Data encryption method and device, electronic equipment, storage medium and product

Abstract

The invention discloses a data encryption method and device, electronic equipment, a storage medium and a product. According to the specific scheme, when it is detected that a to-be-processed task exists at a first preset position, image collection processing is conducted on a to-be-recognized express sheet attached to the to-be-processed task, and a to-be-processed image is obtained; performing image recognition processing on the to-be-processed image based on the image recognition model to obtain an express sheet recognition result of the to-be-recognized express sheet; encrypting the express sheet recognition result based on at least one target data encryption model to obtain a target encryption result, the target data encryption model being a model matched with express sheet features corresponding to the express sheet recognition result; and uploading the target encryption result to an edge server, so that the edge server uploads the target encryption result to a cloud server according to a preset uploading period. According to the invention, encryption processing of the express sheet identification result in the to-be-identified express sheet is realized, and the data security is ensured.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a data encryption method, device, electronic equipment, storage medium and product. Background Art

[0002] In recent years, with the development of the logistics industry, the security of logistics information has gradually emerged. In the process of sorting items, a barcode scanning device with a conveyor belt function is usually used to scan the waybills of items passing under the scanning device, and the scanned waybill information is stored.

[0003] However, the waybill information obtained in the above method is stored in plain text in the local storage space of the scanning device, which may easily lead to data exposure of the item waybill information and the data security is low. Summary of the invention

[0004] The present invention provides a data encryption method, device, electronic device, storage medium and product, which realize the encryption processing of the face sheet recognition result in the face sheet to be recognized and ensure the security of the data.

[0005] According to one aspect of the present invention, there is provided a data encryption method, the method comprising:

[0006] When it is detected that there is a task to be processed at the first preset position, an image of the face sheet to be identified attached to the task to be processed is collected and processed to obtain an image to be processed;

[0007] Performing image recognition processing on the image to be processed based on the image recognition model to obtain a face sheet recognition result of the face sheet to be recognized, wherein the face sheet recognition result at least includes text information and barcode information of the face sheet to be recognized;

[0008] Encrypting the face sheet recognition result based on at least one target data encryption model to obtain a target encryption result, wherein the target data encryption model is a model that matches the face sheet feature corresponding to the face sheet recognition result;

[0009] The target encryption result is uploaded to the edge server, so that the edge server uploads the target encryption result to the cloud server according to the preset upload cycle.

[0010] According to another aspect of the present invention, there is provided a data encryption device, the device comprising:

[0011] An image acquisition module is used to acquire an image of the to-be-processed task attached to the to-be-processed task when detecting that there is a to-be-processed task at the first preset position, and obtain an to-be-processed image;

[0012] An image recognition module is used to perform image recognition processing on the image to be processed based on the image recognition model to obtain a face sheet recognition result of the face sheet to be recognized, wherein the face sheet recognition result at least includes text information and barcode information of the face sheet to be recognized;

[0013] An identification result encryption module, used to encrypt the face sheet identification result based on at least one target data encryption model to obtain a target encryption result, wherein the target data encryption model is a model that matches the face sheet feature corresponding to the face sheet identification result;

[0014] The encryption result uploading module is used to upload the target encryption result to the edge server, so that the edge server uploads the target encryption result to the cloud server according to the preset upload cycle.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the data encryption method of any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the data encryption method of any embodiment of the present invention when executed by a processor.

[0020] According to another aspect of the present invention, there is provided a computer program product, comprising a computer program, wherein the computer program implements the data encryption method according to any embodiment of the present invention when executed by a processor.

[0021] The technical solution of the embodiment of the present invention, when it is detected that there is a task to be processed at the first preset position, performs image acquisition and processing on the face sheet to be identified attached to the task to be processed to obtain the image to be processed, and provide support for subsequent image recognition and encryption of information in the image. Image recognition processing is performed on the image to be processed according to the image recognition model to obtain a face sheet recognition result of the face sheet to be identified. The face sheet recognition result is encrypted according to at least one target data encryption model to obtain a target encryption result. The present invention solves the problems of data exposure and low data security caused by directly storing the face sheet information in plain text in the local storage space in the prior art, and ensures data security by encrypting the face sheet recognition result of the face sheet to be identified and storing the encrypted result in the cloud server.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. 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 creative work.

[0024] Figure 1 is a flow chart of a data encryption method provided by an embodiment of the present invention;

[0025] Figure 2 is a structural example diagram of a method for implementing data encryption provided by an embodiment of the present invention;

[0026] Figure 3 is an example diagram of an image acquisition device and a target light source at a second preset position provided by an embodiment of the present invention;

[0027] Figure 4 is a flow chart of a method for determining a target data encryption model provided by an embodiment of the present invention;

[0028] Figure 5 It is a structural schematic diagram of a data encryption device provided by an embodiment of the present invention;

[0029] Figure 6 It is a schematic diagram of the structure of an electronic device for implementing the data encryption method of an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment 1

[0033] Figure 1 This is a flow chart of a data encryption method provided in the first embodiment of the present invention. This embodiment is applicable to the case where the face sheet recognition result of the face sheet to be recognized attached to the task to be processed is encrypted. The method can be executed by a data encryption device. The data encryption device can be implemented in the form of hardware and / or software. The data encryption device can be configured in electronic devices such as mobile phones, computers or servers. Figure 1 As shown, the method includes:

[0034] S110. When it is detected that there is a task to be processed at the first preset position, image acquisition and processing are performed on the face sheet to be identified attached to the task to be processed to obtain an image to be processed.

[0035] Among them, the first preset position may be a pre-set position for detecting the position of the task to be processed. Optionally, a sensor device may be deployed at the first preset position, and the sensor device at the first preset position may be used to detect whether there is a task to be processed. Optionally, in the scenario of item transportation, the items that currently need to be processed may be regarded as tasks to be processed. The face sheet to be identified may be identification information attached to the task to be processed. For example, if the task to be processed is an express delivery to be processed, the face sheet to be identified may include information such as a barcode, the delivery information of the express delivery, and the transportation company of the express delivery. The image to be processed may be an image obtained after image acquisition of the face sheet to be identified.

[0036] Specifically, a sensor device may be deployed at the first preset position. When the sensor device detects that there is a task to be processed at the first preset position, the image acquisition device performs image acquisition processing on the face sheet to be identified attached to the task to be processed to obtain the image to be processed.

[0037] For example, the task to be processed is an express mail to be processed. Figure 2 , Figure 2This is a structural example diagram for implementing a data encryption method. Figure 2 The code scanning weighing machine includes a code scanning weighing machine, a cloud server and an edge server. The code scanning weighing machine, the edge server and the cloud server are connected in communication. The code scanning weighing machine includes an image acquisition device, a controller and an express transmission module. Among them, the image acquisition device, the controller and the express transmission module are connected in communication. The express transmission module is used to transport the express to be processed to a first preset position. For example, the express transmission module can be a transmission module with a conveyor belt structure. The image acquisition device is used to perform image acquisition processing on the express to be processed at the first preset position. The controller is used to perform image recognition on the image to be processed and encrypt the recognition result.

[0038] When the express delivery transmission module of the barcode scanning weighing machine transports the express to be processed from the starting position to the first preset position, the image acquisition device will perform image acquisition and processing on the face sheet to be identified attached to the express to be processed at the first preset position to obtain the image to be processed.

[0039] In an embodiment of the present invention, a specific method for acquiring an image to be processed may be: when a sensor device at a first preset position detects a task to be processed, an image acquisition device at a second preset position performs image acquisition and processing on the face sheet to be identified attached to the task to be processed to obtain an image to be processed; wherein a target light source is also provided at the second preset position, the target light source acts on the task to be processed, and the target light source is in a working state.

[0040] The second preset position may be a position for image acquisition. The second preset position may be located directly above the first preset position. The image acquisition device may be used to acquire an image of the face sheet to be identified. For example, the image acquisition device may be a camera. The target light source may be used to perform fill light processing on the face sheet to be identified.

[0041] Specifically, when the sensor device at the first preset position detects the task to be processed, the image acquisition device at the second preset position performs image acquisition processing on the face sheet to be identified attached to the task to be processed to obtain the image to be processed. At the same time, during the image acquisition process, the target light source in the working state at the second preset position performs fill light processing on the face sheet to be identified to ensure the clarity and brightness of the acquired image to be processed.

[0042] Exemplarily, in combination with the above examples, Figure 3 is an example diagram of an image acquisition device and a target light source at a second preset position, Figure 3It includes a target light source 1 and an image acquisition device 2. When the express delivery transmission module of the barcode scanning weighing machine transports the express to be processed from the starting position to the first preset position, and the sensor device at the first preset position detects the existence of the express to be processed, the target light source in working state is used to perform fill light processing on the face sheet to be identified attached to the express to be processed, and the image acquisition device at the second preset position performs image acquisition processing on the face sheet to be identified to obtain the image to be processed.

[0043] S120, performing image recognition processing on the image to be processed based on the image recognition model to obtain a waybill recognition result of the waybill to be recognized.

[0044] Among them, the face sheet recognition result includes at least the text information and barcode information of the face sheet to be recognized. The image recognition model can be a model for performing image recognition on the image to be processed. Optionally, the image recognition model can be a recognition model that is pre-trained in the cloud server and issued according to a preset model update cycle. The preset model update cycle can be a pre-set cycle for issuing the updated image recognition model. The text information and barcode information in the face sheet to be recognized can be determined by the image recognition model. For example, taking the face sheet to be recognized as an express delivery face sheet as an example, the text information may include the shipping information in the express delivery face sheet and the name information of the express transportation company. The barcode information may be the barcode information in the express delivery face sheet.

[0045] Specifically, the image to be processed is subjected to image recognition processing through the image recognition model, the text information and / or barcode information corresponding to each area in the image to be processed is determined, and the face sheet recognition result corresponding to the face sheet to be recognized is obtained.

[0046] Exemplarily, in combination with the above example, a pre-trained image recognition model issued by a cloud server according to a preset model update cycle may be stored in the controller of the barcode scanning weighing machine. After the image acquisition device acquires the image to be processed, the image to be processed is sent to the controller. The image to be processed is recognized and processed by the image recognition model in the controller to obtain the text information and barcode information of the express to be recognized. Among them, the text information may include the sender information and recipient information of the express to be processed. The barcode information is the barcode information of the express to be processed.

[0047] In an embodiment of the present invention, a specific method for determining the face sheet recognition result may be: performing image segmentation processing on the image to be processed based on an image recognition model to obtain at least one image area corresponding to the image to be processed; performing region recognition processing on each image area to obtain a region recognition result corresponding to each image area; and determining the face sheet recognition result of the face sheet to be identified based on at least one region recognition result.

[0048] The image region may be a certain region in the image to be processed. The region recognition result may include text information and / or barcode information of the image region. The receipt recognition result may consist of at least one region recognition result.

[0049] Specifically, the image to be processed is subjected to region positioning and image segmentation processing by the image recognition model to obtain at least one image region. Each image region is subjected to recognition processing to determine the region recognition result corresponding to each image region, that is, the text information and / or barcode information contained in each image region is obtained. The face sheet recognition result of the face sheet to be recognized is obtained through the region recognition results of all image regions.

[0050] Exemplarily, in combination with the above example, the image recognition model is a multi-target detection model Yolo-V3 model as an example for explanation. The image to be processed is divided into multiple image areas by the Yolo-V3 model. The bounding box of each image area is known. Each image area is numbered in sequence from top to bottom and from left to right to obtain a numbered image area. Each numbered image area is identified to determine the text information and / or barcode information of each image area. According to the numbering order, the text information and / or barcode information corresponding to each image area is spliced ​​to obtain the face sheet recognition result of the face sheet to be identified. By dividing the image to be processed into at least one image area, fast and accurate recognition of the image to be processed is achieved.

[0051] S130. Encrypt the face sheet recognition result based on at least one target data encryption model to obtain a target encryption result, wherein the target data encryption model is a model that matches the face sheet features corresponding to the face sheet recognition result.

[0052] The target data encryption model may be an encryption model sent by the edge server and matching the features of the delivery note. Each delivery note feature corresponds to a target data encryption model. The delivery note feature may be a feature obtained by extracting features from the delivery note recognition result. For example, if the delivery note recognition result is the recipient address information, the delivery note feature corresponding to the delivery note recognition result may be the delivery area of ​​the express delivery. The target encryption result may be the encrypted data obtained after encrypting the delivery note recognition result.

[0053] Specifically, since the waybill recognition result contains multiple plaintext information, the waybill recognition result can be encrypted using at least one target data encryption model corresponding to the waybill features to obtain a target encryption result corresponding to the waybill to be identified.

[0054] Exemplarily, in combination with the above example, the controller of the barcode scanning weighing machine stores at least one target data encryption model corresponding to the features of the face sheet issued by the edge server. The face sheet recognition result of the face sheet to be recognized is encrypted by at least one target data encryption model to obtain a target encryption result.

[0055] S140: Upload the target encryption result to the edge server, so that the edge server uploads the target encryption result to the cloud server according to a preset upload period.

[0056] Among them, the edge server can be used to send the target data encryption model and receive and upload the target encryption result. The cloud server can be used to send the image recognition model stored therein and receive the target encryption result. The preset upload period can be the upload period of the target encryption result set according to actual needs. For example, if the preset upload period is 30 minutes, the edge server uploads the received target encryption result to the cloud server every 30 minutes.

[0057] Specifically, the target encryption result is uploaded to the edge server, and the edge server uploads the received at least one target encryption result to the cloud server in sequence according to a preset upload cycle.

[0058] Exemplarily, in combination with the above example, after the controller of the barcode scanning weighing machine obtains the target encryption result, the target encryption result can be uploaded to the edge server. In order to avoid excessive consumption of system resources and network resources of the cloud server, the target encryption result can be stored by the edge server first. At the same time, the controller deletes the images to be processed and the receipt recognition results in the local storage space to avoid storing the plaintext information in the local storage space of the barcode scanning weighing machine. After the edge server receives the target encryption result, it determines at least one target data storage space corresponding to the target data encryption model. The target encryption result is stored in at least one target data storage space in different regions.

[0059] When the cloud server detects that its own system conditions meet the preset data upload conditions, for example, when the time interval between the current system time of the cloud server and the system time of receiving the previous target encryption result meets the preset upload period, the cloud server sends a data upload instruction to the edge server, so that the edge server uploads at least one target encryption result to the cloud server for unified storage based on the data upload instruction.

[0060] The technical solution of this embodiment, when it is detected that there is a task to be processed at the first preset position, performs image acquisition and processing on the face sheet to be identified attached to the task to be processed to obtain the image to be processed, and provide support for subsequent image recognition and encryption of information in the image. Image recognition processing is performed on the image to be processed according to the image recognition model to obtain a face sheet recognition result of the face sheet to be identified. The face sheet recognition result is encrypted according to at least one target data encryption model to obtain a target encryption result. The present invention solves the problems of data exposure and low data security caused by directly storing the face sheet information in plain text in the local storage space in the prior art, and ensures data security by encrypting the face sheet recognition result of the face sheet to be identified and storing the encrypted result in the cloud server.

[0061] Embodiment 2

[0062] Figure 4 This is a flow chart of a method for determining a target data encryption model provided by the second embodiment of the present invention. This embodiment is based on the above embodiment. Before encrypting the face sheet recognition result of the face sheet to be recognized, the target data encryption model can also be determined first. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same as or corresponding to the above embodiment are not repeated here. Figure 4 As shown, the method includes:

[0063] S210. Obtain a historical image corresponding to at least one historical waybill.

[0064] The historical bill of lading may be an identification bill of lading attached to a historical processing task. The historical bill of lading may include barcode information and text information of the historical processing task. The historical processing task is a task that has been processed historically. The historical image may be an image obtained by capturing an image of the historical bill of lading. For example, the historical processing task may be a historical transport express, the historical bill of lading is an express bill of lading attached to a historical transport express, and the historical image is an image of the express bill of lading of the historical transport express.

[0065] Specifically, at least one historical image corresponding to a historical waybill may be obtained from a corresponding database or image storage file, so as to determine the features of the waybill through the historical image corresponding to each historical waybill.

[0066] S220: Perform image recognition processing on at least one historical image based on the image recognition model to obtain a historical waybill recognition result.

[0067] The historical waybill recognition result may be a recognition result obtained after performing region segmentation and recognition processing on the historical image. The historical waybill recognition result includes text information and / or barcode information in the historical image.

[0068] Specifically, for at least one historical image, the image recognition model is used to perform image segmentation processing on the current historical image to obtain at least one historical image region corresponding to the current historical image. Region recognition processing is performed on each historical image region of the current historical image to obtain at least one historical region recognition result corresponding to the current historical image. Based on at least one historical region recognition result of the current historical image, the historical face sheet recognition result of the current historical image is determined.

[0069] S230: Perform feature extraction processing on the historical waybill recognition results to obtain at least one waybill feature.

[0070] The feature of the express bill may be a feature used to characterize the historical express bill recognition result. For example, if the historical express bill recognition result is the address information of the express sender, the corresponding feature of the express bill may be the express delivery area.

[0071] Specifically, feature extraction processing is performed on at least one historical waybill recognition result to obtain at least one waybill feature corresponding to the historical image.

[0072] Exemplarily, in combination with the above example, the historical waybill recognition result may include historical area recognition results of multiple historical image areas. Text extraction processing is performed on multiple historical area recognition results to determine the waybill features corresponding to the historical area recognition results. For example, if the historical area recognition result is the recipient address information, then the waybill features corresponding to the historical area recognition result may be the express delivery receiving area. For example, if the historical area recognition result is the abbreviation of the express delivery company and the express delivery sender address information, then the corresponding waybill features may be the full name of the express delivery company and the express delivery sending area.

[0073] S240. Send at least one face sheet feature to the edge server, so that the edge server feeds back at least one target data encryption model that matches the face sheet feature.

[0074] Specifically, multiple data encryption models are stored in the edge server. After the edge server receives at least one face bill feature, the edge server can compare the face bill feature corresponding to the pre-stored data encryption model with the received face bill feature to determine the data encryption model that matches the received face bill feature. And the data encryption model is fed back as the target data encryption model. It should be noted that one face bill feature corresponds to one data encryption model. If the edge server receives at least one face bill feature, the edge server can correspondingly feed back the target data encryption model that matches each face bill feature.

[0075] In an embodiment of the present invention, the edge server determines the target data encryption model in the following manner: for at least one waybill feature, the edge server performs feature matching processing on preset waybill features corresponding to multiple data encryption models to be matched and the current waybill feature to determine the feature matching result, wherein the data encryption model to be matched is a data encryption model sent by the cloud server at a preset time interval; based on the feature matching result, the target data encryption model corresponding to the current waybill feature is determined from multiple data encryption models to be matched and the target data encryption model is sent.

[0076] Among them, the data encryption model to be matched can be a data encryption model that is pre-trained and stored in the edge server. The data encryption model to be matched is a data encryption model sent by the cloud server at a preset time interval. The preset time interval can be a time length set according to actual needs. For example, the cloud server can send multiple data encryption models that match different face sheet features to the edge server at A day, B time and C minute of each natural month. By regularly updating the data encryption model to be matched on the edge server, the target data encryption models in different periods can be made different, making the target encryption result difficult to crack, thereby improving data security.

[0077] The preset bill feature can be understood as the bill feature corresponding to the data encryption model to be matched. The current bill feature can be understood as the bill feature received by the edge server. The feature matching result can be characterized by the feature similarity between the preset bill feature and the received bill feature.

[0078] Specifically, the edge server performs feature matching processing on the preset face order features corresponding to multiple data encryption models to be matched with the received current face order features to obtain feature matching results. If the feature matching result exceeds the preset matching threshold, the data encryption model to be matched corresponding to the current feature matching result is used as the target data encryption model, and the target data encryption model is issued. Among them, the preset matching threshold can be a pre-set standard value of the feature matching result. Through the above, the edge server can issue a target data encryption model that matches each face order feature.

[0079] Exemplarily, in combination with the above example, the feature matching result is the feature similarity, and the preset matching threshold is the preset similarity threshold as an example for explanation. Multiple data encryption models to be matched are stored in the storage space of the edge server. Each data encryption model corresponds to a preset waybill feature. After receiving at least one waybill feature sent by the barcode scanning weighing machine, the edge server performs feature matching processing on the preset waybill feature and the received current waybill feature to obtain feature similarity. If the feature similarity exceeds the preset similarity threshold, the data encryption model to be matched corresponding to the current preset waybill feature is used as the target data encryption model. The target data encryption model is fed back to the controller of the barcode scanning weighing machine.

[0080] It should be noted that the feature similarity may be calculated by calculating the minimum edit distance or Euclidean distance between the preset face sheet feature and the current feature to obtain a calculation result, and using the calculation result as the feature similarity between the preset face sheet feature and the current feature.

[0081] Optionally, based on the feature matching result, a target data encryption model corresponding to the current face order feature is determined from multiple data encryption models to be matched, including: if the feature matching result is greater than a preset matching threshold, the data encryption model to be matched corresponding to the feature matching result is used as a candidate data encryption model; and based on at least one candidate data encryption model, the target data encryption model is determined.

[0082] The preset matching threshold may be a pre-set standard value corresponding to the feature matching result. Optionally, if the feature matching result is feature similarity, the preset matching threshold is a preset similarity threshold. The candidate data encryption model may be a to-be-matched data encryption model whose feature matching result is greater than the preset matching threshold.

[0083] Specifically, if there are one or more data encryption models to be matched whose corresponding feature matching results are greater than a preset matching threshold, these data encryption models to be matched are used as candidate data encryption models. A candidate data encryption model is randomly selected from at least one candidate data encryption model as the target data encryption model. The target data encryption model that matches the features of the face sheet can be quickly determined by the above feature matching method.

[0084] The technical solution of this embodiment provides data support for the subsequent determination of the target data encryption model by obtaining a historical image of at least one historical bill of lading and determining the historical bill of lading recognition result of each historical image according to the image recognition model. Feature extraction is performed on the historical bill of lading recognition result to obtain at least one bill of lading feature. The at least one bill of lading feature is sent to the edge server so that the edge server feeds back at least one target data encryption model that matches the bill of lading feature. Based on this, it is convenient to directly encrypt the bill of lading recognition result by sending the obtained target data encryption model after determining the bill of lading recognition result of the bill of lading to be identified, thereby improving the efficiency of data encryption and ensuring the security of the data.

[0085] Embodiment 3

[0086] Figure 5 Schematic diagram of the structure of a data encryption device provided by Embodiment 3 of the present invention. Figure 5 As shown, the device includes: an image acquisition module 310, an image recognition module 320, a recognition result encryption module 330 and an encryption result upload module 340.

[0087] The image acquisition module 310 is used to perform image acquisition processing on the face sheet to be identified attached to the task to be processed when it is detected that there is a task to be processed at the first preset position, so as to obtain the image to be processed; the image recognition module 320 is used to perform image recognition processing on the image to be processed based on the image recognition model, so as to obtain the face sheet recognition result of the face sheet to be identified, wherein the face sheet recognition result at least includes the text information and barcode information of the face sheet to be identified; the recognition result encryption module 330 is used to encrypt the face sheet recognition result based on at least one target data encryption model, so as to obtain the target encryption result, wherein the target data encryption model is a model that matches the face sheet feature corresponding to the face sheet recognition result; the encryption result uploading module 340 is used to upload the target encryption result to the edge server, so that the edge server uploads the target encryption result to the cloud server according to the preset upload cycle.

[0088] The technical solution of this embodiment, when it is detected that there is a task to be processed at the first preset position, performs image acquisition and processing on the face sheet to be identified attached to the task to be processed to obtain the image to be processed, and provide support for subsequent image recognition and encryption of information in the image. Image recognition processing is performed on the image to be processed according to the image recognition model to obtain a face sheet recognition result of the face sheet to be identified. The face sheet recognition result is encrypted according to at least one target data encryption model to obtain a target encryption result. The present invention solves the problems of data exposure and low data security caused by directly storing the face sheet information in plain text in the local storage space in the prior art, and ensures data security by encrypting the face sheet recognition result of the face sheet to be identified and storing the encrypted result in the cloud server.

[0089] On the basis of the above embodiment, optionally, an image acquisition module is used to obtain an image to be processed by performing image acquisition and processing on the face sheet to be identified attached to the task to be processed based on the image acquisition device at the second preset position when the sensor device at the first preset position detects the task to be processed; wherein a target light source is also provided at the second preset position, the target light source acts on the task to be processed, and the target light source is in a working state.

[0090] Optionally, an image recognition module is used to perform image segmentation processing on the image to be processed based on an image recognition model to obtain at least one image area corresponding to the image to be processed; perform region recognition processing on each image area to obtain a region recognition result corresponding to each image area; and determine the face sheet recognition result of the face sheet to be identified based on at least one region recognition result.

[0091] Optionally, the device also includes an encryption model determination module, which includes: a historical image acquisition unit, used to acquire a historical image corresponding to at least one historical waybill; a historical waybill recognition result determination unit, used to perform image recognition processing on at least one historical image based on an image recognition model to obtain a historical waybill recognition result; a waybill feature determination unit, used to perform feature extraction processing on the historical waybill recognition result to obtain at least one waybill feature; and an encryption model determination unit, used to send at least one waybill feature to an edge server, so that the edge server feeds back at least one target data encryption model that matches the waybill feature.

[0092] Optionally, the encryption model determination unit includes: a feature matching subunit, used for, for at least one waybill feature, the edge server performs feature matching processing on preset waybill features corresponding to multiple data encryption models to be matched with the current waybill feature to determine the feature matching result, wherein the data encryption model to be matched is a data encryption model sent by the cloud server at a preset time interval; a target data encryption model determination subunit, used for determining the target data encryption model corresponding to the current waybill feature from multiple data encryption models to be matched based on the feature matching result and sending the target data encryption model.

[0093] Optionally, the target data encryption model determination subunit is used to use the to-be-matched data encryption model corresponding to the feature matching result as a candidate data encryption model if the feature matching result is greater than a preset matching threshold; and determine the target data encryption model based on at least one candidate data encryption model.

[0094] The data encryption device provided in the embodiment of the present invention can execute the data encryption method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0095] Embodiment 4

[0096] Figure 6 1 is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0097] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0098] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0099] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a data encryption method.

[0100] In some embodiments, the data encryption method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the data encryption method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the data encryption method in any other appropriate manner (e.g., by means of firmware).

[0101] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] The computer program for implementing the data encryption method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0103] Embodiment 5

[0104] Embodiment 5 of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute a data encryption method, the method comprising:

[0105] When it is detected that there is a task to be processed at the first preset position, image acquisition and processing are performed on the face sheet to be identified attached to the task to be processed to obtain the image to be processed; image recognition processing is performed on the image to be processed based on the image recognition model to obtain a face sheet recognition result of the face sheet to be identified, wherein the face sheet recognition result at least includes text information and barcode information of the face sheet to be identified; encryption processing is performed on the face sheet recognition result based on at least one target data encryption model to obtain a target encryption result, wherein the target data encryption model is a model that matches the face sheet features corresponding to the face sheet recognition result; the target encryption result is uploaded to the edge server, so that the edge server uploads the target encryption result to the cloud server according to the preset upload cycle.

[0106] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0107] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0108] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0109] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0110] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0111] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data encryption method, characterized in that: include: When it is detected that there is a task to be processed at the first preset position, an image acquisition process is performed on the face sheet to be identified attached to the task to be processed to obtain an image to be processed; Performing image recognition processing on the image to be processed based on the image recognition model to obtain a face sheet recognition result of the face sheet to be recognized, wherein the face sheet recognition result at least includes text information and barcode information of the face sheet to be recognized; Encrypting the face sheet recognition result based on at least one target data encryption model to obtain a target encryption result, wherein the target data encryption model is a model that matches the face sheet feature corresponding to the face sheet recognition result; The target encryption result is uploaded to the edge server, so that the edge server uploads the target encryption result to the cloud server according to a preset upload period.

2. The method according to claim 1, characterized in that: When it is detected that there is a task to be processed at the first preset position, an image acquisition process is performed on the face sheet to be identified attached to the task to be processed to obtain an image to be processed, including: When the sensor device at the first preset position detects the task to be processed, the image acquisition device at the second preset position performs image acquisition processing on the face sheet to be identified attached to the task to be processed to obtain the image to be processed; Wherein, a target light source is also provided at the second preset position, the target light source acts on the task to be processed, and the target light source is in a working state.

3. The method according to claim 1, characterized in that The performing image recognition processing on the image to be processed based on the image recognition model to obtain the face sheet recognition result of the face sheet to be recognized includes: Performing image segmentation processing on the image to be processed based on the image recognition model to obtain at least one image region corresponding to the image to be processed; Performing region recognition processing on each of the image regions to obtain a region recognition result corresponding to each of the image regions; Based on at least one of the area recognition results, determine the waybill recognition result of the waybill to be recognized.

4. The method according to claim 1, characterized in that: Before encrypting the face sheet recognition result based on at least one target data encryption model, the method further includes: Obtaining at least one historical image corresponding to a historical waybill; Performing image recognition processing on at least one of the historical images based on the image recognition model to obtain a historical bill recognition result; Performing feature extraction processing on the historical bill recognition result to obtain at least one bill feature; At least one of the face sheet features is sent to an edge server, so that the edge server feeds back at least one target data encryption model that matches the face sheet feature.

5. The method according to claim 4, characterized in that The edge server feeds back at least one target data encryption model that matches the features of the face sheet, including: For at least one of the face order features, the edge server performs feature matching processing on the preset face order features corresponding to the multiple data encryption models to be matched and the current face order features to determine the feature matching result, wherein the data encryption model to be matched is a data encryption model sent by the cloud server at a preset time interval; Based on the feature matching result, the target data encryption model corresponding to the current delivery note feature is determined from the multiple data encryption models to be matched and the target data encryption model is issued.

6. The method according to claim 5, characterized in that The step of determining a target data encryption model corresponding to the current document feature from a plurality of data encryption models to be matched based on the feature matching result includes: If the feature matching result is greater than a preset matching threshold, the to-be-matched data encryption model corresponding to the feature matching result is used as a candidate data encryption model; A target data encryption model is determined based on at least one of the candidate data encryption models.

7. A data encryption device, characterized in that: include: An image acquisition module is used to acquire an image of a to-be-processed task attached to the to-be-processed task when detecting that there is a to-be-processed task at the first preset position, and obtain an image to be processed; An image recognition module, used for performing image recognition processing on the image to be processed based on an image recognition model to obtain a face sheet recognition result of the face sheet to be recognized, wherein the face sheet recognition result at least includes text information and barcode information of the face sheet to be recognized; An identification result encryption module, used for encrypting the face sheet identification result based on at least one target data encryption model to obtain a target encryption result, wherein the target data encryption model is a model that matches the face sheet feature corresponding to the face sheet identification result; The encryption result uploading module is used to upload the target encryption result to the edge server, so that the edge server uploads the target encryption result to the cloud server according to a preset upload cycle.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the data encryption method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the data encryption method according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the data encryption method according to any one of claims 1 to 6.

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