A warehouse abnormality tracing method and system

Abstract

The present invention relates to the technical field of warehouse resource management, and specifically discloses a warehouse abnormality tracing method and system, the method comprising: obtaining cargo information in real time at a warehouse entry end, creating cargo labels and cargo features based on the cargo information; creating a cache database named after the cargo labels, obtaining warehouse videos in real time based on a camera system, and locating cargo areas in the warehouse videos based on cargo features; identifying cargo areas, determining cargo changes and staff in each time period, and inputting them into the cache database; receiving a tracing request containing cargo labels and changes input by staff, and tracing in the cache database; the present invention obtains cargo status in real time through a camera in the warehouse, determines cargo changes and nearby staff according to the cargo status, regards nearby staff as the cause of cargo changes, sets them as responsible personnel, and can directly query when tracing is required.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse resource management, and in particular to a warehouse abnormality tracing method and system. Background Art

[0002] "Warehousing" refers to the storage and management of goods, which usually takes place in a warehouse or logistics center. In the commercial field, warehousing is an important part of supply chain management, involving activities such as receiving, storing, picking, packaging and distribution of goods.

[0003] Most of the existing warehousing processes adopt a responsible approach, where each batch of goods is managed by a dedicated staff member. Once a problem occurs, it will be handled by the dedicated staff member. This approach is essentially a one-person-one-post approach, and is overly dependent on the ability of the staff. When the amount of goods is large, the physical and psychological pressure on the staff is very high. How to provide a more relaxed warehousing status attribution solution and reduce the pressure on the staff is a technical problem that the technical solution of the present invention wants to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a warehouse abnormality tracing method and system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for tracing the source of storage anomalies, the method comprising:

[0007] Obtain cargo information in real time at the warehouse entry end, and create cargo labels and cargo features based on the cargo information; the cargo information includes cargo flow information and cargo physical information; the cargo flow information is used to characterize the source and destination of the cargo, and the cargo physical information includes cargo weight and cargo volume; the cargo features are the features of the cargo in the image;

[0008] Create a cache database named after the cargo tag, obtain warehouse videos in real time based on the camera system, and locate the cargo area in the warehouse video based on the cargo features;

[0009] Identify the cargo area, determine the cargo changes and staff in each period, and enter them into the cache database;

[0010] Receive the traceability request containing the goods label and change amount entered by the staff, and perform traceability in the cache database.

[0011] As a further solution of the present invention: the step of obtaining cargo information in real time at the warehousing end and creating cargo labels and cargo features based on the cargo information includes:

[0012] Receive the name of goods, goods flow information and goods physical information uploaded by the staff in real time at the warehouse entry end, and determine the goods number according to the name of goods, upload time and goods flow information as the goods label;

[0013] Query the standard image according to the name of the goods, and calculate the environment filter based on the goods image and the standard image;

[0014] Acquire a cargo image at the warehousing end, correct the cargo image according to the environmental filter, and locate the cargo area in the corrected cargo image;

[0015] The cargo features are extracted from the located cargo area and connected to the cargo labels.

[0016] As a further solution of the present invention: the steps of creating a cache database named after the cargo tag, acquiring warehouse video in real time based on a camera system, and locating the cargo area in the warehouse video based on the cargo features include:

[0017] Create a cache database named after the cargo label and query the cargo features corresponding to the cargo label;

[0018] The camera system acquires warehouse videos in real time and converts the warehouse videos into image frames;

[0019] Traverse each image based on the cargo features to obtain the matching cargo location;

[0020] Taking the cargo location as the center, expand the cargo area;

[0021] The process of expanding the cargo area with the cargo location as the center is as follows:

[0022] Calculate the center of the cargo position, query the nearest obstacle in each direction according to the preset angle step, and simultaneously calculate the closest distance; determine the extended length in that direction according to the closest distance; the extended length is:

[0023] Where D θ is the extension length in the θ direction, d θ is the shortest distance in the θ direction.

[0024] As a further solution of the present invention: the step of traversing each image based on the cargo features to obtain the matching cargo location includes:

[0025] Convert the cargo features to the frequency domain and select the pixel value range in the frequency domain;

[0026] The cargo feature is split into sub-features of different sizes based on the pixel value range; the size of the sub-feature is represented by an information ratio, which is a percentage and is used to represent the ratio of the data volume of the sub-feature to the data volume of the cargo feature;

[0027] Select the smallest sub-feature and traverse each image. When the match is successful, read the sub-features in ascending order of size and traverse the matching. When the largest sub-feature is matched successfully, the matching position is used as the cargo position.

[0028] The traversal order of the image is a rotation order, including clockwise and counterclockwise order; when a cargo position is obtained, the cargo position is used as the traversal starting point of the next image;

[0029] The pixel value range is (μ-iσ,μ+iσ); where μ is the mean of the pixel values ​​in the frequency domain, σ is the standard deviation of the pixel values, and i is a value determined by the sub-feature size, and i is proportional to the sub-feature size.

[0030] As a further solution of the present invention: the steps of identifying the cargo area, determining the cargo change amount and staff in each time period, and inputting into the cache database include:

[0031] Identify the cargo area and determine the cargo location and volume at each moment;

[0032] Obtain the weight of goods with time stamp based on the weighing points pre-installed in the warehouse;

[0033] Count the cargo volume at each time, fit the volume change curve, and correct the volume change curve according to the cargo weight with timestamp;

[0034] The modified volume change curve is derived, and when the derivative value reaches a preset value, an independent variable interval of a preset length is selected with the independent variable corresponding to the derivative value as the center;

[0035] Read the cargo area corresponding to the independent variable interval, identify the cargo area, locate the staff, and determine the responsibility level based on the distance between the staff and the cargo location;

[0036] Statistics include staff members with responsibility levels, independent variable intervals, and changes in goods in the independent variable intervals. The statistical data are sorted based on the left endpoint of the independent variable interval and input into the cache database.

[0037] As a further solution of the present invention: the step of receiving the traceability request containing the goods label and the change amount input by the staff and performing traceability in the cache database includes:

[0038] Receive the traceability request containing the goods label and the change quantity input by the staff;

[0039] The cache database is located based on the cargo label, and the cache database is traversed based on the change amount to match the independent variable interval and the staff with responsibility levels.

[0040] The technical solution of the present invention also provides a storage abnormality tracing system, the system comprising:

[0041] The information filing module is used to obtain cargo information in real time at the warehousing end, and to create cargo labels and cargo features based on the cargo information; the cargo information includes cargo flow information and cargo physical information; the cargo flow information is used to characterize the source and destination of the cargo, and the cargo physical information includes cargo weight and cargo volume; the cargo features are the features of the cargo in the image;

[0042] The video acquisition and recognition module is used to create a cache database named after the cargo label, acquire warehouse videos in real time based on the camera system, and locate the cargo area in the warehouse video based on the cargo features;

[0043] The data backup module is used to identify the cargo area, determine the cargo changes and staff in each period, and input them into the cache database;

[0044] The data traceability module is used to receive traceability requests containing cargo labels and change quantities input by staff, and perform traceability in the cache database.

[0045] As a further solution of the present invention: the information filing module includes:

[0046] The label generation unit is used to receive the name of the goods, the flow information of the goods and the physical information of the goods uploaded by the staff in real time at the warehouse entry end, and determine the goods number as the goods label according to the name of the goods, the upload time and the flow information of the goods;

[0047] A filter generation unit, used for querying a standard image according to a product name, and calculating an environment filter according to the product image and the standard image;

[0048] A region positioning unit, used to obtain a cargo image at the warehousing end, correct the cargo image according to the environmental filter, and locate the cargo region in the corrected cargo image;

[0049] The feature extraction unit is used to extract cargo features in the located cargo area and is connected to the cargo tag.

[0050] As a further solution of the present invention: the video acquisition and recognition module includes:

[0051] A cargo feature query unit, used to create a cache database named after the cargo label, and query the cargo features corresponding to the cargo label;

[0052] A video conversion unit, used for acquiring warehouse videos in real time through a camera system, and converting the warehouse videos into image frames;

[0053] A position matching unit, used to traverse each image based on the characteristics of the goods and obtain the matching goods position;

[0054] The area expansion unit is used to expand the cargo area with the cargo location as the center;

[0055] The process of expanding the cargo area with the cargo location as the center is as follows:

[0056] Calculate the center of the cargo position, query the nearest obstacle in each direction according to the preset angle step, and simultaneously calculate the closest distance; determine the extended length in that direction according to the closest distance; the extended length is:

[0057] Where D θ is the extension length in the θ direction, d θ is the shortest distance in the θ direction.

[0058] As a further solution of the present invention: the position matching unit includes:

[0059] The range selection subunit is used to convert the cargo features into the frequency domain and select the pixel value range in the frequency domain;

[0060] A feature splitting subunit, used for splitting the cargo feature into sub-features of different sizes based on the pixel value range; the size of the sub-feature is represented by an information ratio, which is a percentage and is used to represent the ratio of the data volume of the sub-feature to the data volume of the cargo feature;

[0061] The matching execution subunit is used to select the smallest sub-feature, traverse each image, and when the match is successful, read the sub-features in increasing order of size, perform traversal matching, and when the largest sub-feature is matched successfully, the matching position is used as the cargo position;

[0062] The traversal order of the image is a rotation order, including clockwise and counterclockwise order; when a cargo position is obtained, the cargo position is used as the traversal starting point of the next image;

[0063] The pixel value range is (μ-iσ,μ+iσ); where μ is the mean of the pixel values ​​in the frequency domain, σ is the standard deviation of the pixel values, and i is a value determined by the sub-feature size, and i is proportional to the sub-feature size.

[0064] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention obtains the status of goods in real time through the camera in the warehouse, determines the amount of change in goods and the nearby staff according to the status of goods, regards the nearby staff as the cause of the change in goods, and sets them as responsible personnel. When tracing the source is required, direct inquiry can be made. The present invention has strong real-time performance, does not require full-time personnel, and does not have staff with excessive pressure. It is extremely suitable for existing intelligent warehouses. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0066] Figure 1 A flowchart of a warehouse abnormality tracing method is shown.

[0067] Figure 2 The figure shows a structural block diagram of a warehouse abnormality tracing system. DETAILED DESCRIPTION

[0068] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0069] Figure 1 A flowchart of a method for tracing the source of storage anomalies is shown. In an embodiment of the present invention, a method for tracing the source of storage anomalies is provided, and the method includes:

[0070] Step S100: obtaining cargo information in real time at the warehousing end, and creating cargo labels and cargo features based on the cargo information; the cargo information includes cargo flow information and cargo physical information; the cargo flow information is used to characterize the source and destination of the cargo, and the cargo physical information includes cargo weight and cargo volume; the cargo features are features of the cargo in the image;

[0071] Step S200: creating a cache database named after the cargo tag, acquiring warehouse video in real time based on the camera system, and locating the cargo area in the warehouse video based on the cargo features;

[0072] Step S300: Identify the cargo area, determine the cargo change and staff in each period, and input them into the cache database;

[0073] Step S400: receiving a traceability request containing a cargo label and a change amount input by a staff member, and performing traceability in a cache database.

[0074] The present invention obtains cargo information at the warehousing end, and the cargo information includes cargo circulation information and cargo physical information. The cargo circulation information is not necessary information, and it is mainly used to determine the characteristics of the cargo, indicating from which location the cargo is transported to which location, and which transfer stations the cargo has passed through in the middle; the cargo physical information is the weight and volume of the cargo; by analyzing the cargo information, the unique label of the cargo and the cargo characteristics can be determined, wherein the cargo circulation information is used to determine the cargo label, and the cargo physical information is used to determine the cargo characteristics and correct the cargo label. The cargo label is corrected by the cargo physical information, which can ensure the uniqueness of the cargo label.

[0075] Then, for each batch of goods entering the warehouse, a cache database named after the goods label is established to store the identification data of the corresponding goods. The camera system in the warehouse obtains the warehouse video, and the goods area can be located in the warehouse video in real time based on the goods characteristics, determining where the batch of goods appears in the warehouse at each moment.

[0076] By identifying each cargo area, the amount of cargo at each moment and the nearby staff can be determined, the amount of cargo at each moment can be counted, and the changes can be calculated. At this point, it can be determined that the changes are affected by the corresponding staff, thereby establishing a responsibility relationship.

[0077] Finally, when tracing is needed, the staff inputs the tracing request, locates the corresponding cache database, and reads the changes at each moment and the corresponding responsible personnel. This is the meaning of tracing in this application.

[0078] As a preferred embodiment of the technical solution of the present invention, step S100 is defined as follows: the step of obtaining cargo information in real time at the warehousing end and creating cargo labels and cargo features based on the cargo information includes:

[0079] Receive the name of goods, goods flow information and goods physical information uploaded by the staff in real time at the warehouse entry end, and determine the goods number according to the name of goods, upload time and goods flow information as the goods label;

[0080] Query the standard image according to the name of the goods, and calculate the environment filter based on the goods image and the standard image;

[0081] Acquire a cargo image at the warehousing end, correct the cargo image according to the environmental filter, and locate the cargo area in the corrected cargo image;

[0082] The cargo features are extracted from the located cargo area and connected to the cargo labels.

[0083] The warehousing end is generally a computer device with an information receiving function. When the staff enters the goods into the warehouse, the computer device receives the name of the goods and the flow information of the goods entered by the staff, which is used to generate the goods label; in order to ensure the uniqueness of the generated goods label, this application also records the input time of the staff, connects this information, and then combines it with the conventional conversion model to convert it into digital form to obtain the goods label.

[0084] Furthermore, the standard image of the goods is queried by the name of the goods. The standard image is an image of the goods taken under normal lighting conditions. By comparing it with the goods image, the impact of the environment on the goods can be determined, which is called the environmental filter. The goods image can be corrected by the environmental filter and converted into an image under normal lighting conditions. At this time, the result of identifying the goods image is more accurate.

[0085] By recognizing the corrected image, the cargo area can be located and the cargo features can be extracted.

[0086] Furthermore, the steps of creating a cache database named after the cargo tag, acquiring warehouse video in real time based on the camera system, and locating the cargo area in the warehouse video based on the cargo features include:

[0087] Create a cache database named after the cargo label and query the cargo features corresponding to the cargo label;

[0088] The camera system acquires warehouse videos in real time and converts the warehouse videos into image frames;

[0089] Traverse each image based on the cargo features to obtain the matching cargo location;

[0090] Taking the cargo location as the center, expand the cargo area;

[0091] The process of expanding the cargo area with the cargo location as the center is as follows:

[0092] Calculate the center of the cargo position, query the nearest obstacle in each direction according to the preset angle step, and simultaneously calculate the closest distance; determine the extended length in that direction according to the closest distance; the extended length is:

[0093] Where D θ is the extension length in the θ direction, d θ is the shortest distance in the θ direction.

[0094] For each batch of goods, a cache database named after the goods label is created, and the goods features corresponding to the goods labels are queried. The goods features are traversed and located in the warehouse video to query the location of the goods at different times. An extended area is determined with the goods location as the center. The extended area is a range, and the people within the range can be regarded as responsible persons for the goods at different times.

[0095] Among them, regarding the cargo features, the cargo features can adopt the existing feature extraction scheme. The simplest scheme is to only perform noise reduction on the cargo area located in the cargo image, and then perform contour recognition, and directly use the recognized contour as the cargo feature. In the subsequent matching process, it is only necessary to match the similarity between the two parties to a smaller value.

[0096] As a preferred embodiment of the technical solution of the present invention, the step of traversing each image based on the cargo features to obtain the matching cargo location includes:

[0097] Convert the cargo features to the frequency domain and select the pixel value range in the frequency domain;

[0098] The cargo feature is split into sub-features of different sizes based on the pixel value range; the size of the sub-feature is represented by an information ratio, which is a percentage and is used to represent the ratio of the data volume of the sub-feature to the data volume of the cargo feature;

[0099] Select the smallest sub-feature and traverse each image. When the match is successful, read the sub-features in ascending order of size and traverse the matching. When the largest sub-feature is matched successfully, the matching position is used as the cargo position.

[0100] The traversal order of the image is a rotation order, including clockwise and counterclockwise order; when a cargo position is obtained, the cargo position is used as the traversal starting point of the next image;

[0101] The pixel value range is (μ-iσ,μ+iσ); where μ is the mean of the pixel values ​​in the frequency domain, σ is the standard deviation of the pixel values, and i is a value determined by the sub-feature size, and i is proportional to the sub-feature size.

[0102] In an example of the technical solution of the present invention, the application process of the cargo features is limited, the cargo features are converted into the frequency domain, and then the pixel value range is selected in the frequency domain. Pixel points are extracted from the image features according to the selected pixel value range to obtain sub-features containing different amounts of information. The image is traversed by the sub-features. When the match is successful, the larger feature is read to see if it can be matched. If the largest sub-feature can also be matched, it is considered that the cargo is matched, and the position of the matched cargo is recorded.

[0103] The above processing method can greatly improve the matching efficiency. The logic of improving the matching efficiency is that when a smaller sub-feature does not match, the corresponding position is no longer matched by default, and there is no need to read the larger feature for matching.

[0104] Specifically, regarding the traversal order, when there is a matching position, in the next image, the present application takes the matching position as the starting point and traverses around it clockwise or counterclockwise. This is also a way to improve the matching efficiency, because the speed of the cargo delivery process is stable and will not change suddenly, and the matching positions in the previous and next images are similar.

[0105] As a preferred embodiment of the technical solution of the present invention, the steps of identifying the cargo area, determining the cargo change amount and staff in each time period, and inputting into the cache database include:

[0106] Identify the cargo area and determine the cargo location and volume at each moment;

[0107] Obtain the weight of goods with time stamp based on the weighing points pre-installed in the warehouse;

[0108] Count the cargo volume at each time, fit the volume change curve, and correct the volume change curve according to the cargo weight with timestamp;

[0109] The modified volume change curve is derived, and when the derivative value reaches a preset value, an independent variable interval of a preset length is selected with the independent variable corresponding to the derivative value as the center;

[0110] Read the cargo area corresponding to the independent variable interval, identify the cargo area, locate the staff, and determine the responsibility level based on the distance between the staff and the cargo location;

[0111] Statistics include staff members with responsibility levels, independent variable intervals, and changes in goods in the independent variable intervals. The statistical data are sorted based on the left endpoint of the independent variable interval and input into the cache database.

[0112] The above content specifically defines the identification process of the cargo area. First, the cargo area is identified to determine the cargo position and cargo volume at each moment. The cargo position has been known in the matching process, and the cargo volume only needs to identify the cargo size in the image. Under the background of the prior art, this is very easy. The cargo volume at each moment is counted to fit a volume change curve. During the transportation of cargo, there may be some weighing nodes in the warehouse. The cargo weight is detected at the weighing nodes, and the fitted volume change curve can be corrected by the cargo weight. Since the density of the cargo is known, the accuracy of the identified volume can be determined by the cargo weight and the density of the cargo, and then some accurate coordinates are inserted into the volume change curve to improve the authenticity of the volume change curve.

[0113] By taking the derivative of the volume change curve, the volume change of the goods at each moment can be calculated. The volume change reflects the increase or decrease of the goods. When the derivative is large, it means that the increase or decrease of the goods is more obvious. At this time, take the independent variable with a larger derivative value (corresponding to time) as the center, obtain a time interval, read the goods area within the time interval, identify the goods area, and locate the staff member, who can be the responsible person for the current increase or decrease. According to the distance between the staff member and the goods, the responsibility level of different staff members is determined. Generally, the closer the distance, the higher the responsibility level.

[0114] Finally, the staff with responsibility levels, the independent variable interval, and the cargo change in the independent variable interval are counted and entered into the cache database for subsequent problem tracing. It should be noted that the cargo change is determined by the cargo volume change in the independent variable interval. The cargo volume change can be directly used as the cargo change, or it can be multiplied by the density and the cargo weight change can be used as the cargo change.

[0115] As a preferred embodiment of the technical solution of the present invention, the step of receiving a traceability request containing a cargo label and a change amount input by a staff member and performing traceability in a cache database includes:

[0116] Receive the traceability request containing the goods label and the change quantity input by the staff;

[0117] The cache database is located based on the cargo label, and the cache database is traversed based on the change amount to match the independent variable interval and the staff with responsibility levels.

[0118] The above content is the final application process. The staff who need traceability inputs the traceability request and uploads the cargo label and change amount. The cargo label is used to locate the cache database, and the change amount is used to query the staff and their responsibility level in the cache database.

[0119] Figure 2The structure diagram of a storage abnormality tracing system is shown. In an embodiment of the present invention, a storage abnormality tracing system is provided. The system 10 includes:

[0120] The information filing module 11 is used to obtain cargo information in real time at the warehousing end, and to create cargo labels and cargo features based on the cargo information; the cargo information includes cargo flow information and cargo physical information; the cargo flow information is used to characterize the source and destination of the cargo, and the cargo physical information includes cargo weight and cargo volume; the cargo features are features of the cargo in the image;

[0121] The video acquisition and recognition module 12 is used to create a cache database named after the cargo label, acquire warehouse videos in real time based on the camera system, and locate cargo areas in the warehouse videos based on cargo features;

[0122] The data backup module 13 is used to identify the cargo area, determine the cargo change and staff in each period, and input them into the cache database;

[0123] The data tracing module 14 is used to receive a tracing request containing a cargo label and a change amount input by a staff member, and perform tracing in a cache database.

[0124] Furthermore, the information filing module 11 includes:

[0125] The label generation unit is used to receive the name of the goods, the flow information of the goods and the physical information of the goods uploaded by the staff in real time at the warehouse entry end, and determine the goods number as the goods label according to the name of the goods, the upload time and the flow information of the goods;

[0126] A filter generation unit, used for querying a standard image according to a product name, and calculating an environment filter according to the product image and the standard image;

[0127] A region positioning unit, used to obtain a cargo image at the warehousing end, correct the cargo image according to the environmental filter, and locate the cargo region in the corrected cargo image;

[0128] The feature extraction unit is used to extract cargo features in the located cargo area and is connected to the cargo tag.

[0129] Specifically, the video acquisition and identification module 12 includes:

[0130] A cargo feature query unit, used to create a cache database named after the cargo label, and query the cargo features corresponding to the cargo label;

[0131] A video conversion unit, used for acquiring warehouse videos in real time through a camera system, and converting the warehouse videos into image frames;

[0132] A position matching unit, used to traverse each image based on the characteristics of the goods and obtain the matching goods position;

[0133] The area expansion unit is used to expand the cargo area with the cargo location as the center;

[0134] The process of expanding the cargo area with the cargo location as the center is as follows:

[0135] Calculate the center of the cargo position, query the nearest obstacle in each direction according to the preset angle step, and simultaneously calculate the closest distance; determine the extended length in that direction according to the closest distance; the extended length is:

[0136] Where D θ is the extension length in the θ direction, d θ is the shortest distance in the θ direction.

[0137] Regarding the position matching unit in the above content, the position matching unit includes:

[0138] The range selection subunit is used to convert the cargo features into the frequency domain and select the pixel value range in the frequency domain;

[0139] A feature splitting subunit, used for splitting the cargo feature into sub-features of different sizes based on the pixel value range; the size of the sub-feature is represented by an information ratio, which is a percentage and is used to represent the ratio of the data volume of the sub-feature to the data volume of the cargo feature;

[0140] The matching execution subunit is used to select the smallest sub-feature, traverse each image, and when the match is successful, read the sub-features in increasing order of size, perform traversal matching, and when the largest sub-feature is matched successfully, the matching position is used as the cargo position;

[0141] The traversal order of the image is a rotation order, including clockwise and counterclockwise order; when a cargo position is obtained, the cargo position is used as the traversal starting point of the next image;

[0142] The pixel value range is (μ-iσ,μ+iσ); where μ is the mean of the pixel values ​​in the frequency domain, σ is the standard deviation of the pixel values, and i is a value determined by the sub-feature size, and i is proportional to the sub-feature size.

[0143] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for tracing the source of storage anomalies, characterized in that: The method comprises: Obtain cargo information in real time at the warehousing end, and create cargo labels and cargo features based on the cargo information; the cargo information includes cargo flow information and cargo physical information; the cargo flow information is used to characterize the source and destination of the cargo, and the cargo physical information includes cargo weight and cargo volume; the cargo features are the features of the cargo in the image; Create a cache database named after the cargo tag, obtain warehouse videos in real time based on the camera system, and locate the cargo area in the warehouse video based on the cargo features; Identify the cargo area, determine the cargo changes and staff in each period, and enter them into the cache database; Receive the traceability request containing the goods label and the change amount input by the staff, and perform traceability in the cache database; The steps of obtaining cargo information in real time at the warehousing end and creating cargo labels and cargo features based on the cargo information include: Receive the name of goods, goods flow information and goods physical information uploaded by the staff in real time at the warehouse entry end, and determine the goods number according to the name of goods, upload time and goods flow information as the goods label; Query the standard image according to the name of the goods, and calculate the environment filter based on the goods image and the standard image; Acquire a cargo image at the warehousing end, correct the cargo image according to the environmental filter, and locate the cargo area in the corrected cargo image; Extract cargo features from the located cargo area and connect them to cargo labels; The steps of creating a cache database named after the cargo label, acquiring warehouse video in real time based on the camera system, and locating the cargo area in the warehouse video based on the cargo features include: Create a cache database named after the cargo label and query the cargo features corresponding to the cargo label; The camera system acquires warehouse videos in real time and converts the warehouse videos into image frames; Traverse each converted image frame based on the cargo features to obtain the matching cargo location; Taking the cargo location as the center, expand the cargo area; The process of expanding the cargo area with the cargo location as the center is as follows: Calculate the center of the cargo position, query the nearest obstacle in each direction according to the preset angle step, and simultaneously calculate the closest distance; determine the extended length in that direction according to the closest distance; the extended length is: Where D θ is the extension length in the θ direction, d θ is the shortest distance in the θ direction; The step of traversing each converted image frame based on the cargo feature to obtain the matching cargo location comprises: Convert the cargo features to the frequency domain and select the pixel value range in the frequency domain; The cargo feature is split into sub-features of different sizes based on the pixel value range; the size of the sub-feature is represented by an information ratio, which is a percentage and is used to represent the ratio of the data volume of the sub-feature to the data volume of the cargo feature; Select the smallest sub-feature and traverse each image. When the match is successful, read the sub-features in ascending order of size and traverse the matching. When the largest sub-feature is matched successfully, the matching position is used as the cargo position. The traversal order of the image is a rotation order, including clockwise and counterclockwise order; when a cargo position is obtained, the cargo position is used as the traversal starting point of the next image; The pixel value range is (μ-iσ,μ+iσ); where μ is the mean of the pixel values ​​in the frequency domain, σ is the standard deviation of the pixel values, and i is a value determined by the sub-feature size, and i is proportional to the sub-feature size.

2. The storage abnormality tracing method according to claim 1 is characterized in that: The steps of identifying the cargo area, determining the cargo change amount and staff in each time period, and inputting the data into the cache database include: Identify the cargo area and determine the cargo location and volume at each moment; Obtain the weight of goods with time stamp based on the weighing points pre-installed in the warehouse; Count the cargo volume at each time, fit the volume change curve, and correct the volume change curve according to the cargo weight with timestamp; The modified volume change curve is derived, and when the derivative value reaches a preset value, an independent variable interval of a preset length is selected with the independent variable corresponding to the derivative value as the center; Read the cargo area corresponding to the independent variable interval, identify the cargo area, locate the staff, and determine the responsibility level based on the distance between the staff and the cargo location; Statistics include staff members with responsibility levels, independent variable intervals, and changes in goods in the independent variable intervals. The statistical data are sorted based on the left endpoint of the independent variable interval and input into the cache database.

3. The storage abnormality tracing method according to claim 2 is characterized in that: The step of receiving a traceability request containing a cargo label and a change amount input by a staff member and performing traceability in a cache database includes: Receive the traceability request containing the goods label and the change quantity input by the staff; The cache database is located based on the cargo label, and the cache database is traversed based on the change amount to match the independent variable interval and the staff with responsibility levels.

4. A storage abnormality tracing system, characterized in that: The system comprises: An information filing module is used to obtain cargo information in real time at the warehousing end, and to create cargo labels and cargo features based on the cargo information; the cargo information includes cargo circulation information and cargo physical information; the cargo circulation information is used to characterize the source and destination of the cargo, and the cargo physical information includes cargo weight and cargo volume; the cargo features are the features of the cargo in the image; The video acquisition and recognition module is used to create a cache database named after the cargo label, acquire warehouse videos in real time based on the camera system, and locate the cargo area in the warehouse video based on the cargo features; The data backup module is used to identify the cargo area, determine the cargo changes and staff in each period, and input them into the cache database; The data traceability module is used to receive traceability requests containing cargo labels and changes entered by staff and perform traceability in the cache database; The information filing module includes: The label generation unit is used to receive the name of the goods, the flow information of the goods and the physical information of the goods uploaded by the staff in real time at the warehouse entry end, and determine the goods number as the goods label according to the name of the goods, the upload time and the flow information of the goods; A filter generation unit, used for querying a standard image according to a product name, and calculating an environment filter according to the product image and the standard image; A region positioning unit, used to obtain a cargo image at the warehousing end, correct the cargo image according to the environmental filter, and locate the cargo region in the corrected cargo image; A feature extraction unit, used to extract cargo features in the located cargo area, and connected to the cargo tag; The video acquisition and identification module includes: A cargo feature query unit, used to create a cache database named after the cargo label, and query the cargo features corresponding to the cargo label; A video conversion unit, used for acquiring warehouse videos in real time through a camera system, and converting the warehouse videos into image frames; A position matching unit, used for traversing each converted image frame based on the cargo feature to obtain a matching cargo position; The area expansion unit is used to expand the cargo area with the cargo location as the center; The process of expanding the cargo area with the cargo location as the center is as follows: Calculate the center of the cargo position, query the nearest obstacle in each direction according to the preset angle step, and simultaneously calculate the closest distance; determine the extended length in that direction according to the closest distance; the extended length is: Where D θ is the extension length in the θ direction, d θ is the shortest distance in the θ direction; The position matching unit comprises: The range selection subunit is used to convert the cargo features into the frequency domain and select the pixel value range in the frequency domain; A feature splitting subunit, used for splitting the cargo feature into sub-features of different sizes based on the pixel value range; the size of the sub-feature is represented by an information ratio, which is a percentage and is used to represent the ratio of the data volume of the sub-feature to the data volume of the cargo feature; The matching execution subunit is used to select the smallest sub-feature, traverse each image, and when the match is successful, read the sub-features in increasing order of size, perform traversal matching, and when the largest sub-feature is matched successfully, the matching position is used as the cargo position; The traversal order of the image is a rotation order, including clockwise and counterclockwise order; when a cargo position is obtained, the cargo position is used as the traversal starting point of the next image; The pixel value range is (μ-iσ,μ+iσ); where μ is the mean of the pixel values ​​in the frequency domain, σ is the standard deviation of the pixel values, and i is a value determined by the sub-feature size, and i is proportional to the sub-feature size.

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