CN113118055B - Product detecting and sorting system based on big data - Google Patents
Product detecting and sorting system based on big data Download PDFInfo
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- CN113118055B CN113118055B CN202110349842.8A CN202110349842A CN113118055B CN 113118055 B CN113118055 B CN 113118055B CN 202110349842 A CN202110349842 A CN 202110349842A CN 113118055 B CN113118055 B CN 113118055B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/34—Sorting according to other particular properties
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/36—Sorting apparatus characterised by the means used for distribution
- B07C5/361—Processing or control devices therefor, e.g. escort memory
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/36—Sorting apparatus characterised by the means used for distribution
- B07C5/361—Processing or control devices therefor, e.g. escort memory
- B07C5/362—Separating or distributor mechanisms
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/36—Sorting apparatus characterised by the means used for distribution
- B07C5/38—Collecting or arranging articles in groups
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Abstract
The invention discloses a product detection sorting system based on big data, which belongs to the field of product detection, relates to big data technology and is used for solving the technical problem that the existing automatic detection system cannot intercept the batch of problem products; the quality detection module is used for detecting and analyzing the quality of the product, the manual intervention module is used for allocating proper quality inspectors to carry out deep detection on the product, the deep detection process is that allocation staff detect all high-quality products in a warehouse again through the quality detection module, and the allocation staff supervise the whole detection process; according to the invention, the timer is arranged in the recovery bin, so that all products in the batch are detected again when continuous defective products appear, and quality inspection personnel supervise the products, thereby improving the accuracy of the detection result of the equipment.
Description
Technical Field
The invention belongs to the field of product detection, relates to a big data technology, and particularly relates to a product detection and sorting system based on big data.
Background
In a traditional product conveying production line, product quality inspection mainly depends on manual detection, and sorting of products with different quality levels also mainly depends on manual sorting, so that the manual detection speed is low, the manual sorting efficiency is low, the whole product conveying speed is influenced, and the production efficiency is influenced; meanwhile, the manual detection and sorting accuracy is not high, errors are easy to generate, and the product delivery quality is affected.
The existing product detecting and sorting system can automatically detect and sort the surface flatness and surface scratches of products, but the automatic detection still has the possibility of error, and particularly when the quality of the same batch of products is problematic due to the processing procedure, the existing automatic detecting system can only detect the quality of a single product, but cannot radiate the quality of the same batch of products through the detection results of a plurality of products, so that the aim of intercepting the batch of problematic products is fulfilled.
The invention patent with publication number CN105858197B discloses a product detecting and sorting system, compared with manual detecting and sorting, the product detecting and sorting system realizes full automation, does not need manual work, and saves human resources; the detection speed and the product sorting speed are greatly improved, and the production efficiency is greatly improved; the accuracy of the invention is greatly improved, and the integral quality level of the product can be improved; the invention can detect whether the overall dimension of the product reaches the standard while detecting the surface quality of the product; however, the product detecting and sorting system has the problem that the quality of the products in the same batch cannot be radiated through the detection results of a plurality of products, so that the purpose of intercepting the products with problems in batches is achieved.
In view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a product detecting and sorting system based on big data, which is used for solving the problem that the existing automatic detection system can only detect the quality of a single product, but cannot radiate the quality of the same batch of products according to the detection results of a plurality of products, thereby achieving the purpose of intercepting the batch of problematic products.
The technical problems to be solved by the invention are as follows:
how to provide a product detecting and sorting system capable of intercepting a batch of problem products through detection results of a plurality of products.
The purpose of the invention can be realized by the following technical scheme:
a product detection and sorting system based on big data comprises a sorting server, wherein the sorting server is in communication connection with an inventory analysis module, a quality detection module, a sorting control module, a manual intervention module and a database;
the quality detection module is used for detecting and analyzing the quality of the product, and the specific detection and analysis process of the quality detection module comprises the following steps:
step S1: sequentially detecting products in the same production batch, and calculating the leveling data and the scratch data of the obtained products to obtain the quality coefficient ZL of the products;
step S2: comparing the quality coefficient of the product with a quality coefficient threshold:
if the quality coefficient is smaller than the quality coefficient threshold value, judging that the quality of the product meets the requirement, marking the corresponding product as a high-quality product, and conveying the high-quality product to a to-be-warehoused;
if the quality coefficient is larger than or equal to the quality coefficient threshold value, judging that the product quality does not meet the requirement, marking the corresponding product as an inferior product, conveying the inferior product to a recovery bin, and adding one to the number of the products in the recovery bin;
and step S3: when the recycling bin receives an inferior product, a timer of the recycling bin counts down, the counting-down time length is T1 time, and T1 is a preset value; if the recycling bin receives the inferior products within the countdown time, resetting the timer to count down again, wherein the countdown time length is T1 time; if the recovery bin does not receive the inferior products within the countdown time, the countdown is closed, and the timer is continuously started when the next inferior product enters the recovery bin;
and step S4: if the timer is reset for three times continuously, the quality detection module sends a depth detection signal to the sorting server, the sorting server receives the depth detection signal and then sends the depth detection signal to the manual intervention module, and the manual intervention module receives the depth detection signal and then carries out depth detection on all high-quality products in the to-be-classified bin manually.
Further, the method for calculating the mass coefficient ZL in step S1 includes the following steps:
step S11: calculating the average value of the planeness of each surface of the product to obtain the average planeness of the product, and marking the average planeness as PM;
step S12: summing the number of scratches on each surface of the product to obtain the number of the scratches of the product, and marking the number as GH;
step S13: carrying out dequantization treatment on the average planeness PM and the number GH of the scratches of the product to obtain the numerical value, and utilizing a formula
And obtaining the mass coefficient ZL of the product, wherein both alpha 1 and alpha 2 are proportional coefficients, e is a natural constant, and the value of e is 2.71828.
Further, the manual intervention module is used for allocating proper quality inspectors to carry out deep detection on the products, and the allocation mode of the manual intervention module comprises the following steps:
step P1: acquiring an idle quality inspector in a workshop within a linear distance L1 m from a warehouse to be checked, marking the idle quality inspector as a primary selection employee, wherein L1 is a preset distance value;
and step P2: obtaining basic information of the primary selection staff, wherein the basic information of the primary selection staff comprises the following steps: primarily selecting the name, the time of job entry and the historical quality inspection error rate of the employee;
and step P3: acquiring a linear distance between a primary selected employee and a to-be-warehoused employee, marking the linear distance as JL with the unit of meter, calculating a difference value between system time and the working time of the primary selected employee, marking a calculation result as RS with the unit of month, and marking the historical quality inspection error rate of the primary selected employee as CC;
step P4: by the formula
Obtaining the distribution values of the primary selection employees, wherein both beta 1 and beta 2 are proportionality coefficients, and marking the primary selection employee with the maximum distribution value as a distribution employee;
step P5: and sending the basic information of the distribution staff to a sorting server, and sending a depth detection signal to the communication equipment of the distribution staff by the sorting server.
Furthermore, in the deep detection process, all high-quality products in the warehouse to be detected are detected again by the distribution staff through the quality detection module, and the distribution staff supervises the whole detection process; if the timer is continuously reset for three times in the depth detection process, all products in the same batch are conveyed to a recovery bin for recovery and rework; and if the timer is not reset for three times after the depth detection is finished, sorting the high-quality products in the to-be-sorted bin through the sorting control module.
The invention has the following beneficial effects:
1. the quality detection module is used for detecting and analyzing products in the same production batch, the quality coefficient of the products is obtained through calculation of the planeness and the scratch number of each surface of the products, the smaller the quality coefficient is, the better the quality of the products is, the quality coefficient is compared with the quality coefficient threshold value, whether the quality of the products is qualified or not can be judged, the products in the same batch are temporarily stored in a to-be-positioned bin after being subjected to quality detection until all the products in the same batch pass the quality detection, the products in the batch are uniformly sorted, and the problem that the products in batch are wrong due to the processing procedure when the quality of the products is detected is avoided, the inferior products are directly sorted and stored in the bin after passing the quality detection;
2. the timer is arranged in the recovery bin, when the recovery bin receives inferior products, the timer counts down, when the recovery bin receives the inferior products again before the countdown is finished, the timer resets the countdown, so that the continuity of the defective products is monitored, if the defective products continuously appear in the set time, the condition that a large number of the inferior products or even all the inferior products possibly exist in the same batch of products is reflected, and meanwhile, the possibility of detection errors exists in the products which pass the quality detection in the batch, all the products in the batch are detected again when the continuous defective products appear, and the quality inspection personnel supervise the products, so that the accuracy of the detection result of the equipment is improved, and the problem products which flow to the market and influence brand public praise caused by the quality detection errors existing in the batch of the defective products are avoided;
3. the method comprises the steps that a proper quality inspector is distributed through a manual intervention module to carry out depth detection on a product, the distribution value of the primary employee is obtained through calculation of the linear distance between the primary employee and a to-be-detected warehouse, the difference value between the system time and the working time of the primary employee and the historical quality inspection error rate of the primary employee, the larger the distribution value is, the more the quality inspector is suitable for processing the depth detection, the primary employee with the largest distribution value is marked as a distribution employee, the matching degree of the quality inspector is comprehensively analyzed from multiple angles, the distribution employee can rapidly process the quality detection, and the efficiency of the depth detection is improved;
4. high-quality products in the to-be-determined bin are sorted through the sorting control module and are conveyed to the storage bin, the AGV trolley travels to the to-be-determined bin, workers place the products on a bracket of the AGV trolley, bar codes are pasted on the top surfaces of the products, a pressure sensor on the bracket of the AGV trolley detects the weight of the products and then drives the AGV trolley to enter a scanning area, the traveling path of the AGV trolley is planned through scanned target warehouse information, and the products are conveyed to the corresponding target warehouse.
The storage warehouse is subjected to inventory analysis through an inventory analysis module, after products enter the warehouse to be sorted through quality detection, the expected stacking quantity of the warehouse is calculated according to the target warehouse of the products, meanwhile, the remaining stackable quantity in the target warehouse is compared with the expected stacking quantity, the warehouse corresponding warehouse is cleared when the inventory saturation condition occurs, part of the products are moved away from the warehouse according to the production date of the products placed in the warehouse, the products with longer production dates are preferentially moved away until the stackable quantity is larger than the expected stacking quantity, and then the products in the warehouse to be sorted can be sorted into the warehouses.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a product detecting and sorting system based on big data comprises a sorting server, the sorting server is connected with an inventory analysis module, a quality detection module, a sorting control module, a manual intervention module and a database in a communication manner, the quality detection module is used for detecting and analyzing the quality of a product through the leveling data and the scratch data of the product, the leveling data of the product is an average value of the flatness of each surface of the product, the scratch data of the product is the sum of the quantity of the scratches of each surface of the product, and the specific detection and analysis process of the quality detection module comprises the following steps:
step S1: sequentially detecting products in the same production batch, obtaining the planeness of each surface of the products by a flat crystal interference method, carrying out average calculation on the planeness of each surface of the products to obtain the average planeness of the products, and marking the average planeness as PM;
step S2: acquiring the number of scratches on each surface of the product through image shooting and image processing, summing the number of scratches on each surface of the product to obtain the number of scratches of the product, and marking the number of scratches as GH;
and step S3: carrying out dequantization treatment on the average planeness PM and the number GH of the scratches of the product to obtain the numerical value, and utilizing a formula
Obtaining the mass coefficient ZL of the product, wherein alpha 1 and alpha 2 are proportional systemsThe number, e is a natural constant, and the value of e is 2.71828;
and step S4: comparing the quality coefficient of the product with a quality coefficient threshold:
if the quality coefficient is smaller than the quality coefficient threshold value, judging that the quality of the product meets the requirement, marking the corresponding product as a high-quality product, and conveying the high-quality product to an undetermined warehouse;
if the quality coefficient is larger than or equal to the quality coefficient threshold value, judging that the product quality does not meet the requirement, marking the corresponding product as an inferior product, conveying the inferior product to a recovery bin, and adding one to the number of the products in the recovery bin;
step S5: obtaining the quantity of the inferior products in the recovery bin, comparing the quantity of the inferior products with a recovery threshold value, and if the quantity of the inferior products is less than or equal to the recovery threshold value, not processing the products; if the quantity of the inferior products is larger than the recovery threshold value, the quality detection module sends a recovery signal to the sorting processor;
step S6: when the recovery bin receives an inferior product, a timer of the recovery bin counts down, wherein the counting down time length is T1 time, and T1 is a preset value; for example, T1 takes 5 minutes; if the recycling bin receives the inferior products within the countdown time, resetting the timer to count down again, wherein the countdown time length is T1 time;
step S7: if the timer is continuously reset for three times, the quality detection module sends a depth detection signal to the sorting server, the sorting server receives the depth detection signal and then sends the depth detection signal to the manual intervention module, the manual intervention module receives the depth detection signal and then carries out depth detection on all high-quality products in the warehouse to be detected manually, if the recovery warehouse does not receive inferior products within the countdown time, the countdown is closed, the timer is continuously started when the next inferior product enters the recovery warehouse, and if products continuously have problems within the set time, a large number of inferior products or even all inferior products possibly exist in the products of the same batch, and meanwhile, the possibility of detection errors of the products of the batch which pass the quality detection is represented, so the products of the batch need to be subjected to depth detection;
and if the timer is not continuously reset for three times after all the products in the same batch are detected, sorting the high-quality products in the warehouse to be sorted by the sorting control module.
The manual intervention module is used for allocating proper quality inspectors to carry out deep detection on the products, and the allocation mode of the manual intervention module comprises the following steps:
step P1: acquiring an idle quality inspector within a straight line distance L1 m from a to-be-classified workshop, marking the idle quality inspector as a primary selection employee, wherein L1 is a preset distance value;
step P2: acquiring basic information of the primary selection staff, wherein the basic information of the primary selection staff comprises the following steps: primarily selecting the name, the time of job entry and the historical quality inspection error rate of the employee;
step P3: acquiring a linear distance between the initially selected employee and the to-be-warehoused employee, marking the linear distance as JL (maximum likelihood) with the unit of meter, calculating a difference value between the system time and the working time of the initially selected employee, marking a calculation result as RS with the unit of month, and marking the historical quality inspection error rate of the initially selected employee as CC;
step P4: by the formula
Obtaining the distribution values of the primarily selected employees, wherein both beta 1 and beta 2 are proportionality coefficients, and marking the primarily selected employee with the maximum distribution value as a distribution employee;
step P5: sending basic information of the distribution staff to a sorting server, and sending a depth detection signal to communication equipment of the distribution staff by the sorting server;
in the process of deep detection, the distribution staff detect all the high-quality products in the warehouse again through the quality detection module, and supervise the detection process in the whole process; if the timer is continuously reset for three times in the depth detection process, all products in the same batch are conveyed to a recovery bin for recovery and rework; and if the timer is not reset for three times after the depth detection is finished, sorting the high-quality products in the to-be-sorted bin through the sorting control module.
The sorting control module is used for sorting the high-quality products in the to-be-sorted bin and conveying the products to the storage bin, and the specific sorting process comprises the following steps:
step Q1: controlling an AGV to travel to a to-be-positioned warehouse, placing a product on a bracket of the AGV by a worker, attaching a bar code to the top surface of the product, and driving the AGV to enter a scanning area after a pressure sensor on the bracket of the AGV detects the weight of the product;
and step Q2: when the AGV passes through the scanning area, scanning a bar code on the top surface of a product through a code scanner to obtain basic information of the product and sending the basic information of the product to a sorting control module, wherein the basic information of the product comprises a production date and a serial number of a storage warehouse, the sorting control module obtains a walking path according to the serial number of the storage warehouse and a database, and the sorting control module sends the walking path to a controller of the AGV;
and step Q3: the AGV trolley conveys the products to corresponding storage warehouses according to the walking path, and workers lift the products off to finish product sorting.
The inventory analysis module is used for performing inventory analysis on the storage warehouse, and the specific analysis process comprises the following steps:
step W1: marking the storage bins as warehouses i, i =1,2, \8230:, n and n are positive integers, marking the number of stacked products in the remaining storage space in the warehouses i as SKi, acquiring target storage warehouses of all products in the warehouse to be detected after the quality detection of the same batch of products is completed, acquiring the estimated stacking number of the warehouses i through the target storage warehouses of the products and marking the estimated stacking number as JDi;
step W2: comparing the number SKi of stacked products in the remaining storage space in warehouse i with the expected stacking number marked JDi:
if SKi is less than JDi, judging that the corresponding warehouse is saturated, and performing warehouse cleaning processing on the corresponding warehouse by the inventory analysis module;
if SKi is larger than or equal to JDi, judging that the inventory of the corresponding warehouse is not saturated;
the warehouse cleaning treatment in the step W2 specifically comprises the following steps:
step W21: acquiring the production dates of all products in the warehouse i, calculating the difference between the system time and the production dates of the products, and marking the difference as a warehouse-out value;
step W22: and sorting the products according to the warehouse-out value from high to low to form a travel product sequence, obtaining the expected warehouse cleaning quantity YCi according to a formula YCi = JDi-SKi, and carrying the front YCi products of the product sequence out of a warehouse i.
A product detection and sorting system based on big data is characterized in that a quality detection module carries out detection and analysis on products of the same production batch, a quality coefficient of the products is obtained through calculation of the planeness and the scratch number of each surface of the products, the smaller the quality coefficient is, the better the quality of the products is, the quality coefficient is compared with a quality coefficient threshold value, whether the quality of the products is qualified or not can be judged, the products of the same batch are temporarily stored in a bin to be fixed after the quality detection, until all the products of the same batch pass the quality detection, the products of the batch are sorted uniformly, and the problem that the inferior products pass the quality detection and then are directly sorted into a warehouse for storage when the batch of products with errors are detected due to the processing procedure is avoided; when the recovery bin receives inferior products, the timer counts down, and when the recovery bin receives the inferior products again before the countdown is finished, the timer resets the countdown, so that the continuity of the defective products is monitored, and if the defective products continuously appear within the set time, the fact that a large number of inferior products or even all inferior products possibly exist in the products of the same batch is reflected, and meanwhile, the possibility of detection errors of the products of the batch which pass the quality detection is represented, so that all products of the batch are detected again when the continuous defective products appear, and quality inspection personnel supervise the products, and further the accuracy of the detection result of the equipment is improved; the manual intervention module allocates proper quality inspectors to carry out deep detection on products, the allocation values of the primary-selected employees are obtained through calculation of the linear distances between the primary-selected employees and the to-be-warehoused warehouse, the difference values between the system time and the working time of the primary-selected employees and the historical quality inspection error rates of the primary-selected employees, the larger the allocation values are, the more suitable the quality inspectors are for processing the deep detection, the primary-selected employees with the largest allocation values are marked as the allocation employees, and the matching degree of the quality inspectors is comprehensively analyzed from multiple angles; the sorting control module sorts high-quality products in a to-be-sorted bin and conveys the products to a storage bin, an AGV trolley walks to the to-be-sorted bin, a worker places the products on a bracket of the AGV trolley, a bar code is pasted on the top surface of the products, a pressure sensor on the bracket of the AGV trolley drives the AGV trolley to enter a scanning area after detecting the weight of the products, a walking path of the AGV trolley is planned through scanned target warehouse information, and the products are conveyed to a corresponding target warehouse; the inventory analysis module carries out inventory analysis on the storage warehouses, after products enter to-be-warehoused warehouses through quality detection, the expected stacking number of the warehouses is calculated according to the target warehouses of the products, meanwhile, the remaining stackable number in the target warehouses is compared with the expected stacking number, when the inventory saturation condition occurs, the corresponding warehouses are cleared, part of the products are removed from the warehouses according to the production date of the products placed in the warehouses, the products with the longer production date are preferentially removed until the stackable number is larger than the expected stacking number, and then the products in the to-be-warehoused warehouses can be sorted to each warehouse.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The above formulas are all numerical values obtained by normalization processing, the formula is a formula obtained by acquiring a large amount of data and performing software simulation to obtain the latest real situation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (4)
1. The product detecting and sorting system based on the big data is characterized by comprising a sorting server, wherein the sorting server is in communication connection with an inventory analysis module, a quality detection module, a sorting control module, a manual intervention module and a database;
the quality detection module is used for detecting and analyzing the quality of the product, and the specific detection and analysis process of the quality detection module comprises the following steps:
step S1: sequentially detecting products in the same production batch, and calculating the leveling data and the scratch data of the obtained products to obtain the quality coefficient ZL of the products;
step S2: comparing the quality coefficient of the product with a quality coefficient threshold:
if the quality coefficient is smaller than the quality coefficient threshold value, judging that the quality of the product meets the requirement, marking the corresponding product as a high-quality product, and conveying the high-quality product to an undetermined warehouse;
if the quality coefficient is larger than or equal to the quality coefficient threshold value, judging that the product quality does not meet the requirement, marking the corresponding product as an inferior product, conveying the inferior product to a recovery bin, and adding one to the number of the products in the recovery bin;
and step S3: when the recovery bin receives an inferior product, a timer of the recovery bin counts down, wherein the counting down time length is T1 time, and T1 is a preset value; if the recycling bin receives the inferior products within the countdown time, resetting the timer to count down again, wherein the countdown time length is T1 time; if the recovery bin does not receive the inferior products within the countdown time, the countdown is closed, and the timer is continuously started when the next inferior product enters the recovery bin;
and step S4: if the timer is reset for three times continuously, the quality detection module sends a depth detection signal to the sorting server, the sorting server receives the depth detection signal and then sends the depth detection signal to the manual intervention module, and the manual intervention module receives the depth detection signal and then carries out depth detection on all high-quality products in the to-be-classified bin manually.
2. The big data based product detecting and sorting system according to claim 1, wherein the method for calculating the quality factor ZL in step S1 comprises the following steps:
step S11: calculating the average value of the planeness of each surface of the product to obtain the average planeness of the product, and marking the average planeness as PM;
step S12: summing the number of scratches on each surface of the product to obtain the number of the scratches of the product, and marking the number as GH;
step S13: carrying out dequantization treatment on the average planeness PM and the number GH of the scratches of the product to obtain the numerical value, and utilizing a formula
And obtaining the mass coefficient ZL of the product, wherein both alpha 1 and alpha 2 are proportional coefficients, e is a natural constant, and the value of e is 2.71828.
3. The big data based product detecting and sorting system according to claim 1, wherein the manual intervention module is used for allocating suitable quality inspectors to perform deep inspection on the products, and the allocation manner of the manual intervention module comprises the following steps:
step P1: acquiring an idle quality inspector in a workshop within a linear distance L1 m from a warehouse to be checked, marking the idle quality inspector as a primary selection employee, wherein L1 is a preset distance value;
step P2: acquiring basic information of the primary selection staff, wherein the basic information of the primary selection staff comprises the following steps: primarily selecting the name of an employee, the time of job entry and the historical quality inspection error rate;
step P3: acquiring a linear distance between a primary selected employee and a to-be-warehoused employee, marking the linear distance as JL with the unit of meter, calculating a difference value between system time and the working time of the primary selected employee, marking a calculation result as RS with the unit of month, and marking the historical quality inspection error rate of the primary selected employee as CC;
step P4: by the formula
Obtaining the distribution values of the primarily selected employees, wherein both beta 1 and beta 2 are proportionality coefficients, and marking the primarily selected employee with the maximum distribution value as a distribution employee;
step P5: and sending the basic information of the distribution staff to a sorting server, and sending a depth detection signal to the communication equipment of the distribution staff by the sorting server.
4. The big-data-based product detecting and sorting system according to claim 1, wherein the deep detection process is that the distribution staff detects all the high-quality products in the warehouse again through the quality detection module, and supervises the detection process in the whole process; if the timer is continuously reset for three times in the depth detection process, all products in the same batch are conveyed to a recovery bin for recovery and rework; and if the timer is not continuously reset for three times after the depth detection is finished, the sorting control module sorts the high-quality products in the undetermined warehouse after the depth detection.
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| CN114397307B (en) * | 2021-12-20 | 2023-08-01 | 苏州镁伽科技有限公司 | Method, apparatus, device and storage medium for device inspection |
| CN115423299A (en) * | 2022-08-30 | 2022-12-02 | 深圳市邦迪工程顾问有限公司 | Intelligent building supervision and acceptance method and system based on big data detection |
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