A logistics video tracing system for commodity package detection

Abstract

The present invention discloses a logistics video tracing system for commodity package detection, and relates to the technical field of logistics data processing systems. The logistics video tracing system for commodity package detection includes a logistics acquisition module, a logistics video data extraction module, a three-layer long short-term memory neural network model training module, an environmental parameter analysis and evaluation module, a commodity quality comprehensive analysis module, and a judgment analysis calling video module. The present invention analyzes and trains the commodity package logistics process video data through a three-layer long short-term memory neural network model training module, and calls the logistics video tracing system and takes commodity package adjustment measures after comparing and analyzing the negative analysis value of the comprehensive vibration impact assessment and the negative analysis correction value of the temperature and humidity impact assessment, thereby improving the accuracy of the logistics video tracing evaluation of the commodity package detection, and solving the problem of insufficient accuracy of the logistics video tracing evaluation of the commodity package detection in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics data processing systems, and in particular to a logistics video tracing system for commodity package detection. Background Art

[0002] With the rapid development of e-commerce and the continuous growth of the logistics industry, there is a strong demand for fast and safe delivery of goods, and the logistics video traceability system has come into being. With the development of technologies such as 5G and the Internet of Things, the logistics video traceability system will be more efficient, monitoring and recording the entire process from the issuance to the receipt of goods in real time, ensuring the safety of goods and improving logistics efficiency.

[0003] Logistics video tracing systems usually include video acquisition, data transmission, video analysis and information feedback, which enable real-time monitoring of packages. Through video analysis technology, they can identify the damage of packages and transmit data in real time to users and logistics management terminals via the Internet.

[0004] For example, the invention patent announcement number: CN107730183B announces a network-based automatic transfer and rapid delivery device and intelligent operation system for goods and parcels, including: grid service stations, intelligent machine groups, detection, sorting and storage device systems, transfer and delivery device systems and network operation systems. Through city-wide deployment, convenient and efficient, intelligent unmanned express logistics business acceptance, efficient transfer and delivery of goods and parcels, and intelligent machine delivery and handover.

[0005] For example, the invention patent announcement with announcement number: CN110969377B announces a method, device and electronic device for creating a logistics package information management system, including: when a module loading instruction is detected, the target tool module specified by the module loading instruction is loaded; when a module configuration instruction is detected, the parameter configuration of the loaded target tool module specified by the module configuration instruction is performed; when a publish-subscribe instruction between modules is detected, a publish-subscribe relationship is established between the loaded target tool modules specified by the publish-subscribe instruction; after the parameter configuration and the establishment of the publish-subscribe relationship are completed, a logistics package information management system including the loaded target tool module is obtained.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] Most existing technologies simply determine the status of goods by judging the degree of damage to the product package. However, since product packages have many irregular shapes, there may be situations where the product package is not damaged but the product is damaged. The logistics process is long and consumers generally find out whether the items in the package are damaged after receiving the package. Therefore, there is a problem of insufficient accuracy in the logistics video traceability assessment of product package inspection. Summary of the invention

[0008] The embodiment of the present application solves the problem of insufficient accuracy of logistics video tracing assessment for commodity package detection in the prior art by providing a logistics video tracing system for commodity package detection, thereby achieving the effect of improving the accuracy of logistics video tracing assessment for commodity package detection.

[0009] The embodiment of the present application provides a logistics video tracing system for commodity package detection, including: the logistics acquisition module, which is used to collect commodity package logistics process video data and commodity package environmental parameters; the logistics video data extraction module, which is used to obtain commodity package valid text data, commodity package valid audio data and commodity package valid video data through commodity package logistics process video data identification and extraction; the three-layer long short-term memory neural network model training module, which is used to respectively train commodity package valid text data, commodity package valid audio data and commodity package valid video data through a three-layer long short-term memory neural network model to obtain commodity package detection prediction basic value; the environmental parameter analysis and evaluation module, which is used to obtain vibration impact evaluation negative analysis value and temperature and humidity impact evaluation negative analysis correction value through commodity package environmental parameter evaluation analysis; the commodity quality comprehensive analysis module, which is used to obtain comprehensive commodity quality impact negative analysis value through comprehensive evaluation and analysis of commodity package detection prediction basic value, vibration impact evaluation negative analysis value and temperature and humidity impact evaluation negative analysis correction value; the judgment analysis calling video module, which is used to compare and analyze the comprehensive commodity quality impact negative analysis value with a preset comprehensive commodity quality threshold, and call the logistics video tracing system according to the comparison and analysis result and take commodity package adjustment measures.

[0010] Furthermore, the specific process of collecting the video data of the logistics process of commodity packages and the environmental parameters of commodity packages is: collecting the video data of the logistics process of commodity packages and the environmental parameters of commodity packages in the first logistics detection area and the second logistics detection area through the intelligent video acquisition front-end device; the first logistics detection area is used to describe the detection area where the commodity package is located during the logistics transportation process; the second logistics detection area is used to express the detection area where the commodity package is located during the logistics transfer and storage process.

[0011] Furthermore, the commodity package logistics process video data specifically includes commodity package logistics location data, commodity package transportation status data, commodity package cargo information data, commodity package handling status data and commodity package operation status information.

[0012] Further, the specific process of obtaining valid text data of a commodity package, valid audio data of a commodity package and valid video data of a commodity package by identifying and extracting the video data of the commodity package logistics process is as follows: obtaining the logistics position data of the commodity package and the cargo information data of the commodity package by identifying and extracting the video data of the commodity package logistics process; merging the commodity package logistics position data and the cargo information data of the commodity package of the same commodity package into an original data set of commodity package product information; obtaining the transportation status data of the commodity package, the environmental status data of the commodity package, the operation status information of the commodity package and the handling status data of the commodity package by identifying and extracting the video data of the commodity package logistics process; merging the transportation status data of the commodity package, the environmental status data of the commodity package, the operation status information of the commodity package and the handling status data of the commodity package of the same commodity package into an original video data set of commodity package product status; obtaining the original audio data set of the commodity package by identifying and extracting the video data of the commodity package logistics process; performing data preprocessing on the original data set of commodity package product information, the original audio data set of the commodity package and the original video data set of commodity package product status; obtaining valid text data of the commodity package, valid audio data of the commodity package and valid video data of the commodity package.

[0013] Furthermore, the commodity package environmental parameters include commodity package temperature environmental parameters, commodity package humidity environmental parameters and commodity package vibration environmental parameters.

[0014] Furthermore, the specific process of obtaining the negative analysis value of vibration impact assessment and the negative analysis correction value of temperature and humidity impact assessment through the commodity package environmental parameter evaluation and analysis is as follows: collecting the commodity package vibration environmental parameters in the commodity package environmental parameters through mechanical sensors, the commodity package vibration environmental parameters including the commodity package vibration intensity and the impact force of the commodity package; obtaining the maximum allowable vibration intensity and the maximum allowable impact force of the commodity package through high-thrust electric vibration test equipment; performing mechanical simulation tests on the corresponding commodities in the commodity package, using a scanning electron microscope to identify the microscopic damage structure of the commodity mechanical simulation test, judging the damage of the weak links of the commodities, and analyzing to obtain the correction factor of the weak points of the commodity structure; thereby obtaining the negative analysis value of vibration impact assessment; and obtaining the negative analysis correction value of temperature and humidity impact assessment through analysis of the commodity package temperature environmental parameters and the commodity package humidity environmental parameters in the commodity package environmental parameters.

[0015] Furthermore, the specific process of obtaining the negative analysis value of the comprehensive commodity quality impact is as follows: the negative analysis value of the comprehensive commodity quality impact is obtained by a calculation formula, and the specific calculation formula is as follows:

[0016]

[0017] The product packages are numbered in sequence, S0 represents the number of the product package, and S represents the total number of product package numbers; It represents the negative analysis value of the comprehensive product quality impact of the S0th product package. The basic value of the commodity package detection prediction of the S0th commodity package; represents the negative analysis value of the vibration impact assessment of the S0th product package, It represents the negative analysis correction value of the temperature and humidity impact assessment of the S0th product package. Indicates the package anti-impact correction factor of the S0th product package.

[0018] Furthermore, the specific process of comparing and analyzing the negative analysis value of the comprehensive commodity quality impact with the preset comprehensive commodity quality threshold is: if the negative analysis value of the comprehensive commodity quality impact is not greater than the preset comprehensive commodity quality threshold, it is judged that no commodity quality warning problem has occurred, and no commodity quality and safety warning signal is sent; if the negative analysis value of the comprehensive commodity quality impact is greater than the preset comprehensive commodity quality threshold, it is judged that a commodity quality warning problem has occurred, and a commodity quality and safety warning signal is sent.

[0019] Furthermore, calling the logistics video tracing system and taking commodity package adjustment measures according to the comparative analysis results specifically include: inputting the commodity package logistics process video data into the video surveillance NVR device corresponding to the commodity package, quickly locating the commodity package corresponding to the NVR video information collected by the video surveillance NVR device through the commodity package cargo information data and the commodity package transportation status data, and tracing the commodity package handling status data and the commodity package operation status information of the commodity package in the first logistics detection area and the second logistics detection area through the commodity package corresponding to the NVR video information; the commodity package adjustment measures include repackaging the goods or replacing the commodity package, adjusting the delivery route, and adjusting the transportation method.

[0020] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0021] 1. The three-layer long short-term memory neural network model training module is used to analyze and train the video data of the logistics process of commodity packages. After comparing and analyzing the negative analysis value of the comprehensive vibration impact assessment and the negative analysis correction value of the temperature and humidity impact assessment, the logistics video tracing system is called and commodity package adjustment measures are taken, thereby improving the accuracy of the logistics video tracing assessment of commodity package detection, and solving the problem of insufficient accuracy of the logistics video tracing assessment of commodity package detection in the existing technology.

[0022] 2. Through comprehensive evaluation and analysis of the basic value predicted by commodity package inspection, the negative analysis value of vibration impact assessment, and the negative analysis correction value of temperature and humidity impact assessment, the negative analysis value of comprehensive commodity quality impact is obtained. The basic value, vibration, temperature and humidity are all important factors affecting commodity quality. By combining these factors, the accuracy of prediction of the impact on commodity quality is improved.

[0023] 3. Through comparative analysis of the negative analysis value of the comprehensive product quality impact and the preset comprehensive product quality threshold, the logistics video traceability system is called according to the comparative analysis results and product package adjustment measures are taken to monitor and analyze the factors affecting product quality in real time, quickly identify quality problems, and take prompt measures after comparing with the preset threshold, thereby improving the efficiency of product quality early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the structure of a logistics video tracing system for commodity package detection provided in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of the structure of the negative analysis value of the comprehensive commodity quality impact provided in the embodiment of the present application;

[0026] Figure 3 A schematic diagram of a negative analysis value function image for comprehensive product quality impact provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The embodiment of the present application solves the problem of insufficient accuracy of logistics video tracing assessment for commodity package detection in the prior art by providing a logistics video tracing system for commodity package detection. After analyzing and training commodity package logistics process video data through a three-layer long short-term memory neural network model training module, the logistics video tracing system is called and commodity package adjustment measures are taken, thereby achieving the effect of improving the accuracy of logistics video tracing assessment for commodity package detection.

[0028] The technical solution in the embodiment of the present application is to solve the problem of insufficient accuracy of the logistics video traceability assessment of commodity package detection mentioned above. The overall idea is as follows:

[0029] The three-layer long short-term memory neural network model training module is used to analyze and train the video data of the commodity package logistics process. After a comparative analysis of the negative analysis value of the comprehensive vibration impact assessment and the negative analysis correction value of the temperature and humidity impact assessment, the logistics video traceability system is called and commodity package adjustment measures are taken, thereby improving the accuracy of the logistics video traceability assessment of commodity package detection.

[0030] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0031] like Figure 1 As shown, it is a structural schematic diagram of a logistics video tracing system for commodity package detection provided by an embodiment of the present application. The logistics video tracing system for commodity package detection provided by an embodiment of the present application includes: a logistics acquisition module, which is used to collect commodity package logistics process video data and commodity package environmental parameters; a logistics video data extraction module, which is used to obtain commodity package valid text data, commodity package valid audio data and commodity package valid video data through commodity package logistics process video data recognition and extraction; a three-layer long short-term memory neural network model training module, which is used to train commodity package valid text data, commodity package valid audio data and commodity package valid video data respectively through a three-layer long short-term memory neural network model The module is used to obtain the negative analysis value of vibration impact assessment and the negative analysis correction value of temperature and humidity impact assessment through the environmental parameter analysis of the commodity package; the module is used to obtain the negative analysis value of comprehensive commodity quality impact through the comprehensive analysis of the negative analysis value of vibration impact assessment and the negative analysis correction value of temperature and humidity impact assessment; the module is used to judge and analyze the video calling module, which is used to compare the negative analysis value of comprehensive commodity quality impact with the preset comprehensive commodity quality threshold, call the logistics video tracing system according to the comparison analysis results, and take commodity package adjustment measures.

[0032] In this embodiment, in actual situations, the goods in the product package may include fragile items such as porcelain, glass, tiles, and colored glaze. The product package may not be damaged or dirty on the surface, but in fact the goods have been damaged in the package due to improper operation or severe vibration during transportation. The existing detection technology for product packages cannot accurately judge. Due to various complex situations in long-distance transportation, even if the package is not damaged or violently vibrated, it may still be more prone to damage due to changes in temperature and humidity. The above is the blind spot of the existing detection technology.

[0033] Furthermore, the specific process of collecting video data of the logistics process of commodity packages and environmental parameters of commodity packages is: collecting video data of the logistics process of commodity packages and environmental parameters of commodity packages in the first logistics detection area and the second logistics detection area through intelligent video acquisition front-end equipment; the first logistics detection area is used to describe the detection area where the commodity package is located during the logistics transportation process; the second logistics detection area is used to express the detection area where the commodity package is located during the logistics transfer and storage process.

[0034] In this embodiment, the intelligent video acquisition front-end equipment includes network cameras, analog cameras and video encoders, drone-carried high-definition cameras, industrial cameras, video servers, intelligent mobile devices, etc.

[0035] The network camera is directly connected to the network, supports the H.264 video encoding standard, can be accessed remotely, and is suitable for installation in various environments. Analog cameras and video encoders convert analog signals into digital signals through video encoders, which is convenient for digital processing and network transmission. In some inspection areas that need to cover a large area or are difficult to reach, drones carrying high-definition cameras can be used to collect video data. In situations where high resolution or special imaging requirements are required, such as barcode recognition, quality inspection, etc., industrial cameras can be used with corresponding lenses and light sources. Video servers can encode, compress, store and distribute the collected video streams. Smart mobile devices such as smartphones or tablets collect video data through corresponding applications and perform real-time analysis.

[0036] The first logistics inspection area is used to describe the inspection area where the commodity packages are located during the logistics transportation process, specifically including the cargo hold of an airplane during air transportation, the cabin of a cargo ship during sea transportation, and the truck compartment during land transportation.

[0037] The second logistics inspection area is used to express the inspection area where the commodity packages are located during the logistics transfer and storage process, specifically including the commodity production factory warehouse, sorting center warehouse, logistics transfer station warehouse and other areas.

[0038] Furthermore, the commodity package logistics process video data and commodity package environmental parameters specifically include: the commodity package logistics process video data includes commodity package logistics location data, commodity package transportation status data, commodity package cargo information data, commodity package handling status data and commodity package operation status information; the commodity package environmental parameters include commodity package temperature environmental parameters, commodity package humidity environmental parameters and commodity package vibration environmental parameters.

[0039] In this embodiment, the commodity package logistics location data includes the commodity package logistics longitude and latitude information and the commodity package logistics detection area identification information; it records the exact geographical location of the commodity package, the commodity package logistics detection area identification, and whether it is an airplane cargo hold, a cargo ship cabin, a truck compartment or a factory warehouse, etc.

[0040] The commodity package transportation status data includes commodity package transportation mode information, commodity package transportation stage information and commodity package transportation status information; commodity package transportation mode information such as air transportation, sea transportation, land transportation, etc.; commodity package transportation stage information such as departure, transit, arrival, etc.; commodity package transportation status information such as loading status, unloading status, transportation status, etc.

[0041] Product package environmental status data includes temperature, humidity, light intensity, etc.

[0042] The commodity package cargo information data includes commodity type, commodity quantity, commodity weight, commodity size, etc.

[0043] The commodity package handling status data includes commodity package handling status video analysis results, such as motion detection, abnormal behavior detection, etc.

[0044] The operation status information of the commodity package includes the operation processing time information of the commodity package and the operation processing personnel information of the commodity package; the operation processing time information of the commodity package includes the time of inspection, loading and unloading, sorting and other operations; the operation processing personnel information of the commodity package includes the information of the employees who perform various operations.

[0045] The above data can be collected through data collection terminals (workbench scanning, packaging review table, PDA, gantry collection, automatic sorting line collection, etc.), collecting nodes of the entire sorting process of the package, and uploaded to the site data center or group data center through local area network, wide area network, private network, etc.

[0046] Further, the specific process of obtaining valid text data of commodity packages, valid audio data of commodity packages and valid video data of commodity packages by identifying and extracting the video data of the commodity package logistics process is as follows: identifying and extracting the video data of the commodity package logistics process to obtain the logistics position data of the commodity package and the commodity package cargo information data; merging the commodity package logistics position data and the commodity package cargo information data of the same commodity package into a commodity package product information original data set; identifying and extracting the video data of the commodity package logistics process to obtain the commodity package transportation status data, commodity package environmental status data, commodity package operation status information and commodity package handling status data; merging the commodity package transportation status data, commodity package environmental status data, commodity package operation status information and commodity package handling status data of the same commodity package into a commodity package product status original video data set; identifying and extracting the video data of the commodity package logistics process to obtain the commodity package audio original data set; performing data preprocessing on the commodity package product information original data set, the commodity package audio original data set and the commodity package product status original video data set; obtaining valid text data of the commodity package, valid audio data of the commodity package and valid video data of the commodity package.

[0047] In this embodiment, data preprocessing includes denoising, clipping, scaling, etc., so as to more clearly identify the package information.

[0048] The video data of the logistics process of commodity packages can also be collected through the site video surveillance network: mainly through IPCamera, NVR, DVR, CVR and other equipment, the site is monitored with zero blind spots, no dead angles, and around the clock. All IPCamera devices are gathered to the video surveillance room of the first logistics detection area and the second logistics detection area through POE switches. All video surveillance information is stored in the hard disk of the recorder. Generally, mainstream recorders store video information for 3 months.

[0049] Remote video can be viewed through the monitoring terminal. 1. Supports window layout display of various specifications, presenting different default options under widescreen and normal screen resolutions; at the same time, users can customize window layout according to needs. 2. Supports full-screen display mode and supports multiple screens on one machine. 3. Supports commonly used shortcut operations on the window: monitoring point information, switch sound, volume control, main / sub stream switching, instant playback, electronic zoom, emergency recording, capture, continuous capture, wall, smart preview. 4. Supports custom toolbars on the window, including whether the toolbar is automatically hidden, whether the buttons on the toolbar are displayed, and the display order. 5. Supports network disconnection and reconnection, and the timeout time and reconnection times can be adjusted. 6. Supports adjustment of video parameters: brightness, contrast, hue, and saturation. 7. Supports picture adaptation / stretching. 8. Supports real-time preview of different manufacturers, different models, and different devices.

[0050] Furthermore, the specific process of obtaining the basic value of commodity package detection prediction is as follows: the valid text data of the commodity package is used as the commodity text feature data set, the valid audio data of the commodity package is used as the commodity audio feature data set, and the valid video data of the commodity package is used as the commodity video feature data set; the text feature data set of the commodity text feature data set is processed by natural language processing to obtain a valid commodity text feature data set, and the valid commodity text feature data set is input into the first-layer long short-term memory neural network model, and the hidden layer state of the neurons of the first-layer long short-term memory neural network model is obtained after training the first-layer long short-term memory neural network model; the audio feature data set of the commodity audio feature is processed by audio spectrum analysis technology to obtain a valid commodity audio feature data set, and the valid commodity audio feature data set is compared with the hidden layer state of the neurons of the first-layer long short-term memory neural network model. The states are spliced ​​and input into the second-layer long short-term memory neural network model, and the hidden layer states of the neurons in the second-layer long short-term memory neural network model are obtained after training the second-layer long short-term memory neural network model; the visual feature data set is processed by visual feature extraction technology to obtain a valid data set of product video features, and the valid data set of product video features is spliced ​​with the hidden layer states of the neurons in the second-layer long short-term memory neural network model and input into the third-layer long short-term memory neural network model, and the hidden layer states of the neurons in the third-layer long short-term memory neural network model are obtained after training the third-layer long short-term memory neural network model, and the hidden layer states of the neurons in the third-layer long short-term memory neural network model are input into the fully connected layer of the long short-term memory neural network model; the basic value of product package detection prediction is output through training the fully connected layer of the long short-term memory neural network model.

[0051] In this embodiment, in terms of specific implementation steps, deep learning frameworks such as TensorFlow and PyTorch are used to build and train a three-layer long short-term memory neural network model; Python or other programming languages ​​that support TensorFlow / PyTorch are used. Required libraries are installed, such as TensorFlow library, Keras library, PyTorch library, NumPy library, etc. The three-layer architecture of the long short-term memory neural network model is designed and the model is compiled, and the optimizer, loss function and evaluation index are specified; the model parameters are optimized through multiple iterations (epochs); the performance of the long short-term memory neural network model is evaluated using the validation set and the test set to obtain a mature long short-term memory neural network model.

[0052] Text feature processing means converting text data into feature representation, such as word embedding processing or bag-of-words representation processing. These features are input to the first-layer long short-term memory neural network model. The first-layer long short-term memory neural network model learns the semantic information of the text sequence and generates the hidden state of each neuron.

[0053] Audio feature processing refers to extracting audio features, such as extracting audio features through MFCC (Mel-frequency cepstral coefficients).

[0054] Visual feature extraction techniques include low-level features of images, such as edges, textures, or high-level features, such as the output of object detection or classification.

[0055] The visual features are concatenated with the output of the hidden layer state of neurons in the second-layer LSTM neural network model, and then input into the third-layer LSTM neural network model, which is able to combine information from three modalities: text, audio, and vision.

[0056] The hidden layer state of neurons in the third layer of the long short-term memory neural network model is a high-level feature representation that integrates three modal information.

[0057] When concatenating features from different modalities, it is often necessary to transform them to have the same dimension or to fuse them through a learned embedding layer. For example, a shared embedding layer can be used to transform feature vectors from different modalities for processing in a three-layer LSTM neural network model.

[0058] The fully connected layer of the three-layer long short-term memory neural network model trains and predicts the output. The fully connected layer of the three-layer long short-term memory neural network model contains one or more neurons, which generate the final prediction results, such as the product status prediction results, based on the learned feature representation. The basic value of the product package detection prediction ranges from 0 to 1.

[0059] Furthermore, collecting commodity package environmental parameters specifically includes: collecting commodity package environmental parameters through mechanical sensors, the commodity package environmental parameters include commodity package temperature environmental parameters, commodity package humidity environmental parameters and commodity package vibration environmental parameters.

[0060] In this embodiment, mechanical sensors: such as temperature sensors, humidity sensors, light sensors, air pressure sensors, and vibration sensors. The vibration environment parameters of the commodity package can also be obtained through a texture analyzer. A texture analyzer is an instrument for measuring the texture of a material, and is mainly used in the field of evaluating the texture and mechanical properties of a material. The texture analyzer evaluates the texture of a sample by measuring its physical properties (such as hardness, elasticity, viscosity, cohesion, etc.), specifically including hardness, elasticity, viscosity, cohesion, chewiness, and compressibility. Hardness: The resistance of the sample surface, usually used to describe the hardness of the food or the firmness of the shell. Elasticity: The ability of the sample to restore its original shape after being subjected to external force, which can reflect the tenderness of the food or the elasticity of the meat. Viscosity: The feeling of adhesion of the sample to the tongue during chewing or cutting, often used to describe the viscosity of sauces or pulp. Cohesion: The ability of the sample's internal resistance to external compression, often used to evaluate the chewing feel and structure of the food. Chewability: An indicator that combines hardness, elasticity, and viscosity to describe the chewing experience of food in the mouth. Breaking force: The amount of force that a sample needs to be stretched or compressed until it breaks. Compressibility: The degree to which a sample deforms when subjected to external pressure.

[0061] Furthermore, the specific process of obtaining the negative analysis value of vibration impact assessment and the negative analysis correction value of temperature and humidity impact assessment through the evaluation and analysis of commodity package environmental parameters is as follows: collecting the commodity package vibration environmental parameters in the commodity package environmental parameters through mechanical sensors, the commodity package vibration environmental parameters include the commodity package vibration intensity and the impact force of the commodity package; obtaining the maximum allowable vibration intensity and the maximum allowable impact force of the commodity package through analysis with high-thrust electric vibration test equipment; performing mechanical simulation tests on the corresponding commodities in the commodity package, using a scanning electron microscope to identify the microscopic damage structure of the commodity mechanical simulation test, judging the damage of the weak links of the commodities, and analyzing to obtain the correction factor of the weak points of the commodity structure; thereby obtaining the negative analysis value of vibration impact assessment; obtaining the negative analysis correction value of temperature and humidity impact assessment through analysis of the commodity package temperature environmental parameters and the commodity package humidity environmental parameters in the commodity package environmental parameters.

[0062] The negative analysis value of vibration impact assessment is obtained through this analysis and calculation. The negative analysis value of vibration impact assessment is used to describe the degree of negative impact of accumulated vibration on the product when the product is packaged in a vibration environment.

[0063] In this embodiment, the negative analysis value of vibration impact assessment can be obtained by collecting a large amount of vibration and product damage data, analyzing them using statistical methods, and establishing a relationship model between vibration intensity and product damage rate. The specific calculation formula is as follows:

[0064]

[0065] It represents the negative analysis value of vibration impact assessment of the S0th product package.

[0066] The product packages are numbered in sequence, S0 represents the number of the product package, and S represents the total number of product package numbers.

[0067] Set up vibration commodity package video logistics node monitoring points, and number the vibration commodity package video logistics node monitoring points in sequence, Z0 represents the number of the vibration commodity package video logistics node monitoring point, and Z represents the total number of the vibration commodity package video logistics node monitoring points.

[0068] Indicates the starting time point of the transportation of the S0th package. Indicates the current timing point of the product package transportation, in minutes.

[0069] It indicates the vibration intensity of the monitoring point of the video logistics node of the S0th commodity package and the Z0th vibrating commodity package; the unit of vibration intensity is meters per second squared. The vibration of the equipment in the actual working environment is measured by accelerometer sensors, displacement sensors, etc. The measured signal can be analyzed by mechanical analysis software to obtain the vibration intensity.

[0070] It represents the maximum allowable vibration intensity of the S0th commodity package and the Z0th vibrating commodity package video logistics node monitoring point; the high-thrust electric vibration test equipment can simulate the vibration environment in actual work, and the commodity package is forced to vibrate to test its performance and reliability under extreme conditions, and the maximum allowable vibration intensity can be obtained at which vibration intensity the commodity package will cause inherent damage.

[0071] It is the commodity structure weakness point correction factor of the preset S0th commodity package and Z0th vibration commodity package video logistics node monitoring point obtained from the database, and the value range is 1-10.

[0072] In a specific embodiment, the commodity structural weakness correction factor of the S0th commodity package and the Z0th vibration commodity package video logistics node monitoring point is input into a pre-set mapping set according to the real-time vibration intensity and / or commodity damage condition to obtain the corresponding correction factor, and the mapping set can be established through the vibration intensity and / or commodity damage condition in the historical data and the corresponding correction factor; when in use, the corresponding commodity structural weakness correction factor is obtained according to the vibration intensity and / or commodity damage condition, and the correction factor in the database can be obtained by performing material tests on the commodities in the commodity package, especially mechanical tests on the materials of fragile commodities, such as compression tests, bending tests and impact tests, and then using a scanning electron microscope to observe the microstructure of the material, identify the damage of the weak links of the commodity, set complete damage to 10, and no damage to 1, and obtain the average value after 1000 mechanical tests.

[0073] Indicates the impact force of the S0th commodity package and the Z0th vibrating commodity package video logistics node monitoring point; the impact force unit is Newton; the video data of the experimental simulation loading and unloading process is processed by combining video analysis software with experimental simulation impact force analysis. By identifying the movement and changes of commodity packages in video data, combined with object recognition and time analysis, the speed change and acceleration before and after the impact can be calculated, thereby calculating the impact force of the commodity package. For example, by calculating the speed difference of the commodity before and after the impact and the impact duration, the magnitude of the impact force can be obtained.

[0074] It represents the maximum allowable impact force of the S0th commodity package and the Z0th vibrating commodity package video logistics node monitoring point; the unit of impact force is Newton; laboratory tests, such as drop tests, rolling tests or impact tests, are carried out to record information such as the height of the commodity drop, the angle and force of the impact, so as to simulate the critical value of impact force corresponding to the collision and damage of commodities that may be encountered during actual transportation and operation, and obtain the maximum allowable impact force.

[0075] The larger the negative analysis value of vibration impact assessment is, the greater the cumulative negative impact of vibration on the product package is. The greater the cumulative negative impact of vibration on the product package is, the greater the vibration load on the product is, the more likely the product is to be damaged, and the more it is necessary to adjust the anti-vibration condition of the product package.

[0076] If the vibration intensity is fixed at 2.5 meters per second squared, the maximum allowable vibration intensity is fixed at 5 meters per second squared, the correction factor for the weak point of the product structure is fixed at 5, the maximum allowable impact force is fixed at 1000 Newtons, and the difference between the current timing time point of the product package transportation and the starting timing time point of the product package transportation is 1 minute, then the impact force is used as the independent variable, and the negative analysis value of the vibration impact assessment is used as the dependent variable; Table 1 is an example table of negative analysis values ​​of vibration impact assessment, and Table 1 is shown below.

[0077] Table 1 Example of negative analysis values ​​for vibration impact assessment

[0078]

[0079] According to Table 1, the negative analysis value of the vibration impact assessment increases with the increase of the impact intensity. In actual situations, the greater the impact intensity, the greater the negative degree of vibration impact on the goods in the product package, which makes the negative analysis value of the vibration impact assessment larger, which conforms to the objective law and provides a quantitative basis for subsequent adjustments to the vibration conditions of the product package.

[0080] In this embodiment, even if the transportation time is short, if the temperature and humidity fluctuate greatly during the transportation, fragile goods may still be damaged. For example, if the temperature and humidity are not properly controlled during the transportation from the warehouse to the transportation vehicle and then to the destination warehouse or the final consumer, fragile goods may break due to thermal expansion and contraction. In addition, during the transportation process, the brittleness of fragile goods increases due to thermal expansion and contraction, and they are more likely to break under the same impact force.

[0081] The negative analysis correction value of temperature and humidity impact assessment is obtained through the calculation formula. The specific calculation formula is as follows:

[0082]

[0083] It represents the negative analysis correction value of the temperature and humidity impact assessment of the S0th product package.

[0084] Set commodity package video logistics node environmental monitoring points, and number the commodity package video logistics node environmental monitoring points in sequence, W0 represents the number of the commodity package video logistics node environmental monitoring point, and W represents the total number of commodity package video logistics node environmental monitoring points.

[0085] represents the ambient temperature of the environmental monitoring point of the video logistics node of the S0th commodity package and the W0th commodity package; It is the negative impact factor of the ambient temperature of the preset S0th commodity package and W0th commodity package video logistics node environmental monitoring point on the commodity obtained from the database.

[0086] Indicates the ambient humidity of the environmental monitoring point of the video logistics node of the S0th commodity package and the W0th commodity package; It is the negative impact factor of the ambient humidity on the commodity at the preset S0th commodity package and W0th commodity package video logistics node environmental monitoring point obtained from the database.

[0087] The negative impact factor of ambient temperature on the product and the negative impact factor of ambient humidity on the product range from 1 to 10.

[0088] In a specific embodiment, the negative impact factor of ambient temperature on the commodity is input into a pre-set mapping set according to the real-time temperature and / or commodity damage condition to obtain a corresponding correction factor. The mapping set can be established through the temperature and / or commodity damage condition in the historical data and the corresponding correction factor; when in use, the corresponding negative impact factor of ambient temperature on the commodity is obtained according to the temperature and / or commodity damage condition.

[0089] In a specific embodiment, the negative impact factor of ambient humidity on commodities is input into a pre-set mapping set according to the real-time humidity and / or commodity damage conditions to obtain a corresponding correction factor. The mapping set can be established through the humidity and / or commodity damage conditions in historical data and the corresponding correction factors. When in use, the corresponding negative impact factor of ambient humidity on commodities is obtained according to the humidity and / or commodity damage conditions.

[0090] The correction factor in the database can be obtained through experimental simulation analysis. For example, taking the goods at 25 degrees Celsius and 30% air humidity as the benchmark, it is set to 1 in unit; the damage of the goods at 50 degrees Celsius or 60% air humidity when subjected to the same impact force is tested respectively. The damage is obtained through non-destructive testing technology. If the goods are broken, it is set to 10 in unit. If the goods are not broken, it is set to between 1 and 10 according to the test results of the non-destructive testing technology, and the average value is taken from multiple experiments.

[0091] The larger the negative analysis correction value of the temperature and humidity impact assessment is, the greater the cumulative negative impact of temperature and humidity on the product is. The greater the cumulative negative impact of temperature and humidity on the product is, the greater the impact of vibration stress on the product due to changes in temperature and humidity in the same vibration environment is, and the more likely the product is to be damaged, and the environment where the product is packaged needs to be adjusted.

[0092] Furthermore, the specific process of obtaining the negative analysis value of the comprehensive commodity quality impact is as follows: The negative analysis value of the comprehensive commodity quality impact is obtained by a calculation formula, and the specific calculation formula is as follows:

[0093]

[0094] It represents the negative analysis value of the comprehensive product quality impact of the S0th product package. The basic value of the product package detection prediction for the S0th product package, represents the negative analysis value of the vibration impact assessment of the S0th product package, It represents the negative analysis correction value of the temperature and humidity impact assessment of the S0th product package. is the package anti-impact correction factor of the preset S0th product package obtained from the database.

[0095] In this embodiment, if Figure 2 As shown, it is a schematic diagram of the structure of the negative analysis value of the comprehensive product quality impact provided in the embodiment of the present application.

[0096] In a specific embodiment, the package anti-impact correction factor of the S0th commodity package is input into a preset mapping set according to the real-time impact force and the parameter of the commodity package's impact force reduction effect on the commodity to obtain the corresponding correction factor. The mapping set can be established by the correction factors corresponding to the impact force and the parameter of the commodity package's impact force reduction effect on the commodity in the historical data; when used, the corresponding commodity structural weakness correction factor is obtained according to the impact force and the parameter of the commodity package's impact force reduction effect on the commodity. The correction factors in the database can be obtained by actually testing the impact force reduction effect of the commodity package on the commodity. For example, in a 100 Newton impact test, the commodity is subjected to a force of 30 Newtons under the protection of the package, then The value range of the package impact resistance correction factor is between 0 and 1. The larger the package impact resistance correction factor is, the weaker the package impact resistance is, and vice versa.

[0097] like Figure 3 As shown, it is a schematic diagram of the image of the negative analysis value function of the comprehensive commodity quality impact provided in the embodiment of the present application; when the commodity package detection prediction basic value is taken as a fixed value of 0.8, the function curve a represents the schematic curve of the negative analysis value function of the comprehensive commodity quality impact when the negative analysis value of the vibration impact assessment and the package anti-impact correction factor are taken as a fixed value of 1.3, and the negative analysis correction value of the temperature and humidity impact assessment is used as the independent variable; the function curve b represents the schematic curve of the negative analysis value function of the comprehensive commodity quality impact when the negative analysis value of the vibration impact assessment and the package anti-impact correction factor are taken as a fixed value of 1.5, and the negative analysis correction value of the temperature and humidity impact assessment is used as the independent variable; the function curve c represents the schematic curve of the negative analysis value function of the comprehensive commodity quality impact when the negative analysis value of the vibration impact assessment and the package anti-impact correction factor are taken as a fixed value of 1.7, and the negative analysis correction value of the temperature and humidity impact assessment is used as the independent variable.

[0098] It can be seen from the figure that with the increase of the negative analysis correction value of the temperature and humidity impact assessment, the negative analysis value of the comprehensive product quality impact increases. The negative analysis value of the comprehensive product quality impact is used to describe the level of the comprehensive negative impact of various factors on product quality under the logistics video traceability system; when the negative analysis correction value of the temperature and humidity impact assessment is small, the comprehensive product quality impact negative analysis value function is not much different under the product of different vibration impact assessment negative analysis values ​​and the package anti-impact correction factor. At this time, it means that the package has strong resistance to temperature and humidity, which makes the negative impact of external environmental factors on the product weaker. With the increase of the negative analysis correction value of the temperature and humidity impact assessment, that is, the weaker the package resistance to temperature and humidity, the greater the negative analysis value of the comprehensive product quality impact.

[0099] Furthermore, the specific process of comparing and analyzing the negative analysis value of the comprehensive commodity quality impact with the preset comprehensive commodity quality threshold is as follows: if the negative analysis value of the comprehensive commodity quality impact is not greater than the preset comprehensive commodity quality threshold, it is judged that no commodity quality warning problem has occurred, and no commodity quality and safety warning signal is sent; if the negative analysis value of the comprehensive commodity quality impact is greater than the preset comprehensive commodity quality threshold, it is judged that a commodity quality warning problem has occurred, and a commodity quality and safety warning signal is sent.

[0100] In this embodiment, the comprehensive commodity quality threshold is preset by consulting industry experts and scholars, and the threshold is set based on their experience and knowledge.

[0101] Furthermore, based on the comparative analysis results, the logistics video tracing system is called and commodity package adjustment measures are taken, specifically including: inputting the commodity package logistics process video data into the video surveillance NVR device corresponding to the commodity package, quickly locating the commodity package corresponding to the NVR video information collected by the video surveillance NVR device through the commodity package cargo information data and the commodity package transportation status data, and tracing the commodity package handling status data and the commodity package operation status information of the commodity package in the first logistics inspection area and the second logistics inspection area through the commodity package corresponding to the NVR video information; the commodity package adjustment measures include repackaging goods or replacing commodity packages, adjusting delivery routes, and adjusting transportation methods.

[0102] In this embodiment, the logistics video tracing system can be called using the logistics video tracing software. The logistics video tracing software 1. supports quick retrieval, video, and picture quick positioning by package number; 2. supports quick retrieval, video, and picture quick positioning by job code; 3. supports quick retrieval, video, and picture quick positioning by device ID; 4. supports quick retrieval, video, and picture quick positioning by user ID; 5. supports quick retrieval, video, and picture quick positioning of automatic sorting lines and grid numbers; 6. supports quick retrieval, video, and picture quick positioning by time period; 7. supports assembly line package video tracing.

[0103] In addition, the logistics video tracing software also includes an electronic map function, which facilitates the quick call of historical video data of the corresponding video surveillance NVR device.

[0104] Electronic map template, 1. Can import site CAD drawings; 2. Can mark the distribution of cameras and the direction of cameras on the electronic map; 3. Click a camera icon to perform real-time preview, playback, restart and other operations; 4. Can be combined with site positioning equipment to locate the trajectory of people, vehicles and packages on the site; 5. Can realize the function of video linkage.

[0105] It may be necessary to repackage the goods, use more shock-resistant and moisture-proof materials, etc., and adjust the delivery routes to avoid goods passing through areas with frequent vibrations or extreme climates. If it is found that certain methods of transportation have a greater impact on the quality of the goods, it may be necessary to switch to more secure methods of transportation, such as cold chain transportation, air transportation, etc.

[0106] In summary, the logistics video traceability system used for commodity package inspection has the following features: 1. On-demand retrieval and visual traceability; 2. Information superposition and precise positioning; 3. Data analysis and decision-making assistance; 4. On-site video that can be called up at any time.

[0107] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0112] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A logistics video tracing system for commodity package detection, characterized in that: include: Logistics collection module, logistics video data extraction module, three-layer long short-term memory neural network model training module, environmental parameter analysis and evaluation module, commodity quality comprehensive analysis module and judgment analysis and video call module; The logistics collection module is used to collect the video data of the commodity package logistics process and the commodity package environmental parameters; The logistics video data extraction module is used to obtain valid text data, valid audio data and valid video data of the commodity package through the video data of the commodity package logistics process identification and extraction; The three-layer long short-term memory neural network model training module is used to respectively train the commodity package valid text data, the commodity package valid audio data and the commodity package valid video data through the three-layer long short-term memory neural network model to obtain a commodity package detection prediction basic value; The specific process of obtaining the commodity package detection prediction basic value is as follows: taking the valid text data of the commodity package as the commodity text feature data set, the valid audio data of the commodity package as the commodity audio feature data set, and the valid video data of the commodity package as the commodity video feature data set, performing text feature processing on the commodity text feature data set through natural language processing to obtain a valid commodity text feature data set, inputting the valid commodity text feature data set into the first-layer long short-term memory neural network model, obtaining the first-layer long short-term memory neural network model neuron hidden layer state after training the first-layer long short-term memory neural network model, performing audio feature processing on the commodity audio feature data set through audio spectrum analysis technology to obtain a valid commodity audio feature data set, and comparing the valid commodity audio feature data set with the first-layer long short-term memory neural network model neuron hidden layer state. splicing and inputting them into the second-layer long short-term memory neural network model, obtaining the hidden layer state of the neurons of the second-layer long short-term memory neural network model after training the second-layer long short-term memory neural network model, performing visual feature processing on the product video feature data set through visual feature extraction technology to obtain a valid product video feature data set, splicing the valid product video feature data set with the hidden layer state of the neurons of the second-layer long short-term memory neural network model and inputting them into the third-layer long short-term memory neural network model, obtaining the hidden layer state of the neurons of the third-layer long short-term memory neural network model after training the third-layer long short-term memory neural network model, and inputting the hidden layer state of the neurons of the third-layer long short-term memory neural network model into the fully connected layer of the long short-term memory neural network model, and outputting the basic value of product package detection prediction through the training of the fully connected layer of the long short-term memory neural network model; The environmental parameter analysis and evaluation module is used to obtain a vibration impact evaluation negative analysis value and a temperature and humidity impact evaluation negative analysis correction value through commodity package environmental parameter evaluation and analysis; The specific process of obtaining the negative analysis value of vibration impact assessment and the negative analysis correction value of temperature and humidity impact assessment through the commodity packaging environmental parameter assessment is as follows: The mechanical sensor is used to collect the commodity package vibration environment parameters among the commodity package environment parameters, where the commodity package vibration environment parameters include the commodity package vibration intensity and the commodity package impact intensity; The maximum allowable vibration intensity and maximum allowable impact force of the product package are analyzed by high-thrust electric vibration test equipment; Through mechanical simulation testing of the corresponding products in the product package, a scanning electron microscope is used to identify the microscopic damage structure of the product mechanical simulation test, determine the damage of the weak links of the product, and analyze and obtain the correction factor of the weak points of the product structure; The negative analysis value of vibration impact assessment is obtained by comprehensively considering the vibration intensity of the commodity package, the impact intensity of the commodity package, the maximum allowable vibration intensity of the commodity package, the maximum allowable impact intensity and the correction factor of the weak points of the commodity structure; The negative analysis correction value of the temperature and humidity impact assessment is obtained by analyzing the commodity package temperature environment parameter and the commodity package humidity environment parameter in the commodity package environment parameter; The commodity quality comprehensive analysis module is used to obtain a comprehensive commodity quality impact negative analysis value through comprehensive evaluation and analysis of a commodity package detection prediction basic value, a vibration impact assessment negative analysis value, and a temperature and humidity impact assessment negative analysis correction value; The judgment analysis calling video module is used to compare and analyze the negative analysis value of the comprehensive commodity quality impact with the preset comprehensive commodity quality threshold, call the logistics video tracing system according to the comparison analysis result and take commodity package adjustment measures.

2. The logistics video tracing system for commodity package detection as claimed in claim 1, characterized in that: The specific process of collecting the video data of the commodity package logistics process and the commodity package environmental parameters is as follows: Collecting the video data of the commodity package logistics process and the commodity package environmental parameters in the first logistics detection area and the second logistics detection area through the intelligent video acquisition front-end device; The first logistics detection area is used to describe the detection area where the commodity package is located during the logistics transportation process; The second logistics detection area is used to express the detection area where the commodity package is located during the logistics transfer and storage process.

3. The logistics video tracing system for commodity package detection as claimed in claim 1, characterized in that: The commodity package logistics process video data specifically includes commodity package logistics location data, commodity package transportation status data, commodity package cargo information data, commodity package handling status data and commodity package operation status information.

4. The logistics video tracing system for commodity package detection as claimed in claim 1, characterized in that: The specific process of obtaining the valid text data, valid audio data and valid video data of the commodity package by identifying and extracting the video data of the commodity package logistics process is as follows: Identify and extract the commodity package logistics process video data to obtain the commodity package logistics location data and commodity package cargo information data; Combining the commodity package logistics location data and the commodity package cargo information data of the same commodity package into a commodity package commodity information original data set; Identify and extract the commodity package logistics process video data to obtain commodity package transportation status data, commodity package environment status data, commodity package operation status information and commodity package handling status data; Combining the commodity package transportation status data, commodity package environment status data, commodity package operation status information and commodity package handling status data of the same commodity package into a commodity package commodity status original video data set; Recognize and extract the video data of the commodity package logistics process to obtain the original audio data set of the commodity package; Preprocessing the original data set of product package product information, the original data set of product package audio, and the original data set of product package product status; Obtain valid text data of the commodity package, valid audio data of the commodity package, and valid video data of the commodity package.

5. The logistics video tracing system for commodity package detection as claimed in claim 1, characterized in that: The specific process of obtaining the commodity package detection prediction basic value is as follows: Use a deep learning framework to build and train a three-layer LSTM neural network model, use validation sets and test sets to evaluate the performance of the LSTM neural network model, and obtain a mature LSTM neural network model. Text feature processing means converting text data into features; Audio feature processing means extracting audio features through MFCC; Visual feature extraction techniques include low-level features of images; A shared embedding layer is used to convert feature vectors of different modalities. The fully connected layer of the three-layer long short-term memory neural network model is trained and output is predicted. The fully connected layer of the three-layer long short-term memory neural network model contains one or more neurons, and generates the final product status prediction result based on the learned feature representation. The basic value of commodity package detection prediction ranges from 0 to 1.

6. The logistics video tracing system for commodity package detection as claimed in claim 1, characterized in that: The specific process of obtaining the negative analysis value of vibration impact assessment and the negative analysis correction value of temperature and humidity impact assessment through the commodity packaging environmental parameter assessment is as follows: It represents the negative analysis value of vibration impact assessment of the S0th commodity package; The product packages are numbered in sequence, S0 represents the number of the product package, and S represents the total number of product package numbers; Set up vibration commodity package video logistics node monitoring points, and number the vibration commodity package video logistics node monitoring points in sequence, where Z0 represents the number of the vibration commodity package video logistics node monitoring point, and Z represents the total number of the vibration commodity package video logistics node monitoring points; Indicates the starting time point of the transportation of the S0th package. Indicates the current timing point of the product package transportation, in minutes; It indicates the vibration intensity of the video logistics node monitoring point of the S0th commodity package and the Z0th vibrating commodity package. The unit of vibration intensity is meter per second squared. It represents the maximum permissible vibration intensity of the monitoring point of the video logistics node of the Z0th vibrating commodity package of the S0th commodity package; The correction factor of the weak point of the commodity structure of the preset S0th commodity package and the Z0th vibration commodity package video logistics node monitoring point obtained from the database, with a value range of 1-10; It indicates the impact intensity of the S0th commodity package at the Z0th vibration commodity package video logistics node monitoring point; It represents the maximum permissible impact force of the S0th commodity package at the Z0th vibration commodity package video logistics node monitoring point; It represents the negative analysis correction value of the temperature and humidity impact assessment of the S0th product package; Set commodity package video logistics node environmental monitoring points, and number the commodity package video logistics node environmental monitoring points in sequence, W0 represents the number of the commodity package video logistics node environmental monitoring point, and W represents the total number of commodity package video logistics node environmental monitoring points; represents the ambient temperature of the environmental monitoring point of the video logistics node of the S0th commodity package and the W0th commodity package; is the negative impact factor of the ambient temperature of the preset S0th commodity package and W0th commodity package video logistics node environmental monitoring point on the commodity obtained from the database; Indicates the ambient humidity of the environmental monitoring point of the video logistics node of the S0th commodity package and the W0th commodity package; is the negative impact factor of the ambient humidity on the commodity at the preset S0th commodity package and W0th commodity package video logistics node environmental monitoring point obtained from the database; The negative impact factor of ambient temperature on the product and the negative impact factor of ambient humidity on the product range from 1 to 10.

7. The logistics video tracing system for commodity package detection as claimed in claim 1, characterized in that: The specific process of obtaining the negative analysis value of the comprehensive commodity quality impact is as follows: The negative analysis value of the comprehensive commodity quality impact is obtained through the calculation formula. The specific calculation formula is as follows: The product packages are numbered in sequence, S0 represents the number of the product package, and S represents the total number of product package numbers; It represents the negative analysis value of the comprehensive product quality impact of the S0th product package. The basic value of the commodity package detection prediction of the S0th commodity package; represents the negative analysis value of the vibration impact assessment of the S0th product package, It represents the negative analysis correction value of the temperature and humidity impact assessment of the S0th product package. Indicates the package anti-impact correction factor of the S0th product package.

8. The logistics video tracing system for commodity package detection as claimed in claim 1, characterized in that: The specific process of comparing and analyzing the negative analysis value of the comprehensive product quality impact with the preset comprehensive product quality threshold is as follows: If the negative analysis value of the comprehensive product quality impact is not greater than the preset comprehensive product quality threshold, it is judged that no product quality warning problem has occurred and no product quality safety warning signal is sent; If the negative analysis value of the comprehensive product quality impact is greater than the preset comprehensive product quality threshold, it is judged that a product quality warning problem occurs and a product quality safety warning signal is sent.

9. The logistics video tracing system for commodity package detection as claimed in claim 1, characterized in that: The logistics video tracing system is called according to the comparative analysis results and commodity package adjustment measures are taken, specifically including: Input the video data of the logistics process of the commodity package into the video surveillance NVR device corresponding to the commodity package, quickly locate the commodity package corresponding to the video information collected by the video surveillance NVR device through the commodity package cargo information data and the commodity package transportation status data, and trace the commodity package handling status data and the commodity package operation status information of the commodity package in the first logistics detection area and the second logistics detection area through the commodity package corresponding to the NVR video information; Product package adjustment measures include repackaging or replacing product packages, adjusting delivery routes, and adjusting transportation methods.

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