CN113138588A

CN113138588A - Method, apparatus and computer-readable storage medium for product tracing

Info

Publication number: CN113138588A
Application number: CN202010617284.4A
Authority: CN
Inventors: 朱嘉; 崔学坤; B·恩吉姆; R·德库尔塞勒
Original assignee: Saint Gobain Glass France SAS
Current assignee: Saint Gobain Glass France SAS; Compagnie de Saint Gobain SA
Priority date: 2020-06-30
Filing date: 2020-06-30
Publication date: 2021-07-20
Also published as: WO2022002081A1

Abstract

Embodiments of the present disclosure relate to methods, apparatuses, and computer-readable storage media for product tracing. The method comprises the following steps: determining whether the first object reaches a first location; in response to determining that the first object reaches the first location, determining an identity of the first object; generating a first message comprising an identification of the first object; and causing a second message to be moved for a second location upstream of the first location, the second message including an identification of the first object.

Description

Method, apparatus and computer-readable storage medium for product tracing

Technical Field

Embodiments of the present disclosure relate generally to smart manufacturing and, more particularly, to methods, apparatuses, and computer-readable storage media for product tracing.

Background

Object detection is a key task in automation industries such as smart manufacturing. Industrial controls need to know when an object or object reaches a particular location. Common types of object sensing technologies include electromechanical, pneumatic, capacitive, and photoelectric sensors. However, these sensors generally have high requirements for use environments and cannot be used under severe conditions such as high temperatures. In addition, these sensors typically have a relatively high cost to achieve object identification.

Disclosure of Invention

According to embodiments of the present disclosure, a method, apparatus, system, and computer-readable storage medium for product tracing are provided.

In a first aspect, a computer-implemented method for product tracing is provided. The method comprises the following steps: determining whether the first object reaches a first location; in response to determining that the first object reaches the first location, determining an identity of the first object; generating a first message comprising an identification of the first object; and causing a second message to be moved for a second location upstream of the first location, the second message including an identification of the first object.

In a second aspect, a computing device is provided. The apparatus comprises: a processing unit; and a memory coupled to the processing unit and storing instructions that, when executed by the processing unit, cause the computing device to perform the method according to the first aspect.

In a third aspect, a system for product traceability is provided. The system comprises: a plurality of modules disposed in a process stream, each module of the plurality of modules comprising: an object detector configured to detect whether an object reaches a position of the object detector; and a computing device according to the second aspect, the computing device being coupled to the object detector.

In a fourth aspect, there is provided a computer-readable storage medium storing computer-executable instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to the first aspect.

It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:

FIG. 1 shows a schematic diagram of a tracking method according to some embodiments of the present disclosure;

FIG. 2 illustrates a schematic diagram of a tracking method according to some embodiments of the present disclosure;

FIG. 3 illustrates a schematic diagram of a tracking method according to some embodiments of the present disclosure;

FIG. 4 illustrates a flow diagram of a tracking method according to some embodiments of the present disclosure;

FIG. 5 illustrates a schematic diagram of exception handling, according to some embodiments of the present disclosure;

FIG. 6 illustrates a schematic diagram of exception handling, according to some embodiments of the present disclosure;

FIG. 7 illustrates a schematic diagram of process parameter correlation according to some embodiments of the present disclosure;

FIG. 8 illustrates a flow diagram of a tracking method according to some embodiments of the present disclosure;

FIG. 9 illustrates a schematic diagram of a production line system according to some embodiments; and

FIG. 10 illustrates a block diagram of a computing device capable of implementing some embodiments of the present disclosure.

Detailed Description

The concepts of the present disclosure will now be described with reference to various exemplary embodiments shown in the drawings. It should be understood that these examples are described merely to enable those skilled in the art to better understand and further practice the present disclosure, and are not intended to limit the scope of the present disclosure in any way. It should be noted that where feasible, similar or identical reference numerals may be used in the figures and similar or identical reference numerals may denote similar or identical elements. It will be appreciated by those skilled in the art from the following description that alternative embodiments of the structures and/or methods illustrated herein may be employed without departing from the principles and concepts of the disclosure as described.

In the context of the present disclosure, the term "comprising" and its various variants can be understood as open-ended terms, which mean "including but not limited to"; the term "based on" may be understood as "based at least in part on"; the term "one embodiment" may be understood as "at least one embodiment"; the term "another embodiment" may be understood as "at least one other embodiment". Other terms that may be present but are not mentioned herein should not be construed or limited in a manner that would contradict the concept upon which the embodiments of the disclosure are based unless explicitly stated.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms, and these terms are not associated with the order of the corresponding elements. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the embodiments. As used herein, the term "and/or" includes any and all combinations of one or more of the listed terms.

Fig. 1 shows a schematic diagram of a tracking method according to some embodiments of the present disclosure. As shown in FIG. 1, the objects 1-6 pass through the production line in sequence, and the object Detector_i-1And a Detector_iIs arranged on a production line and is used for detecting whether an object reaches the position of the Detector or not, wherein the Detector of the Detector_i-1Located at the Detector_iUpstream. In some embodiments, the object detector may have only a function of detecting the presence or absence of an object, without requiring a complicated image detection and recognition function, and thus, at a relatively low cost. For example, the output of the detector may be 1 if an object is detected, and 0 otherwise. Pyrometers, pressure sensors, and the like may also be used to implement the function of the object detector.

In this example, the Identification (ID) of the object that reaches various locations (e.g., where the object detection point or detector is located) may be recorded. In addition to the ID of the object, the arrival time of each object at the corresponding detection point may be recorded. For example, at the location of each detector, when an object is detected, a corresponding message may be generated containing the ID of the object and optionally including the time at which the detector detected the object, i.e. the time at which the object arrived at the location of the detector. For ease of discussion, reference will be made below to both the ID of the object and the time of arrival, however, it should be understood that some embodiments of the present disclosure do not require the time of arrival to be recorded.

The ID of the object (e.g., component/part identification (CPID)) enables a manufacturer or the like to identify the component and the part. Typically, an Original Equipment Manufacturer (OEM) defines specifications for a part or part that is part of its final product (e.g., an automobile). Identification (ID) means uniquely identifying an object. The basic principle of marking is that each object can be identified even if objects of the same shape and material are manufactured in series. The ID of the object may be printed as text, or encoded as a bar code or Data Matrix Code (DMC) or an Electronic Product Code (EPC)/radio frequency tag (RFID) attached to the object (e.g., component or part). Since the ID reader cannot operate for a long period of time due to a severe environment, virtual tracking, such as high temperature, highly corrosive atmosphere, narrow places, and the like, may not be avoided. In the embodiment of the present disclosure, the IDs of these entities are not necessary, but may be used to cross-check the results of the virtual trace, so that the location can be flexibly set. The approach of virtual tracking of embodiments of the present disclosure may alleviate or even eliminate such problems.

For example, such information may be recorded using a linear data structure, such as a queue or a stack, where the queue is a first-in-first-out (FIFO) type of linear data structure and the stack is a last-in-first-out (LIFO) type of linear data structure. It should be understood that the queues or stacks described herein do not represent actual storage in a storage device, but merely represent logical representations of such data. Instead, these data may be stored in a more optimized manner in the storage device. In the example shown in fig. 1, queues may be advantageous because such a pipeline design is well suited for using queues to record information such as the ID of an object. Alternatively, in some pipelines, it may be desirable to temporarily store some products in a warehouse or at a temporary site. For example, some process flows may require cooling, which requires that the hot product be placed in a temporary station. For example, in glass production, these storage stations can slow the production rate of glass to the next processing station, acting as a buffer. In such a case, the product that is eventually entered into such a temporary repository needs to be taken out first, possibly using a stack to record information such as the ID of the object. It should be understood that linear data structures such as queues and stacks are only preferred examples of data structures, and that other data structures may be used to record such data or information.

The length of a linear data structure such as a queue or stack is dynamic depending on the production line. As shown in FIG. 1, Queue_i-1And Queue_iDetector for respectively recording Detector_i-1And a Detector_iID and arrival time of the detected object. For convenience, embodiments of the present disclosure will be discussed below by way of example in terms of queues. However, it should be understood that the inventive concepts of the present disclosure may also be applied to other data structures such as stacks.

In the queue, the new element is pushed to the beginning of the queue. When an object with an ID of 6 enters the Detector, as shown in FIG. 2_i-1At the position, the Detector_i-1The object is detected. Then, a message including the ID 6 and arrival time (as a time stamp) of the object is generated and added to the Queue_i-1As shown in fig. 2.

When the object enters a new position, the last element in the queue of the last position is popped up, a message containing the ID of the element and the time of reaching the new position is generated, the message is pushed to the current queue, and the like, so as to realize traceability. As shown in FIG. 3, once an object with ID3 enters the Detector_iWhere the location, Queue_i-1Will pop up and will contain the ID of the element and arrive at the Detector_iTime of arrival push to Queue_iOf the bottom plate. It should be understood that the push and pop described herein do not represent the operation of a true storage situation in a storage device,but merely a logical representation of such data. Instead, these data may be stored in a more optimized manner in the storage device.

For example, a Detector_i-1And a Detector_iCan be considered as the inlet and outlet of one module in the flow direction of the production line. Queue can be used_i-1The objects inside the module are tracked along the sequence of movements. In a production line, detection sensors may be installed at the entrance and exit of a workstation/process to enable object tracking between the two.

Some embodiments of the present disclosure may be implemented using a modular design. For example, fig. 4 illustrates a flow diagram of a method 400 of operation of a single tracking module in accordance with some embodiments of the present disclosure. For example, the tracking module may be disposed at the Detector shown in fig. 1_iAt the location where the Detector will be located_iShown as object detector 402. It should be understood that the method of operation 400 may also be applied to tracking modules disposed at other detectors.

At block 404, it is determined whether the output of the object detector 402 has changed. For example, the output value of the object detector 402 may be continuously monitored or periodically checked. If it is determined at block 404 that there has been no change in the output of the object detector 402, a return to block 402 continues to check the output value of the object detector 402.

If it is determined at block 404 that the output of the object detector 402 has changed, it is determined at block 406 whether the output value has changed from 1 to 0 or from 0 to 1. If it is determined at block 406 that the output value has changed from 1 to 0, proceed to block 408, indicating that the object has left the location, a timestamp is recorded as

If the value changes from 0 to 1 at block 406, indicating an object arrives, at block 410, from the previous linear data structure (e.g., Queue)_i-1Bottom) pops up a message and gets from Queue_i-1Extracts the ID of the object (particularly from the ejected message) and records the timestamp as the time stamp at block 412

It should be understood that although 1 is used to indicate that an object arrives and 0 is used to indicate that an object does not arrive, other numbers can be used to indicate corresponding meanings. For example, 0 may be used to indicate that an object arrives and 1 may be used to indicate that an object does not arrive.

At block 414, it is determined whether the object arrival time matches the expected arrival time. For example, an anomaly may occur because an object may be taken from the pipeline or a new object may be added to the pipeline, and the arrival time of the detected object may not match the expected arrival time.

If it is determined at block 414 that the object arrival time matches the expected time, the method 400 proceeds to block 416, at which time the ID of the object is compared to the expected time

Together generating a new message. Alternatively, this message may also contain only the ID. At block 418, the new message is pushed to the Detector_iLinear data structure of (2), e.g. pushing new messages to Queue_iAs a Queue_iNew elements of (2).

If it is determined at block 414 that the object arrival time does not match the expected arrival time, the method 400 proceeds to block 420. At block 420, the specifics of the abnormal situation are determined. For example, it may be determined whether the object arrives at the location earlier or later than the expected arrival time. If it is determined at block 420 that the object arrives at the location later than the expected arrival time, a timeout condition is included, which will be described in detail below in conjunction with FIG. 5. In this case, the method 400 proceeds to block 422. At block 422, one or more IDs for timeouts are pushed to the Detector_iAnd may also push the expected arrival time into the anomalous linear data structure (e.g., queue or stack). If it is determined at block 420 that the object arrives at the location later than the expected arrival time, the method 400 further proceeds to block 416 where the ID of the object will be determined and will include the ID of the object and optionally the arrival time

Push messages to Queue_iIn (1).

In case of timeout, the Queue can be obtained by traversing the Queue of the previous location_i-1To determine which element's expected arrival time matches the arrival time of the object. For example, for a queue, the queue may be traversed starting with the element at the end of the queue (i.e., the element that was pushed to the queue earliest). For a stack, the stack may be traversed starting with the element that started the stack (i.e., the element that was most recently pushed to the stack). In this way, the ID of the element can be determined as the ID of the object.

If it is determined at block 420 that the object arrives at the location earlier than the expected arrival time, then this is the case for an inserted object, which will be described in detail below in connection with FIG. 6. In this case, the method 400 proceeds to block 424. At block 424, an ID and arrival time including the object is generated

The message of (2). It should be understood that the ID of the object may be defined in a manner distinct from a normal object, and may be redefined at the checkpoint. For example, the insertion object and the regular object may be defined by different encoding manners. At block 426, the message is pushed to a Detector_iLinear data structure of (2), e.g. pushing the message to the Queue_iAs a Queue_iNew elements of (2).

In some embodiments, the time interval is calculated

To determine if the movement of the object between the two positions has timed out. For ease of calculation, it may be further simplified to use only the time stamps of the incoming detectors to calculate the motion time interval, i.e.,

it is clear that,

where d is the dimension of the object in the direction of movement,

is the average moving speed between the two. Alternatively, the time stamp of the departure detector may also be used to calculate the movement interval, i.e.,

since the time stamp of the entry of the object into each position is recorded in a corresponding queue, for example for the object 3 shown in fig. 3, it is possible to use

Slave computer object_i-1To the Detector_iTime interval of movement of wherein

Representing Queue_iThe first element of (i.e., the topmost element or the latest push to Queue)_iThe element of (d) of the time stamp,

is Queue_i-1The last element of (i.e., the bottom-most element or to be selected from Queue)_i-1Popped element) in which n is_i-1＝len(Queue_i-1)-1。

In production, the time interval for each object to move from one position to the next is substantially the same for a certain production cycle. If an abnormal situation occurs, such as an operator taking the object away or the object breaking during transport, a timeout will result.

Fig. 5 shows an example of a timeout. Detector_i-1And a Detector_iIs a distance of l_iWith an average moving speed of

Then the time t is shifted_iIs equal to

A slight deviation may be caused due to the instability of the transmission speed.

Assuming that the object 3 (object with ID 3) is removed from the conveyor belt, the actual travel time

Is equal to

This is because

In fact the arrival time of the object 4, here g_nIs the gap between the object 3 and the object 4 plus the dimension of the object in the direction of movement. Thus, can be

Defined as the reference time for the timeout determination, here

Indicating a buffering time, considering that the speed is not absolutely stable. In general,

is an empirical value and should be less than

Reference time

Also reflects (at the Detector)_i-1And a Detector_iIn between) production cycles of the process.

If passing through

Minus

To calculate t_i ^′Then the value will be closest

This can be used to determine the actual arrival at the Detector when a timeout occurs_iID of the object of (1). This is particularly useful, for example, when removing multiple objects, as can be calculated for t_iAnd make it and

to determine the Detector_iThe actual ID of the object at (a).

In some embodiments, the average speed of motion is not easily obtained

Thus reference time

It can also be determined by statistics or machine learning, so that the details of the kinematics inside the module need not be known. For example, a previous number of objects (e.g., five) from the Detector may be statistically calculated_i-1Move to the Detector_iAverage of the time required. In addition, the movement time of each object can be continuously recorded for judging an abnormal value or a reference time change caused by the movement speed adjustment.

For example, whether to timeout may be determined by the following formula:

here, j is Queue_i-1To indicate the actual detected object. Thus, by calculating the minimum movement time interval, the details of the kinematics inside the module can be deduced.

In some casesIn an embodiment, due to the use of queues, Queue_i-1Last element, i.e. enter Queue first_i-1Will also be the first to reach the Detector_i. It can be seen that in the foregoing formula, when a timeout occurs, Queue will be traversed_i-1All elements of (2) to calculate the closest

Including the element at the end of the queue. This is because it is considered that if a timeout occurs, the element at the end of the Queue still gets the minimum value (i.e., j ═ len (Queue)_i-1) -1), it may mean that there is a pause in the transmission.

If j is<len(Queue_i-1)1, then one or more objects leave the movement sequence, Queue_i-1Element (index) entered earlier than jth element in>j) Will be pushed to an exception linear data structure, such as an exception queue or exception stack.

These exception elements are moved to an exception linear data structure (e.g., exception queue or exception stack) instead of simply being deleted, and may also be better checked and tracked. Since the exception element contains a timestamp, it can be correlated with an exception condition in actual production to find the cause.

Alternatively, the anomalies may be recorded by means of a flag. For example, the time, time interval, location, cause of the anomaly may be marked for association with the anomaly element.

Further, multiple exception queues may be used to record object information for different exception situations, e.g., a queue of broken objects, a queue of defective objects, a queue of removed objects, etc. Of course, this may require other information to classify the abnormal condition, such as a Programmable Logic Controller (PLC) signal indicating open door, a test result (NOK) of the inspection device, and the like. Thus, the use of exception queues may be highly customized because different production lines have different exceptions. However, the classification of the abnormal situation may be achieved by a simple filter, i.e. timeout plus condition a/condition B/. The concepts of the embodiments of the present disclosure are described above with reference to exception queues as examples only, however, it should be understood that these exception queues may each be replaced by other linear data structures such as exception stacks.

In some embodiments, an exception queue may be created for each part of the production line (e.g., at a Detector_i-1And a Detector_iIn between), then an algorithm is used to classify the anomalous object by associating with the anomalous signal (e.g., alarm). For example, machine learning algorithms may be used to classify anomalous data or anomalous flows.

Since the classification is performed almost immediately after the time-out judgment and the signal automatic reading function such as PLC is used, the management of the abnormal flow can be realized very efficiently. The classified data can be used for subsequent statistical analysis and predictive maintenance.

In some embodiments, an object is replaced in the production line after being removed.

Fig. 6 shows a schematic diagram of a tracking method in such a case, according to some embodiments of the present disclosure. As shown in fig. 6, the object 7 is inserted into the movement sequence and placed in front of the object 3. In this case, when the Detector_iWith a new detection value, t_iWill be equal to

For example, there are two conditions that can be used to judge this, as shown in the following equation:

here, the first and second liquid crystal display panels are,

and

are respectively detectors_iDetecting a current timestamp and a next timestamp of the object;

is a Detector_i-1Last timestamp, n, of the detected object_i-1＝len(Queue_i-1) -1. In this case, the object ID and

will form a new element and be pushed to Queue_iOf the bottom plate. The ID of the object being processed may be defined in a manner distinct from a normal object and may be redefined at the checkpoint.

While an embodiment of inserting an object in a product line is described above in conjunction with FIG. 6, those skilled in the art will appreciate that the process of inserting multiple objects simultaneously may be similar to a timeout determination where multiple objects are removed.

In some embodiments, the accuracy of the virtual trace as described above may be verified by cross-checking. For example, cross-checking may be performed using a count of objects, i.e., the number of objects entering a station minus the number of removed objects plus the number of re-injected objects minus the number of damaged objects equals the number of objects leaving the station.

For example, one or more checkpoints may be set. The object may be provided with a physical marking, e.g. a two-dimensional code, a bar code, etc. For example, if the IDs are printed on the object in the form of Data Matrix Codes (DMCs), it is possible to check whether the actual ID sequence coincides with the expected ID queue by installing a scanner as a check point at a specific position. If the check result is inconsistent, the queue may be readjusted based on the actual scan result. At this point, it may be checked whether the configuration of the virtual tracking algorithm is correct, e.g.

Whether the set value of (a) is reasonable. The concepts of the embodiments of the present disclosure are described above with reference to queues as examples only, however, it should be understood that these queues may be replaced by other suitable linear data structures such as stacks.

In embodiments of the present disclosure, the checkpoint is not relied upon to enable traceability, but rather is used as a means of verification. In practice, relying entirely on a scanner or RFID reader to achieve traceability would be costly. Under certain harsh conditions, these devices cannot be installed and used (e.g., RFID tags cannot survive in environments in excess of 400 ℃). Virtual tracking undoubtedly has the advantages of flexibility and cost savings.

However, in some cases, checkpoints are necessary. That is, when the order of movement is interrupted and the order of objects is manually destroyed, the order of the IDs in a linear data structure, such as a queue or stack, can be reorganized by subsequently checkpointing.

In production traceability applications, it is often necessary to associate an object ID with a process parameter for each process in order to associate an indicator, such as product quality, with the production process in order to perform product failure analysis and optimize process control.

FIG. 7 illustrates an example of the association of an object ID with a process parameter. Due to the Sensor_kDetector closest to the Detector_iIs fixed, so that the distance l can be used_kAnd time stamp

To derive and extract the corresponding process parameters. For example, according to Sensor_kAt the sampling frequency of

Measured under measurement, or in the closest proximity

The measured value. For example, the use of production cycles is also conceivable

The sampling frequency is set to help correlate the object ID with the process parameters.

The values of certain process parameters may vary greatly with or without the object, for example, the surface temperature of the object. This type of process parameter sensor can also be considered as an object detector. For example, a queue may be assigned to such an object detector, and the elements of the queue may comprise measured values of process parameters in addition to the object ID, the time stamp.

Through the modular design, the virtual traceable system can be easily deployed to a production line. For example, the object detector is installed at a critical site on a production line and then connected to an edge computing device or other computing device over an industrial network. The virtual trace program is to be deployed on a computing device. The computing devices may be implemented in a distributed fashion at various sites and interconnected in a communication means such as a network. Alternatively, the computing device may also be realized centrally and receive detection signals from the respective object detectors for the determination.

In some embodiments, various sensors, PLCs, etc. may be connected to the network in order to manage anomaly flows, associate object IDs with production conditions, etc. In addition, when the object is tracked, the ID of the object, the time stamp of entering each detection position, relevant process parameters, measurement results and the like can be written into a database in real time, and therefore a database with the product as the center is established.

Fig. 8 illustrates a flow diagram of a tracking method 800 according to some embodiments of the present disclosure. FIG. 8 may be implemented by a Detector disposed at the Detector shown in FIG. 1_iA computing device at the site of the site.

At block 802, it is determined whether a first object has reached a first location. For example, the first location may be a Detector as shown in FIG. 3_iWhere it is located and the first object may be the object 3. Of course, the first position may also be any other suitable position on the production line, e.g. a Detector_i-1The location of the location.

At block 804, in response to determining that the first object reaches the first location, an identity of the first object is determined. In some embodiments, the data may be received from a Detector_i-1To determine the identity of the first object. For example, it can be received from the Detector_i-1Queue of (1)_i-1The identification of the first object, i.e., ID3, is obtained.

At block 806, a first message is generated that includes an identification of the first object. The first message may include an identification of the first object, e.g., ID3, and may also include a time of arrival of the first object at the first location, e.g., ID3 as shown in FIG. 3.

At block 808, a second message is caused to be moved at a second location upstream from the first location, the second message including an identification of the first object. As shown in FIG. 3, the second position may be a Detector_i-1At the location, for example, the message containing ID3 can be queued from Queue_i-1Pop up. The message may be deleted directly or moved to another data structure (e.g., another queue) or other storage space at the second location. It should be appreciated that although a sequential order is shown in FIG. 8, the order of

blocks

806 and 808 may be reversed or performed in parallel.

For example, the first message may be pushed to a linear data structure of the first location and the second message may be popped from a linear data structure of the second location. These linear data structures may be implemented by queues or stacks. Alternatively, any other suitable data structure may be used.

In some embodiments, the method 800 further comprises: determining an expected arrival time of a second object at the first location; and in response to determining that the time of arrival of a third object at the first location does not match the expected arrival time, generating a third message indicating that the second object is not expected to arrive at the first location. The third message may include an identification of the second object and optionally an expected arrival time of the second object at the first location. In this manner, anomaly detection may be implemented, as indicated at block 414 of FIG. 4.

In some embodiments, determining the expected arrival time comprises: determining a time of arrival of the second object at the second location, an average speed of movement between the first location and the second location, and a distance between the first location and the second location; and determining the expected arrival time based on the time of arrival of the second object at the second location, the average moving speed between the first location and the second location, and the distance between the first location and the second location.

In some embodiments, determining the expected arrival time comprises: determining a reference motion time between the second location and the first location by statistics or machine learning; and determining the expected arrival time based on the reference motion time.

In some embodiments, the time at which the third object arrives at the first location does not match the expected arrival time comprises: the third object arrives at the first location after a time window defined by the expected arrival time, the third message including an identification of the second object and the expected arrival time of the second object. Further, pushing the third message into an anomalous linear data structure of the first location, determining an identity of the third object, generating a fourth message including the identity of the third object, and pushing the fourth message into a linear data structure of the first location. In this way, time-out detection may be achieved, for example, by the methods shown in fig. 4-5.

In some embodiments, the time at which the third object arrives at the first location does not match the expected arrival time comprises: the third object reaches the first location before a time window defined by the expected arrival time. The third message includes an identification of the third object and a time of arrival of the third object at the first location. The method 800 further includes pushing the third message to a linear data structure of the first location. The identity of the third object may be obtained and defined in a different manner than a conventional object. In this way, detection and tracking of the re-injected object may be achieved.

In some embodiments, the third message may be associated with an anomaly signal associated with the first location at an expected arrival time of the second object. In this way, the cause of the occurrence of the anomaly may be analyzed, thereby facilitating subsequent improvements in the tracking method and/or process flow.

In some embodiments, the method 800 further comprises: associating a process parameter of a process associated with the first location with the first object based on the identification of the first object. In this manner, a product can be associated with various process parameters, thereby facilitating the provision of queries and product traceability.

In some embodiments, method 800 further comprises determining whether a fourth object reaches the second location; in response to determining that the fourth object reaches the second location, determining an identity of the fourth object; generating a fifth message comprising an identification of the fourth object; and causing a sixth message to be moved for a third location upstream of the second location, the sixth message including an identification of the fourth object. The second position may be a Detector as shown in FIG. 3_i-1Where it is located, and the third location may be an upstream Detector_i-2The location of the location.

In some embodiments, the first object includes a physical identifier, e.g., a two-dimensional code, a bar code, etc. The method 800 includes extracting a physical identification of the first object from the first object; and determining whether the extracted physical marker on the first object and the marker obtained from the second location coincide for cross-checking. For example, the physical identity of the object may be extracted from the first object by reading a code or the like.

In accordance with embodiments of the present disclosure, by virtue of the modular approach, production traceability can be quickly deployed to various stations on a production line and the output signals of the object detectors used to record the identity and optionally the time stamps of products at various parts of the production line. In addition, embodiments of the present disclosure allow for the quick addition of new sites to the tracking process, and also facilitate modifying, skipping, and deleting tracking of one or more parts. The scheme has no strict requirements on the production line, can be suitable for various production lines, and can be rapidly deployed to various production lines.

Quality control and rapid tracking and analysis of product failures are facilitated by printing indicia on the product (e.g., part or part) and providing relevant information. The user can conveniently obtain the relevant information of the product, thereby improving the production process utilizing the parts or the parts and further advancing to realizing intelligent manufacturing.

With the modular configuration, embodiments of the present disclosure can be applied to a variety of complex pipelines. For example, FIG. 9 shows a schematic diagram of a production line system 900 according to some embodiments. In the production line system 900, detectors are provided at a plurality of stations and a corresponding module is provided at each detector. For example, FIFO 901 represents a module disposed at a site for implementing the methods and functions shown in fig. 1-8. As shown in fig. 9.

FIFOs

901 and 904 form an upstream and downstream pipeline,

FIFOs

902 and 905 form an upstream and downstream pipeline, and

FIFOs

903 and 906 form an upstream and downstream pipeline. The three sub-pipelines are merged together at block 907 and split into two sub-pipelines,

LIFO

909 and 910 at block 908.

FIG. 10 shows a schematic block diagram of a device 1000 that can be used to implement embodiments of the present disclosure. Method 400 as shown in fig. 4 and method 800 as shown in fig. 8 may be implemented by device 1000. Device 1000 may be implemented at or may be communicatively coupled to each detector.

As shown in fig. 10, device 1000 includes a Central Processing Unit (CPU)1001 that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM)1002 or computer program instructions loaded from a storage unit 1008 into a Random Access Memory (RAM) 1003. In the RAM 1003, various programs and data necessary for the operation of the device 1000 can also be stored. The CPU 1001, ROM 1002, and RAM 1003 are connected to each other via a bus 1004. An input/output (I/O) interface 1005 is also connected to bus 1004.

A number of components in device 1000 are connected to I/O interface 1005, including: an input unit 1006 such as a keyboard, a mouse, and the like; an output unit 1007 such as various types of displays, speakers, and the like; a storage unit 1008 such as a magnetic disk, an optical disk, or the like; and a communication unit 1009 such as a network card, a modem, a wireless communication transceiver, or the like. The communication unit 1009 allows the device 1000 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

Various processes and processes described above, such as

method

400 or 800, may be performed by the processing unit 1001. For example, in some embodiments, the

method

400 or 800 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 1000 via ROM 1002 and/or communications unit 1009. When the computer program is loaded into RAM 1003 and executed by CPU 1001, one or more steps of

method

400 or 800 described above may be performed. Alternatively, in other embodiments, the CPU 1001 may be configured to perform the

method

400 or 800 by any other suitable means (e.g., by way of firmware).

The present disclosure may be methods, apparatus, systems, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for carrying out various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, Python, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A computer-implemented method for product tracing, comprising:

determining whether the first object reaches a first location;

in response to determining that the first object reaches the first location, determining an identity of the first object;

generating a first message comprising an identification of the first object; and

causing a second message to be moved at a second location upstream from the first location, the second message including an identification of the first object.

2. The method of claim 1, further comprising pushing the first message to a linear data structure of the first location, and

wherein causing the second message to be moved comprises: causing the second message to pop up from the linear data structure at the second location.

3. The method of claim 2, wherein the linear data structure comprises a queue or a stack.

4. The method of claim 1, further comprising:

determining an expected arrival time of a second object at the first location; and

in response to determining that a time of arrival of a third object at the first location does not match the expected arrival time, generating a third message indicating that the second object is not expected to arrive at the first location.

5. The method of claim 4, wherein determining the expected arrival time comprises:

determining a time of arrival of the second object at the second location, an average speed of movement between the first location and the second location, and a distance between the first location and the second location; and

determining the expected arrival time based on a time of arrival of the second object at the second location, an average moving speed between the first location and the second location, and a distance between the first location and the second location.

6. The method of claim 4, wherein determining the expected arrival time comprises:

determining a reference motion time between the second location and the first location by statistics or machine learning; and

determining the expected arrival time based on the reference motion time.

7. The method of claim 4, wherein the third message includes an identification of the second object and an expected arrival time of the second object at the first location when the third object arrives at the first location after a time window defined by the expected arrival time;

the method further comprises the following steps: pushing the third message into an anomalous linear data structure at the first location;

determining an identity of the third object and generating a fourth message comprising the identity of the third object; and

pushing the fourth message to a linear data structure of the first location.

8. The method of claim 4, wherein the third message includes an identification of the third object and a time at which the third object arrived at the first location when the third object arrived at the first location before a time window defined by the expected arrival time,

the method also includes pushing the third message to a linear data structure of the first location.

9. The method of claim 4, further comprising:

associating the third message with an exception signal associated with the first location at an expected arrival time of the second object.

10. The method according to any one of claims 1-8, further comprising:

associating a process parameter of a process associated with the first location with the first object based on the identification of the first object.

11. The method according to any one of claims 1-8, further comprising:

determining whether a fourth object reaches the second location;

in response to determining that the fourth object reaches the second location, determining an identity of the fourth object;

generating a fifth message comprising an identification of the fourth object; and

causing a sixth message to be moved at a third location upstream of the second location, the sixth message including an identification of the fourth object.

12. The method of any of claims 1-8, wherein determining the identity of the first object comprises:

obtaining an identification of the first object from the second location.

13. The method of claim 12, wherein the first object comprises a physical marker, the method further comprising:

extracting a physical identification of the first object from the first object; and

determining whether the extracted physical marker on the first object and the marker obtained from the second location coincide for cross-checking.

14. A computing device, comprising:

a processing unit; and

a memory coupled to the processing unit and including instructions stored thereon that, when executed by the processing unit, cause the computing device to perform the method of any of claims 1-13.

15. A system for product traceability, comprising:

a plurality of modules disposed in a process stream, each module of the plurality of modules comprising:

an object detector configured to detect whether an object reaches a position of the object detector; and

the computing device of claim 14, the computing device coupled to the object detector.

16. A computer-readable storage medium storing computer-executable instructions that, when executed by at least one processor, cause the at least one processor to perform the method of any one of claims 1-13.