Detection method and system for non-woven bags

Abstract

The present invention discloses a non-woven bag detection method and system, which matches the molding logic of the non-woven bag according to the shape type of the non-woven bag, so as to introduce the molding logic of the non-woven bag, thereby controlling the molding of the non-woven fabric based on the molding logic of the non-woven bag, and outputting the non-woven bag. At the same time, the detection image of the non-woven bag is collected, and a plurality of areas are divided based on the detection image; corresponding features are determined according to the identification of the plurality of areas, so as to define a correlation coefficient based on the association of the plurality of features, and trigger a combination of the plurality of features according to the correlation coefficient, so as to control the feature combination, so as to match the molding coefficient based on the feature combination, and define the molding grade according to the molding coefficient, and define the overall grade of the non-woven bag according to the plurality of molding grades and the material grade of the non-woven bag, thereby ensuring the molding effect of the non-woven bag, and at the same time, facilitating the control of each molding node of the molding logic of the non-woven bag.

Description

Technical Field

[0001] The present invention relates to the technical field of non-woven bags, and in particular to a detection method and system for non-woven bags. Background Art

[0002] With the development of science and technology, non-woven bags are formed by non-woven fabrics through multiple forming nodes and are used to hold objects. At this time, the non-woven bags are formed at multiple forming process nodes, and the shaping of each side and the holding groove is completed in turn. However, there is no control over the forming level of the non-woven bags, nor is there any control based on each forming node, which affects the forming effect of the non-woven bags. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides a non-woven bag detection method and system, which matches the molding logic of the non-woven bag according to the shape type of the non-woven bag, so as to introduce the molding logic of the non-woven bag, thereby controlling the molding of the non-woven fabric based on the molding logic of the non-woven bag, and outputting the non-woven bag. At the same time, the detection image of the non-woven bag is collected, and multiple areas are divided based on the detection image; the corresponding features are determined according to the identification of multiple areas, so as to define the correlation coefficient based on the association of multiple features, and trigger the combination of multiple features according to the correlation coefficient, so as to control the feature combination, so as to match the molding coefficient based on the feature combination, and define the molding grade according to the molding coefficient, and define the overall grade of the non-woven bag according to multiple molding grades and the material grade of the non-woven bag, thereby ensuring the molding effect of the non-woven bag, and at the same time, it is convenient to control each molding node of the molding logic of the non-woven bag.

[0004] In order to solve the above technical problems, an embodiment of the present invention provides a non-woven bag detection method, which is applied to the detection scenario of non-woven bags;

[0005] The detection method of the non-woven bag comprises:

[0006] Collecting the shaping information of the non-woven bag, and defining the shape type of the non-woven bag according to the shaping information of the non-woven bag;

[0007] Match the forming logic of the non-woven bag based on the shape type of the non-woven bag, and trigger the forming of the non-woven fabric according to the forming logic of the non-woven bag to output the non-woven bag;

[0008] Collecting a detection image of a non-woven bag and dividing it into multiple areas based on the detection image;

[0009] Determining corresponding features based on the identification of multiple regions and associating the multiple features;

[0010] A correlation coefficient is defined based on the correlation of multiple features, and a combination of multiple features is triggered according to the correlation coefficient to construct multiple feature combinations;

[0011] The forming coefficient is matched based on each feature combination, and the forming grade is defined according to the forming coefficient. The overall grade of the non-woven bag is defined according to multiple forming grades and the material grade of the non-woven bag.

[0012] Optionally, the collecting of the shaping information of the non-woven bag and defining the shape type of the non-woven bag according to the shaping information of the non-woven bag include:

[0013] Acquiring a communication signal input by a user via remote communication;

[0014] Outputting multiple information based on the analysis of the communication signal, the multiple information including user information, non-woven bag order information and non-woven bag shaping information;

[0015] Screening the shaping information of the non-woven bag based on multiple information to complete the collection of the shaping information of the non-woven bag;

[0016] Traverse the shaping information of non-woven bags, and filter shaping keywords based on the shaping information of non-woven bags;

[0017] The shape type of the non-woven bag is defined according to the stereotyped keywords and the shape type learning model. At this time, the shape type learning model is trained based on the stereotyped keywords and shape types in the past.

[0018] Optionally, matching the forming logic of the non-woven bag based on the shape type of the non-woven bag, and triggering the forming of the non-woven fabric according to the forming logic of the non-woven bag to output the non-woven bag includes:

[0019] The shape type of fixed non-woven bag;

[0020] Associate the shape type and forming logic table of non-woven bags;

[0021] Match the forming logic of the non-woven bag based on the shape type of the non-woven bag and the forming logic table. At this time, match the shape type of the non-woven bag according to the mapping relationship between the shape type and the forming logic table;

[0022] The forming logic is collected, and a forming process table for the non-woven bag is constructed based on the forming logic. At this time, the forming process table presents various forming nodes of the non-woven bag.

[0023] Optionally, the matching of the forming logic of the non-woven bag based on the shape type of the non-woven bag and triggering the forming of the non-woven fabric according to the forming logic of the non-woven bag to output the non-woven bag further includes:

[0024] Trigger the forming monitoring of non-woven bags based on each forming node;

[0025] Trigger the forming process of non-woven fabric to non-woven bag according to each forming node of the non-woven bag to complete the forming of the non-woven bag;

[0026] According to the formation monitoring of non-woven bags, images of each stage of the non-woven bag formation process are collected, and abnormal parts are defined based on image recognition of each stage image;

[0027] Based on the abnormal part, the repair of the next forming node is triggered until the non-woven bag is output.

[0028] Optionally, collecting a detection image of the non-woven bag and dividing the detection image into multiple areas includes:

[0029] After the non-woven bag is formed, locate the position of the non-woven bag;

[0030] Based on the location of the non-woven bag, the surrounding cameras are triggered, and a camera combination is constructed, which takes the location of the non-woven bag as the position center;

[0031] The non-woven bag is photographed in multiple directions according to the camera combination to obtain images in multiple directions;

[0032] Constructing a detection image of the non-woven bag based on multiple images in different directions, and using the detection image of the non-woven bag as a panoramic image;

[0033] The closed-loop contour line is highlighted based on the detection image, and multiple areas are divided according to the closed-loop contour line.

[0034] Optionally, determining corresponding features based on identification of multiple regions and associating multiple features includes:

[0035] Collect multiple areas and locate the spatial relationship between multiple areas;

[0036] Defining recognition priorities among multiple areas based on the spatial relationship between the multiple areas and the outer contour of the non-woven bag;

[0037] Triggering sequential identification of multiple areas based on identification priority;

[0038] In the sequential recognition of multiple regions, multiple regions are sequentially input into the feature learning model, and the corresponding features are output in sequence. At this time, the feature learning model is trained based on the previous regions and features.

[0039] Associate multiple features.

[0040] Optionally, defining a correlation coefficient based on the correlation of multiple features, and triggering a combination of multiple features according to the correlation coefficient to construct multiple feature combinations, includes:

[0041] Define core features among multiple features and associate them with the remaining features based on the core features;

[0042] Define the corresponding correlation coefficient based on the correlation between the core feature and the remaining features;

[0043] Collect multiple correlation coefficients and build a combination relationship based on the multiple correlation coefficients and recognition priorities. At this time, mark the multiple correlation coefficients within a preset range, and combine the features corresponding to the marked multiple correlation relationships to form a feature combination. At the same time, match the recognition priority to the feature combination;

[0044] Based on the listing of multiple feature combinations, an integration mechanism is constructed for the multiple feature combinations. In the integration mechanism, the similarity of the multiple feature combinations is defined, and the similarity is compared with the preset similarity threshold. The similarity of the multiple feature combinations is integrated to improve the multiple feature combinations.

[0045] Optionally, matching the forming coefficients based on the combination of each feature, defining the forming grade according to the forming coefficients, and defining the overall grade of the non-woven bag according to the multiple forming grades and the material grade of the non-woven bag include:

[0046] Freeze each feature combination;

[0047] Matching the corresponding forming coefficients according to each feature combination and the forming table. At this time, the forming mapping relationship in the forming table associates the feature combination and the forming coefficient;

[0048] The forming level is defined based on the forming coefficient and the number of feature combinations. In this case, each feature combination matches the corresponding forming level.

[0049] Collect multiple forming levels and define the forming priority of each feature combination according to the forming process of non-woven bags and multiple forming levels;

[0050] The molding sequence of each feature combination is triggered according to the molding priority, and at the same time, the material grade of the non-woven bag is collected.

[0051] Optionally, matching the forming coefficients based on the combination of features, defining the forming grades according to the forming coefficients, and defining the overall grade of the non-woven bag according to the multiple forming grades and the material grade of the non-woven bag further includes:

[0052] Correlate multiple molding grades and material grades of non-woven bags;

[0053] The overall grade of the non-woven bag is defined according to multiple forming grades and the material grade of the non-woven bag, and the shaping grade in the final shaping process is matched based on the overall grade of the non-woven bag.

[0054] In addition, an embodiment of the present invention further provides a non-woven bag detection system, the non-woven bag detection system comprising:

[0055] The shape area module is used to collect the shaping information of the non-woven bag and define the shape type of the non-woven bag according to the shaping information of the non-woven bag;

[0056] A forming module, which is used to match the forming logic of the non-woven bag based on the shape type of the non-woven bag, and trigger the forming of the non-woven fabric according to the forming logic of the non-woven bag to output the non-woven bag;

[0057] A region module is used to collect detection images of non-woven bags and divide them into multiple regions based on the detection images;

[0058] A feature module, configured to determine corresponding features based on the identification of multiple regions and associate multiple features;

[0059] A feature combination module is used to define a correlation coefficient based on the correlation of multiple features, and trigger the combination of multiple features according to the correlation coefficient to construct multiple feature combinations;

[0060] The grade module is used to match the forming coefficient based on each feature combination, and define the forming grade according to the forming coefficient, and define the overall grade of the non-woven bag according to multiple forming grades and the material grade of the non-woven bag.

[0061] In an embodiment of the present invention, through the method in the embodiment of the present invention, the forming logic of the non-woven bag is matched according to the shape type of the non-woven bag, so as to introduce the forming logic of the non-woven bag, thereby controlling the forming of the non-woven fabric based on the forming logic of the non-woven bag, and outputting the non-woven bag. At the same time, the detection image of the non-woven bag is collected, and multiple areas are divided based on the detection image; the corresponding features are determined according to the identification of multiple areas, so as to define the correlation coefficient based on the association of multiple features, and trigger the combination of multiple features according to the correlation coefficient, so as to control the feature combination, so as to match the forming coefficient based on the feature combination, and define the forming grade according to the forming coefficient, and define the overall grade of the non-woven bag according to multiple forming grades and the material grade of the non-woven bag, thereby ensuring the forming effect of the non-woven bag. At the same time, dynamic detection is performed during the forming process of the non-woven bag, so as to control each forming node of the forming logic of the non-woven bag. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 1 is a flow chart of a method for detecting a non-woven bag in an embodiment of the present invention;

[0064] Figure 2 1 is a flow chart of S11 in the method for detecting a non-woven bag in an embodiment of the present invention;

[0065] Figure 3 1 is a flow chart of S12 in the method for detecting a non-woven bag in an embodiment of the present invention;

[0066] Figure 4 1 is a flow chart of S13 in the method for detecting a non-woven bag in an embodiment of the present invention;

[0067] Figure 5 1 is a flow chart of S14 in the method for detecting a non-woven bag in an embodiment of the present invention;

[0068] Figure 6 1 is a flow chart of S15 in the method for detecting a non-woven bag in an embodiment of the present invention;

[0069] Figure 7 1 is a flow chart of S16 in the method for detecting a non-woven bag in an embodiment of the present invention;

[0070] Figure 8 Schematic diagram of the structure of the non-woven bag detection system in an embodiment of the present invention;

[0071] Figure 9 The figure shows a hardware diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0073] Example

[0074] See also Figures 1 to 9 , a non-woven bag detection method is applied to the detection scenario of non-woven bags; the non-woven bag detection method includes:

[0075] Step S11: collecting the shaping information of the non-woven bag, and defining the shape type of the non-woven bag according to the shaping information of the non-woven bag;

[0076] Step S12: matching the forming logic of the non-woven bag based on the shape type of the non-woven bag, and triggering the forming of the non-woven fabric according to the forming logic of the non-woven bag to output the non-woven bag;

[0077] Step S13: collecting a detection image of the non-woven bag and dividing the detection image into multiple areas;

[0078] Step S14: determining corresponding features based on the identification of the multiple regions, and associating the multiple features;

[0079] Step S15: defining a correlation coefficient based on the correlation of the multiple features, and triggering a combination of the multiple features according to the correlation coefficient to construct multiple feature combinations;

[0080] Step S16: matching the forming coefficients based on the feature combinations, defining the forming grades according to the forming coefficients, and defining the overall grade of the non-woven bag according to the multiple forming grades and the material grade of the non-woven bag.

[0081] In an embodiment of the present invention, through the method in the embodiment of the present invention, the forming logic of the non-woven bag is matched according to the shape type of the non-woven bag, so as to introduce the forming logic of the non-woven bag, thereby controlling the forming of the non-woven fabric based on the forming logic of the non-woven bag, and outputting the non-woven bag. At the same time, the detection image of the non-woven bag is collected, and multiple areas are divided based on the detection image; the corresponding features are determined according to the identification of multiple areas, so as to define the correlation coefficient based on the association of multiple features, and trigger the combination of multiple features according to the correlation coefficient, so as to control the feature combination, so as to match the forming coefficient based on the feature combination, and define the forming grade according to the forming coefficient, and define the overall grade of the non-woven bag according to multiple forming grades and the material grade of the non-woven bag, thereby ensuring the forming effect of the non-woven bag. At the same time, dynamic detection is performed during the forming process of the non-woven bag, so as to control each forming node of the forming logic of the non-woven bag.

[0082] refer to Figure 2 In step S11, the shaping information of the non-woven bag is collected, and the shape type of the non-woven bag is defined according to the shaping information of the non-woven bag;

[0083] In the specific implementation process of the present invention, the specific steps may be:

[0084] S111: Acquire a communication signal input by the user via remote communication;

[0085] S112: Outputting a plurality of information according to the analysis of the communication signal, the plurality of information including user information, non-woven bag order information, and non-woven bag shaping information;

[0086] S113: Filtering the shaping information of the non-woven bag based on the multiple pieces of information to complete the collection of the shaping information of the non-woven bag;

[0087] S114: traversing the shaping information of the non-woven bag, and filtering shaping keywords based on the shaping information of the non-woven bag;

[0088] S115: defining the shape type of the non-woven bag according to the stereotyped keywords and the shape type learning model. At this time, the shape type learning model is trained based on the stereotyped keywords and shape types in the past.

[0089] In an embodiment of the present application, a communication signal input by a user via remote communication is obtained, and signal analysis is performed on the communication signal so as to output multiple information based on the analysis of the communication signal. Optionally, the multiple information includes user information, non-woven bag order information, and non-woven bag shaping information.

[0090] At this time, the stereotyped information of the non-woven bag is filtered based on multiple information to complete the collection of the stereotyped information of the non-woven bag; the stereotyped information of the non-woven bag is traversed to facilitate further processing of the stereotyped information of the non-woven bag, thereby filtering stereotyped keywords based on the stereotyped information of the non-woven bag, and then realizing keyword matching of the stereotyped information of the non-woven bag, so as to define the shape type of the non-woven bag according to the stereotyped keywords and the shape type learning model, thereby ensuring the accuracy of the shape type of the non-woven bag. At this time, the shape type learning model is trained based on previous stereotyped keywords and shape types.

[0091] refer to Figure 3 In step S12, the forming logic of the non-woven bag is matched based on the shape type of the non-woven bag, and the forming of the non-woven fabric is triggered according to the forming logic of the non-woven bag to output the non-woven bag;

[0092] In the specific implementation process of the present invention, the specific steps may be:

[0093] S121: The shape type of fixed non-woven bag;

[0094] S122: Associating the shape type and forming logic table of non-woven bags;

[0095] S123: Matching the forming logic of the non-woven bag based on the shape type of the non-woven bag and the forming logic table. At this time, matching is performed according to the mapping relationship between the shape type of the non-woven bag and the forming logic table;

[0096] S124: collecting the forming logic and constructing a forming process table for the non-woven bag according to the forming logic. At this time, the forming process table presents various forming nodes of the non-woven bag;

[0097] S125: triggering the forming monitoring of the non-woven bag based on each forming node;

[0098] S126: triggering the forming process of the non-woven fabric to the non-woven bag according to each forming node of the non-woven bag to complete the forming of the non-woven bag;

[0099] S127: collecting images of each stage of the non-woven bag during the forming process according to the forming monitoring of the non-woven bag, and defining abnormal parts based on image recognition of each stage image;

[0100] S128: Triggering the repair of the next forming node based on the abnormal part until the non-woven bag is output.

[0101] In an embodiment of the present application, the shape type of the non-woven bag is fixed to facilitate the association of the shape type of the non-woven bag and the molding logic table, so as to match the molding logic of the non-woven bag based on the shape type of the non-woven bag and the molding logic table, and then introduce the molding logic of the non-woven bag, so as to perform corresponding shaping for the non-woven fabric molding to the non-woven bag, and perform targeted shaping for the non-woven bag. At this time, the shape type of the non-woven bag is matched with the mapping relationship in the molding logic table, so as to intelligently select the corresponding molding logic according to the shape type of the non-woven bag, thereby managing and controlling the molding logic of the non-woven bag.

[0102] Furthermore, the forming logic is collected, and a forming process table for the non-woven bag is constructed based on the forming logic. At this time, the forming process table presents each forming node of the non-woven bag, so as to facilitate the management and control of each forming node of the non-woven bag, thereby triggering the forming monitoring of the non-woven bag based on each forming node, realizing the monitoring and adjustment of the non-woven bag during the forming process, and dynamically following up the forming process of the non-woven bag.

[0103] At this time, the forming process of the non-woven fabric to the non-woven bag is triggered according to each forming node of the non-woven bag to complete the forming of the non-woven bag, thereby realizing the forming of the non-woven bag. At the same time, according to the forming monitoring of the non-woven bag, the various stage images of the non-woven bag in the forming process are collected, and the abnormal part is defined according to the image recognition of each stage image; based on the abnormal part, the repair of the next forming node is triggered until the non-woven bag is output, so as to make real-time adjustments to each forming node in the forming of the non-woven bag and make timely repairs to the next forming node for the abnormal part.

[0104] At the same time, the forming logic of the non-woven bag is matched according to the shape type of the non-woven bag, so that the forming logic of the non-woven bag is introduced, so as to control the forming of the non-woven fabric based on the forming logic of the non-woven bag and output the non-woven bag.

[0105] refer to Figure 4 , in step S13, a detection image of the non-woven bag is collected, and a plurality of regions are divided based on the detection image;

[0106] In the specific implementation process of the present invention, the specific steps may be:

[0107] S131: After the non-woven bag is formed, the position of the non-woven bag is positioned;

[0108] S132: triggering the surrounding cameras based on the location of the non-woven bag, and constructing a camera combination with the location of the non-woven bag as the center of the camera combination;

[0109] S133: photographing the non-woven bag in multiple directions according to the camera combination to obtain images in multiple directions;

[0110] S134: constructing a detection image of the non-woven bag based on the multiple images in different directions, and using the detection image of the non-woven bag as a panoramic image;

[0111] S135: highlighting a closed-loop contour line based on the detection image, and dividing the closed-loop contour line into a plurality of regions.

[0112] In an embodiment of the present application, after the non-woven bag is formed, the position of the non-woven bag is located so that the camera responds to the position of the non-woven bag, thereby triggering the surrounding cameras based on the position of the non-woven bag, and constructing a camera combination, which takes the position of the non-woven bag as the position center; according to the camera combination, the non-woven bag is photographed in multiple directions to obtain multiple images in different directions, so that different sides of the non-woven bag can be presented based on multiple images, and then different sides of the non-woven bag can be detected in real time.

[0113] At this time, a detection image of the non-woven bag is constructed based on images in multiple directions, and the detection image of the non-woven bag is used as a panoramic image; the closed-loop contour line is highlighted based on the detection image, and multiple areas are divided according to the closed-loop contour line to facilitate the definition of multiple areas, thereby managing and controlling multiple areas, and then completing the image analysis of the detection image to define the actual situation of the non-woven bag, thereby realizing intelligent identification of the non-woven bag.

[0114] refer to Figure 5 , S14: determining corresponding features according to the identification of multiple regions, and associating the multiple features;

[0115] In the specific implementation process of the present invention, the specific steps may be:

[0116] S141: collecting multiple regions and locating the spatial relationship between the multiple regions;

[0117] S142: defining recognition priorities among the multiple regions according to the spatial relationship between the multiple regions and the outer contour of the non-woven bag;

[0118] S143: triggering sequential identification of multiple areas based on identification priorities;

[0119] S144: In the sequential recognition of multiple regions, the multiple regions are sequentially input into a feature learning model, and corresponding features are sequentially output. In this case, the feature learning model is trained based on previous regions and features.

[0120] S145: Associating multiple features.

[0121] In an embodiment of the present application, multiple areas are collected and the spatial relationship between the multiple areas is located; the recognition priority between the multiple areas is defined based on the spatial relationship between the multiple areas and the outer contour of the non-woven bag; the orderly recognition of the multiple areas is triggered based on the recognition priority, and then the recognition of the multiple areas is completed, so as to output multiple features based on the recognition of the multiple areas.

[0122] At this time, in the orderly identification of multiple areas, multiple areas are input into the feature learning model in sequence, and the corresponding features are output in sequence. At this time, the feature learning model is trained based on previous areas and features; multiple features are associated, and a feature set is constructed for the associated multiple features to facilitate overall control of the feature set, thereby realizing the detection of non-woven bags.

[0123] refer to Figure 6 , S15: defining a correlation coefficient based on the correlation of multiple features, and triggering a combination of multiple features according to the correlation coefficient to construct multiple feature combinations;

[0124] In the specific implementation process of the present invention, the specific steps may be:

[0125] S151: defining a core feature among multiple features, and associating the core feature with the remaining features;

[0126] S152: defining a corresponding correlation coefficient based on the correlation between the core feature and the remaining features;

[0127] S153: Collect multiple correlation coefficients and construct a combination relationship based on the multiple correlation coefficients and recognition priorities. At this time, multiple correlation coefficients within a preset range are marked, and features corresponding to the marked multiple correlation relationships are combined to form a feature combination. At the same time, the recognition priority is matched to the feature combination.

[0128] S154: Listing is performed based on multiple feature combinations, and an integration mechanism is constructed for the multiple feature combinations. In the integration mechanism, similarities of the multiple feature combinations are defined, and the similarities are compared with a preset similarity threshold. The similarities of the multiple feature combinations are integrated to improve the multiple feature combinations.

[0129] In an embodiment of the present application, a core feature is defined among multiple features, and the core feature is associated with the remaining features, so as to compare the core feature with the remaining features, so as to define a corresponding correlation coefficient based on the association between the core feature and the remaining features, thereby introducing the correlation coefficient, and then controlling the core feature and the remaining features based on the correlation coefficient.

[0130] At the same time, multiple correlation coefficients are collected, and a combination relationship is constructed based on the multiple correlation coefficients and recognition priorities. At this time, multiple correlation coefficients in a preset range are marked, and the features corresponding to the marked multiple correlation relationships are combined to form a feature combination. At the same time, the recognition priority is matched to the feature combination, so as to introduce recognition priority for the feature combination and be compatible with the influence of recognition order, thereby integrating multiple feature combinations.

[0131] In addition, multiple feature combinations are listed and an integration mechanism is constructed for the multiple feature combinations. In the integration mechanism, the similarity of the multiple feature combinations is defined, and the similarity is compared with the preset similarity threshold. The similarity of the multiple feature combinations is integrated to improve the multiple feature combinations and ensure the accuracy of the multiple feature combinations.

[0132] refer to Figure 7 , S16: matching the forming coefficient based on each feature combination, and defining the forming grade according to the forming coefficient, and defining the overall grade of the non-woven bag according to the multiple forming grades and the material grade of the non-woven bag;

[0133] In the specific implementation process of the present invention, the specific steps may be:

[0134] S161: freeze each feature combination;

[0135] S162: Matching corresponding forming coefficients according to each feature combination and the forming table. At this time, the forming mapping relationship in the forming table associates the feature combination and the forming coefficient;

[0136] S163: defining a forming level based on the forming coefficient and the number of feature combinations, wherein each feature combination matches a corresponding forming level;

[0137] S164: Collect multiple forming levels and define the forming priority of each feature combination according to the forming process of the non-woven bag and the multiple forming levels;

[0138] S165: Triggering the molding sequence of each feature combination according to the molding priority, and at the same time, collecting the material grade of the non-woven bag;

[0139] S166: Associate multiple molding grades and material grades of non-woven bags;

[0140] S167: defining an overall grade of the non-woven bag according to the plurality of forming grades and the material grade of the non-woven bag, and matching a shaping grade in a final shaping process based on the overall grade of the non-woven bag.

[0141] In an embodiment of the present application, a detection image of a non-woven bag is collected, and a plurality of regions are divided based on the detection image; corresponding features are determined according to the identification of the plurality of regions, so that a correlation coefficient is defined based on the correlation of the plurality of features, and a combination of the plurality of features is triggered according to the correlation coefficient, so that the feature combination is controlled, so that a molding coefficient is matched based on the feature combination, and a molding grade is defined according to the molding coefficient, and an overall grade of the non-woven bag is defined according to the plurality of molding grades and the material grade of the non-woven bag, so as to ensure the molding effect of the non-woven bag, and at the same time, dynamic detection is performed during the molding process of the non-woven bag, so as to control each molding node of the molding logic of the non-woven bag.

[0142] At this time, freeze each feature combination; match the corresponding forming coefficient according to each feature combination and the forming table. At this time, the forming mapping relationship in the forming table associates the feature combination and the forming coefficient, so as to define the forming level based on the forming coefficient and the number of feature combinations. At this time, each feature combination matches the corresponding forming level, so as to present the difficulty of forming through the forming level, and then carry out the forming of the non-woven bag in different processes, so as to facilitate the forming control of each process.

[0143] At the same time, multiple forming grades are collected, and the forming priority of each feature combination is defined according to the forming process of the non-woven bag and multiple forming grades; the forming order of each feature combination is triggered according to the forming priority, and at the same time, the material grade of the non-woven bag is collected; multiple forming grades and the material grade of the non-woven bag are associated; the overall grade of the non-woven bag is defined according to the multiple forming grades and the material grade of the non-woven bag, and the forming grade in the final forming process is matched based on the overall grade of the non-woven bag, so as to strictly limit the final forming process and control it with the overall grade of the non-woven bag, thereby ensuring the forming accuracy of the output non-woven bag.

[0144] Therefore, the forming logic of the non-woven bag is matched according to the shape type of the non-woven bag, so that the forming logic of the non-woven bag is introduced, so that the forming of the non-woven fabric is controlled based on the forming logic of the non-woven bag, and the non-woven bag is output. At the same time, the detection image of the non-woven bag is collected, and multiple areas are divided based on the detection image; the corresponding features are determined according to the identification of multiple areas, so that the correlation coefficient is defined based on the association of multiple features, and the combination of multiple features is triggered according to the correlation coefficient, so as to control the feature combination, so as to match the forming coefficient based on the feature combination, and define the forming grade according to the forming coefficient, and define the overall grade of the non-woven bag according to multiple forming grades and the material grade of the non-woven bag, so as to ensure the forming effect of the non-woven bag. At the same time, dynamic detection is performed during the forming process of the non-woven bag, so as to control each forming node of the forming logic of the non-woven bag.

[0145] Example

[0146] See also Figure 8 , Figure 8 Schematic diagram of the structure of the non-woven bag detection system in an embodiment of the present invention.

[0147] like Figure 8 As shown, a non-woven bag detection system, the non-woven bag detection system includes:

[0148] The shape area module 21 is used to collect the shaping information of the non-woven bag and define the shape type of the non-woven bag according to the shaping information of the non-woven bag;

[0149] a forming module 22 for matching the forming logic of the non-woven bag based on the shape type of the non-woven bag, and triggering the forming of the non-woven fabric according to the forming logic of the non-woven bag to output the non-woven bag;

[0150] The region module 23 is used to collect a detection image of the non-woven bag and divide the detection image into multiple regions;

[0151] a shaping module 24 for determining corresponding features based on the identification of the multiple regions and associating the multiple features;

[0152] A feature combination module 25 is configured to define a correlation coefficient based on the correlation of multiple features, and trigger the combination of multiple features according to the correlation coefficient to construct multiple feature combinations;

[0153] The grade module 26 is used to match the forming coefficients based on the combination of each feature, define the forming grade according to the forming coefficients, and define the overall grade of the non-woven bag according to the multiple forming grades and the material grade of the non-woven bag.

[0154] Example

[0155] See also Figure 9, refer to the following Figure 9 An electronic device 40 according to this embodiment of the present invention will be described. Figure 9 The electronic device 40 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present invention.

[0156] like Figure 9 As shown, the electronic device 40 is a general-purpose computing device. Components of the electronic device 40 may include, but are not limited to, at least one processing unit 41, at least one storage unit 42, and a bus 43 connecting different system components (including the storage unit 42 and the processing unit 41).

[0157] The storage unit stores program codes, which can be executed by the processing unit 41, so that the processing unit 41 performs the steps according to various exemplary embodiments of the present invention described in the above “Example Method” section of this specification.

[0158] The storage unit 42 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0159] The storage unit 42 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0160] Bus 43 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0161] The electronic device 40 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 40, and / or any device that enables the electronic device 40 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 44. Furthermore, the electronic device 40 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 45. Figure 9 As shown, the network adapter 45 communicates with other modules of the electronic device 40 via the bus 43. Figure 9Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 40, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup planning systems.

[0162] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0163] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be performed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium, which may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Furthermore, the computer program instructions are stored therein, and when executed by a computer, the computer executes the above methods.

[0164] In addition, the above is a detailed introduction to the detection method and system for non-woven bags provided in the embodiments of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for detecting non-woven bags, characterized in that: Applied to the detection scenario of non-woven bags; The detection method of the non-woven bag comprises: Collecting the shaping information of the non-woven bag, and defining the shape type of the non-woven bag according to the shaping information of the non-woven bag; Matching the forming logic of the non-woven bag based on the shape type of the non-woven bag, and triggering the forming of the non-woven fabric according to the forming logic of the non-woven bag to output the non-woven bag, including triggering the forming monitoring of the non-woven bag based on each forming node; triggering the forming process of the non-woven fabric to the non-woven bag according to each forming node of the non-woven bag to complete the forming of the non-woven bag; collecting each stage image of the non-woven bag in the forming process according to the forming monitoring of the non-woven bag, and defining abnormal parts according to image recognition of each stage image; triggering the repair of the next forming node based on the abnormal part until the non-woven bag is output; Collecting a detection image of a non-woven bag and dividing it into multiple areas based on the detection image; Determining corresponding features based on the identification of multiple regions and associating the multiple features; A correlation coefficient is defined based on the correlation of multiple features, and a combination of multiple features is triggered according to the correlation coefficient to construct multiple feature combinations; The forming coefficient is matched based on each feature combination, and the forming grade is defined according to the forming coefficient. The overall grade of the non-woven bag is defined according to multiple forming grades and the material grade of the non-woven bag.

2. The method for detecting non-woven bags according to claim 1, characterized in that: The collecting of the shaping information of the non-woven bag and defining the shape type of the non-woven bag according to the shaping information of the non-woven bag include: Acquiring communication signals input by users via remote communication; Outputting multiple information based on the analysis of the communication signal, the multiple information including user information, non-woven bag order information and non-woven bag shaping information; Screening the shaping information of the non-woven bag based on multiple information to complete the collection of the shaping information of the non-woven bag; Traverse the shaping information of non-woven bags, and filter shaping keywords based on the shaping information of non-woven bags; The shape type of the non-woven bag is defined according to the stereotyped keywords and the shape type learning model. At this time, the shape type learning model is trained based on the stereotyped keywords and shape types in the past.

3. The method for detecting non-woven bags according to claim 2, characterized in that: The method of matching the forming logic of the non-woven bag based on the shape type of the non-woven bag and triggering the forming of the non-woven fabric according to the forming logic of the non-woven bag to output the non-woven bag includes: The shape type of fixed non-woven bag; Associate the shape type and forming logic table of non-woven bags; Match the forming logic of the non-woven bag based on the shape type of the non-woven bag and the forming logic table. At this time, match the shape type of the non-woven bag according to the mapping relationship between the shape type and the forming logic table; The forming logic is collected, and a forming process table for the non-woven bag is constructed based on the forming logic. At this time, the forming process table presents various forming nodes of the non-woven bag.

4. The method for detecting non-woven bags according to claim 1, characterized in that: The collecting of the detection image of the non-woven bag and dividing the detection image into multiple areas include: After the non-woven bag is formed, locate the position of the non-woven bag; Based on the location of the non-woven bag, the surrounding cameras are triggered, and a camera combination is constructed, which takes the location of the non-woven bag as the position center; The non-woven bag is photographed in multiple directions according to the camera combination to obtain images in multiple directions; Constructing a detection image of the non-woven bag based on multiple images in different directions, and using the detection image of the non-woven bag as a panoramic image; The closed-loop contour line is highlighted based on the detection image, and multiple areas are divided according to the closed-loop contour line.

5. The method for detecting non-woven bags according to claim 4, characterized in that: The determining corresponding features based on the identification of the multiple regions and associating the multiple features includes: Collect multiple areas and locate the spatial relationship between multiple areas; Defining the recognition priority among multiple areas according to the spatial relationship between the multiple areas and the outer contour of the non-woven bag; Triggering sequential identification of multiple areas based on identification priority; In the sequential recognition of multiple regions, multiple regions are sequentially input into the feature learning model, and the corresponding features are output in sequence. At this time, the feature learning model is trained based on the previous regions and features. Associate multiple features.

6. The method for detecting non-woven bags according to claim 5, characterized in that: The method of defining a correlation coefficient based on the correlation of multiple features and triggering a combination of multiple features according to the correlation coefficient to construct multiple feature combinations includes: Define core features among multiple features and associate them with the remaining features based on the core features; Define the corresponding correlation coefficient based on the correlation between the core feature and the remaining features; Collect multiple correlation coefficients and build a combination relationship based on the multiple correlation coefficients and recognition priorities. At this time, mark the multiple correlation coefficients within a preset range, and combine the features corresponding to the marked multiple correlation relationships to form a feature combination. At the same time, match the recognition priority to the feature combination; Based on the listing of multiple feature combinations, an integration mechanism is constructed for the multiple feature combinations. In the integration mechanism, the similarity of the multiple feature combinations is defined, and the similarity is compared with the preset similarity threshold. The similarity of the multiple feature combinations is integrated to improve the multiple feature combinations.

7. The method for detecting a non-woven bag according to claim 6, wherein: The forming coefficient is matched based on each feature combination, and the forming grade is defined according to the forming coefficient. The overall grade of the non-woven bag is defined according to multiple forming grades and the material grade of the non-woven bag, including: Freeze each feature combination; Matching the corresponding forming coefficients according to each feature combination and the forming table. At this time, the forming mapping relationship in the forming table associates the feature combination and the forming coefficient; The forming level is defined based on the forming coefficient and the number of feature combinations. In this case, each feature combination matches the corresponding forming level. Collect multiple forming levels and define the forming priority of each feature combination according to the forming process of non-woven bags and multiple forming levels; The molding sequence of each feature combination is triggered according to the molding priority, and at the same time, the material grade of the non-woven bag is collected.

8. The method for detecting non-woven bags according to claim 7, characterized in that: The method of matching the forming coefficients based on the combination of each feature, defining the forming grade according to the forming coefficients, and defining the overall grade of the non-woven bag according to the multiple forming grades and the material grade of the non-woven bag also includes: Correlate multiple molding grades and material grades of non-woven bags; The overall grade of the non-woven bag is defined according to multiple forming grades and the material grade of the non-woven bag, and the shaping grade in the final shaping process is matched based on the overall grade of the non-woven bag.

9. A non-woven bag detection system, characterized in that: The non-woven bag detection system is applied to the non-woven bag detection method according to any one of claims 1 to 8, and the non-woven bag detection system includes: The shape area module is used to collect the shaping information of the non-woven bag and define the shape type of the non-woven bag according to the shaping information of the non-woven bag; A forming module is used to match the forming logic of the non-woven bag based on the shape type of the non-woven bag, and trigger the forming of the non-woven fabric according to the forming logic of the non-woven bag to output the non-woven bag, including triggering the forming monitoring of the non-woven bag based on each forming node; triggering the forming process of the non-woven fabric to the non-woven bag according to each forming node of the non-woven bag to complete the forming of the non-woven bag; collecting various stage images of the non-woven bag in the forming process according to the forming monitoring of the non-woven bag, and defining abnormal parts based on image recognition of each stage image; triggering the repair of the next forming node based on the abnormal part until the non-woven bag is output; A region module is used to collect detection images of non-woven bags and divide them into multiple regions based on the detection images; A feature module, configured to determine corresponding features based on the identification of multiple regions and associate multiple features; A feature combination module is used to define a correlation coefficient based on the correlation of multiple features, and trigger the combination of multiple features according to the correlation coefficient to construct multiple feature combinations; The grade module is used to match the forming coefficient based on each feature combination, and define the forming grade according to the forming coefficient, and define the overall grade of the non-woven bag according to multiple forming grades and the material grade of the non-woven bag.

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