Logistics package data processing method, device and equipment

Abstract

The invention discloses a data processing method, device and equipment for logistics parcels, the method is applied to the technical field of logistics, and the method comprises the steps: constructing a preset database according to bar codes and RFID tags corresponding to the logistics parcels; temperature data of the logistics parcels are collected in real time through a temperature control sensor; establishing an R tree index according to the temperature data collected by the temperature control sensor, the position information of the logistics package and the position information of the temperature control sensor; obtaining a temperature data set of the logistics parcel according to the real-time temperature collected by the temperature control sensor and the R tree index; detecting a real-time bar code of the logistics package through a transportation vehicle of the logistics package and bar code detection equipment installed in a warehouse; and verifying the real-time bar code according to a preset database, and uploading the real-time bar code and the temperature data set to a cloud platform when the verification is passed. According to the invention, whole-process monitoring and quality tracing of the logistics parcels are realized.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device and equipment for processing data of logistics packages. Background Art

[0002] During the logistics data monitoring process of logistics packages, traditional scanning equipment is often unable to accurately read barcodes due to abnormal environments. The wiring cost of the wired temperature control system is high and it is not suitable for mobile devices. The wireless system has the problem of unstable data transmission, making it difficult to achieve real-time monitoring and traceability of information throughout the entire process. Summary of the Invention

[0003] The present invention provides a data processing method, device and equipment for logistics packages, which can realize full-process monitoring and quality traceability of logistics packages.

[0004] In one aspect, the present invention provides a method for processing logistics package data, the method comprising: Build a preset database based on the barcodes and RFID tags corresponding to logistics packages; The temperature data of the logistics package is collected in real time through the temperature control sensors installed in the transportation vehicle and the warehouse of the logistics package; Establishing an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package, and the location information of the temperature control sensor; Obtaining a temperature dataset of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index; Detecting the real-time barcode of the logistics package through the transport vehicle of the logistics package and the barcode detection equipment installed in the warehouse; The real-time barcode is verified according to the preset database, and if the verification passes, the real-time barcode and the temperature data set are uploaded to a cloud platform.

[0005] In an exemplary embodiment, the step of constructing a preset database based on the barcodes and RFID tags corresponding to the logistics packages includes: Generate a barcode for the logistics package based on first attribute information of the logistics package; the first attribute information includes at least one of the name, specification, and production batch of the logistics package; generating an RFID tag for the logistics package based on second attribute information of the logistics package, wherein the second attribute information includes at least one of a production time, a shelf life, ingredients, and transportation requirements of the logistics package; Generate a CRC check code for the logistics package based on the barcode and the RFID tag corresponding to the logistics package; The correspondence between the barcode, the RFID tag and the CRC check code of the logistics package is stored in the preset database.

[0006] In an exemplary embodiment, verifying the real-time barcode according to the preset database includes: Obtaining the real-time RFID tag of the logistics package; Generate a real-time CRC check code for the logistics package based on the real-time barcode and the real-time RFID tag; Searching the preset database for a CRC check code that matches the real-time barcode to obtain a target CRC check code; Comparing the CRC check code with the real-time CRC check code to see if they are consistent; If the CRC check code is consistent with the real-time CRC check code, it is determined that the real-time barcode verification has passed.

[0007] In an exemplary embodiment, obtaining the temperature dataset of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index includes: According to the real-time temperature collected by the temperature control sensor, searching the R-tree index for a logistics package corresponding to the real-time temperature, and establishing a binding relationship between the real-time temperature and the logistics package; An abnormality detection is performed on the real-time temperature. If the real-time temperature is an abnormal temperature, the real-time temperature is filtered out to obtain a temperature data set of the logistics package.

[0008] In an exemplary embodiment, establishing an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package, and the location information of the temperature control sensor includes: Based on the temperature data collected by the temperature control sensor and the dynamic model of the system, the temperature data is optimally estimated to obtain the target temperature of the logistics package; Synchronize the clocks of the temperature control sensor and the RFID reader using the NTP protocol, and add timestamps to the target temperature of the logistics package and the RFID tag respectively; The location information of the logistics package is determined by the GPS module, and an R-tree index is established based on the location information of the logistics package and the location information of the temperature control sensor; the R-tree is used to store the target temperature corresponding to the logistics package at different locations, the RFID tag, and the timestamp.

[0009] In an exemplary embodiment, the barcode detection device includes a barcode recognition model, which is used to detect the real-time barcode of the logistics package; the training method of the barcode recognition model includes: Acquire a sample barcode image, wherein the sample barcode image is marked with a sample barcode label; Input the sample barcode image into the RF-DETR-base model to obtain the sample original feature map extracted from each channel in the model; Perform global average pooling and full connection processing on the channel dimension of the original feature map of the samples of each channel to obtain the weight coefficient of each channel; Calculate the product of the sample original feature map of each channel and the weight coefficient of each channel to obtain the sample barcode feature of each channel; Performing feature fusion processing on the sample barcode features of each channel to obtain a sample fusion feature; and predicting a sample barcode result based on the sample fusion feature; The RF-DETR-base model is trained according to the difference between the sample barcode result and the sample barcode label to obtain the barcode recognition model.

[0010] In an exemplary embodiment, the detecting anomaly of the real-time temperature includes: According to the type of goods corresponding to the logistics package, set the normal temperature threshold corresponding to the logistics package; If the real-time temperature exceeds the normal temperature threshold, determining that the real-time temperature is abnormal; In the case of the real-time temperature anomaly, the method further includes: Determining the current abnormality type according to the abnormality judgment result of the real-time temperature; Query the exception handling strategy corresponding to the current exception type in the knowledge graph, and execute the exception handling strategy for the logistics package; the knowledge graph includes a correspondence between multiple preset exception types and multiple preset exception handling strategies.

[0011] Another aspect provides a data processing device for logistics packages, the device comprising: A preset database construction module is used to construct a preset database based on the barcode and RFID tag corresponding to the logistics package; A temperature acquisition module is used to collect temperature data of the logistics package in real time through temperature control sensors installed in the transportation vehicle and warehouse of the logistics package; An R-tree construction module is configured to establish an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package, and the location information of the temperature control sensor; A temperature data set construction module is used to obtain the temperature data set of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index; A real-time barcode detection module is used to detect the real-time barcode of the logistics package through the transport vehicle of the logistics package and the barcode detection equipment installed in the warehouse; The data uploading module is used to verify the real-time barcode according to the preset database, and upload the real-time barcode and the temperature data set to the cloud platform if the verification is passed.

[0012] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the data processing method for logistics packages as described above.

[0013] On the other hand, a computer storage medium is provided, which stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by a processor to implement the data processing method for logistics packages as described above.

[0014] Another aspect provides a computer program product or computer program, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to implement the above-described method for processing logistics package data.

[0015] The data processing method, device, and equipment for logistics packages provided by the present invention have the following technical effects: The present invention constructs a preset database based on the barcode and RFID tag corresponding to the logistics package; collects the temperature data of the logistics package in real time through the temperature control sensor installed in the transportation vehicle and the warehouse of the logistics package; establishes an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package and the location information of the temperature control sensor; obtains the temperature data set of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index; detects the real-time barcode of the logistics package through the barcode detection equipment installed in the transportation vehicle and the warehouse of the logistics package; verifies the real-time barcode according to the preset database, and uploads the real-time barcode and the temperature data set to the cloud platform if the verification passes. The present invention can realize high-precision reading of barcode information in complex environments, and realize multi-target recognition by combining RFID technology; combines the temperature control sensor with the barcode detection and recognition system, records the transportation temperature and flow information of the goods through the barcode, and realizes full-process monitoring and quality traceability of the logistics package. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages of the embodiments of this specification or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a schematic diagram of a data processing system for logistics packages provided in an embodiment of this specification; Figure 2 This is a flow chart of a data processing method for a logistics package provided in an embodiment of this specification; Figure 3 This is a flowchart of a method for building a preset database based on barcodes and RFID tags corresponding to logistics packages provided in an embodiment of this specification; Figure 4 1 is a flow chart of a method for establishing an R-tree index based on temperature data collected by the temperature control sensor, location information of the logistics package, and location information of the temperature control sensor, provided in an embodiment of this specification; Figure 5 This is a flowchart of a barcode recognition model training method provided in an embodiment of this specification; Figure 6 This is a structural diagram of a data processing device for logistics packages provided in an embodiment of this specification; Figure 7 This is a structural diagram of a server provided in an embodiment of this specification. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first", "second", etc. in the description 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 numbers used in this way are interchangeable 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 server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] See also Figure 1 , Figure 1 This is a schematic diagram of a data processing system for logistics packages provided in an embodiment of this specification. Figure 1 As shown, the data processing system of the logistics package may include at least a server 01 and a client 02.

[0021] Specifically, in the embodiments of this specification, the server 01 may include a standalone server, a distributed server, or a server cluster consisting of multiple servers. It may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Server 01 may include a network communication unit, a processor, and memory, etc. Specifically, the server 01 can be used to build a preset database based on the barcode and RFID tag corresponding to the logistics package; collect the temperature data of the logistics package in real time through the temperature control sensors installed in the transportation vehicle and the warehouse of the logistics package; establish an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package and the location information of the temperature control sensor; obtain the temperature data set of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index; detect the real-time barcode of the logistics package through the barcode detection equipment installed in the transportation vehicle and the warehouse of the logistics package; verify the real-time barcode according to the preset database, and if the verification is passed, upload the real-time barcode and the temperature data set to the cloud platform.

[0022] Specifically, in the embodiments of this specification, the client 02 may include physical devices such as smartphones, desktop computers, tablet computers, laptops, digital assistants, smart wearable devices, smart speakers, in-vehicle terminals, and smart TVs. It may also include software running on physical devices, such as web pages provided by service providers to users, or applications provided by these service providers to users. Specifically, the client 02 may be used to query barcode and temperature datasets for logistics packages online.

[0023] The following describes a data processing method for logistics packages of the present invention. Figure 2 It is a flow chart of a data processing method for logistics packages provided in an embodiment of this specification. This specification provides method operation steps as described in the embodiment or flow chart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or server product is executed, it can be executed in sequence or in parallel (for example, in a parallel processor or multi-threaded processing environment) according to the method shown in the embodiment or the accompanying drawings. Specifically, Figure 2 As shown, the method may include: S201: Build a preset database based on the barcode and RFID tag corresponding to the logistics package. In the examples of this specification, barcodes record basic information about goods, such as name, specifications, and batch, which are simple and commonly used data. This information can be quickly read by scanning devices to meet the needs of rapid identification. RFID tags store detailed data, including the goods' production time, shelf life, ingredients, and shipping requirements.

[0024] S203: The temperature data of the logistics package is collected in real time through the temperature control sensors installed in the transportation vehicle of the logistics package and the warehouse.

[0025] In the embodiments of this specification, multiple temperature control sensors can be installed in transportation equipment and warehouse environments to collect temperature data of goods in real time.

[0026] S205: Establish an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package, and the location information of the temperature control sensor.

[0027] In the embodiment of this specification, the R-tree is a spatial index structure that can efficiently process queries and matches of spatial data, and stores the location information of the temperature control sensor and the location information of the goods in the R-tree.

[0028] S207: Obtain a temperature dataset of the logistics package according to the real-time temperature collected by the temperature control sensor and the R-tree index.

[0029] S209: Detecting the real-time barcode of the logistics package through the transport vehicle of the logistics package and the barcode detection equipment installed in the warehouse.

[0030] In the embodiments of this specification, edge computing devices driven by the RF-DETR-base real-time object detection model can be installed at key locations in transport vehicles and warehouses to accurately detect and identify barcodes.

[0031] S2011: Verify the real-time barcode according to the preset database, and if the verification is passed, upload the real-time barcode and the temperature data set to the cloud platform.

[0032] In the embodiment of this specification, the detected barcode information and temperature control data are uploaded to the cloud platform through the Internet of Things technology, and blockchain technology is used to ensure that the data cannot be tampered with and is fully traceable.

[0033] In some embodiments, as Figure 3 As shown, the preset database is constructed based on the barcode and RFID tag corresponding to the logistics package, including: S20101: Generate a barcode for the logistics package based on first attribute information of the logistics package; the first attribute information includes at least one of the following: the name of the goods, specifications, and production batch of the logistics package; S20103: Generating an RFID tag for the logistics package based on second attribute information of the logistics package; the second attribute information includes at least one of the production time, shelf life, ingredients, and transportation requirements of the logistics package; S20105: Generate a CRC check code for the logistics package based on the barcode and the RFID tag corresponding to the logistics package; S20107: Storing the correspondence between the barcode, the RFID tag, and the CRC check code of the logistics package in the preset database.

[0034] In the embodiments of this specification, the first attribute information of a logistics package is obtained, which is basic information about the logistics package, such as the name of the goods, specifications, batch, and other simple and commonly used data. This information can be quickly read by a barcode scanning device to meet the need for rapid identification. The second attribute information of the logistics package is obtained, and an RFID tag for the logistics package is generated. The second attribute information includes at least one of the production time, shelf life, ingredients, and transportation requirements of the logistics package. Exemplarily, the RFID tag stores detailed data, including the production time, shelf life, ingredients, and transportation requirements of the goods.

[0035] The basic cargo information contained in the barcode and the detailed data stored in the RFID tag are processed using the SHA-256 hash algorithm to generate a data fingerprint. The SHA-256 algorithm converts the input data into a fixed-length hash value that is unique and irreversible. A mapping table is established to associate the barcode data fingerprint with the RFID tag data fingerprint and record the corresponding relationship between the two. During the storage and transmission of barcode and RFID tag data, cyclic redundancy check (CRC) technology is used. For each tag's data, a specific polynomial is calculated to generate a CRC check code and store it with the data.

[0036] In some embodiments, verifying the real-time barcode according to the preset database includes: Obtaining the real-time RFID tag of the logistics package; Generate a real-time CRC check code for the logistics package based on the real-time barcode and the real-time RFID tag; Searching the preset database for a CRC check code that matches the real-time barcode to obtain a target CRC check code; Comparing the CRC check code with the real-time CRC check code to see if they are consistent; If the CRC check code is consistent with the real-time CRC check code, it is determined that the real-time barcode verification has passed.

[0037] In the embodiments of this specification, when reading tag data, the CRC checksum is recalculated and compared with the stored checksum. Based on the real-time barcode and the real-time RFID tag, a real-time CRC checksum is generated for the logistics package. A CRC checksum matching the real-time barcode is searched in the preset database to obtain a target CRC checksum. The CRC checksum is then compared with the real-time CRC checksum to ensure consistency.

[0038] If the two are consistent, it means that no error occurred during the data transmission and storage process; if they are inconsistent, it means that the data may be damaged and needs to be processed accordingly, such as rereading or error correction.

[0039] In some embodiments, obtaining the temperature dataset of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index includes: According to the real-time temperature collected by the temperature control sensor, searching the R-tree index for a logistics package corresponding to the real-time temperature, and establishing a binding relationship between the real-time temperature and the logistics package; An abnormality detection is performed on the real-time temperature. If the real-time temperature is an abnormal temperature, the real-time temperature is filtered out to obtain a temperature data set of the logistics package.

[0040] In the embodiment of this specification, when temperature data is collected, the corresponding goods are quickly found through the R-tree index to achieve dynamic binding of temperature data and goods; and the isolation forest detection algorithm is applied to detect anomalies in the collected temperature data.

[0041] In some embodiments, as Figure 4 As shown, the R-tree index is established based on the temperature data collected by the temperature control sensor, the location information of the logistics package, and the location information of the temperature control sensor, including: S2051: Optimizing the temperature data based on the temperature data collected by the temperature control sensor and the system's dynamic model to obtain a target temperature for the logistics package. S2053: Synchronize the clocks of the temperature control sensor and the RFID reader / writer using the NTP protocol, and add a timestamp to the target temperature of the logistics package and the RFID tag respectively; S2055: Determine the location information of the logistics package through the GPS module, and establish an R-tree index based on the location information of the logistics package and the location information of the temperature control sensor; the R-tree is used to store the target temperature corresponding to the logistics package at different locations, the RFID tag, and the timestamp.

[0042] In the embodiments of this specification, multiple temperature control sensors are installed in transportation equipment and warehouse environments to collect temperature data of goods in real time. A Kalman filter is designed to fuse the temperature data collected by multiple temperature control sensors. The Kalman filter performs optimal estimation of the temperature data based on the measurement values of the sensors and the dynamic model of the system through two steps of prediction and update. The NTP protocol is used to synchronize the clocks of the temperature control sensor and the RFID reader. The NTP protocol adjusts the clock of the device by communicating with the time server so that its time accuracy reaches the millisecond level, and adds an accurate timestamp to each collected temperature data and RFID tag reading data. The location coordinates of the goods are determined by GPS, and an R-tree index is established based on the location coordinates of the goods. The R-tree is a spatial index structure that can efficiently handle queries and matching of spatial data, and the location information of the temperature control sensor and the location information of the goods are stored in the R-tree. When temperature data is collected, the corresponding goods are quickly found through the R-tree index, realizing dynamic binding of temperature data and goods. The isolation forest detection algorithm is applied to detect anomalies in the collected temperature data. The isolation forest algorithm divides data points into different leaf nodes by constructing a random binary tree. The path length of the data point in the tree determines whether it is an outlier. Temperature values judged to be abnormal are filtered out from the data set.

[0043] In some embodiments, the barcode detection device includes a barcode recognition model, and the barcode recognition model is used to detect the real-time barcode of the logistics package; Figure 5 As shown, the training method of the barcode recognition model includes: S501: Acquire a sample barcode image, where the sample barcode image is marked with a sample barcode label; S503: Input the sample barcode image into the RF-DETR-base model to obtain the sample original feature map extracted from each channel in the model; S505: Perform global average pooling and full connection processing on the channel dimension of the original feature map of the sample of each channel to obtain the weight coefficient of each channel; S507: Calculate the product of the sample original feature map of each channel and the weight coefficient of each channel to obtain the sample barcode feature of each channel; S509: performing feature fusion processing on the sample barcode features of each channel to obtain a sample fusion feature; and predicting a sample barcode result based on the sample fusion feature; S5011: Training the RF-DETR-base model according to the difference between the sample barcode result and the sample barcode label to obtain the barcode recognition model.

[0044] In the embodiments of this specification, a channel attention mechanism is integrated into the RF-DETR-base model. This mechanism calculates the importance weight of each channel and obtains a weight coefficient for each channel by performing global average pooling and fully connected layer processing on the channel dimension of the feature map. The obtained weight coefficient is multiplied by the original feature map channel by channel, enhancing the feature response of important channels and suppressing the features of unimportant channels, thereby improving the barcode feature extraction capability. A feature pyramid network (FPN) is used to process barcodes of different sizes. The FPN consists of bottom-up and top-down paths. The bottom-up path extracts feature maps of different scales through convolutional layers, while the top-down path transmits high-level semantic information to lower layers. Horizontal connections and fusion operations are performed between feature maps of different scales, allowing the model to simultaneously utilize feature information at different scales, improving the detection accuracy of barcodes of different sizes. When performing barcode detection on an edge computing device, the detection distance is measured in real time, and the detection distance information is obtained through the device's ranging sensor or other related technologies. The resolution of the input image is automatically adjusted based on the detection distance. When the detection distance is far, the resolution of the input image is appropriately reduced; when the detection distance is close, the resolution of the input image is increased. Knowledge distillation technology is applied to compress the original RF-DETR-base model, and the compressed model is quantized using an 8-bit quantization algorithm, converting the floating-point parameters in the model into 8-bit integers to reduce the model's storage space and computational complexity. Target loss data is determined based on the difference between the sample barcode result and the sample barcode label. The RF-DETR-base model parameters are then adjusted based on the target loss data until the training end conditions are met. The RF-DETR-base model at the end of training is then determined as the barcode recognition model. The barcode recognition model can then be used to identify the captured barcode image and obtain the barcode information, achieving fast and accurate barcode recognition.

[0045] In some embodiments, the detecting anomaly of the real-time temperature includes: According to the type of goods corresponding to the logistics package, set the normal temperature threshold corresponding to the logistics package; If the real-time temperature exceeds the normal temperature threshold, determining that the real-time temperature is abnormal; In the case of the real-time temperature anomaly, the method further includes: Determining the current abnormality type according to the abnormality judgment result of the real-time temperature; Query the exception handling strategy corresponding to the current exception type in the knowledge graph, and execute the exception handling strategy for the logistics package; the knowledge graph includes a correspondence between multiple preset exception types and multiple preset exception handling strategies.

[0046] In the embodiments of this specification, different temperature threshold ranges are pre-set according to the type of goods, and a sliding window mechanism is used to perform statistical analysis on temperature data over a period of time. The temperature threshold is adaptively adjusted according to the changing trend and distribution of the data. When the temperature of the goods exceeds the adjusted threshold, it is determined to be abnormal. When the system detects an abnormal situation, the A* algorithm and the Dijkstra algorithm are integrated to perform emergency path planning. The AStar (A*) algorithm is a direct and effective search method for solving the shortest path in a static grid. A logistics anomaly handling knowledge base is constructed, and common abnormal situations, handling methods and related knowledge information in the logistics transportation process are organized into a knowledge graph. A graph neural network is used to reason about the knowledge graph. The neural network can learn the feature representations of nodes and edges in the knowledge graph, and perform reasoning and matching in the knowledge graph based on the current abnormal situation to recommend appropriate disposal solutions.

[0047] This technical solution provides a full-process monitoring method for logistics transportation based on RF-DETR-base model recognition. By building the SOTA real-time target detection model RF-DETR-base, barcode detection and recognition are performed on the edge. At the same time, combined with temperature control sensors, the transportation temperature and flow information of the goods are recorded through barcodes, realizing full-process monitoring and quality traceability of logistics, ensuring transportation safety and controllable quality.

[0048] It can be seen from the technical solutions provided by the above embodiments of this specification that the embodiments of this specification construct a preset database based on the barcode and RFID tag corresponding to the logistics package; collect the temperature data of the logistics package in real time through the temperature control sensor installed in the transportation vehicle and warehouse of the logistics package; establish an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package and the location information of the temperature control sensor; obtain the temperature data set of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index; detect the real-time barcode of the logistics package through the barcode detection equipment installed in the transportation vehicle and warehouse of the logistics package; verify the real-time barcode according to the preset database, and upload the real-time barcode and the temperature data set to the cloud platform if the verification passes. The present invention can realize high-precision reading of barcode information in complex environments, and realize multi-target recognition by combining RFID technology; combine the temperature control sensor with the barcode detection and identification system, record the transportation temperature and flow information of the goods through the barcode, and realize full-process monitoring and quality traceability of the logistics package.

[0049] The embodiment of this specification also provides a data processing device for logistics packages, such as Figure 6 As shown, the device includes: A preset database construction module 610 is used to construct a preset database based on the barcode and RFID tag corresponding to the logistics package; The temperature collection module 620 is used to collect the temperature data of the logistics package in real time through the temperature control sensors installed in the transportation vehicle and the warehouse of the logistics package; An R-tree construction module 630 is configured to construct an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package, and the location information of the temperature control sensor; A temperature data set construction module 640 is configured to obtain a temperature data set of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index; A real-time barcode detection module 650 is used to detect the real-time barcode of the logistics package through the transport vehicle of the logistics package and the barcode detection equipment installed in the warehouse; The data uploading module 660 is used to verify the real-time barcode according to the preset database, and upload the real-time barcode and the temperature data set to the cloud platform if the verification is passed.

[0050] In some embodiments, the preset database construction module includes: a barcode generating unit, configured to generate a barcode for the logistics package based on first attribute information of the logistics package; the first attribute information including at least one of the name, specification, and production batch of the logistics package; a label generating unit, configured to generate an RFID label for the logistics package based on second attribute information of the logistics package; the second attribute information including at least one of a production time, a shelf life, ingredients, and transportation requirements of the logistics package; A check code generating unit, configured to generate a CRC check code for the logistics package according to the barcode and the RFID tag corresponding to the logistics package; A storage unit is used to store the correspondence between the barcode, the RFID tag and the CRC check code of the logistics package in the preset database.

[0051] In some embodiments, the data upload module includes: A label acquisition unit, configured to acquire a real-time RFID label of the logistics package; A real-time check code generation unit, configured to generate a real-time CRC check code for the logistics package based on the real-time barcode and the real-time RFID tag; A target check code search unit is used to search the preset database for a CRC check code that matches the real-time barcode to obtain a target CRC check code; A comparing unit, configured to compare the CRC check code with the real-time CRC check code to see if they are consistent; The verification unit is configured to determine that the real-time barcode verification has passed if the CRC verification code is consistent with the real-time CRC verification code.

[0052] In some embodiments, the temperature dataset building module includes: a binding relationship establishing unit, configured to search the R-tree index for a logistics package corresponding to the real-time temperature collected by the temperature control sensor, and establish a binding relationship between the real-time temperature and the logistics package; The filtering unit is used to detect abnormalities in the real-time temperature. If the real-time temperature is abnormal, the real-time temperature is filtered out to obtain a temperature data set of the logistics package.

[0053] In some embodiments, the R-tree construction module includes: a target temperature determination unit, configured to optimally estimate the temperature data collected by the temperature control sensor and a dynamic model of the system to obtain a target temperature for the logistics package; A timestamp adding unit, configured to synchronize the clocks of the temperature control sensor and the RFID reader using the NTP protocol, and to add timestamps to the target temperature of the logistics package and the RFID tag respectively; An R-tree establishment unit is used to determine the location information of the logistics package through a GPS module, and to establish an R-tree index based on the location information of the logistics package and the location information of the temperature control sensor; the R-tree is used to store the target temperature corresponding to the logistics package at different locations, the RFID tag, and the timestamp.

[0054] In some embodiments, the barcode detection device includes a barcode recognition model, and the barcode recognition model is used to detect the real-time barcode of the logistics package; the device also includes: A sample image acquisition module is used to acquire a sample barcode image, wherein the sample barcode image is marked with a sample barcode label; A sample feature map acquisition module is used to input the sample barcode image into the RF-DETR-base model to obtain the sample original feature map extracted from each channel in the model; The weight coefficient acquisition module is used to perform global average pooling and full connection processing on the channel dimension of the original feature map of the samples of each channel to obtain the weight coefficient of each channel; The sample barcode feature determination module is used to calculate the product of the sample original feature map of each channel and the weight coefficient of each channel to obtain the sample barcode feature of each channel; The sample fusion module is used to perform feature fusion processing on the sample barcode features of each channel to obtain sample fusion features; and predict the sample barcode results based on the sample fusion features; A training module is used to train the RF-DETR-base model according to the difference between the sample barcode result and the sample barcode label to obtain the barcode recognition model.

[0055] In some embodiments, the filtering unit is further configured to set a normal temperature threshold corresponding to the logistics package according to the type of goods corresponding to the logistics package; and if the real-time temperature exceeds the normal temperature threshold, determine that the real-time temperature is abnormal; In the event of an abnormal real-time temperature, the device further comprises: An abnormality type determination module, used to determine the current abnormality type according to the abnormality judgment result of the real-time temperature; A strategy determination module is used to query the knowledge graph for an exception handling strategy corresponding to the current exception type and execute the exception handling strategy for the logistics package; the knowledge graph includes a correspondence between multiple preset exception types and multiple preset exception handling strategies.

[0056] The device and method embodiments in the device embodiments are based on the same inventive concept.

[0057] An embodiment of this specification provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the data processing method for logistics packages provided in the above method embodiment.

[0058] An embodiment of the present invention also provides a computer storage medium, which can be set in a terminal to store at least one instruction or at least one program related to a data processing method for a logistics package in a method embodiment. The at least one instruction or at least one program is loaded and executed by the processor to implement the data processing method for the logistics package provided by the above method embodiment.

[0059] Embodiments of the present invention further provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to implement the logistics package data processing method provided in the above method embodiment.

[0060] Optionally, in the embodiments of this specification, the storage medium may be located in at least one of the multiple network servers in the computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard drive, a magnetic disk, or an optical disk, among other media capable of storing program code.

[0061] The memory described in the embodiments of this specification can be used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required for functions, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0062] The data processing method for logistics packages provided in the embodiments of this specification can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Taking running on a server as an example, Figure 7 This is a hardware structure diagram of a server for a data processing method for logistics packages provided in an embodiment of this specification. Figure 7 As shown, the server 700 may vary significantly depending on configuration or performance. It may include one or more central processing units (CPUs) 710 (CPUs 710 may include, but are not limited to, processing devices such as microprocessors (MCUs) or programmable logic devices (FPGAs), memory 730 for storing data, and one or more storage media 720 (e.g., one or more mass storage devices) for storing applications 723 or data 722. The memory 730 and storage media 720 may be either transient or persistent storage. The program stored in the storage medium 720 may include one or more modules, each of which may include a series of instruction operations on the server. Furthermore, the CPU 710 may be configured to communicate with the storage medium 720 to execute the series of instruction operations in the storage medium 720 on the server 700. The server 700 may also include one or more power supplies 760, one or more wired or wireless network interfaces 750, one or more input and output interfaces 740, and / or one or more operating systems 721, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0063] The input / output interface 740 can be used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the server 700. In one embodiment, the input / output interface 740 may include a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the input / output interface 740 may be a radio frequency (RF) module for wireless communication with the Internet.

[0064] It can be understood by those skilled in the art that Figure 7 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 7 More or fewer components than shown, or with Figure 7 Different configurations shown.

[0065] From the embodiments of the data processing method, device, electronic device or storage medium of the logistics package provided by the present invention, it can be seen that the present invention constructs a preset database based on the barcode and RFID tag corresponding to the logistics package; collects the temperature data of the logistics package in real time through the temperature control sensor installed in the transportation vehicle and warehouse of the logistics package; establishes an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package and the location information of the temperature control sensor; obtains the temperature data set of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index; detects the real-time barcode of the logistics package through the barcode detection equipment installed in the transportation vehicle and warehouse of the logistics package; verifies the real-time barcode according to the preset database, and uploads the real-time barcode and the temperature data set to the cloud platform if the verification passes. The present invention can realize high-precision reading of barcode information in complex environments, and realize multi-target recognition by combining RFID technology; combines the temperature control sensor with the barcode detection and recognition system, records the transportation temperature and flow information of the goods through the barcode, and realizes full-process monitoring and quality traceability of the logistics package.

[0066] It should be noted that the order in which the embodiments of this specification are presented is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions are of specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0067] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, equipment, and storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant portions, refer to the descriptions of the method embodiments.

[0068] Those skilled in the art will understand that all or part of the steps of implementing the above embodiments may be accomplished by hardware, or by a program instructing the relevant hardware to accomplish the steps. The program may be stored in a computer storage medium, and the above-mentioned storage medium may be a read-only memory, a disk, or an optical disk, etc.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data processing method for logistics packages, characterized in that: The method comprises: Build a preset database based on the barcodes and RFID tags corresponding to logistics packages; The temperature data of the logistics package is collected in real time through the temperature control sensors installed in the transportation vehicle and the warehouse of the logistics package; Establishing an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package, and the location information of the temperature control sensor; Obtaining a temperature dataset of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index; Detecting the real-time barcode of the logistics package through the transport vehicle of the logistics package and the barcode detection equipment installed in the warehouse; The real-time barcode is verified according to the preset database, and if the verification passes, the real-time barcode and the temperature data set are uploaded to a cloud platform.

2. The method according to claim 1, characterized in that The method of constructing a preset database based on the barcode and RFID tag corresponding to the logistics package includes: Generate a barcode for the logistics package based on first attribute information of the logistics package; the first attribute information includes at least one of the name, specification, and production batch of the logistics package; generating an RFID tag for the logistics package based on second attribute information of the logistics package, wherein the second attribute information includes at least one of a production time, a shelf life, ingredients, and transportation requirements of the logistics package; Generate a CRC check code for the logistics package based on the barcode and the RFID tag corresponding to the logistics package; The correspondence between the barcode, the RFID tag and the CRC check code of the logistics package is stored in the preset database.

3. The method according to claim 2, characterized in that The verifying the real-time barcode according to the preset database includes: Obtaining the real-time RFID tag of the logistics package; Generate a real-time CRC check code for the logistics package based on the real-time barcode and the real-time RFID tag; Searching the preset database for a CRC check code that matches the real-time barcode to obtain a target CRC check code; Comparing the CRC check code with the real-time CRC check code to see if they are consistent; If the CRC check code is consistent with the real-time CRC check code, it is determined that the real-time barcode verification has passed.

4. The method according to claim 1, wherein The step of obtaining the temperature data set of the logistics package according to the real-time temperature collected by the temperature control sensor and the R-tree index includes: According to the real-time temperature collected by the temperature control sensor, searching the R-tree index for a logistics package corresponding to the real-time temperature, and establishing a binding relationship between the real-time temperature and the logistics package; An abnormality detection is performed on the real-time temperature. If the real-time temperature is an abnormal temperature, the real-time temperature is filtered out to obtain a temperature data set of the logistics package.

5. The method according to claim 4, characterized in that The establishing of an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package, and the location information of the temperature control sensor includes: Based on the temperature data collected by the temperature control sensor and the dynamic model of the system, the temperature data is optimally estimated to obtain the target temperature of the logistics package; Synchronize the clocks of the temperature control sensor and the RFID reader using the NTP protocol, and add timestamps to the target temperature of the logistics package and the RFID tag respectively; The location information of the logistics package is determined by the GPS module, and an R-tree index is established based on the location information of the logistics package and the location information of the temperature control sensor; the R-tree is used to store the target temperature corresponding to the logistics package at different locations, the RFID tag, and the timestamp.

6. The method according to claim 1, characterized in that The barcode detection device includes a barcode recognition model, which is used to detect the real-time barcode of the logistics package; the training method of the barcode recognition model includes: Acquire a sample barcode image, wherein the sample barcode image is marked with a sample barcode label; Input the sample barcode image into the RF-DETR-base model to obtain the sample original feature map extracted from each channel in the model; Perform global average pooling and full connection processing on the channel dimension of the original feature map of the samples of each channel to obtain the weight coefficient of each channel; Calculate the product of the sample original feature map of each channel and the weight coefficient of each channel to obtain the sample barcode feature of each channel; Performing feature fusion processing on the sample barcode features of each channel to obtain a sample fusion feature; and predicting a sample barcode result based on the sample fusion feature; The RF-DETR-base model is trained according to the difference between the sample barcode result and the sample barcode label to obtain the barcode recognition model.

7. The method according to claim 4, characterized in that The abnormality detection of the real-time temperature includes: According to the type of goods corresponding to the logistics package, set the normal temperature threshold corresponding to the logistics package; If the real-time temperature exceeds the normal temperature threshold, determining that the real-time temperature is abnormal; In the case of the real-time temperature anomaly, the method further includes: Determining the current abnormality type according to the abnormality judgment result of the real-time temperature; Query the exception handling strategy corresponding to the current exception type in the knowledge graph, and execute the exception handling strategy for the logistics package; the knowledge graph includes a correspondence between multiple preset exception types and multiple preset exception handling strategies.

8. A data processing device for logistics packages, characterized in that: The device comprises: A preset database construction module is used to construct a preset database based on the barcode and RFID tag corresponding to the logistics package; A temperature acquisition module is used to collect temperature data of the logistics package in real time through temperature control sensors installed in the transportation vehicle and warehouse of the logistics package; An R-tree construction module is configured to establish an R-tree index based on the temperature data collected by the temperature control sensor, the location information of the logistics package, and the location information of the temperature control sensor; A temperature data set construction module is used to obtain the temperature data set of the logistics package based on the real-time temperature collected by the temperature control sensor and the R-tree index; A real-time barcode detection module is used to detect the real-time barcode of the logistics package through the transport vehicle of the logistics package and the barcode detection equipment installed in the warehouse; The data uploading module is used to verify the real-time barcode according to the preset database, and upload the real-time barcode and the temperature data set to the cloud platform if the verification is passed.

9. An electronic device, characterized in that: The device includes: a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the data processing method for logistics packages as described in any one of claims 1-7.

10. A computer storage medium, characterized in that The computer storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the data processing method for logistics packages as described in any one of claims 1-7.

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