RELIABILITY-BASED VERIFICATION AND SELECTIVE DATA RECOVERY SYSTEM FOR MULTI-SOURCE PRODUCT DATA.

Abstract

The invention relates to a reliability-based verification and selective data retrieval system and method for multi-source product data, enabling the verification of data obtained from different data acquisition methods for a physical product at the source and data field level, and selective data retrieval from the physical product based on the verification result. Within the system, data fields within the image data obtained from the physical product are identified, these data fields are associated with their corresponding regions on the physical product, and they are compared at the source level by matching them with corresponding data fields obtained from different product data sources. The field-based reliability level is determined by considering at least one of the data acquisition characteristics and inter-source comparison and inconsistency results for each data field, and it is evaluated whether the relevant data field meets the specified verification condition.Based on the validation result, the data field requiring re-acquisition is identified, the corresponding region on the physical product is selected as the target region for re-acquisition, and at least one data acquisition parameter is selected based on the nature of the identified data acquisition deficiency. The selected data acquisition parameter is adjusted based on the previous data acquisition result, limiting the re-acquisition process to the target region, and new data is obtained from that target region. The re-acquired data is fed back into the validation cycle, re-evaluated according to the validation condition of the relevant data field, thus creating a feedback technical relationship between the validation result and the subsequent physical data acquisition process.Thanks to this invention, instead of repeatedly re-evaluating the entire physical product under the same data acquisition conditions, selective data re-acquisition and re-validation of the acquired data are achieved by adjusting the data acquisition conditions from the physical region corresponding to the data field requiring verification.

Description

RELIABILITY-BASED ON MULTI-SOURCE PRODUCT DATA. VERIFICATION AND SELECTIVE DATA RECOVERY SYSTEM Technological Field: The invention relates to optically detectable data and machine-generated data on physical products. Obtaining readable identifiers through technical means, different data acquisition methods. correlating data obtained from pathways and product data sources and Verification of unreliable data by selectively analyzing the physical product It relates to data acquisition and verification systems for the purpose of re-recovery. The invention specifically involves the acquisition of an image from a physical product via an imaging unit. data processing, identification derived from machine-readable product identifiers. Product information extracted from data and image data, and one or more products The correlation of corresponding data obtained from the data source on a data field basis, different Checking the consistency between data obtained from sources, data acquisition. determining its quality and the reliability level for each data field separately. It relates to a multi-source verification system that enables its evaluation. The invention also allows for the detection of inconsistencies between sources or a specific data field. If the reliability level related to this does not meet the defined verification condition, Determining the corresponding region of the data field on the physical product, this the selection of the region as the target data acquisition region and the data acquisition process of the product Selective data redirection enables redirection to the selected target region instead of the entire data set. It is related to the acquisition structure. The invention, however, allows for the acquisition of previous data during the data re-acquisition process. Depending on the result, at least one data acquisition parameter may be adjusted, selected obtaining new data from the physical product area and the re-obtained data The response obtained from other data sources is fed back into the validation process. Feedback-based data acquisition and validation that allows for re-validation with data. It is related to its structure. In this context, the invention involves acquiring data from a physical product and transferring data fields to the physical product. Corresponding regions correlation, multi-source data matching, cross-source inconsistency detection, data acquisition quality and field-based reliability determination, target region The data acquisition selection will be adjusted based on the previous data acquisition result. parameter selection, editing of the selected data acquisition parameter, selector 1 data retrieval and re-verification processes are interconnected. It is located in the field of feedback data acquisition and validation systems where it is implemented. It is receiving. State of the Art: Known methods for obtaining information about physical products electronically. in systems; barcodes, QR codes and similar machine-readable identifiers perception, display of textual or visual information on the product surface product information is obtained from the resulting image data through the units. extraction and comparison of this information with records found in product data sources. Techniques such as linking products are utilized. In this context, product identity, product... Name, content information, production information, and similar data fields are different ways of acquiring data. It can be obtained electronically using [method]. Optical data acquisition processes performed on the surface of a physical product The quality of the image data is not fixed. The viewing angle varies depending on the viewing unit and the product. The distance between the surfaces, perspective distortion, whether the product or packaging surface is flat or not curved shape, ambient lighting, surface reflection, shadowing, motion-induced physical features such as blur, character size, print quality, and the location of the displayed area The conditions result in different accuracy and perceptibility of different data areas on the product surface. This can lead to results at certain levels. Due to these physical conditions, only one image acquisition process can result in the same product. While some data fields are obtained with sufficient quality, other data fields are incomplete, corrupted, or It can be obtained with low reliability. The entire image is a single data set. If evaluated as such, it will be re-evaluated with sufficiently qualified data fields. Separating data fields that require data acquisition becomes difficult, and the product The inability to acquire data in only a limited area prevents the entire image from being captured. This can lead to reacquisition or reprocessing. Product ID, derived from a machine-readable product identifier, is the physical product ID. Product information and one or more product data points optically extracted from its surface Although the corresponding records obtained from the source are associated with the same physical product Data is obtained through various different methods of data acquisition. Any of these sources with missing, incorrect or outdated data in one of them and from the physical product the detection error that occurs during optical data acquisition differs from each other indistinguishable from each other, direct combination of information from different sources. 2 This can lead to the creation of an incorrect or inconsistent product data set. It is possible. Therefore, in addition to obtaining data from different sources, determining which data field is suitable for which purpose is crucial. ensuring that the data source is protected, and that the same or corresponding data fields are maintained. matching between sources and validating the data fields in question. Their situations need to be determined individually. Inconsistencies between resources. Even if such a discrepancy exists, the data field to which it relates cannot be determined. data fields with sufficient reliability can also be reacquired or reused. This can lead to processing. Another technical problem encountered in known systems is the result of optical data acquisition. reliability is not determined on a data field basis or acquisition related to the data field its quality along with corresponding data obtained from different data sources. It is not being evaluated. In this case, the data is resulting from low image quality. Distinguishing between an extraction error and a mismatch found in the product data source. This can become difficult, and data fields with insufficient reliability are considered verified data. It is available for use. Only a limited data area on the physical product surface was obtained with insufficient quality. In this case, the entire product image needs to be retaken and the entire image needs to be redone. processing; repeating image acquisition and image processing operations, The increase in the amount of data to be processed necessitates the reuse of processor and memory resources. the unnecessary use and exchange of data with remote product data sources This can lead to repetition in this manner. However, it yields low reliability results. The use of the acquired data field without re-verification, physical product and electronic This creates a risk of incorrectly associating product registrations. Another technical limitation in current approaches is the difference between the verification result and the physical product. The subsequent data acquisition process performed involved the data area and the physical product area. This refers to the absence or inadequacy of a feedback relationship at the data level. After determining that the reliability of the area is insufficient, the product image Instead of re-acquiring the entire data set, the data space in question can be transferred to the physical product. the identification of the corresponding region and the process of acquiring new data specific to that region. the amount of data to be acquired and reprocessed It is important in terms of limiting [the scope of the restrictions]. 3 However, simply identifying the physical region from which data will be retrieved is also not enough. This may not be sufficient in this situation. Data for a data field with insufficient reliability. If the acquisition process is repeated under the same conditions as the initial data acquisition process, the risk is low. the same depending on the continuation of the physical or optical condition that causes the reliability or a similar data acquisition error may occur again. Therefore, verification is necessary. The result is not only in the selection of the region from which data will be re-collected, but also in the matter in question. At least one data acquisition parameter of the next data acquisition process relating to the region It is also needed for its use in regulation. In this context, focusing, exposure, and imaging area are considered during data acquisition. from parameters such as image scale or the nature of the data acquisition process at least one of them is modified based on the previous data acquisition result; initial data acquisition conditions that reduce reliability in the process are repeated in subsequent data acquisition processes. This is important in terms of reducing the probability of such a thing happening. The newly obtained data also... instead of direct acceptance, based on previous data acquisition results and other data sources. The provided data needs to be re-verified taking into account the corresponding information. Data retrieval and revalidation processes are performed with feedback. Another technical problem encountered in its implementation is the sequential nature of the validation condition. If it cannot be provided in the data acquisition cycles, it will be repeated for the same data field. It is the uncontrolled repetition of acquisition processes. On the physical product surface permanent print defect, surface geometry, inaccessible viewing angle or data similar physical conditions that prevent the field from being obtained with sufficient optical quality As long as this continues, repeating the data acquisition process will reduce the level of reliability. without providing a sufficient increase in display, data processing, memory and data transfer This can lead to the repetition of the processes. Therefore, the data acquisition cycle should be repeated; the number of reacquisitions performed, reliability observed in successive cycles the change, taking into account the verification result or another defined termination condition the restriction and controlled use of data areas where the verification condition cannot be met It needs to be separated in this way. Known technical approaches to extracting data from physical products together. When evaluated, data obtained from different data acquisition methods have the same physical form. Associating data fields with a product alone determines their reliability. It may not be sufficient in terms of quality. Especially optically, via the physical product. The product ID is determined via a machine-readable identifier using the data obtained. 4 The corresponding data obtained from external product data sources have different acquisition conditions and The fact that it has different sources of error means that the data in question is only combined. Instead, it requires separate evaluation based on source and data domain. In addition, the reliability of a data field may be insufficient or different data may be present. there is an inconsistency between the sources regarding the data field in question Determining this alone does not resolve the inadequacy of physical data acquisition conditions. The corresponding problematic data field identified electronically on the physical product. If it is not associated with the region, the subsequent data acquisition process will only be possible through verbal agreement. It is not possible to direct the issue to the region and the product to a wider area. It may be necessary to re-view part or all of it. Furthermore, even if the physical region from which data will be re-acquired is determined, the following will be the case: Performing the data acquisition process under the same conditions as the initial data acquisition process, low if the viewing or acquisition conditions that give rise to reliability continue This can lead to a recurrence of the same or similar data acquisition failures. Therefore, in addition to determining the region from which data will be re-collected, previous data acquisition... Depending on the outcome and the identified data acquisition inadequacy, subsequent data acquisition will be carried out. Selecting and editing at least one data acquisition parameter to be used in the process. the need arises. On the other hand, the re-obtained data is considered as directly verified data. instead of being verified, it should be subjected to a re-verification process and re-verified. The result should determine the next data acquisition cycle if necessary. Thus... In structures where there is no feedback relationship, the verification result is from the physical product. the target region of the next data acquisition operation to be performed or data acquisition It does not create a control input that changes the conditions. Therefore, in systems for extracting data from physical products, different types of data are used. Product data obtained from acquisition methods, based on source and data field. correlation, determination of data acquisition characteristics of data fields, from different sources Identifying discrepancies between the obtained data, data acquisition. The reliability of data fields is determined separately by considering their quality and inter-source consistency. separate evaluation of the physical product of the data field requiring data acquisition. Determining the corresponding region on it, acquiring target data from that region. selection of the region, at least one data point to be used in the data acquisition process The acquisition parameter is selected based on the previous data acquisition result, and the organization and reprocessing of the retrieved data and feeding it back into the validation cycle There is a need for technical development in this regard. Consequently, data obtained through data acquisition methods other than the physical product is only instead of obtaining, combining or evaluating it electronically; word the subject is verifying the data at the source and data field level, and the quality of data acquisition. Identifying inconsistencies between sources and detecting data areas with insufficient reliability. i.e., associates the data field in question with the corresponding region on the physical product, redirecting the data acquisition process to the specified target region, previous data acquisition Depending on the outcome and the identified data acquisition deficiency, at least one will be organized. selects the data acquisition parameter, edits the selected data acquisition parameter, re- feeding the obtained data back to the validation cycle and sequentially verifying the validation condition. If this cannot be achieved in the data acquisition cycles, then the defined cycle in question an integrated and backward system that enables continuation or termination according to a boundary condition A feed-in data acquisition and validation system is needed. The purpose of the invention: The purpose of the invention is to create a product from a physical product through different methods of data acquisition. Instead of simply collecting or aggregating data, the data in question Cross-validation on a source and data domain basis is insufficient, or reliability is lacking. Identifying data fields that are inconsistent with other sources and depending on the validation result. selectively re-evaluating the data acquisition process performed from the physical product. an integrated and feedback-based data acquisition and validation system that enables its execution It is about developing. Another objective of the invention is to obtain optically acquired data from the physical product surface, Identity data derived from machine-readable product identifiers and one or more Data field-based comparison of corresponding data from multiple product data sources. by establishing a relationship, data obtained from different data acquisition methods regarding the same physical product The aim is to determine whether the situation confirms each other. Another aim of the invention is to share all image data obtained from the product surface. instead of applying a reliability assessment, data extracted from image data the evaluation of each data field separately and the reliability of each data field. the level of data acquisition in the field in question and the data obtained from different data sources The aim is to ensure that the correspondence between the data is determined by taking into account the agreement between the corresponding data. 6 Another purpose of the invention is to obtain the same or corresponding data from different data sources. If inconsistencies are detected between data fields, the said inconsistency will be investigated. to separate the data field it is associated with from other data fields and to physically isolate this data field The goal is to identify the corresponding area on the product. Another purpose of the invention is to verify that the reliability level does not meet the defined verification condition. or the physical product of the data field that was determined to be incompatible with other data sources. select the corresponding region on it as the target data acquisition region and new data by directing the acquisition process to the target region in question instead of the entire product; The goal is to perform a new data acquisition. Another purpose of the invention is to analyze the relevant data field in the selective data acquisition process. taking into account the previous data acquisition result and the nature of the identified data acquisition deficiency by selecting at least one data acquisition parameter to be configured in the next data acquisition cycle. to select and determine the selected data acquisition parameter based on the previous data acquisition result. The goal is to regulate. This includes focusing, exposure, image area, and image scale. or at least one of the similar parameters affecting the quality of data acquisition is determined for the data. by being selected and regulated in relation to the inability to acquire, the selected Differentiated data acquisition conditions compared to the initial data acquisition process from the target region. under which new data is obtained and data acquisition conditions that lead to low reliability The aim is to reduce the likelihood of it being repeated exactly. Another aim of the invention is to analyze the new data obtained as a result of selective data acquisition. Instead of being accepted as directly verified data, the previous data acquisition result and recalculated by taking into account the corresponding data from different data sources. The aim is to ensure verification and re-evaluation of the verification condition. Another purpose of the invention is to acquire data, define the data field, and provide a physical representation of the data field. associating with the region, determining the quality of data acquisition, comparing sources, Domain-based reliability assessment, inconsistency detection, target region selection, data to be processed. Selecting the acquisition parameter, editing the selected data acquisition parameter, Selective data retrieval and revalidation processes are linked together in a feedback loop. by performing the verification within the feed cycle, the result is then transmitted through the subsequent physical data. a closed document that specifies the target region of the acquisition process and at least one data acquisition condition. The goal is to create a cyclical structure. Another aim of the invention is to detect data occurring within a limited data area on the surface of the physical product. reflection, shadowing, perspective distortion, surface curvature, motion-induced blurring, 7 Product loss due to insufficient character resolution or print quality. The goal is to reduce the need to re-acquire and process the entire image. Another purpose of the invention is to re-evaluate data fields that have been determined to have sufficient reliability. by excluding the data acquisition and reprocessing cycle, the physical data to be re-acquired to limit the product area and the amount of image data to be reprocessed; thus Repeated image acquisition and image processing operations, and the associated processor, The goal is to reduce the use of memory and data transfer resources. Another purpose of the invention is to obtain product information from a machine-readable product identifier. identity, product data obtained optically from the physical product surface, and product data by identifying discrepancies between the corresponding records obtained from the sources, a result of associating a physical product with an incorrect electronic product registration The goal is to reduce the number of faulty product data sets. Another purpose of the invention is to source data obtained from different data sources. ensuring that the information is protected during the verification process and that the same or corresponding data fields remain the same. by enabling data to be evaluated in relation to its sources. The absence or inconsistency at the source is not verified before being transferred to other data fields. The goal is to prevent its transfer. Another purpose of the invention is to re-data with data fields that satisfy the validation condition. separating the data fields that require acquisition and through the physical product Based on the verification result, the scope of the next data acquisition operation to be carried out will be determined. to determine. Another objective of the invention is to enable selective data acquisition and revalidation processes. redata to prevent uncontrolled repetition for the same data field The aim is to ensure that the acquisition cycle is monitored according to a defined boundary condition. within the scope of data acquisition, reliability as a result of successive cycles. The change occurring at this level is a result of verification or another defined factor. Data retrieval by taking into account at least one of the termination conditions. The aim is to decide whether to continue or terminate the cycle. Thus, the verification condition is met due to physical or optical conditions. In situations where this is not possible, the same data acquisition and data processing operations are unnecessary. The aim is to reduce the frequency of repetition in this manner. Another aim of the invention is to achieve selective data reacquisition and revalidation as a result. Data fields that meet the validation requirement are considered validated product data. 8 data fields that do not meet the validation requirement are considered to have insufficient reliability. By enabling the separation of low-reliability data into verified data, The aim is to prevent its use as such. The invention thus involves not only obtaining and combining data relating to the physical product, instead of classifying or presenting it to the user; data is obtained from different data acquisition methods. The reliability and compatibility of the data obtained will be assessed based on the physical product. the next data acquisition operation will target both the target region and at least one data acquisition It defines the parameter, the physical counterpart region of the unreliable data area. selective data retrieval is performed and the retrieval data is an integrated data acquisition and validation system that feeds back into the validation loop It aims to reveal this. Explanation of the Figures Figure 1: Reliability-based validation and selective re-validation in multi-source product data. The general technical structure of the data acquisition system and the basic units that make up the system. It is a schematic view showing the connections between them. Figure 2: Optical data and machine-readable identifier from the physical product. data acquisition, image data preprocessing, data fields and the corresponding regions of the identified data fields on the physical product. Schematic showing the data acquisition and image processing structure that enables correlation. It is appearance. Figure 3: Data obtained from the physical product from different product data sources. associating data with other data, matching data fields, cross-source comparison and inconsistency detection, determination of data acquisition quality, field-based many that enable reliability assessment and verification processes to be carried out This is a schematic view illustrating the source verification structure. Figure 4: Data identified as having insufficient reliability or inconsistencies between sources. Selecting the target region of the field on the physical product, in re-acquisition of data Determining the data acquisition parameter to be adjusted, the previous parameter in question The arrangement depends on the data acquisition result, the data acquisition process is directed towards the selected target. directing data to the region, obtaining data again from the relevant region, the obtained data re-validation and data retrieval by feeding feedback into the validation process enabling the cycle to be continued or terminated according to a defined boundary condition. This is a schematic view illustrating the selective data acquisition and feedback structure. 9 References: 1. Reliability-based validation and selective data re-data processing in multi-source product data. acquisition system 2. Data acquisition unit 3. Display unit 4. Optical data sensing unit 5. Machine-readable identifier detection unit 6. Image preprocessing unit 7. Image segmentation unit 8. Perspective correction unit 9. Image enhancement unit 10. Optical character and data extraction unit 11. Data field definition unit 12. Unit for determining the physical counterpart region 13. Product identification unit 14. Resource attribution unit 15. Primary product data source 16. Second product data source 17. Source data acquisition unit 18. Data field mapping unit 19. Unit of comparison between sources 20. Discrepancy detection unit 21. Data acquisition quality determination unit 22. Domain-based reliability determination unit 23. Reliability comparison unit 24. Unit for determining verification conditions. 25. Verification unit 26. Data field definition unit requiring data retrieval. 27. Target area selection unit 28. Data Acquisition Control Unit 29. Data acquisition parameter editing unit 30. Regional imaging control unit 31. Feedback unit 32. Re-verification unit 33. Data reception and separation unit 34. Data standardization unit 35. Processing unit 36. Memory unit 37. Communications Unit 38. Local data storage unit 39. Output unit 40. User interface 41. Data acquisition parameter selection unit 42. Data acquisition cycle limiting unit Description of the Invention: An invention is the acquisition of data relating to a physical product through various data acquisition methods. Relating the acquired data based on source and data field, data field acquisition. determining their qualities and reliability separately, obtained from different sources Identifying discrepancies between data, re-acquiring data. Determining the corresponding region of the data field on the physical product, data Selectively directing the acquisition process to the region in question, reacquiring data. At least one data acquisition parameter to be used in the process must match the previous data acquisition result. Validation of data obtained through selection and arrangement depending on the circumstances. Multi-source product that enables re-evaluation by feeding back into the cycle. with reliability-based verification and selective data acquisition system (1) in its data It is related. Reliability-based validation and selective data re-data processing in multi-source product data. data acquisition system (1), basically data acquisition that enables data to be obtained from the physical product. unit (2), the process that enables the execution of operations performed by the system. unit (35) is responsible for storing the data and operating parameters used during the process. memory unit (36) that enables data exchange with different product data sources communication unit (37) that enables its implementation and the obtained or verified It includes a local data storage unit (38) that enables data to be stored when needed. In different implementation forms of the system, the output unit (39) and user interface (40) It can be used in connection with the system. However, the output unit (39) and user interface (40), verification of the invention and selective data acquisition cycle 11 not mandatory for implementation, verification result or system status These are complementary units that can be used for the purpose of transferring data to the external environment. Data acquisition unit (2) is the optically detectable data located on the physical product. and is structured for obtaining machine-readable identifiers. Data acquisition unit (2), the whole of the physical product or a limited unit determined on the physical product It can be configured to obtain data regarding the region. Data acquisition unit (2) includes an imaging unit (3), an optical data sensing unit (4) and a machine. There are readable identifier sensing units (5). These units are the same. either integrated within a physical device or separate devices that enable data communication with each other. It can be implemented in the form of units. The imaging unit (3) enables the acquisition of image data from the physical product surface. It provides. The display unit (3) provides an image that covers the entire product. It also obtains regional image data covering a specific section of the product surface. It is structured in such a way that it can perform a wide range of imaging during the system's first data acquisition cycle. the use of the region, and then only re-acquisition in the next data acquisition cycle It is possible to use a more limited viewing area for the region requiring it. is happening. Optical data sensing unit (4), image obtained by imaging unit (3) Identifying optically detectable information regions within the data and the aforementioned It is used to transfer the regions to subsequent image processing operations. Optical data sensing unit (4) detects textual, digital, symbolic information on the physical product. or the perception of regions containing data resulting from a combination of these. It provides opportunities. Machine-readable identifier detection unit (5), barcode associated with the physical product, It enables the detection of QR codes or similar machine-readable identifiers. Data obtained from the machine-readable identifier allows the physical product to be presented electronically. in identifying the physical product and corresponding to it in different product data sources. It is used in identifying incoming records. Machine-readable identifier data obtained by sensing unit (5) and optical data through sensing unit (4) The data obtained is acquired through different data acquisition methods, and the system is located in the same physical environment. It allows the user to compare product data from multiple sources. Image data obtained by the imaging unit (3) is processed by the image preprocessing unit. (6) makes it suitable for subsequent data extraction processes. Image front 12 The processing unit (6) can improve the detectability of the image data or improve the image data. It can perform preliminary operations to prepare the product for subsequent processing. These operations can be applied to the entire image or to a specific part of the image. It can also be applied to the region. The image segmentation unit (7) divides the image data into different image or data regions. It separates them. In this way, the physical product surface contains objects located in different positions. data fields are considered as separate regions instead of being treated as a single whole. It is ensured that it can be processed. The image segmentation unit (7) is determined by the image segmentation unit (7). regions, text fields on the product, descriptive fields, or areas separated by the system. There may be other optical data regions that need to be evaluated. The perspective correction unit (8) is opposite the display unit (3) of the physical product. perspective that occurs depending on the location or geometry of the product surface It helps to reduce defects, especially in product surface imaging. not perpendicular to the axis, the product surface is curved, or the image of the data area In cases where it is at an angle, the perspective correction unit (8) is the relevant image. to make the region more suitable for data extraction It is used. Image enhancement unit (9), image that affects the perceptibility in image data. It enables the adjustment of its properties. Image enhancement process improves the image. to the whole or to a specific region separated by the image segmentation unit (7) This can be implemented. Thus, unnecessary image regions with sufficient reliability are avoided. While preventing reprocessing, it is inadequate in terms of data extraction. a more intensive image enhancement process on the identified image regions It can be accomplished. Optical character and data extraction unit (10), product related to the processed image data. It enables the extraction of characters and data values. Optical characters and data. The output obtained by the subtraction unit (10) is a single data point relating to the entire image. instead of being kept as separate data fields, through the data field definition unit (11) It is organized in this way. Data field determination unit (11) can separate the data extracted from the physical product. It defines data fields. Defining data fields separately ensures that each field is part of the system. to be able to apply a different acquisition quality and reliability assessment to the data field This ensures that a data acquisition failure occurring in a data domain is resolved in the same way. 13 necessitates the re-acquisition of other data fields within the image. It does not bring. Physical correspondence area determination unit (12), data area determination unit (11) each defined data field, and the corresponding data field on the physical product surface. It associates it with the region. The physical correspondence region is within the image data. coordinates, boundaries, regional location information, or data area on the physical product with another form of regional identification that allows it to be rediscovered This can be determined. Thanks to this association, reliability in the electronic environment can be established. From which region on the physical product will a data field that has been determined to be insufficient be re-examined? It can be determined that it needs to be obtained. Physical correspondence region determination unit (12), the data area within the first image by associating the spatial information with the identity of the data field, that spatial information... This allows for its reuse in the next data acquisition cycle. In this context... The start and end coordinates of the data area within the image, the area surrounding the data area. regional boundary, central location of the data area, relative position within the viewing area location or a combination of these in defining the physical correspondence region It can be used. With the initial data acquisition of the physical product or display unit. In the implementation modes where the location changes between re-acquisition and physical Machine-readable on the product for the purpose of redefining the corresponding region. the identifier, a distinguishable visual region, or relating to the product geometry It is possible to use positional information as a reference. Thus, in an electronic environment... between the identified problematic data area and the physical product region to be re-imaged This ensures that the spatial relationship is preserved in subsequent data acquisition cycles. Product identification unit (13), in particular machine readable identifier detection Identify the physical product using the descriptive data obtained by unit (5). It determines the product identity, the source attribution unit (14) by the first product. It is associated with the data source (15) and the second product data source (16) and the relevant product. It is used in identifying the corresponding source records. The first product data source (15) and the second product data source (16) relate to the same physical product. It can include data records that are independent of each other or sourced from different origins. These data sources are located on physically separate servers. It is not mandatory; different datasets, different record systems, different data This can be done through providers or different verification sources. 14 The system can be implemented to work with more than two product data sources. The terms first product data source (15) and second product data source (16) refer to multi-source To explain that the verification structure can be performed with at least two different sources. It is used. Source data acquisition unit (17), first product data source (15) and second product data It enables the receipt of corresponding data for the relevant physical product from the source (16). The source data receiving unit (17) communicates with remote data sources via the communication unit (37). It can communicate as well as obtain data from locally accessible data sources. is able to obtain. Data obtained from different data acquisition methods, data standardization unit (34) Data can be converted into comparable data formats. standardization unit (34), expressed in different forms among data sources Data format, character structure, and measurements that enable matching of corresponding data. the regulation of differences in unit, separator, spelling or data representation It can provide. Data standardization unit (34) provides the content meaning of the source data. Instead of altering it, it works by creating a suitable representation for comparison. Data field matching unit (18) with data fields extracted from the physical product image. The same or obtained from the first product data source (15) and the second product data source (16) It associates the corresponding data fields. Thus, for example, through a physical product... A given data field is only compared with its corresponding data, and incorrect comparison of data fields with different characteristics It is being blocked. The inter-source comparison unit (19) is by the data field matching unit (18). It compares the related data on a source basis. The comparison process is complete. matching, matching within a certain tolerance, formal equivalence, or comparison by using another comparison rule appropriate to the nature of the data field This can be achieved. Thus, the system processes data from different sources. It can evaluate whether they are compatible with each other on a data field basis. Inconsistency detection unit (20), inter-source comparison unit (19) As a result of the comparison performed, the same or corresponding data fields were found. It identifies the inconsistencies found. The inconsistency detection unit (20) identifies the inconsistency. not only its existence but also which data field the discrepancy is associated with It is structured in such a way that it will also determine inconsistencies in a single data field. to spread across all product data or trigger the reacquisition of the entire dataset It can be prevented. Data acquisition quality determination unit (21), each optically obtained from the physical product It determines the acquisition quality of a data field. Data acquisition quality refers to the acquisition quality of the relevant data field. image clarity, character distinguishability, regional image quality, perspective level of distortion, contrast, blur, reflection, shadowing, resolution, or data. one or more of these similar characteristics that can affect the reliability of the subtraction process This can be determined by taking into account the entire image. Instead of assigning values, this is done on a data field basis. Field-based reliability determination unit (22), data acquisition quality determination unit (21) the nature of the acquisition provided and, where necessary, the unit of comparison between sources (19) and using the results of the inconsistency detection unit (20) for each data field It determines the level of reliability. Thus, optically sufficient quality is obtained. However, the optical acquisition quality was poor due to a data field that did not match the source records. For this reason, a data field with decreasing reliability can be separated. Reliability comparison unit (23), domain-based reliability determination unit (22) (24) Unit for determining the condition for verifying the level of reliability determined by It compares it with the validation condition determined by [the relevant authority]. Validation condition The determination unit (24) ensures that verification is only possible over a fixed numerical threshold. It is not limited to its implementation. Verification requirements include: reliability level, resources. compatibility between data acquisition quality, type of data field, or a combination of these. It may include one or more conditions based on its evaluation. Verification unit (25), reliability comparison unit (23), inter-source at least from the results provided by the comparison unit (19) and the inconsistency detection unit (20) using one of them to check whether the relevant data field satisfies the validation condition. It determines. Data fields that satisfy the validation condition are retrieved again. It can be excluded from the cycle, if it does not meet the verification condition or if the resources are not available. Data fields that have discrepancies are subject to data retrieval. is being evaluated. Data field determination unit (26) requiring data retrieval (25) the data field or data fields from which data needs to be retrieved depending on the result This determination is based solely on the low level of reliability. No. Inconsistencies between sources, inadequate data acquisition or verification quality. 16 Different situations, such as the condition not being met, may also lead to the retrieval of data from the relevant data area. This can lead to it being designated as a field requiring attention. Target region selection unit (27), data area determination unit requiring data acquisition again (12) Data area and physical correspondence area determination unit determined by (26) Data will be re-acquired on the physical product using the relationship established by It selects the target region. By selecting the target region, the system controls all physical increasing reliability without requiring the product to be re-displayed. The process of acquiring new data on the physical location where the required data field is located. is able to accomplish this. The data acquisition parameter selection unit (41) is the data field requiring re-acquisition of data. previous data acquisition result and data acquisition quality determination unit (21) taking into account the acquisition nature determined by [the relevant authority] in the next data acquisition cycle It selects at least one data acquisition parameter to be modified. In this way, the system ensures that each Instead of changing all data acquisition parameters in the case of reacquisition, one or more parameters associated with the identified data acquisition deficiency It can select. Data acquisition parameter selection unit (41), data acquisition to be edited When determining the parameter, the data acquisition quality determination unit (21) determines it. It can take into account the type of deficiency encountered. Focusing on the deficiency in the data field. or data relating to focusing if it is determined to be related to blurring. acquisition parameter, related to the brightness level or image saturation of the deficiency If so, an exposure data acquisition parameter, the image of the data field obtaining data on image scale if it is not of sufficient size. the parameter and data field are appropriately positioned within the display area. If not available, a data acquisition parameter for the imaging region. It is possible to select multiple acquisition deficiencies that may coexist in a data field. In this case, the data acquisition parameter selection unit (41) has more than one data acquisition parameter. They can select them together. Thus, the data acquisition process is carried out by all predetermined groups. instead of modifying the parameters in the same way, the detected data acquisition deficiency based on the selection of the related parameter or parameters It can be accomplished. Data acquisition parameter editing unit (29), data acquisition parameter selection unit (41) selects at least one data acquisition parameter from the previous data acquisition result It is adjusted accordingly. The data acquisition parameters that can be adjusted are; focusing, 17 exposure, imaging area, image scale, or acquisition quality of image data There may be another imaging parameter that affects it. Multiple parameters It is also possible to arrange them together. Thus, the data acquisition process can be repeated, along with the initial data. It differs from simply repeating the acquisition process under the same conditions. Editing the data acquisition parameter allows you to adjust the selected parameter compared to the previous data acquisition. by changing it to a value different from the value used in the cycle This can be done. The direction and amount of regulation are determined for the relevant data field. This can be determined according to the nature of data acquisition. As a result of re-acquiring data... If the verification condition is still not met, subsequent data acquisition will be permitted. In the process, the same parameter can be set to a different value, as can the data acquisition parameter. a different parameter or a combination of multiple parameters by the selection unit (41) It can also be selected. Thus, data acquisition in successive data acquisition cycles. Instead of repeating the same conditions, the results obtained from previous cycles are used. It is possible to differentiate them depending on the situation. Regional imaging control unit (30), target region selection unit (27) It controls the imaging process for the selected physical product region. Regional The imaging control unit (30) targets the data acquisition area of ​​the imaging unit (3). It is possible to limit the area or select only the target area from the obtained image. This allows the corresponding portion to be used in the reacquisition cycle. This reduces the amount of image data that needs to be reprocessed. An implementation... regional imaging control unit (30), imaging unit (3) by physically or optically directing the imaging field to the target area, new This ensures that image data is primarily obtained from the target region. Another In the implementation method, the imaging unit (3) provides a wider image than the target region. obtaining the area, but the regional imaging control unit (30) physical counterpart using the positional information provided by the regional determination unit (12) only re-acquisition of the image portion corresponding to the target region and subsequent image It transfers the data to the processing cycle. Thus, selective data acquisition and imaging are performed. by physically or optically limiting the field of view or by the resulting image data This can be done by selecting the part that is related to the target region. Data acquisition control unit (28), target region selection unit (27), data acquisition parameter selection unit (41), data acquisition parameter editing unit (29) and regional new data acquisition cycle in connection with the imaging control unit (30) 18 It controls the execution. Data acquisition control unit (28), target after the region is determined and the necessary data acquisition parameters are adjusted the data acquisition unit (2) obtaining new data regarding the region in question It provides. Thanks to this structure, in the initial data acquisition process, for example, depending on the viewing conditions When it is necessary to re-acquire a data field that could not be obtained with sufficient reliability, The system doesn't simply generate a "take a new image" command. Instead, it asks which data... The area is identified as problematic, the physical counterpart region of the data area in question. is located, the target area to be reacquired is selected, based on the result of the previous acquisition. The data acquisition parameter to be used in the process is determined, the relevant parameter The process is being organized and new data acquisition is being carried out in the target region. The feedback unit (31) receives data through the data acquisition control unit (28) It passes the new data back to the validation structure. Thus, the re-obtained data is the same as the first one. It is not directly accepted as a result independent of the data acquisition process; the system It is re-included in the verification cycle. via the feedback unit (31) The reacquired data transferred to the validation structure acquires new data for the relevant data field. It can be used to determine its quality and reliability level. Again If the determined reliability level does not meet the verification requirement, then... Result, data field determination unit requiring data acquisition again (26), target region selection unit (27), data acquisition parameter selection unit (41) and data acquisition parameter In creating the next data acquisition cycle via the regulatory unit (29) It can be used. Thus, feedback is only based on the re-acquired data. not transferring it to the verification unit, but the subsequent data of the re-verification result. closed-loop control that allows the acquisition process to have an impact on the target and conditions of data acquisition. It establishes a cyclical control relationship. Reverification unit (32) retrieved via feedback unit (31) The data obtained from the first product data source (15) is the previous data belonging to the relevant data field. the corresponding data obtained from the second product data source (16) and from the verification condition determined by the verification condition determination unit (24) It re-evaluates with at least one of them. As a result of the re-validation, the relevant data field whether its reliability has become sufficient and the inconsistencies between sources It is determined whether or not it has been resolved. 19 The re-verification unit (32) confirms that the verification condition is met If determined, the relevant data field is determined by the data acceptance and separation unit (33). It is considered verified data. The verification condition is not met. In these cases, the relevant data field can be transferred back to the data acquisition cycle, or data with insufficient reliability if a defined boundary condition is met. They can be separated. Data acquisition cycle limiting unit (42), for the same data space uncontrolled data acquisition and revalidation cycles It is used to prevent its continuation in this manner. Data retrieval cycle limiting unit (42), number of re-acquisitions, verification result, reliability change or other termination defined by the system. the continuation of the data acquisition cycle depends on at least one of the following conditions, or It ensures that a decision is made to terminate it. In one form of implementation, the data acquisition cycle limiting unit (42) is the same It tracks the number of data reacquisitions performed for the data field and the defined reacquisitions. If the acquisition threshold is reached, a new data acquisition cycle will be initiated. It prevents. In another form of implementation, limiting the cycle, sequentially sufficient change in the reliability level of the data space as a result of reacquisitions This can be achieved depending on whether it occurs or not. Thus, the system, physically in cases where it is not possible to obtain sufficient quality data due to circumstances It does not repeat the same operations continuously. Data acquisition cycle limitation unit (42) by the cycle If a decision is made to terminate it, the validation status and finality of the relevant data field will be recorded. Reliability information is transferred to the data acceptance and separation unit (33). Data acceptance and separation unit (33), keeping the data field in question separate from the validated data fields It can mark data as having insufficient reliability, thus creating new ones for the same data field. This can prevent a data acquisition cycle from automatically restarting. This structure terminates the data acquisition cycle and processes the data space. It establishes a direct control relationship between determining the situation. Data acceptance and separation unit (33), data according to the result of verification or reverification. It allocates its fields to different processing states. The data field that satisfies the validation condition. Data that does not meet the verification condition and is not verified is considered as verified data. Data not allowed to be added to the new cycle by the acquisition cycle limiting unit (42) The field is being separated as data with insufficient reliability. Thus, it is considered unreliable. the unintentional inclusion of a data point into a validated dataset is being prevented. Data retrieval for data fields determined to have sufficient reliability in the system It is essential that it is not included in the cycle. Therefore, more than one in a physical product image having too many data fields and only one of them meeting the validation condition If it does not provide, the target region selection unit (27) can only select the data area in question. It can define the physical counterpart region as its goal for reacquiring it. This structure, Re-capturing the entire product image and reprocessing all data fields. Instead, it creates a data acquisition and processing cycle limited to the problematic area. For example, the first, second, and third data fields in an image of a physical product. in a defined implementation method, the first and second data fields have sufficient data It has acquisition quality and source compatibility, and the third data field is reflection. or it may be determined to have low acquisition quality due to ambiguity. This In this case, the first and second data fields are excluded from the data acquisition cycle, The physical counterpart region of the third data field can be selected as the target region. Data acquisition parameter selection unit (41), the nature of the deficiency in the third data field depending on the parameter to be adjusted and the data acquisition parameter The regulatory unit (29) regulates the relevant parameter. Regional imaging control The unit (30) displays the operation to the region where the third data field is located. directing and reacquisition control unit (28) new acquisition cycle It is carrying out. Another form of realization is one that is optically obtained from a physical product. While the acquisition quality of the data domain is sufficient, the data domain in question is a primary product. The second product may be compatible with data source (15) and incompatible with data source (16). The inconsistency detection unit (20) associates the inconsistency with the relevant data field. and the validation unit (25) reconfigures the data field depending on the validation condition. It evaluates only errors related to optical image quality. not only, but also verifying domain-based inconsistencies that arise between different data sources. It can be included in the cycle. Another form of implementation is the machine-readable identifier sensing unit (5) Product identity obtained from optical characteristics and data from the physical product surface. A relationship is established between the product information obtained by the extraction unit (10) and the product 21 source records corresponding to the identity determined by the identification unit (13) The source is determined through the source attribution unit (14). Thus, the physical product potential issues that may arise if associated with an incorrect electronic product registration Source inconsistencies also include inter-source comparison unit (19) and inconsistency detection. The unit can be determined by (20). In a sample working form of the invention, the data acquisition unit (2) is the first data from the physical product. It performs the acquisition process and the imaging unit (3) displays the image of the product surface. It obtains the data. Machine-readable identifier sensing unit (5) with the physical product. detects the associated identifier and the product identification unit (13) It determines the product ID from the identifier. Image data image preprocessing unit (6), image segmentation unit (7), perspective correction unit (8) and image enhancement unit (9) is processed via the optical character and data extraction unit (10) image Product data within it is extracted and separate data by the data field determination unit (11). They are determined as areas. The physical counterpart area determination unit (12), the determined data It associates the fields with their corresponding regions on the physical product. Using the product ID, the relevant product data is attributed by the resource attribution unit (14). The sources are determined and the first product data is obtained through the source data receiving unit (17). Corresponding data are obtained from source (15) and second product data source (16). Made comparable by the data standardization unit (34) when necessary data field matching unit (18) with corresponding data fields It is related. The inter-source comparison unit (19) compares the matched data, The inconsistency detection unit (20) identifies the data fields where inconsistencies are found, data acquisition The quality determination unit (21) determines the acquisition quality of data fields obtained from the physical product. determines and the field-based reliability determination unit (22) determines the reliability of each data field. It forms the level. Reliability comparison unit (23) condition for verifying the determined reliability level compares it with the verification condition created by the determination unit (24) and The validation unit (25) determines the validation status of the relevant data field. Validation Identifying a data field that does not meet the condition requires data retrieval. selected by unit (26) and by physical counterparty area determination unit (12) Target region selection unit using the previously established spatial relationship (27) The repurchase zone on the physical product is determined by [the company / organization]. 22 The data acquisition parameter selection unit (41) is based on the previous acquisition quality of the relevant data field. It selects at least one data acquisition parameter to be configured accordingly and data acquisition The parameter editing unit (29) selects the parameter from the previous data acquisition cycle. It differentiates. The regional imaging control unit (30) selects the target for data acquisition. directs to the region and data acquisition control unit (28) data acquisition unit (2) It enables the acquisition of new data from the target region. The newly acquired data provides feedback. The data field is transferred from unit (31) to the revalidation unit (32) and the relevant data field It will be re-evaluated. If the verification condition is met as a result of re-verification, data acceptance and The separation unit (33) accepts the relevant data field as verified data. Verification If the condition is not met, the data acquisition cycle limiting unit (42) It determines whether a new data acquisition cycle will be allowed. Allowing a new cycle If provided, the re-verification result will determine the next target region and data acquisition. It is used in parameter determination processes; if the boundary condition is met, then... The cycle is terminated and the relevant data field is deemed insufficient by the data acceptance and separation unit (33). It is separated into data based on reliability. The processing unit (35) is used for data acquisition, image processing, data field determination, and sourcing. correlation, comparison, reliability determination, verification, target region selection, data acquisition parameter selection and adjustment, data re-acquisition, feedback, and It ensures that at least one of the re-verification processes is carried out. Processing unit (35) can perform all of these functions in a single processing structure. in a structure where functions are distributed among multiple processing components It can be accomplished. The memory unit (36) stores the image data used during the operation of the system. areas, physical correspondence zone information, reliability assessments, verification conditions, data acquisition parameters and status regarding the data acquisition cycle It can store information temporarily or permanently. Thus, with the initial data acquisition result... Comparison between subsequent data acquisition results and re-acquisition. The process can be carried out based on previous acquisition results. Communication unit (37), system and first product data source (15), second product data source (16) enables the exchange of data between other external data sources. Communication unit (37) one or more of wired or wireless communication structures This can be done through the system. The system allows local access to data sources. 23 in a form of implementation that does not require external communication It can be configured to work. Local data storage unit (38), product IDs, previously verified data fields, systems that can be used in verification results or data retrieval cycles It can ensure the storage of data. The local data storage unit (38) can store the same product. reuse of previously verified data or data sources relating to In situations where access is temporarily unavailable, the system uses local data. It can support his / her work. The output unit (39) is the output of the system outputs obtained as a result of the verification process to the external environment. It enables the transfer of the output unit (39) verified data, insufficient reliability. Data, source incompatibility, or the need for retrieval of data. It is structured in a way that can transmit its knowledge. However, the technical operation of the invention has a specific output. It does not depend on the form. User interface (40), system status or verification result to the user so that it can be monitored and data acquisition can be carried out in appropriate implementation methods. This enables the relevant guidance to be conveyed to the user. User interface (40), a complementary to the basic verification and feedback cycle of the invention independent of the It is a structure, and the technical essence of the system does not depend on providing the user with specific information. Display unit (3), optical data sensing in different implementation forms of the invention. unit (4), machine readable identifier detection unit (5), processing unit (35), memory unit (36), communication unit (37), local data storage unit (38), output unit (39) and The user interface (40) can be integrated within a single device. also on different devices or system components that communicate with each other This can be achieved. Therefore, the technical scope of the invention is specific to a particular mobile device. It is not limited to a fixed display device or a specific form of hardware. The application of the invention is not limited to a specific type of product. It also applies to physical products. at least one optically detectable data field and / or a machine-readable one the identification number must be found and at least one corresponding piece of data relating to the product in question must be provided. If data can be obtained from the data source, the system can distribute it to different product groups. It can be implemented. Thus, the system's operating principle is based on the commercial application of specific product information. regardless of its health-related or user-oriented meaning, from the physical product based on the technical nature of the data obtained and its verification with different sources is being carried out. 24 The area-based reliability assessment used in the invention is based on the physical product image. Instead of assigning a single reliability result to all data fields, the data fields are differentiated from each other. allowing for evaluation taking into account independent or reciprocal relationships. This structure ensures the protection of sufficiently reliable areas, not only. It is possible to recover and reprocess the problematic data area. The feedback structure used in the invention ensures that the verification process is only a final result. by preventing its production, the verification result will then be carried out from the physical product. This makes it one of the control inputs of the data acquisition cycle. In this context As a result of the verification, in determining the data field from which data will be re-obtained, the data field In selecting the physical counterparty region, the data acquisition parameter to be adjusted in determining, regulating the parameter in question and acquiring new data It is used to initiate the cycle. Within this control relationship, the first data The evaluation of the acquisition result is limited to generating only a verification output. not remaining; the physical product region from which data will be re-obtained as a result of verification, next data acquisition parameters to be configured in the cycle and the data acquisition cycle again It is used to determine whether or not it will be continued. Thus, physical The nature of the data obtained from the product affects the verification result, while the verification result... the scope of subsequent physical data acquisition and the conditions for data acquisition By changing it, it creates a bidirectional and feedback-based technical control relationship. Reliability-based validation and selective data re-data processing in multi-source product data. acquisition system (1) is a linear system that accepts the initial data acquisition result without modification. Unlike data processing structures, it evaluates the outcome of data acquisition; assessment. Based on the result, the scope of the subsequent physical data acquisition process and at least one acquisition closed-loop system that modifies the parameter and re-evaluates the new acquisition result. It creates a technical structure. Thus, unnecessary data fields are required for sufficient reliability. While the re-acquisition and reprocessing of data in this form is reduced, data with insufficient reliability Differentiated data acquisition from corresponding regions of the fields on the physical product It is ensured that it is reproduced and verified under these conditions. Industrial Application of the Invention: The invention relates to optically detectable data and machine-generated data on physical products. Obtaining readable identifiers, corresponding data from different data sources. The data is insufficient to verify and ensure reliability, or it is inconsistent between sources. Selective recovery of specified data fields from the physical product It can be used in required industrial data acquisition and verification applications. The invention includes data acquisition, imaging, optical sensing, and mechanical imaging. Readable identifier detection, image processing, data comparison, verification, Communication, processing, memory, and data storage structures are separate technical units. Alternatively, these units can be manufactured as a single integrated device, either in part or in whole. or can be implemented within the system structure. The system also includes existing imaging, product It can be implemented in conjunction with identification and data validation infrastructures. The invention relates to production, packaging, quality control, product identification, warehouse and inventory management, In logistics, retail, and similar fields, data on physical products can be transferred to electronic products. It can be used in applications where verification with records is required. Physical product. at least one optically detectable data field or machine-readable field The identifier is found and one or more corresponding data relating to the product are used. If it can be sourced from the manufacturer, the system can be applied to different product groups. Selective data retrieval is only performed on physical devices that do not meet the validation requirement. Performing this on the product area, repeatedly obtaining the entire product image. It can be used to reduce the need for data collection and processing. Re-data Termination of the acquisition cycle according to the defined boundary condition constitutes sufficient reliability. In situations where this is not possible, repeated data acquisition and processing operations must be kept under control. It allows for its retention. Thanks to the invention's selective data acquisition structure, data on the physical product can be retrieved. The physical counterpart region of the area that only requires data retrieval from its fields. can be designated as the target region; at least one will be used in the data acquisition process. The data acquisition parameter can be adjusted depending on the previous data acquisition result and the relevant Data retrieved from the region can be fed back into the validation cycle. Thus, the system, different processes where data relating to physical products is acquired and verified They can be applied to industrial systems in an integrated or modular form. 26

Claims

1. The source of data obtained from different data acquisition methods relating to a physical product. and verification on a data field basis, and physical verification depending on the verification result. in multi-source product data that enables selective data retrieval from the product reliability-based verification and selective data retrieval system (1), The feature must be at least one that enables the extraction of image data from the physical product. Data acquisition unit (2) containing imaging unit (3), acquired image data Data field definition unit (11) which defines the data fields within it, each defined a physical product that associates a data field with its corresponding region on the physical product The corresponding region determination unit (12), data fields obtained from the physical product obtained from the first product data source (15) and the second product data source (16) Data field matching unit that associates with corresponding data fields (18), Cross-source comparison of related data fields on a source basis. Comparison unit (19), data fields as a result of inter-source comparison. inconsistency detection unit (20) which determines the discrepancy between the physical product Data acquisition characteristics that determine the data acquisition characteristics of each data field obtained. determination unit (21), comparison between sources with data acquisition quality and for each data field based on at least one of the inconsistency detection results Field-based reliability determination unit (22) which determines the level of reliability. reliability level, inter-source consistency, quality of data acquisition, or data area a validation condition that specifies a validation requirement dependent on at least one of the following types The determination unit (24) determines whether the relevant data field satisfies the said validation condition. The verification unit (25) that determines that it does not provide, depending on the verification result data acquisition that identifies the data area requiring reacquisition data field determination unit (26), the physical product of the said data field selects the corresponding region on it as the target region from which data will be re-acquired. target region selection unit (27), the nature of the identified data acquisition deficiency data acquisition that selects at least one data acquisition parameter to be configured accordingly parameter selection unit (41), selected data acquisition parameter previous data Data acquisition parameter regulation based on acquisition result. unit (29) that limits the data acquisition process to the selected target region regional imaging control unit (30), regulated data acquisition parameter using a system that controls the acquisition of new data from the selected target region. Data acquisition control unit (28), verification structure of the reacquired data 27 transferring feedback units (31) and the data that is fed back to the relevant data field Revalidation, which allows for re-evaluation based on the verification condition. It is characterized by containing unit (32).

2. Reliability-based validation of multi-source product data according to Claim 1 and Selective data acquisition system (1) and its feature is the display unit (3) optically detectable information within the image data obtained by Optical data sensing unit (4) that determines the regions, barcode associated with the physical product, a machine that detects a QR code or similar machine-readable identifier The readable identifier detection unit (5) uses the said identifier Product identification unit (13) which identifies the physical product, determined product identity with first product data source (15) and second product source (16) associating source association unit (14) and from the said data sources The source data acquisition unit that enables the retrieval of corresponding data relating to the physical product. (17) is characterized by its inclusion.

3. Reliability-based validation of multi-source product data according to Claim 1 and Selective data acquisition system (1) and its feature is the display unit (3) making the image data obtained suitable for subsequent data extraction processes. The image preprocessing unit (6) brings the image data and separates the image data from each other. or image segmentation unit (7) that divides into data regions, product surface geometry or reduces perspective distortions caused by viewing position perspective correction unit (8), affecting the perceptibility in image data Image enhancement unit (9) which regulates image properties and processed image Optical character and data extractors that derive product-related characters and data values ​​from data. It is characterized by containing a data extraction unit (10).

4. Reliability-based validation of multi-source product data according to Claim 1 and Selective data retrieval system (1) and its feature is field-based reliability. Condition for verifying the level of reliability determined by the determination unit (22) comparing with the verification condition determined by the determination unit (24) It is characterized by containing a reliability comparison unit (23).

5. Reliability-based validation of multi-source product data according to Claim 1 and It is a selective data acquisition system (1), and its feature is that it uses different data acquisition methods. before the obtained data is matched by the data field matching unit (18) data format, character structure, unit of measurement, separator, spelling, or data representation By modifying at least one of the 28 differences, the data can be compared in a comparable format. It is characterized by containing a data standardization unit (34) that transforms the data.

6. Reliability-based validation of multi-source product data according to Claim 1 and Selective data acquisition system (1) and its feature is; revalidation unit (32) The data field determined to meet the verification condition is called verified data. (42) permission was not granted to initiate a new data acquisition cycle. Data acceptance and separation that identifies a defined data field as data with insufficient reliability. data acquisition and revalidation for the same data field with unit (33) continuation or termination of cycles according to a defined boundary condition by including the controlling data acquisition cycle limiting unit (42) It is characterized by...

7. Reliability-based validation of multi-source product data according to Claim 6 and Selective data reacquisition system (1) and its feature is; data reacquisition cycle the number of re-acquisitions of the limiting unit (42), the verification result or changes in reliability level in successive reacquisition cycles will continue the data acquisition cycle depending on at least one of the changes. or will terminate and the cycle will be terminated before the verification condition is met. in case the data acceptance and separation unit (33) of the relevant data field is insufficient by structuring it in a way that will allow it to be separated as reliable data. It is characterized by...

8. Reliability-based validation of multi-source product data according to Claim 1 and Selective data acquisition system (1) and its feature is; the system acquires data, data Field identification, cross-referenced comparison, field-based reliability determination, Verification, target region selection, data acquisition parameter selection and editing, executing data acquisition, feedback, and revalidation functions The processing unit (35) provides image data, data fields, and physical counterpart region. information, reliability assessments, verification conditions, data acquisition from the parameters or status information regarding the data acquisition cycle It is characterized by containing a memory unit (36) which holds a small number of units.

9. Reliability-based validation of multi-source product data according to Claim 1 and Selective data acquisition system (1), and its feature is; first product data source (15), second product data source (16) or data exchange with an external data source communication unit (37) and product identities, verified data fields, 29. Verification results or system relating to data retrieval cycle. by including a local data storage unit (38) that stores at least one of its data It is characterized by...

10. Reliability-based validation of multi-source product data according to Claim 1 and Selective data reacquisition system (1) and its feature is; verified data, insufficient data reliability, source inconsistencies, or the need to re-acquire data. output unit (39) which transmits at least one of the related system outputs to the external environment and system User interface that allows monitoring of status or verification result. (40) is characterized by its inclusion.

11. Reliability-based validation of multi-source product data according to Claim 1 and Selective data acquisition system (1) and its feature is; physical correspondence region the determination unit (12), the start and end of the data field in the image coordinates, regional boundary surrounding the data area, central location of the data area or at least one of its relative positions within the viewing area targeting the next data acquisition cycle by associating it with the data field. by generating spatial information that can be used in determining the region It is characterized by...

12. Reliability-based validation of multi-source product data according to Claim 11 and Selective data acquisition system (1) is characterized by the physical product or the first data acquisition cycle and the re-acquisition cycle of the imaging unit (3) the physical correspondence zone determination unit in case of a change of position between them (12), detected by machine-readable identifier detection unit (5) machine-readable identifier, a distinguishable image region, and product. using at least one piece of positional information relating to its geometry as a reference It is characterized by determining the physical counterpart region from which data will be re-acquired. is being done.

13. Reliability-based validation of multi-source product data according to Claim 1 and Selective data acquisition system (1) and its feature is; data acquisition parameter selection. determined by the unit (41), data acquisition quality determination unit (21) If the deficiency is related to blurriness or focusing, then focus... related data acquisition parameters, such as brightness level or image saturation. If relevant, the exposure data acquisition parameter, data field if the image scale is insufficient in the image. acquisition parameter or location of the data field in the display area if relevant, the data acquisition parameter for the imaging region It is characterized by its choice.

14. Reliability-based validation of multi-source product data according to Claim 13 and Selective data acquisition system (1) and its feature is; data acquisition parameter by the regulation unit (29), data acquisition parameter selection unit (41) the selected data acquisition parameter used in the previous data acquisition cycle It will change the value to a different value and the revalidation result will be a validation. If the condition is not met, the same data will not be retrieved in the next data acquisition cycle. will set the acquisition parameter to a different value or the data acquisition parameter If a different data acquisition parameter is selected by the selection unit (41) Configuring it to regulate the different data acquisition parameters in question It is characterized by...

15. The source of data obtained from different data acquisition methods relating to a physical product. and verification on a data field basis, and physical verification depending on the verification result. It is a method for selectively re-acquiring data from a product, and its characteristic is that it is physical. Obtaining image data from the product, data within the obtained image data. the identification of the data fields, and the physical product of each identified data field. associating the data fields obtained from the physical product with the corresponding region. obtained from the first product data source (15) and the second product data source (16) matching data fields with corresponding data fields, source-based matching of the matched data fields. Identifying discrepancies between data fields by comparison, physical Determining the data acquisition characteristics of each data field obtained from the product, data from the results of comparing the acquisition quality and sources and identifying inconsistencies the level of reliability for each data field depending on at least one of them determination, reliability level, inter-source consistency, quality of data acquisition or Determining the validation condition that depends on at least one of the data field types, relevant whether the data field satisfies the aforementioned validation condition Identification, data requiring re-acquisition depending on the verification result. determining the area, the physical location previously associated with that data area the corresponding region is selected as the target region for data acquisition, the relevant data previous data acquisition results of the area and the identified data acquisition deficiencies Selection of at least one data acquisition parameter to be adjusted according to its nature, depending on the previous data acquisition result of the selected data acquisition parameter the arrangement, the re-acquisition process with the selected target region The 31 limitation is the target selected using the regulated data acquisition parameter. Obtaining new data from the region and verifying the newly obtained data. by feeding it back into the loop and re-evaluating it according to the validation condition of the relevant data field. It is characterized by its verification.

16. Reliability-based validation of multi-source product data according to Claim 15 and It is a selective data retrieval method, characterized by its presence on the physical product. Detection of a machine-readable identifier, from that identifier Identifying the product identity of the physical product, determining the identified product identity. the relevant product on the first product data source (15) and the second product source (16) linking it with records and from those product records to the physical product It is characterized by the acquisition of corresponding data.

17. Reliability-based validation of multi-source product data according to Claim 15 and It is a selective data recovery method, characterized by its ability to retrieve data from the physical product. image data, image preprocessing before data fields are identified, image At least one of the following operations: segmentation, perspective correction, and image enhancement. implementation and processing of image data into a physical product. characterized by the extraction of related optical characters or data values. is being done.

18. Reliability-based validation of multi-source product data according to Claim 15 and It is a selective data retrieval method, characterized by its ability to retrieve data for each data field. acquisition quality image clarity, character distinguishability, regional image quality, level of perspective distortion, contrast, blur, reflection, shadowing, or It should be determined by taking into account at least one of the resolution characteristics and the said Data acquisition quality and corresponding data obtained from different product data sources. by jointly evaluating the compatibility between them, the relevant data field It is characterized by determining the level of reliability.

19. Reliability-based validation of multi-source product data according to Claim 15 and It is a selective data retrieval method, characterized by the fact that it requires data retrieval. depending on the nature of the identified data acquisition deficiency regarding the data area Obtaining data regarding focus, exposure, imaging area, or image scale. Selection of at least one of the parameters, the selected data acquisition parameter To set a value different from the one used in the previous data acquisition cycle. and the aforementioned regulated data acquisition parameter from the selected target region It is characterized by the process of retrieving data using this method. 32 20. Reliability-based validation of multi-source product data according to Claim 15 and It is a selective data retrieval method, characterized by the fact that the re-retrieved data Revalidation of the relevant data field by feeding it back into the validation cycle, If the validation condition is met, the relevant data field will be considered verified data. If accepted as valid, data will be re-data if the verification condition is not met. acquisition count, verification result, or consecutive data re-acquisition cycles a defined limit that depends on at least one of the changes in the level of confidence Continuation or termination of the data acquisition cycle depending on the condition and if the cycle is terminated before the verification condition is met, the relevant data characterized by the decomposition of the field as data with insufficient reliability. is being done. 33

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