impurity rate according to the rack tube pelletizing data collection device to obtain the impurity rate of each sampling sample; The impurity rate model analysis device is connected to the model operation device, and is used to receive the impurity rate of each sample of the batch and the corresponding license plate data, as well as the random sampling identification ID of the batch, calculate the average value of the impurity rate of all the samples of the batch, and compare the average value of the impurity rate with the impurity rate of the sample marked with the random sampling identification ID, and output the comparison result to the impurity rate inspection result device; The impurity rate inspection result device is respectively connected to the impurity rate model analysis device and the impurity rate warning device, and is used to make a judgment based on the comparison result transmitted by the impurity rate model analysis device. If the average value of the impurity rate is greater than or equal to the impurity rate of the sample marked with the random sampling identification ID, and the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is greater than or equal to the high threshold, then the impurity rate warning device is notified to issue a warning; if the average value of the impurity rate is less than the impurity rate of the sample marked with the random sampling identification ID, and the absolute value of the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is less than the low threshold, then the impurity rate warning device is notified to issue a warning; otherwise, no warning is issued; Impurity content warning equipment is used for impurity content warning.

[0007] Furthermore, preferably, the rack pipe pelletizing data acquisition device has a built-in MQTT protocol, and the concrete actual weight, sampling weight, license plate number, and random identification ID are collected and received in real time through the protocol. After receiving the sampling weight data, the received data is transmitted to the model operation device through the output interface of the rack pipe pelletizing data acquisition device; Wherein, the sampling weight is the weight of the sample taken; The actual weight of the concrete is obtained by crushing the concrete in the rack tube during sampling of the pelletized material and then weighing the concrete.

[0008] Further, preferably, the impurity rate model of the rack tube pelletizing is specifically: Impurity rate 1 = Round (concrete actual weight 1 / sampling weight 1*100,2) Impurity rate 2 = Round (concrete actual weight 2 / sampling weight 2*100,2) Impurity rate 3 = Round (concrete weight 3 / sampling weight 3*100,2) Impurity rate N = Round (concrete actual weight N / sampling weight N * 100, 2) The impurity rate 1 is the impurity rate of the first car sample, the concrete actual weight 1 is the concrete actual weight of the first car sample, and the sampling weight 1 is the sampling weight of the first car sample; The impurity content 2 is the impurity content of the samples taken from the second vehicle, the concrete actual weight 2 is the concrete actual weight of the samples taken from the second vehicle, and the sampling weight 2 is the sampling weight of the samples taken from the second vehicle; By analogy, the impurity content N is the impurity content of the samples taken from the Nth car, the actual weight of concrete N is the actual weight of concrete of the samples taken from the Nth car, and the sampling weight N is the sampling weight of the samples taken from the Nth car.

[0009] Further, preferably, in the impurity rate model analysis device, assuming an actual scenario, the rack tube pelletizing random sampling device marks the sampling ID on the second car sample according to the rules, then the specific method of the impurity rate result analysis is: Impurity rate 1 = Round (concrete actual weight 1 / sampling weight 1*100,2) Impurity rate 2 (sampling ID) = Round (concrete actual weight 2 / sampling weight 2*100,2) Impurity rate 3 = Round (concrete weight 3 / sampling weight 3*100,2) Impurity rate N = Round (concrete weight N / sampling weight N*100,2) Average impurity content = (impurity content 1 + impurity content 3 + impurity content N) / (N-1) The analysis result of the impurity rate = if the average impurity rate is greater than or equal to the impurity rate 2 (sampling ID), True is output; if the average impurity rate is less than the impurity rate 2 (sampling ID), False is output.

[0010] Further, preferably, in the impurity content inspection result device, the value of the warning trigger is calculated as follows: If the analysis result value of the impurity rate is True, the result of subtracting the impurity rate 2 (sampling ID) from the average impurity rate is compared with the high threshold. If it is greater than or equal to the high threshold, True is output, otherwise False is output. If the analysis result value of the impurity rate is False, the absolute value of the result of subtracting the impurity rate 2 (sampling ID) from the average impurity rate is compared with the low threshold. If it is less than the absolute value, True is output, otherwise False is output.

[0011] Furthermore, preferably, the high threshold is set to 0.5 and the low threshold is set to 0.2.

[0012] Further, preferably, when no warning is given, the impurity rate of the sample with the sampling identification ID is output as the impurity rate of the current batch.

[0013] In the present invention, after multiple trucks of rack tube pelletizing materials enter the factory in the same batch and arrive at a designated unloading location, they are sampled by the rack tube pelletizing random sampling device.

[0014] The present invention aims to establish inspection standardization, data accuracy and data rationality of impurity content in scrap steel rack pipe pellets. The inspection result of impurity content directly affects the final deduction of the pelletized material of the rack pipe, and the final deduction affects the cost settlement between the enterprise and the supplier. Through the application of the present invention, the accuracy and fairness of the quality inspection of the pelletized material of the scrap steel rack pipe can be improved, the influence of human factors can be reduced, and immeasurable economic losses to the enterprise can be avoided. It can also effectively control the integrity risk problems that may arise in the steel industry during the inspection of the impurity content of the pelletized material of the scrap steel rack pipe.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] Through the application of the device of the present invention, the automation and standardization of the inspection of the impurity content of rack pipe pellets can be achieved, so as to improve the accuracy and fairness of the inspection of the impurity content of rack pipe pellets for enterprises and reduce the influence of human factors (including human misjudgment or intentional reduction of the impurity content, which brings immeasurable economic losses to enterprises); and through the application of the present invention, the integrity risk problems that may arise in the steel industry during the inspection of the rack pipe pellets of scrap steel materials can be effectively controlled. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of the impurity content inspection device for rack tube pellets of the present invention; Figure 2 It is a schematic diagram of data processing of the impurity content inspection device for rack tube pellets of the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with embodiments.

[0019] Those skilled in the art will appreciate that the following examples are only used to illustrate the present invention and should not be considered to limit the scope of the present invention. If no specific techniques or conditions are specified in the examples, the techniques or conditions described in the literature in the art or the product specifications are used. If the manufacturer of the materials or equipment used is not specified, they are all conventional products that can be purchased. Example 1

[0020] like Figure 1 As shown, the impurity rate inspection device for rack pipe pelletizing includes a rack pipe pelletizing random sampling device 100, a rack pipe pelletizing data collection device 200, a model operation device 300, an impurity rate model analysis device 400, an impurity rate inspection result device 500 and an impurity rate early warning device 600; The rack tube pelletizing random sampling device 100 is used to collect the weight and corresponding license plate number of the samples taken from each vehicle of the same batch of rack tube pelletizing materials; the rack tube pelletizing random sampling device 100 also has a built-in random sampling algorithm, which randomly selects a sample of a vehicle according to the rule, and marks the selected sample with a random sampling ID according to the rule, and encodes it as a unique identifier randomly selected, and then sends it to the rack tube pelletizing data collection device 200 together with the weight and corresponding license plate number of the previously taken sample; The rack pipe pelletizing data collection device 200 is connected to the rack pipe pelletizing random sampling device 100, and is used to receive data from the rack pipe pelletizing random sampling device 100, and transmit the received data to the model operation device 300; The model operation device 300 is equipped with a rack tube pelletizing impurity rate model; The model operation device 300 is connected to the rack tube pelletizing data collection device 200, and is used to calculate the impurity content according to the rack tube pelletizing data collection device 200 to obtain the impurity content of each sample; The impurity rate model analysis device 400 is connected to the model operation device 300, and is used to receive the impurity rate of each sample of the batch and the corresponding license plate data, as well as the random sampling identification ID of the batch, calculate the average value of the impurity rate of all the samples of the batch, and compare the average value of the impurity rate with the impurity rate of the sample marked with the random sampling identification ID, and output the comparison result to the impurity rate inspection result device 500; The impurity rate inspection result device 500 is connected to the impurity rate model analysis device 400 and the impurity rate warning device 600 respectively, and is used to make a judgment based on the comparison result transmitted by the impurity rate model analysis device 400. If the average value of the impurity rate is greater than or equal to the impurity rate of the sample marked with the random sampling identification ID, and the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is greater than or equal to the high threshold, then the impurity rate warning device 600 is notified to issue a warning; if the average value of the impurity rate is less than the impurity rate of the sample marked with the random sampling identification ID, and the absolute value of the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is less than the low threshold, then the impurity rate warning device 600 is notified to issue a warning; otherwise, no warning is issued; The impurity content warning device 600 is used for making an impurity content warning. Example 2

[0021] like Figure 1 As shown, the impurity rate inspection device for rack pipe pelletizing includes a rack pipe pelletizing random sampling device 100, a rack pipe pelletizing data collection device 200, a model operation device 300, an impurity rate model analysis device 400, an impurity rate inspection result device 500 and an impurity rate early warning device 600; The rack tube pelletizing random sampling device 100 is used to collect the weight and corresponding license plate number of the samples taken from each vehicle of the same batch of rack tube pelletizing materials; the rack tube pelletizing random sampling device 100 also has a built-in random sampling algorithm, which randomly selects a sample of a vehicle according to the rule, and marks the selected sample with a random sampling ID according to the rule, and encodes it as a unique identifier randomly selected, and then sends it to the rack tube pelletizing data collection device 200 together with the weight and corresponding license plate number of the previously taken sample; The rack pipe pelletizing data collection device 200 is connected to the rack pipe pelletizing random sampling device 100, and is used to receive data from the rack pipe pelletizing random sampling device 100, and transmit the received data to the model operation device 300; The model operation device 300 is equipped with a rack tube pelletizing impurity rate model; The model operation device 300 is connected to the rack tube pelletizing data collection device 200, and is used to calculate the impurity content according to the rack tube pelletizing data collection device 200 to obtain the impurity content of each sample; The impurity rate model analysis device 400 is connected to the model operation device 300, and is used to receive the impurity rate of each sample of the batch and the corresponding license plate data, as well as the random sampling identification ID of the batch, calculate the average value of the impurity rate of all the samples of the batch, and compare the average value of the impurity rate with the impurity rate of the sample marked with the random sampling identification ID, and output the comparison result to the impurity rate inspection result device 500; The impurity rate inspection result device 500 is connected to the impurity rate model analysis device 400 and the impurity rate warning device 600 respectively, and is used to make a judgment based on the comparison result transmitted by the impurity rate model analysis device 400. If the average value of the impurity rate is greater than or equal to the impurity rate of the sample marked with the random sampling identification ID, and the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is greater than or equal to the high threshold, then the impurity rate warning device 600 is notified to issue a warning; if the average value of the impurity rate is less than the impurity rate of the sample marked with the random sampling identification ID, and the absolute value of the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is less than the low threshold, then the impurity rate warning device 600 is notified to issue a warning; otherwise, no warning is issued; The impurity content warning device 600 is used for making an impurity content warning.

[0022] Specifically, the rack pipe pelletizing data collection device 200 has a built-in MQTT protocol, and collects and receives the actual weight of concrete, sampling weight, license plate number, and random identification ID in real time through the protocol. After receiving the sampling weight data, the received data is transmitted to the model operation device 300 through the output interface of the rack pipe pelletizing data collection device 200; Wherein, the sampling weight is the weight of the sample taken; The actual weight of the concrete is obtained by crushing the concrete in the rack tube during sampling of the granulated material and then weighing the concrete.

[0023] Specifically, the impurity rate model of rack tube pelletizing is as follows: Impurity rate 1 = Round (concrete actual weight 1 / sampling weight 1*100,2) Impurity rate 2 = Round (concrete actual weight 2 / sampling weight 2*100,2) Impurity rate 3 = Round (concrete weight 3 / sampling weight 3*100,2) Impurity rate N = Round (concrete actual weight N / sampling weight N * 100, 2) The impurity rate 1 is the impurity rate of the first car sample, the concrete actual weight 1 is the concrete actual weight of the first car sample, and the sampling weight 1 is the sampling weight of the first car sample; The impurity content 2 is the impurity content of the samples taken from the second vehicle, the concrete actual weight 2 is the concrete actual weight of the samples taken from the second vehicle, and the sampling weight 2 is the sampling weight of the samples taken from the second vehicle; By analogy, the impurity content N is the impurity content of the samples taken from the Nth car, the actual weight of concrete N is the actual weight of concrete of the samples taken from the Nth car, and the sampling weight N is the sampling weight of the samples taken from the Nth car.

[0024] Specifically, in the impurity rate model analysis device 400, assuming an actual scenario, the rack tube pelletizing random sampling device 100 marks the sampling ID of the second car sample according to the rule, then the specific method of the impurity rate result analysis is: Impurity rate 1 = Round (concrete actual weight 1 / sampling weight 1*100,2) Impurity rate 2 (sampling ID) = Round (concrete actual weight 2 / sampling weight 2*100,2) Impurity rate 3 = Round (concrete weight 3 / sampling weight 3*100,2) Impurity rate N = Round (concrete weight N / sampling weight N*100,2) Average impurity content = (impurity content 1 + impurity content 3 + impurity content N) / (N-1) The analysis result of the impurity rate = if the average impurity rate is greater than or equal to the impurity rate 2 (sampling ID), True is output; if the average impurity rate is less than the impurity rate 2 (sampling ID), False is output.

[0025] Specifically, in the impurity rate inspection result device 500, The value for the alert trigger is calculated as follows: If the analysis result value of the impurity rate is True, the result of subtracting the impurity rate 2 (sampling ID) from the average impurity rate is compared with the high threshold. If it is greater than or equal to the high threshold, True is output, otherwise False is output. If the analysis result value of the impurity rate is False, the absolute value of the result of subtracting the impurity rate 2 (sampling ID) from the average impurity rate is compared with the low threshold. If it is less than the absolute value, True is output, otherwise False is output.

[0026] Specifically, the high threshold is set to 0.5 and the low threshold is set to 0.2.

[0027] Specifically, when no warning is given, the impurity rate of the sampling identification ID is output as the impurity rate of the current batch. Example 3

[0028] like Figure 1 As shown, the impurity rate inspection device for rack pipe pelletizing includes a rack pipe pelletizing random sampling device 100, a rack pipe pelletizing data collection device 200, a model operation device 300, an impurity rate model analysis device 400, an impurity rate inspection result device 500 and an impurity rate early warning device 600; After multiple trucks of rack tube pelletizing materials enter the factory in the same batch, they arrive at the designated unloading location; the rack tube pelletizing random sampling device 100 is used to collect the weight and corresponding license plate number of the samples taken from each truck of the same batch of rack tube pelletizing materials; the rack tube pelletizing random sampling device 100 also has a built-in random sampling algorithm, which randomly selects a sample of a truck according to the rule, and marks the selected sample with a random sampling ID according to the rule, which is encoded as the randomly selected unique identifier, and then sent to the rack tube pelletizing data collection device 200 together with the weight and corresponding license plate number of the previously taken sample; The rack pipe pelletizing data collection device 200 is connected to the rack pipe pelletizing random sampling device 100, and is used to receive data from the rack pipe pelletizing random sampling device 100, and transmit the received data to the model operation device 300; The model operation device 300 is equipped with a rack tube pelletizing impurity rate model; The model running device 300 is connected to the rack pipe pelletizing data collection device 200, and is used to calculate the impurity rate according to the rack pipe pelletizing data collection device 200 to obtain the impurity rate of each sample; the model running device 300 has a built-in running engine of the Flink stream processing framework, through which the rack pipe pelletizing impurity rate model with data is started and run, and then based on the combination of Spark big data real-time calculation and batch calculation, the model analysis and data real-time calculation are realized; The impurity rate model analysis device 400 is connected to the model operation device 300, and is used to receive the impurity rate of each sample of the batch and the corresponding license plate data, as well as the random sampling identification ID of the batch, calculate the average value of the impurity rate of all the samples of the batch, and compare the average value of the impurity rate with the impurity rate of the sample marked with the random sampling identification ID, and output the comparison result to the impurity rate inspection result device 500; The impurity rate inspection result device 500 is connected to the impurity rate model analysis device 400 and the impurity rate warning device 600 respectively, and is used to make a judgment based on the comparison result transmitted by the impurity rate model analysis device 400. If the average value of the impurity rate is greater than or equal to the impurity rate of the sample marked with the random sampling identification ID, and the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is greater than or equal to the high threshold, then the impurity rate warning device 600 is notified to issue a warning; if the average value of the impurity rate is less than the impurity rate of the sample marked with the random sampling identification ID, and the absolute value of the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is less than the low threshold, then the impurity rate warning device 600 is notified to issue a warning; otherwise, no warning is issued; The device 500 for checking the result of the impurity content is used to set the threshold value, compare the difference between the average value of the impurity content and the impurity content of the sampling mark, and obtain the early warning trigger instruction; 500 has a built-in visual threshold function, high threshold setting, low threshold setting, and when the analysis result instruction of the impurity content received from 400 is True, the high threshold is used, otherwise the low threshold is used; The impurity content warning device 600 is used for making an impurity content warning.

[0029] The impurity content warning device 600 mainly receives the warning trigger instruction and triggers the warning through the instruction judgment. When the warning trigger value 500 is True, the impurity content warning device will push the warning value to the enterprise-related platform for event alarm, and initiate a joint review of quality objections through the enterprise quality inspection management system to determine whether re-sampling is required. If re-sampling is required, the rack tube pelletizing random sampling device will re-randomly sample and re-go through the process. When the impurity rate warning device 600 obtains the output interface warning trigger value of the impurity rate inspection result device 500 as False, the impurity rate warning device 600 will pass the impurity rate 2 (sampling identifier) ​​= Round (concrete actual weight 2 / sampling weight 2*100, 2) as the impurity rate of the current batch to the upstream and downstream systems, such as the company's procurement system, inspection system, and access control system, and store it in the rack pipe pelletizing impurity rate database 800.

[0030] Specifically, the rack pipe pelletizing data collection device 200 has a built-in MQTT protocol, and collects and receives the actual weight of concrete, sampling weight, license plate number, and random identification ID in real time through the protocol. After receiving the sampling weight data, the received data is transmitted to the model operation device 300 through the output interface of the rack pipe pelletizing data collection device 200; Wherein, the sampling weight is the weight of the sample taken; The actual weight of the concrete is obtained by crushing the concrete in the rack tube during sampling of the granulated material and then weighing the concrete.

[0031] Specifically, the impurity rate model of rack tube pelletizing is as follows: Impurity rate 1 = Round (concrete actual weight 1 / sampling weight 1*100,2) Impurity rate 2 = Round (concrete actual weight 2 / sampling weight 2*100,2) Impurity rate 3 = Round (concrete weight 3 / sampling weight 3*100,2) Impurity rate N = Round (concrete actual weight N / sampling weight N * 100, 2) The impurity rate 1 is the impurity rate of the first car sample, the concrete actual weight 1 is the concrete actual weight of the first car sample, and the sampling weight 1 is the sampling weight of the first car sample; The impurity content 2 is the impurity content of the samples taken from the second vehicle, the concrete actual weight 2 is the concrete actual weight of the samples taken from the second vehicle, and the sampling weight 2 is the sampling weight of the samples taken from the second vehicle; By analogy, the impurity content N is the impurity content of the samples taken from the Nth car, the actual weight of concrete N is the actual weight of concrete of the samples taken from the Nth car, and the sampling weight N is the sampling weight of the samples taken from the Nth car.

[0032] Specifically, in the impurity rate model analysis device 400, assuming an actual scenario, the rack tube pelletizing random sampling device 100 marks the sampling ID of the second car sample according to the rule, then the specific method of the impurity rate result analysis is: Impurity rate 1 = Round (concrete actual weight 1 / sampling weight 1*100,2) Impurity rate 2 (sampling ID) = Round (concrete actual weight 2 / sampling weight 2*100,2) Impurity rate 3 = Round (concrete weight 3 / sampling weight 3*100,2) Impurity rate N = Round (concrete weight N / sampling weight N*100,2) Average impurity content = (impurity content 1 + impurity content 3 + impurity content N) / (N-1) The analysis result of the impurity rate = if the average impurity rate is greater than or equal to the impurity rate 2 (sampling ID), True is output; if the average impurity rate is less than the impurity rate 2 (sampling ID), False is output.

[0033] Specifically, in the impurity rate inspection result device 500, The value for the alert trigger is calculated as follows: If the analysis result value of the impurity rate is True, the result of subtracting the impurity rate 2 (sampling ID) from the average impurity rate is compared with the high threshold. If it is greater than or equal to the high threshold, True is output, otherwise False is output. If the analysis result value of the impurity rate is False, the absolute value of the result of subtracting the impurity rate 2 (sampling ID) from the average impurity rate is compared with the low threshold. If it is less than the absolute value, True is output, otherwise False is output.

[0034] Specifically, the high threshold is set to 0.5 and the low threshold is set to 0.2.

[0035] Specifically, when no warning is given, the impurity rate of the sampling identification ID is output as the impurity rate of the current batch.

[0036] like Figure 2 As shown, the rack pipe pelletizing random sampling device 100 randomly samples the vehicles in the current batch, such as: sampling from the left side of vehicle 1, sampling from the right side of vehicle 2, and sampling from vehicle N. At the same time, the rack pipe pelletizing random sampling device 100 randomly selects a vehicle from multiple vehicles, and randomly marks a random identification ID according to the above rules. Finally, the data of the actual weight of concrete, sampling weight, and license plate number are obtained, and transmitted to the rack pipe pelletizing data acquisition device 200. The rack pipe pelletizing data acquisition device 200 automatically creates a Kafka data acquisition stream, and produces the collected data through the Kafka data acquisition stream as a data producer. The model running device 300 has a built-in model running engine 301; in the model running engine 301, the running engine of the Flink stream processing framework is adopted, Spark provides a rich set of operators, and Spark is used as a memory distributed computing framework to parse the model to realize real-time computing of the built-in algorithm with data. The model running device 300 has a built-in model algorithm and consumes the Kafka data stream, that is, according to the data transmitted by the rack pipe cutting data acquisition device 200, the impurity rate of the corresponding data is calculated.

[0037] The impurity rate model analysis device 400 has a built-in model analysis engine 401; the model analysis engine 401 receives the impurity rate of each sample and the corresponding license plate data, as well as the sampling identification ID data, calculates the average value of the impurity rate of all the sampled samples in the batch, and compares the average value of the impurity rate with the impurity rate of the sampling identification ID, and outputs the comparison result to the impurity rate inspection result device 500; The result instruction engine 501 is built in the impurity rate inspection result device 500; the result instruction engine 501 makes a judgment based on the comparison result transmitted by the impurity rate model analysis device 400. If the average value of the impurity rate is greater than or equal to the impurity rate of the sampling mark, and the difference between the average value of the impurity rate and the impurity rate of the sampling mark is greater than or equal to the high threshold, the impurity rate warning device 600 is notified to issue a warning; if the average value of the impurity rate is less than the impurity rate of the sampling mark, and the absolute value of the difference between the average value of the impurity rate and the impurity rate of the sampling mark is less than the low threshold, the impurity rate warning device 600 is notified to issue a warning; otherwise, no warning is issued; The impurity rate warning device 600 has a built-in warning engine 601; when the warning engine 601 obtains the warning trigger value of the impurity rate inspection result device 500 as True, the warning engine 601 will push the warning value to the enterprise-related platform for event alarm, and initiate a joint review of quality objections through the enterprise quality inspection management system to determine whether re-sampling is required. If re-sampling is required, the rack tube pelletizing random sampling device will re-randomly sample and re-go through the process; when the warning trigger value is False, the impurity rate warning device will output the impurity rate inspection result data through the output interface 700 through the output interface.

[0038] The rack tube pellet impurity database 800 is used to store the impurity of the current batch, and is also used to store the impurity of each sample corresponding to the warning time and the corresponding license plate data, as well as the sampling ID data.

[0039] The output interface 700 is used to provide an output interface to provide the impurity content test results to the enterprise inspection and testing platform, supporting the clean quality inspection of the impurity content test of scrap steel rack pipe pelletizing materials.

[0040] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The device for testing the impurity content of the rack tube pellets is characterized by: It comprises a rack tube pelletizing random sampling device (100), a rack tube pelletizing data acquisition device (200), a model operation device (300), an impurity content model analysis device (400), an impurity content inspection result device (500) and an impurity content early warning device (600); The rack tube pelletizing random sampling device (100) is used to collect the weight and corresponding license plate number of the samples taken from each vehicle of the same batch of rack tube pelletizing materials. The rack tube pelletizing random sampling device (100) also has a built-in random sampling algorithm, which randomly selects a sampling sample from a vehicle and marks the selected sample with a random sampling ID according to the rule. The random sampling ID is encoded as a unique identifier selected randomly, and then sent to the rack tube pelletizing data collection device (200) together with the weight and corresponding license plate number of the previously taken sample. The rack tube pelletizing data collection device (200) is connected to the rack tube pelletizing random sampling device (100) and is used to receive data transmitted from the rack tube pelletizing random sampling device (100) and transmit the received data to the model operation device (300); The model operation device (300) is equipped with a rack tube pelletizing impurity rate model; The model operation device (300) is connected to the rack tube pelletizing data collection device (200) and is used to calculate the impurity content according to the rack tube pelletizing data collection device (200) to obtain the impurity content of each sample; The impurity rate model analysis device (400) is connected to the model operation device (300) and is used to receive the impurity rate of each sample of the batch and the corresponding license plate data, as well as the random sampling identification ID of the batch, calculate the average value of the impurity rate of all the samples of the batch, and compare the average value of the impurity rate with the impurity rate of the sample marked with the random sampling identification ID, and output the comparison result to the impurity rate inspection result device (500); The impurity rate inspection result device (500) is respectively connected to the impurity rate model analysis device (400) and the impurity rate warning device (600), and is used to make a judgment based on the comparison result transmitted by the impurity rate model analysis device (400). If the average value of the impurity rate is greater than or equal to the impurity rate of the sample marked with the random sampling identification ID, and the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is greater than or equal to the high threshold, then the impurity rate warning device (600) is notified to issue a warning; if the average value of the impurity rate is less than the impurity rate of the sample marked with the random sampling identification ID, and the absolute value of the difference between the average value of the impurity rate and the impurity rate of the sample marked with the random sampling identification ID is less than the low threshold, then the impurity rate warning device (600) is notified to issue a warning; Otherwise, no warning will be given; The impurity content warning device (600) is used for providing impurity content warning.

2. The device for inspecting impurity content of rack tube pellets according to claim 1 is characterized in that: The rack pipe pelletizing data collection device (200) has a built-in MQTT protocol, and collects and receives the actual weight of concrete, the sampling weight, the license plate number, and the random identification ID in real time through the protocol. After receiving the sampling weight data, the received data is transmitted to the model operation device (300) through the output interface of the rack pipe pelletizing data collection device (200); Wherein, the sampling weight is the weight of the sample taken; The actual weight of the concrete is obtained by crushing the concrete in the rack tube during sampling of the granulated material and then weighing the concrete.

3. The device for inspecting impurity content of rack tube pellets according to claim 1 is characterized in that: The specific impurity rate model of rack tube pelletizing is: Impurity rate 1 = Round (concrete actual weight 1 / sampling weight 1*100,2) Impurity rate 2 = Round (concrete actual weight 2 / sampling weight 2*100,2) Impurity rate 3 = Round (concrete weight 3 / sampling weight 3*100,2) Impurity rate N = Round (concrete actual weight N / sampling weight N * 100, 2) The impurity rate 1 is the impurity rate of the first car sample, the concrete actual weight 1 is the concrete actual weight of the first car sample, and the sampling weight 1 is the sampling weight of the first car sample; The impurity content 2 is the impurity content of the samples taken from the second vehicle, the concrete actual weight 2 is the concrete actual weight of the samples taken from the second vehicle, and the sampling weight 2 is the sampling weight of the samples taken from the second vehicle; By analogy, the impurity content N is the impurity content of the samples taken from the Nth car, the actual weight of concrete N is the actual weight of concrete of the samples taken from the Nth car, and the sampling weight N is the sampling weight of the samples taken from the Nth car.

4. The device for inspecting impurity content of rack tube pellets according to claim 1 is characterized in that: In the impurity rate model analysis device (400), assuming an actual scenario, the rack tube pelletizing random sampling device (100) marks the sampling ID on the second car sample according to the rule, then the specific method of the impurity rate result analysis is: Impurity rate 1 = Round (concrete actual weight 1 / sampling weight 1*100,2) Impurity rate 2 (sampling ID) = Round (concrete actual weight 2 / sampling weight 2*100,2) Impurity rate 3 = Round (concrete weight 3 / sampling weight 3*100,2) Impurity rate N = Round (concrete weight N / sampling weight N*100,2) Average impurity content = (impurity content 1 + impurity content 3 + impurity content N) / (N-1) The analysis result of the impurity rate = if the average impurity rate is greater than or equal to the impurity rate 2 (sampling ID), True is output; if the average impurity rate is less than the impurity rate 2 (sampling ID), False is output.

5. The device for inspecting impurity content of rack tube pellets according to claim 4 is characterized in that: In the impurity rate inspection result device (500), the value of the warning trigger is calculated as follows: If the analysis result value of the impurity rate is True, the result of subtracting the impurity rate 2 (sampling ID) from the average impurity rate is compared with the high threshold. If it is greater than or equal to the high threshold, True is output, otherwise False is output. If the analysis result value of the impurity rate is False, the absolute value of the result of subtracting the impurity rate 2 (sampling ID) from the average impurity rate is compared with the low threshold. If it is less than the absolute value, True is output, otherwise False is output.

6. The device for inspecting impurity content of rack tube pellets according to claim 1 or 5, characterized in that: The high threshold is set to 0.5 and the low threshold is set to 0.

2.

7. The device for inspecting impurity content of rack tube pellets according to claim 1, characterized in that: When no warning is given, the impurity rate of the sample with the ID will be output as the impurity rate of the current batch.

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