A Big Data-Based Intelligent Logistics Management Method and System

Abstract

An embodiment of the present invention provides a big data-based intelligent logistics management method and system, belonging to the field of logistics information technology. The method includes: classifying data of first logistics information of a target logistics vehicle to obtain a target cargo type of the first logistics information; screening attention targets for the first logistics information according to the target cargo type to obtain second logistics information; obtaining target spatial information of the second logistics information according to target three-dimensional data, and measuring environmental information of the target logistics vehicle by a target sensor to obtain initial internal environmental parameters; adjusting the initial internal environmental parameters according to the target spatial information to obtain target internal environmental parameters; performing data clustering on the second logistics information according to the target internal environmental parameters to obtain a target logistics cluster; predicting the state of the target logistics cluster according to the target internal environmental parameters to obtain a target logistics state; and adjusting logistics strategies for the target logistics vehicle according to the target logistics state to obtain target logistics strategies.

Description

Technical Field

[0001] The present invention relates to the field of logistics information technology, and in particular, to a smart logistics management method and system based on big data. Background Art

[0002] With the development of the Internet, the logistics industry has faced unprecedented opportunities and challenges. The Internet has not only greatly promoted the booming development of e-commerce, but also made logistics services an indispensable part of people's daily lives. People can transport items through logistics and enjoy unprecedented convenience. However, in this process, for items that are easily damaged, there is a lack of effective protection measures, which not only directly affects the customer experience, but also significantly increases the workload of delivery staff. For example, for easily damaged items such as agricultural products, mineral products, equipment, etc., if there is a lack of effective control and preservation measures during the logistics process, the items may be damaged during transportation, resulting in the inability to use the goods received by the customer. This increases the abnormal handling cost of logistics companies, prolongs the waiting time of customers, and reduces customer satisfaction. Summary of the Invention

[0003] The main purpose of the embodiments of the present invention is to provide a smart logistics management method and system based on big data, aiming to solve the problem in the related technology that for items that are easily damaged during the logistics service process, there is a lack of effective protection measures, which not only directly affects the customer experience, but also significantly increases the workload of delivery staff.

[0004] In a first aspect, the embodiments of the present invention provide a smart logistics management method based on big data, including:

[0005] Obtain the first logistics information corresponding to the target logistics vehicle, and perform data classification on the first logistics information to obtain the target cargo type corresponding to the first logistics information;

[0006] According to the target cargo type, perform attention target screening on the first logistics information to obtain the second logistics information corresponding to the target logistics vehicle;

[0007] Obtain the target space information corresponding to the second logistics information according to the target three-dimensional data, and measure the environmental information inside the target logistics vehicle by a target sensor to obtain the corresponding initial internal environment parameters;

[0008] Adjust the initial internal environment parameters according to the target space information to obtain the target internal environment parameters corresponding to the second logistics information;

[0009] Perform data clustering on the second logistics information according to the target internal environment parameters to obtain the target logistics cluster;

[0010] Perform state prediction on the target logistics cluster according to the target internal environmental parameters to obtain the target logistics state corresponding to the target logistics cluster;

[0011] Adjust the logistics strategy of the target logistics vehicle according to the target logistics state to obtain the target logistics strategy corresponding to the target logistics vehicle.

[0012] In a second aspect, an embodiment of the present invention provides a big data-based intelligent logistics management system, including:

[0013] A data classification module, configured to obtain the first logistics information corresponding to the target logistics vehicle and perform data classification on the first logistics information to obtain the target cargo type corresponding to the first logistics information;

[0014] A data screening module, configured to perform focus target screening on the first logistics information according to the target cargo type to obtain the second logistics information corresponding to the target logistics vehicle;

[0015] A data acquisition module, configured to obtain the target spatial information corresponding to the second logistics information according to the target three-dimensional data, and measure the environmental information inside the target logistics vehicle by using a target sensor to obtain the corresponding initial internal environmental parameters;

[0016] A parameter adjustment module, configured to adjust the initial internal environmental parameters according to the target spatial information to obtain the target internal environmental parameters corresponding to the second logistics information;

[0017] A data clustering module, configured to perform data clustering on the second logistics information according to the target internal environmental parameters to obtain a target logistics cluster;

[0018] A state prediction module, configured to perform state prediction on the target logistics cluster according to the target internal environmental parameters to obtain the target logistics state corresponding to the target logistics cluster;

[0019] A logistics management module, configured to adjust the logistics strategy of the target logistics vehicle according to the target logistics state to obtain the target logistics strategy corresponding to the target logistics vehicle.

[0020] An embodiment of the present invention provides a big data-based intelligent logistics management method and system. The method includes: obtaining first logistics information corresponding to a target logistics vehicle, and classifying the data of the first logistics information to obtain a target cargo type corresponding to the first logistics information; screening attention targets for the first logistics information according to the target cargo type to obtain second logistics information corresponding to the target logistics vehicle, so as to obtain items that need attention in the target logistics vehicle; obtaining target space information corresponding to the second logistics information according to the target three-dimensional data, and measuring the internal environment information of the target logistics vehicle by a target sensor to obtain corresponding initial internal environment parameters; adjusting the initial internal environment parameters according to the target space information to obtain target internal environment parameters corresponding to the second logistics information. Furthermore, accurate environmental parameters corresponding to the second logistics information can be accurately obtained according to the target space information, thereby providing good support for subsequent logistics status judgment; clustering the data of the second logistics information according to the target internal environment parameters to obtain a target logistics cluster; predicting the status of the target logistics cluster according to the target internal environment parameters to obtain a target logistics status corresponding to the target logistics cluster. Therefore, predicting the status according to the target logistics cluster with similar characteristics can improve the accuracy of predicting the status of a single logistics information and reduce risks; finally, adjusting the logistics strategy of the target logistics vehicle according to the target logistics status to obtain a target logistics strategy corresponding to the target logistics vehicle. Thus, it is ensured that the target items corresponding to the second logistics information are fully protected during transportation and the transportation quality is improved. This method not only improves customer satisfaction, but also reduces the workload of delivery staff. In addition, it solves the problem in the related technology that there is a lack of effective protection measures for easily damaged items in the logistics service process, which not only directly affects the customer experience, but also significantly increases the workload of delivery staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic flowchart of a big data-based intelligent logistics management method provided by an embodiment of the present invention;

[0023] Figure 2 It is a schematic module structure diagram of a big data-based intelligent logistics management system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] The flowcharts shown in the accompanying drawings are only illustrative examples, and do not necessarily include all contents and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged, so the actual execution order may be changed according to the actual situation.

[0026] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0027] The embodiments of the present invention provide a big data-based intelligent logistics management method and system. Among them, the big data-based intelligent logistics management method can be applied to a terminal device, and the terminal device can be an electronic device such as a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device. The terminal device can be a server or a server cluster.

[0028] The following will describe in detail some embodiments of the present invention with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0029] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a big data-based intelligent logistics management method provided by an embodiment of the present invention.

[0030] As Figure 1 shown, the big data-based intelligent logistics management method includes steps S101 to S107.

[0031] Step S101, obtain the first logistics information corresponding to the target logistics vehicle, and perform data classification on the first logistics information to obtain the target cargo type corresponding to the first logistics information.

[0032] Exemplarily, the target logistics vehicle refers to those vehicles that are performing transportation tasks, and the first logistics information is the commodity information loaded in the target logistics vehicle. The first logistics information includes, but is not limited to, the origin, destination, type, quantity, weight, volume, etc. of each commodity. Thus, data classification is performed on the first logistics information according to the type in the first logistics information to obtain the corresponding target cargo type, and the target cargo type is one of the easily damaged type, easily destroyed type, not easily damaged type, etc. Among them, the commodities corresponding to the easily destroyed type usually have a short shelf life, are sensitive to environmental conditions such as temperature and humidity, and are easily destroyed.

[0033] Step S102: Screen the attention targets for the first logistics information according to the target cargo type to obtain the second logistics information corresponding to the target logistics vehicle.

[0034] Exemplarily, when the target cargo type is the easily destroyed type or a cargo type that is easily affected by the external environment, the first logistics information corresponding to the target cargo type is determined as the second logistics information. That is, the second logistics information is the commodities or items affected by environmental parameters during transportation.

[0035] Step S103: Obtain the target space information corresponding to the second logistics information according to the target three-dimensional data, and measure the environmental information inside the target logistics vehicle by the target sensor to obtain the corresponding initial internal environmental parameters.

[0036] Exemplarily, in the loading area, such as key positions like warehouses and loading and unloading platforms, high-precision three-dimensional scanning devices are installed, such as laser scanners, structured light scanners, or portable three-dimensional scanners. And the positions and angles of the scanning devices are reasonably planned to ensure that they can completely cover the entire loading area. Strategies such as multi-point scanning or scanning path planning are adopted to ensure that each commodity is scanned comprehensively and without dead angles. For example, fixed scanning devices can be installed at the four corners of the loading area, and mobile scanners can be set at key loading points to achieve seamless coverage. During the cargo loading process, each commodity is scanned in real time by the three-dimensional scanning device to obtain its accurate target three-dimensional data, and it is transmitted to the database in real time through the network for storage and processing.

[0037] Exemplarily, in the database, the target three-dimensional data will be associated and stored with the unique identifier of each commodity (such as commodity barcodes, two-dimensional codes, RFID tags, etc.). Thus, according to the unique identifier of each commodity in the second logistics information, the target space information corresponding to the second logistics information is extracted from the target three-dimensional data. The target space information is used to characterize the space information presented after the second logistics information is loaded in the target logistics vehicle.

[0038] Exemplarily, the target sensor includes, but is not limited to, a temperature sensor, a humidity sensor, a barometric pressure sensor, a light sensor, etc. Thus, the corresponding initial internal environment parameters are obtained by measuring the environmental information inside the target logistics vehicle with the target sensor. The initial internal environment parameters include, but are not limited to, temperature information, humidity information, barometric pressure information, light intensity information, etc.

[0039] Step S104: Adjust the initial internal environment parameters according to the target space information to obtain the target internal environment parameters corresponding to the second logistics information.

[0040] Exemplarily, determine the specific installation location of the target sensor inside the target logistics vehicle. Thus, according to the Euclidean distance formula, calculate the distance information between the target space information corresponding to each second logistics information and the specific installation location of the target sensor, and then analyze the possible influence on the environmental parameters (such as temperature, humidity, etc.) of the target space information based on the distance information. For example: If the target space information is close to the target sensor, it may mean that the environmental parameters corresponding to the second logistics information are less different from the measurement results of the target sensor, and vice versa.

[0041] Exemplarily, adjust the initial internal environment parameters according to the calculated distance information using a preset parameter adjustment rule to obtain the target internal environment parameters corresponding to the second logistics information.

[0042] For example, the preset parameter adjustment rules include a temperature adjustment rule: for every 1-meter increase in the distance information, the temperature should be increased by 0.5°C; a humidity adjustment rule: the humidity adjustment value can be set as a percentage change in the distance information, such as for every additional 1-meter distance, the humidity increases by 10 percentage points, etc. Thus, after obtaining the distance information, use the preset parameter adjustment rules to adjust the initial internal environment parameters to obtain the target internal environment parameters corresponding to the second logistics information.

[0043] In some embodiments, adjusting the initial internal environment parameters according to the target space information to obtain the target internal environment parameters corresponding to the second logistics information includes: obtaining the peripheral logistics information corresponding to the second logistics information according to the target space information, and obtaining the first space information corresponding to the peripheral logistics information and the second space information corresponding to the second logistics information from a database; performing data superposition on the peripheral logistics information according to the first space information to obtain a third space information; obtaining the current space information corresponding to the peripheral logistics information from the target three-dimensional data, calculating the similarity between the current space information and the third space information to obtain the first extrusion information corresponding to the peripheral logistics information; calculating the similarity between the second space information and the target space information to obtain the second extrusion information corresponding to the second logistics information; fusing the first extrusion information and the second extrusion information to determine the target airtight information corresponding to the second logistics information; and adjusting the initial internal environment parameters according to the target airtight information to obtain the target internal environment parameters corresponding to the second logistics information.

[0044] Exemplarily, access the database according to the target space information, extract all the logistics information adjacent to the target space information and determine it as the peripheral logistics information corresponding to the second logistics information, and then obtain the first space information corresponding to the peripheral logistics information and the second space information corresponding to the second logistics information from the database. The first space information is used to characterize the space information required for the peripheral logistics information when it is not loaded in the target logistics vehicle. The second space information is used to characterize the space required for the second logistics information when it is not loaded in the target logistics vehicle.

[0045] Exemplarily, perform information merging on the first space information corresponding to the peripheral logistics information, so as to obtain the space information required for all the peripheral logistics information when it is not extruded in the target logistics vehicle, that is, obtain the third space information. The third space information is used to characterize the space information required for all the peripheral logistics information when it is loaded without extrusion in the target logistics vehicle.

[0046] Exemplarily, use the previously collected target three-dimensional data to obtain the current space information associated with the peripheral logistics information, that is, the current space information is the real space information formed by the peripheral logistics information near the second logistics information.

[0047] Exemplarily, similarity measurement methods (such as cosine similarity, Euclidean distance, etc.) are used to calculate the similarity between the current spatial information and the third spatial information. The actual state of the surrounding logistics information is evaluated through the similarity value to obtain the corresponding first extrusion information after the surrounding logistics information is loaded. Similarly, the similarity between the second spatial information and the target spatial information is calculated, and the second extrusion information is obtained through the similarity result. The first extrusion information is used to characterize the extrusion degree of the surrounding logistics information after loading. The greater the similarity, the smaller the extrusion degree; the smaller the similarity, the greater the extrusion degree. The second extrusion information is used to characterize the extrusion degree of the second logistics information after loading. The greater the similarity, the smaller the extrusion degree; the smaller the similarity, the greater the extrusion degree.

[0048] Exemplarily, methods such as weighted average and logistic regression are used to fuse the first extrusion information and the second extrusion information to obtain the target airtight information between the second logistics information and the surrounding logistics information. The target airtight information is used to characterize the environmental circulation degree of the second logistics information in the target logistics vehicle.

[0049] Exemplarily, according to the target airtight information, the initial environmental parameters are adjusted accordingly using preset rules to obtain the corresponding target internal environmental parameters of the second logistics information under the target spatial information.

[0050] Specifically, by adjusting the initial environmental parameters according to the target airtight information, the corresponding target internal environmental parameters of the second logistics information under the target spatial information are obtained, providing good support for improving the accuracy of the second logistics information state prediction in the future.

[0051] Step S105: Perform data clustering on the second logistics information according to the target internal environmental parameters to obtain the target logistics clusters.

[0052] Exemplarily, data clustering is performed using a clustering algorithm according to the target internal environmental parameters and the attribute information corresponding to the second logistics information, so as to obtain the target logistics clusters, where each cluster in the target logistics clusters has the same state change trend.

[0053] In some embodiments, the performing data clustering on the second logistics information according to the target internal environmental parameters to obtain the target logistics clusters includes: calculating the similarity of the second logistics information according to the target internal environmental parameters to obtain the corresponding target association information of the second logistics information; performing data clustering on the second logistics information according to the target association information to obtain the target logistics clusters.

[0054] Exemplarily, a suitable similarity calculation method is selected, such as cosine similarity, Euclidean distance, or Pearson correlation coefficient, to measure the similarity degree between the target internal environmental parameters corresponding to each logistics information in the second logistics information, and a similarity value is obtained. When the similarity value is greater than a preset value, it is determined that the target association information corresponding to the second logistics information has a high degree of association; when the similarity value is less than or equal to the preset value, it is determined that the target association information corresponding to the second logistics information has a low degree of association.

[0055] Exemplarily, data clustering is performed on the second logistics information according to the target association information. When the target association information has a high degree of association, it is determined to be in the same cluster, thereby obtaining the target logistics cluster.

[0056] In some embodiments, the second logistics information includes at least a first sub-logistics information and a second sub-logistics information, the target internal environmental parameters include a first environmental parameter corresponding to the first sub-logistics information and a second environmental parameter corresponding to the second sub-logistics information, and the obtaining of the target association information corresponding to the second logistics information by performing a similarity calculation on the second logistics information according to the target internal environmental parameters includes: obtaining a first attribute information corresponding to the first sub-logistics information and a second attribute information corresponding to the second sub-logistics information; determining a first change trend corresponding to the first sub-logistics information according to the first attribute information and the first environmental parameter; determining a second change trend corresponding to the second sub-logistics information according to the second attribute information and the second environmental parameter; and calculating the similarity between the first change trend and the second change trend to determine the target association information corresponding to the first sub-logistics information and the second sub-logistics information.

[0057] Exemplarily, the second logistics information includes at least a first sub-logistics information and a second sub-logistics information, and the target internal environmental parameters include a first environmental parameter corresponding to the first sub-logistics information and a second environmental parameter corresponding to the second sub-logistics information.

[0058] Exemplarily, a first item type corresponding to the first sub-logistics information is obtained, and thus the first attribute information corresponding to the first sub-logistics information is determined according to the first item type, and a second item type corresponding to the second sub-logistics information is obtained, and thus the second attribute information corresponding to the second sub-logistics information is determined according to the second item type.

[0059] Exemplarily, data statistics and trend analysis are performed using the first attribute information and the first environmental parameter. For example, the damage trend of the commodity corresponding to the first sub-logistics information, that is, the first change trend, is determined through a time series model in combination with the first attribute information and the first environmental parameter. The first change trend is used to characterize the damage speed of the first sub-logistics information corresponding to the first environmental parameter. For example, the change in the damage speed of the commodity corresponding to the first sub-logistics information under the first attribute information due to the temperature in the first environmental parameter.

[0060] Similarly, data statistics and trend analysis are performed using the second attribute information and the second environmental parameter. For example, the damage trend of the commodity corresponding to the second sub-logistics information, that is, the second change trend, is determined through a time series model in combination with the second attribute information and the second environmental parameter. The second change trend is used to characterize the damage speed of the second sub-logistics information corresponding to the second environmental parameter. For example, the change in the damage speed of the commodity corresponding to the second sub-logistics information under the second attribute information due to the temperature in the second environmental parameter.

[0061] Exemplarily, a suitable similarity calculation method is determined, such as cosine similarity, Euclidean distance, or correlation analysis. The first change trend and the second change trend are input into the similarity calculation method to calculate the similarity value between them. This similarity value reflects the degree of association of the quality changes between the first sub-logistics information and the second sub-logistics information. Thus, based on the calculated similarity result, the degree of association between the first sub-logistics information and the second sub-logistics information is analyzed. A higher similarity value indicates that the target association information between the first sub-logistics information and the second sub-logistics information has a high degree of association. A lower similarity value indicates that the target association information between the first sub-logistics information and the second sub-logistics information has a low degree of association.

[0062] In some embodiments, determining the first change trend corresponding to the first sub-logistics information according to the first attribute information and the first environmental parameter and determining the second change trend corresponding to the second sub-logistics information according to the second attribute information and the second environmental parameter includes: using the attribute characterization layer of the trend prediction model to perform information characterization on the first attribute information to obtain a first characterization vector; using the parameter characterization layer of the trend prediction model to perform information characterization on the first environmental parameter to obtain a second characterization vector; using the information combination layer of the trend prediction model to perform information matching on the first attribute information and the first environmental parameter by using the first characterization vector and the second characterization vector to obtain a plurality of first combination information corresponding to the first sub-logistics information; using the trend prediction layer of the trend prediction model to perform change prediction on the plurality of first combination information respectively to obtain a plurality of first predicted trends; using the trend fusion layer of the trend prediction model to perform information fusion on the plurality of first predicted trends to obtain the first change trend corresponding to the first sub-logistics information; using the attribute characterization layer to perform information characterization on the second attribute information to obtain a third characterization vector; using the parameter characterization layer to perform information characterization on the second environmental parameter to obtain a fourth characterization vector; using the information combination layer to perform information matching on the second attribute information and the second environmental parameter by using the third characterization vector and the fourth characterization vector to obtain a plurality of second combination information corresponding to the second sub-logistics information; using the trend prediction layer to perform change prediction on the plurality of second combination information respectively to obtain a plurality of second predicted trends; using the trend fusion layer to perform information fusion on the plurality of second predicted trends to obtain the second change trend corresponding to the second sub-logistics information.

[0063] Exemplarily, the trend prediction model includes an attribute characterization layer, a parameter characterization layer, an information combination layer, a trend prediction layer, and a trend fusion layer.

[0064] Exemplarily, in the attribute characterization layer, it is used to process and transform the first attribute information, extract features from the first attribute information, and generate a first characterization vector. The first characterization vector represents the essential features of the first attribute information and can better support subsequent calculations.

[0065] Exemplarily, in the parameter characterization layer, the one-hot characterization algorithm is used to convert the first environmental parameter into a vector form, and then features are extracted from the first environmental parameter to generate a second characterization vector. The second characterization vector can effectively capture the key features of the environmental parameter and help with subsequent parameter matching and trend prediction.

[0066] Exemplarily, in the information combination layer, the first attribute information and the first environmental parameters are effectively combined by using principal component analysis or key feature extraction to generate a plurality of first combined information. Then, in the trend prediction layer, a classification network is used to predict the changes of each first combined information to obtain a plurality of first predicted trends. The first predicted trends reflect the change direction and amplitude under the combination of specific attribute information and environmental parameters.

[0067] Exemplarily, in the trend fusion layer, fusion algorithms such as weighted average or Bayesian fusion are used to fuse the information of a plurality of first predicted trends to obtain a comprehensive first change trend. The first change trend can more comprehensively reflect the change law of the first sub-logistics information under various combined data.

[0068] Exemplarily, in the attribute characterization layer, it is used to process and transform the second attribute information, extract the features of the second attribute information, and generate a third characterization vector. The third characterization vector represents the essential features of the second attribute information and can better support subsequent calculations.

[0069] Exemplarily, in the parameter characterization layer, the one-hot characterization algorithm is used to convert the second environmental parameter into a vector form, and then the features of the second environmental parameter are extracted to generate a fourth characterization vector. The fourth characterization vector can effectively capture the key features of the environmental parameters and help with subsequent parameter matching and trend prediction.

[0070] Exemplarily, in the information combination layer, the second attribute information and the second environmental parameters are effectively combined by using principal component analysis or key feature extraction to generate a plurality of second combined information. Then, in the trend prediction layer, a classification network is used to predict the changes of each second combined information to obtain a plurality of second predicted trends. The second predicted trends reflect the change direction and amplitude under the combination of specific attribute information and environmental parameters.

[0071] Exemplarily, in the trend fusion layer, fusion algorithms such as weighted average or Bayesian fusion are used to fuse the information of a plurality of second predicted trends to obtain a comprehensive second change trend. The second change trend can more comprehensively reflect the change law of the second sub-logistics information under various combined data.

[0072] Step S106: Perform state prediction on the target logistics cluster according to the target internal environmental parameters to obtain the target logistics state corresponding to the target logistics cluster.

[0073] Exemplarily, a sub-cluster corresponding to the target logistics cluster is obtained, and then sub-environment parameters corresponding to each sub-logistics information in the sub-cluster are obtained from the target internal environment parameters. Thus, the item status of the sub-logistics information is predicted using the sub-environment parameters according to the neural network model to obtain the sub-logistics status corresponding to each sub-logistics information. Then, according to the voting mechanism, the sub-logistics status corresponding to each sub-logistics information in the sub-cluster is counted, and the logistics status with the most predictions in the sub-cluster is obtained. Furthermore, the logistics status with the most predictions in the sub-cluster is determined as the final logistics status corresponding to the sub-cluster. Thus, the above steps are performed for each sub-cluster in the target logistics cluster to obtain the final logistics status corresponding to each sub-cluster in the target logistics cluster, and thus the target logistics status corresponding to the target logistics cluster is obtained.

[0074] In some embodiments, the target logistics cluster includes at least one target sub-cluster, and the target sub-cluster includes at least third sub-logistics information and fourth sub-logistics information. The obtaining of the target logistics status corresponding to the target logistics cluster by predicting the status of the target logistics cluster according to the target internal environment parameters includes: obtaining third attribute information corresponding to the third sub-logistics information and fourth attribute information corresponding to the fourth sub-logistics information; obtaining third environment parameters corresponding to the third sub-logistics information and fourth environment parameters corresponding to the fourth sub-logistics information from the target internal environment parameters; performing information prediction on the third attribute information and the third environment parameters according to the status prediction model to obtain a first prediction result corresponding to the third sub-logistics information; performing information prediction on the fourth attribute information and the fourth environment parameters according to the status prediction model to obtain a second prediction result corresponding to the fourth sub-logistics information; and fusing the first prediction result and the second prediction result to obtain the target logistics status corresponding to the target logistics cluster.

[0075] Exemplarily, the target logistics cluster includes at least one target sub-cluster, and the target sub-cluster includes at least third sub-logistics information and fourth sub-logistics information. Then, the third item type corresponding to the third sub-logistics information is obtained, and thus the third attribute information corresponding to the third sub-logistics information is determined according to the third item type, and the fourth item type corresponding to the fourth sub-logistics information is obtained, and thus the fourth attribute information corresponding to the fourth sub-logistics information is determined according to the fourth item type.

[0076] Exemplarily, the third environment parameters corresponding to the third sub-logistics information are obtained from the target internal environment parameters according to the unique identifier of the third sub-logistics information, and the fourth environment parameters corresponding to the fourth sub-logistics information are obtained from the target internal environment parameters according to the unique identifier of the fourth sub-logistics information.

[0077] Exemplarily, a suitable prediction model is selected, such as a time series analysis model, a machine learning model (such as random forest, support vector machine, etc.) or a deep learning model (such as long short-term memory network, LSTM), etc. The selected state prediction model is trained using a historical data set to optimize the model parameters so that it can accurately predict the logistics state.

[0078] Exemplarily, the third attribute information and the third environmental parameter are input into the trained state prediction model for state prediction to obtain a first prediction result corresponding to the third sub-logistics information. And the fourth attribute information and the fourth environmental parameter are input into the trained state prediction model to obtain a second prediction result corresponding to the fourth sub-logistics information. Then, the evidence theory is used to perform probability fusion on the first prediction result and the second prediction result to obtain a target prediction result, and further, the logistics state corresponding to the maximum probability in the target prediction result is determined as the sub-logistics state corresponding to the target sub-cluster. Thus, the sub-logistics state corresponding to each target sub-cluster in the target logistics cluster is obtained, and further, the sub-logistics states corresponding to each target sub-cluster are combined into the target logistics state corresponding to the target logistics cluster. Thus, by performing fusion analysis on the logistics state according to each sub-logistics information in the target sub-cluster, not only can the accuracy and reliability of the prediction be improved, but also a more scientific basis can be provided for subsequent logistics management and decision-making.

[0079] In some embodiments, the fusing the first prediction result and the second prediction result to obtain the target logistics state corresponding to the target logistics cluster includes: quantifying the third environmental parameter to obtain a first quantization result, and calculating a reliability degree according to the first quantization result to obtain first reliable information corresponding to the third environmental parameter; determining first weight information corresponding to the first prediction result according to the first reliable information; quantifying the fourth environmental parameter to obtain a second quantization result, and calculating a reliability degree according to the second quantization result to obtain second reliable information corresponding to the fourth environmental parameter; determining second weight information corresponding to the second prediction result according to the second reliable information; performing prediction fusion on the first prediction result and the second prediction result according to the first weight information and the second weight information to obtain the target logistics state corresponding to the target logistics cluster; wherein, the first weight information and the second weight information are obtained according to the following formula:

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] Among them, represents the i-th first quantization result corresponding to the j-th third environmental parameter, represents the first reliable information corresponding to the j-th third environmental parameter, m represents the quantity information corresponding to the first quantization result or the second quantization result, and n represents the number of parameter types corresponding to the third environmental parameter or the fourth environmental parameter; represents the i-th second quantization result corresponding to the j-th fourth environmental parameter, represents the second reliable information corresponding to the j-th fourth environmental parameter; represents the first weight information, represents the second weight information, and ln represents the natural logarithm.

[0085] Exemplarily, the parameter data of each type of parameter in the third environmental parameter is normalized to obtain the first quantization result. For example, the third environmental parameter includes multiple type parameters such as temperature, humidity, and air pressure. Therefore, the parameter data when the type parameter in the third environmental parameter is temperature, the parameter data when the type parameter is humidity, and the parameter data when the type parameter is air pressure are normalized respectively, so that multiple temperature parameters in the third environmental parameter are on the same scale, multiple humidity parameters are on the same scale, and multiple air pressure parameters are on the same scale, providing support for subsequent measurement of the stability of the parameter data corresponding to the type parameters in the third environmental parameter.

[0086] Exemplarily, according to the following formula, the reliability degree is calculated using the first quantization result to obtain the first reliable information corresponding to the third environmental parameter.

[0087] ;

[0088] Among them, represents the i-th first quantization result corresponding to the j-th third environmental parameter, represents the first reliable information corresponding to the j-th third environmental parameter, m represents the quantity information corresponding to the first quantization result, n represents the number of parameter types corresponding to the third environmental parameter, and ln represents the natural logarithm.

[0089] Exemplarily, the above formula can more accurately reflect the information amount degree of each environmental parameter by comprehensively considering multiple quantization results of the third environmental parameter through ln representing the natural logarithm. The greater the information amount, the greater the reliability degree of the corresponding prediction result.

[0090] Exemplarily, the parameter data of each type parameter in the fourth environmental parameter is normalized to obtain a second quantization result. For example, the fourth environmental parameter includes multiple type parameters such as temperature, humidity, and air pressure. Therefore, the parameter data when the type parameter in the fourth environmental parameter is temperature, the parameter data when the type parameter is humidity, and the parameter data when the type parameter is air pressure are normalized respectively, so that multiple temperature parameters in the fourth environmental parameter are on the same scale, multiple humidity parameters are on the same scale, and multiple air pressure parameters are on the same scale, providing support for subsequently measuring the stability of the parameter data corresponding to the type parameters in the fourth environmental parameter.

[0091] Exemplarily, according to the following formula, the reliability degree is calculated using the second quantization result to obtain the second reliable information corresponding to the fourth environmental parameter.

[0092] ;

[0093] where m represents the quantity information corresponding to the second quantization result, and n represents the number of parameter types corresponding to the fourth environmental parameter; represents the i-th second quantization result corresponding to the j-th fourth environmental parameter, represents the second reliable information corresponding to the j-th fourth environmental parameter; ln represents the natural logarithm.

[0094] Exemplarily, the above formula can more accurately reflect the information content degree of each environmental parameter by comprehensively considering multiple quantization results of the fourth environmental parameter through ln representing the natural logarithm. The greater the information content, the greater the reliability degree of the corresponding prediction result.

[0095] Exemplarily, after obtaining the first reliable information and the second reliable information, the first weight information corresponding to the first prediction result and the second weight information corresponding to the second prediction result are obtained according to the following formula.

[0096] ;

[0097] ;

[0098] where, represents the first reliable information corresponding to the j-th third environmental parameter, and n represents the number of parameter types corresponding to the third environmental parameter or the fourth environmental parameter; represents the second reliable information corresponding to the j-th fourth environmental parameter; represents the first weight information, represents the second weight information.

[0099] Exemplarily, a weighted probability fusion is performed on the first prediction result and the second prediction result according to the first weight information and the second weight information to obtain a target prediction result, and then the logistics state corresponding to the maximum probability in the target prediction result is determined as the sub-logistics state corresponding to the target sub-cluster. Thus, the sub-logistics state corresponding to each target sub-cluster in the target logistics cluster is obtained, and then the sub-logistics states corresponding to each target sub-cluster are combined into the target logistics state corresponding to the target logistics cluster. Thus, by performing fusion analysis on the logistics state according to each sub-logistics information in the target sub-cluster, not only can the accuracy and reliability of the prediction be improved, but also a more scientific basis can be provided for subsequent logistics management and decision-making.

[0100] Step S107, adjust the logistics strategy of the target logistics vehicle according to the target logistics state to obtain the target logistics strategy corresponding to the target logistics vehicle.

[0101] Exemplarily, set the abnormal state as there being damage or destruction. Obtain the sub-logistics state corresponding to each target sub-cluster in the target logistics cluster from the target logistics state. When the sub-logistics state is the abnormal state, obtain the sub-logistics information included in the target sub-cluster, and then obtain the target logistics strategy corresponding to the target logistics vehicle from the preset strategy.

[0102] For example, the target logistics strategy is to increase air circulation in the area specified by the target space information to reduce temperature and humidity and prevent further damage. Or if weather conditions permit, open the tarpaulin of the vehicle to increase ventilation while preventing excessive sunlight from causing a temperature rise. The target logistics strategy can also be that if the temperature and humidity conditions of the current path are not conducive to the preservation of goods, it will be recommended to adjust the transportation path and select a more suitable transportation environment, etc.

[0103] In some embodiments, after obtaining the target logistics strategy corresponding to the target logistics vehicle, the method further includes: obtaining the corresponding first internal environment parameter measured by the target sensor for the internal environment information of the target logistics vehicle after applying the target logistics strategy; adjusting the first internal environment parameter according to the target space information to obtain the second internal environment parameter corresponding to the second logistics information; predicting the state of the target logistics cluster according to the second internal environment parameter to obtain the latest logistics state corresponding to the target logistics cluster.

[0104] Exemplarily, after applying the target logistics strategy for a period of time, reuse the target sensor to measure the parameter of the internal environment information of the target logistics vehicle to obtain the first internal environment parameter corresponding to the target logistics vehicle, and then continue to adjust the first internal environment parameter according to the target space information, so as to obtain the second internal environment parameter corresponding to the second logistics information.

[0105] Exemplarily, the status of each target sub-cluster in the target logistics cluster is predicted again according to the second internal environment parameter, so as to obtain the current logistics status corresponding to each target sub-cluster, and then the latest logistics status corresponding to the target logistics cluster is formed according to the current logistics status corresponding to each target sub-cluster.

[0106] In some embodiments, after obtaining the latest logistics status, the method further includes: when the target logistics vehicle arrives at the target location, comparing the latest logistics status with a preset status to obtain a target comparison result; determining the target distribution strategy corresponding to the target logistics cluster from a target mapping table according to the target comparison result; and performing logistics processing on the target logistics cluster according to the target distribution strategy to obtain a logistics processing result corresponding to the target logistics cluster.

[0107] Exemplarily, the preset status is that there is damage or destruction. When the target logistics vehicle arrives at the target location, the latest logistics status is compared with the preset status to obtain a target comparison result. When the target comparison result is equal, that is, when the latest logistics status is that there is damage or destruction, the corresponding sub-cluster is obtained from the target logistics cluster according to the latest logistics status, and then the associated logistics information corresponding to the sub-cluster is obtained.

[0108] Exemplarily, a target distribution strategy corresponding to the associated logistics information is determined by using the target mapping table. The target distribution strategy is a predefined set of strategies, which includes specific operations to be taken in different situations. For example: obtaining the first information of the target user and the second information of the target merchant from the associated logistics information, and then sending a notice to the target user and the target merchant to inform them of the current logistics status and potential problems. This notice not only includes the status information of damage or destruction, but may also put forward the following requirements: for the target user, confirm the receiving status and ask whether they wish to continue receiving the goods or choose to reject them; for the target merchant, consult whether there is an emergency handling plan, such as options for replacing the goods, refunding or re-distributing, etc. After the notice is sent, wait for the feedback from the target merchant and the target user. If the target user and the target merchant decide to continue the distribution, the distribution level corresponding to the associated logistics information is set to the highest, so as to give priority to the distribution. Thus, the corresponding logistics processing result is obtained.

[0109] Specifically, by predicting the logistics status of the second logistics information in the target logistics vehicle, the logistics strategy is adjusted, so as to ensure that the target item corresponding to the second logistics information is fully protected during transportation and improve the transportation quality. Moreover, by predicting the latest logistics status of the second logistics information, the status of the second logistics information can be timely informed to the merchant and the user, which can reduce the waiting time of the user and also reduce the work burden of the delivery staff.

[0110] Please refer toFigure 2 , Figure 2 A smart logistics management system 200 for big data provided by an embodiment of the present application. The smart logistics management system 200 for big data includes a data classification module 201, a data screening module 202, a data acquisition module 203, a parameter adjustment module 204, a data clustering module 205, a status prediction module 206, and a logistics management module 207. Among them, the data classification module 201 is configured to obtain first logistics information corresponding to a target logistics vehicle, and perform data classification on the first logistics information to obtain a target cargo type corresponding to the first logistics information; the data screening module 202 is configured to perform a focus target screening on the first logistics information according to the target cargo type to obtain second logistics information corresponding to the target logistics vehicle; the data acquisition module 203 is configured to obtain target spatial information corresponding to the second logistics information according to target three-dimensional data, and measure the environmental information inside the target logistics vehicle by a target sensor to obtain corresponding initial internal environmental parameters; the parameter adjustment module 204 is configured to adjust the initial internal environmental parameters according to the target spatial information to obtain target internal environmental parameters corresponding to the second logistics information; the data clustering module 205 is configured to perform data clustering on the second logistics information according to the target internal environmental parameters to obtain a target logistics cluster; the status prediction module 206 is configured to perform a status prediction on the target logistics cluster according to the target internal environmental parameters to obtain a target logistics status corresponding to the target logistics cluster; the logistics management module 207 is configured to adjust a logistics strategy for the target logistics vehicle according to the target logistics status to obtain a target logistics strategy corresponding to the target logistics vehicle.

[0111] In some embodiments, during the process of the parameter adjustment module 204 adjusting the initial internal environmental parameters according to the target spatial information to obtain the target internal environmental parameters corresponding to the second logistics information, it performs:

[0112] Obtain the surrounding logistics information corresponding to the second logistics information according to the target spatial information, and obtain the first spatial information corresponding to the surrounding logistics information and the second spatial information corresponding to the second logistics information from a database;

[0113] Perform data superposition on the surrounding logistics information according to the first spatial information to obtain a third spatial information;

[0114] Obtain the current spatial information corresponding to the surrounding logistics information from the target three-dimensional data, calculate the similarity between the current spatial information and the third spatial information, and obtain the first extrusion information corresponding to the surrounding logistics information;

[0115] Calculate the similarity between the second spatial information and the target spatial information to obtain the second extrusion information corresponding to the second logistics information;

[0116] Fuse the first extrusion information and the second extrusion information to determine the target sealing information corresponding to the second logistics information;

[0117] Adjust the initial internal environment parameters according to the target sealing information to obtain the target internal environment parameters corresponding to the second logistics information.

[0118] In some embodiments, when the data clustering module 205 performs data clustering on the second logistics information according to the target internal environment parameters to obtain the target logistics cluster, it executes:

[0119] Perform a similarity calculation on the second logistics information according to the target internal environment parameters to obtain the target association information corresponding to the second logistics information;

[0120] Perform data clustering on the second logistics information according to the target association information to obtain the target logistics cluster.

[0121] In some embodiments, the second logistics information includes at least first sub-logistics information and second sub-logistics information, and the target internal environment parameters include the first environmental parameters corresponding to the first sub-logistics information and the second environmental parameters corresponding to the second sub-logistics information. When the data clustering module 205 performs a similarity calculation on the second logistics information according to the target internal environment parameters to obtain the target association information corresponding to the second logistics information, it executes:

[0122] Obtain the first attribute information corresponding to the first sub-logistics information and the second attribute information corresponding to the second sub-logistics information;

[0123] Determine the first change trend corresponding to the first sub-logistics information according to the first attribute information and the first environmental parameters;

[0124] Determine the second change trend corresponding to the second sub-logistics information according to the second attribute information and the second environmental parameters;

[0125] Calculate the similarity between the first change trend and the second change trend to determine the target association information corresponding to the first sub-logistics information and the second sub-logistics information.

[0126] In some embodiments, during the process of determining the first change trend corresponding to the first sub-logistics information according to the first attribute information and the first environmental parameters and determining the second change trend corresponding to the second sub-logistics information according to the second attribute information and the second environmental parameters, the data clustering module 205 performs the following:

[0127] Use the attribute representation layer of the trend prediction model to perform information representation on the first attribute information to obtain a first representation vector;

[0128] Use the parameter representation layer of the trend prediction model to perform information representation on the first environmental parameters to obtain a second representation vector;

[0129] Use the information combination layer of the trend prediction model to perform information matching on the first attribute information and the first environmental parameters using the first representation vector and the second representation vector to obtain multiple first combination information corresponding to the first sub-logistics information;

[0130] Use the trend prediction layer of the trend prediction model to perform change prediction on multiple pieces of the first combination information respectively to obtain multiple first predicted trends;

[0131] Use the trend fusion layer of the trend prediction model to perform information fusion on multiple first predicted trends to obtain the first change trend corresponding to the first sub-logistics information;

[0132] Use the attribute representation layer to perform information representation on the second attribute information to obtain a third representation vector;

[0133] Use the parameter representation layer to perform information representation on the second environmental parameters to obtain a fourth representation vector;

[0134] Use the information combination layer to perform information matching on the second attribute information and the second environmental parameters using the third representation vector and the fourth representation vector to obtain multiple second combination information corresponding to the second sub-logistics information;

[0135] Use the trend prediction layer to perform change prediction on multiple pieces of the second combination information respectively to obtain multiple second predicted trends;

[0136] Use the trend fusion layer to perform information fusion on multiple second predicted trends to obtain the second change trend corresponding to the second sub-logistics information.

[0137] In some embodiments, the target logistics cluster includes at least one target sub-cluster, and the target sub-cluster includes at least third sub-logistics information and fourth sub-logistics information. When the state prediction module 206 performs state prediction on the target logistics cluster according to the target internal environment parameters to obtain the target logistics state corresponding to the target logistics cluster, it executes:

[0138] Obtain the third attribute information corresponding to the third sub-logistics information and the fourth attribute information corresponding to the fourth sub-logistics information;

[0139] Obtain the third environmental parameter corresponding to the third sub-logistics information and the fourth environmental parameter corresponding to the fourth sub-logistics information from the target internal environment parameters;

[0140] Perform information prediction on the third attribute information and the third environmental parameter according to the state prediction model to obtain the first prediction result corresponding to the third sub-logistics information;

[0141] Perform information prediction on the fourth attribute information and the fourth environmental parameter according to the state prediction model to obtain the second prediction result corresponding to the fourth sub-logistics information;

[0142] Fuse the first prediction result and the second prediction result to obtain the target logistics state corresponding to the target logistics cluster.

[0143] In some embodiments, when the state prediction module 206 fuses the first prediction result and the second prediction result to obtain the target logistics state corresponding to the target logistics cluster, it executes:

[0144] Quantify the third environmental parameter to obtain a first quantization result, and calculate the reliability according to the first quantization result to obtain the first reliable information corresponding to the third environmental parameter;

[0145] Determine the first weight information corresponding to the first prediction result according to the first reliable information;

[0146] Quantify the fourth environmental parameter to obtain a second quantization result, and calculate the reliability according to the second quantization result to obtain the second reliable information corresponding to the fourth environmental parameter;

[0147] Determine the second weight information corresponding to the second prediction result according to the second reliable information;

[0148] Perform prediction fusion on the first prediction result and the second prediction result according to the first weight information and the second weight information to obtain the target logistics state corresponding to the target logistics cluster;

[0149] Among them, the first weight information and the second weight information are obtained according to the following formula:

[0150] ;

[0151] ;

[0152] ;

[0153] ;

[0154] Among them, represents the i-th first quantization result corresponding to the j-th third environmental parameter, represents the first reliable information corresponding to the j-th third environmental parameter, m represents the quantity information corresponding to the first quantization result or the second quantization result, and n represents the number of parameter types corresponding to the third environmental parameter or the fourth environmental parameter; represents the i-th second quantization result corresponding to the j-th fourth environmental parameter, represents the second reliable information corresponding to the j-th fourth environmental parameter; represents the first weight information, represents the second weight information, and ln represents the natural logarithm.

[0155] In some embodiments, during the process after the logistics management module 207 obtains the target logistics strategy corresponding to the target logistics vehicle, it also executes:

[0156] Obtain the corresponding first internal environmental parameter measured by the target sensor for the internal environment information of the target logistics vehicle after applying the target logistics strategy;

[0157] Adjust the first internal environmental parameter according to the target space information to obtain the second internal environmental parameter corresponding to the second logistics information;

[0158] Predict the state of the target logistics cluster according to the second internal environmental parameter to obtain the latest logistics state corresponding to the target logistics cluster.

[0159] In some embodiments, during the process after the logistics management module 207 obtains the latest logistics state, it also executes:

[0160] When the target logistics vehicle arrives at the target location, compare the latest logistics state with the preset state to obtain a target comparison result;

[0161] Determine the target distribution strategy corresponding to the target logistics cluster from the target mapping table according to the target comparison result;

[0162] Perform logistics processing on the target logistics cluster according to the target distribution strategy to obtain the logistics processing result corresponding to the target logistics cluster.

[0163] In some embodiments, the intelligent logistics management system 200 for big data can be applied to a terminal device.

[0164] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described intelligent logistics management system 200 for big data can refer to the corresponding process in the foregoing embodiments of the intelligent logistics management method based on big data, and will not be elaborated herein.

[0165] The embodiment of the present invention further provides a storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of the intelligent logistics management methods provided in the specification of the embodiment of the present invention.

[0166] Among them, the storage medium can be an internal storage unit of the terminal device in the foregoing embodiment, such as the hard disk or memory of the terminal device. The storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk equipped on the terminal device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0167] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations. In the hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component can have multiple functions, or one function or step can be executed by several physical components in cooperation. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or can be implemented as hardware, or can be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. The computer storage medium includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technologies, CD-ROM, digital versatile disk (DVD), or other optical disk storage, magnetic cartridges, tapes, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, a communication medium typically contains computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

[0168] It should be understood that the term "and / or" used in the specification and appended claims of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. It should be noted that in this article, the term "comprises", "comprising", or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or system. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or system comprising the element.

[0169] The serial numbers of the embodiments of the present invention above are only for description and do not represent the advantages or disadvantages of the embodiments. The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A smart logistics management method based on big data, characterized in that: The method comprises: Obtaining first logistics information corresponding to a target logistics vehicle, and performing data classification on the first logistics information to obtain a target cargo type corresponding to the first logistics information; Performing a focus target screening on the first logistics information according to the target cargo type to obtain second logistics information corresponding to the target logistics vehicle; Obtaining target space information corresponding to the second logistics information according to the target three-dimensional data, and measuring the internal environment information of the target logistics vehicle according to the target sensor to obtain corresponding initial internal environment parameters; Obtain the surrounding logistics information corresponding to the second logistics information according to the target spatial information, and obtain the first spatial information corresponding to the surrounding logistics information and the second spatial information corresponding to the second logistics information from the database; superimpose the surrounding logistics information according to the first spatial information to obtain the third spatial information; obtain the current spatial information corresponding to the surrounding logistics information from the target three-dimensional data, calculate the similarity between the current spatial information and the third spatial information, and obtain the first extrusion information corresponding to the surrounding logistics information; calculate the similarity between the second spatial information and the target spatial information, and obtain the second extrusion information corresponding to the second logistics information; fuse the first extrusion information and the second extrusion information to determine the target closed information corresponding to the second logistics information; adjust the initial internal environment parameters according to the target closed information, and obtain the target internal environment parameters corresponding to the second logistics information; Performing data clustering on the second logistics information according to the target internal environment parameters to obtain a target logistics cluster; Predicting the state of the target logistics cluster according to the target internal environment parameters to obtain the target logistics state corresponding to the target logistics cluster; Adjusting the logistics strategy of the target logistics vehicle according to the target logistics state to obtain the target logistics strategy corresponding to the target logistics vehicle; The target logistics cluster includes at least one target sub-cluster, and the target sub-cluster includes at least third sub-logistics information and fourth sub-logistics information. The state prediction of the target logistics cluster according to the target internal environment parameter to obtain the target logistics state corresponding to the target logistics cluster includes: Obtain third attribute information corresponding to the third sub-logistics information and fourth attribute information corresponding to the fourth sub-logistics information; Obtaining a third environmental parameter corresponding to the third sub-logistics information and a fourth environmental parameter corresponding to the fourth sub-logistics information from the target internal environmental parameter; Performing information prediction on the third attribute information and the third environmental parameter according to the state prediction model to obtain a first prediction result corresponding to the third sub-logistics information; Performing information prediction on the fourth attribute information and the fourth environmental parameter according to the state prediction model to obtain a second prediction result corresponding to the fourth sub-logistics information; quantifying the third environmental parameter to obtain a first quantification result, and calculating the reliability according to the first quantification result to obtain first reliable information corresponding to the third environmental parameter; Determine first weight information corresponding to the first prediction result according to the first reliable information; quantifying the fourth environmental parameter to obtain a second quantification result, and calculating the reliability according to the second quantification result to obtain second reliable information corresponding to the fourth environmental parameter; Determine second weight information corresponding to the second prediction result according to the second reliable information; According to the first weight information and the second weight information, the first prediction result and the second prediction result are predicted and fused to obtain the target logistics state corresponding to the target logistics cluster; The first weight information and the second weight information are obtained according to the following formula: ； ； ； ； in, represents the i-th first quantization result corresponding to the j-th third environmental parameter, represents the first reliable information corresponding to the j-th third environmental parameter, m represents the quantity information corresponding to the first quantization result or the second quantization result, and n represents the number of parameter types corresponding to the third environmental parameter or the fourth environmental parameter; represents the i-th second quantization result corresponding to the j-th fourth environmental parameter, represents the second reliable information corresponding to the j-th fourth environmental parameter; represents the first weight information, represents the second weight information, and ln represents the natural logarithm.

2. The method according to claim 1, characterized in that: The step of clustering the second logistics information according to the target internal environment parameter to obtain a target logistics cluster includes: Performing similarity calculation on the second logistics information according to the target internal environment parameter to obtain target association information corresponding to the second logistics information; The second logistics information is clustered according to the target association information to obtain the target logistics cluster.

3. The method according to claim 2, characterized in that The second logistics information at least includes first sub-logistics information and second sub-logistics information, the target internal environment parameter includes a first environment parameter corresponding to the first sub-logistics information and a second environment parameter corresponding to the second sub-logistics information, and the target association information corresponding to the second logistics information is obtained by performing similarity calculation on the second logistics information according to the target internal environment parameter, including: Obtaining first attribute information corresponding to the first sub-logistics information and second attribute information corresponding to the second sub-logistics information; Determine a first change trend corresponding to the first sub-logistics information according to the first attribute information and the first environmental parameter; Determine a second change trend corresponding to the second sub-logistics information according to the second attribute information and the second environmental parameter; The similarity between the first change trend and the second change trend is calculated to determine the target association information corresponding to the first sub-logistics information and the second sub-logistics information.

4. The method according to claim 3, characterized in that The trend prediction model includes an attribute representation layer, a parameter representation layer, an information combination layer, a trend prediction layer, and a trend fusion layer, and determines a first change trend corresponding to the first sub-logistics information according to the first attribute information and the first environmental parameter, and determines a second change trend corresponding to the second sub-logistics information according to the second attribute information and the second environmental parameter, including: Using the attribute representation layer of the trend prediction model to represent the first attribute information, obtaining a first representation vector; Using the parameter representation layer of the trend prediction model to represent the first environmental parameter, obtaining a second representation vector; Using the information combination layer of the trend prediction model to match the first attribute information with the first environmental parameter using the first characterization vector and the second characterization vector, to obtain a plurality of first combination information corresponding to the first sub-logistics information; Using the trend prediction layer of the trend prediction model to respectively predict changes of the plurality of first combination information, to obtain a plurality of first prediction trends; Using the trend fusion layer of the trend prediction model to perform information fusion on a plurality of the first prediction trends, obtaining the first change trend corresponding to the first sub-logistics information; Using the attribute representation layer to represent the second attribute information to obtain a third representation vector; Using the parameter characterization layer to characterize the second environmental parameter to obtain a fourth characterization vector; Using the information combination layer, using the third characterization vector and the fourth characterization vector, to match the second attribute information and the second environmental parameter to obtain a plurality of second combination information corresponding to the second sub-logistics information; Using the trend prediction layer to respectively predict changes of the plurality of second combination information to obtain a plurality of second predicted trends; The trend fusion layer is used to fuse the information of the plurality of the second prediction trends to obtain the second change trend corresponding to the second sub-logistics information.

5. The method according to any one of claims 1 to 4, characterized in that After obtaining the target logistics strategy corresponding to the target logistics vehicle, the method further includes: After obtaining and applying the target logistics strategy, the target sensor measures the environmental information inside the target logistics vehicle to obtain a corresponding first internal environmental parameter; Adjusting the first internal environment parameter according to the target space information to obtain a second internal environment parameter corresponding to the second logistics information; The state of the target logistics cluster is predicted according to the second internal environment parameter to obtain the latest logistics state corresponding to the target logistics cluster.

6. The method according to claim 5, characterized in that After obtaining the latest logistics status, the method further includes: When the target logistics vehicle arrives at the target location, the latest logistics status is compared with the preset status to obtain a target comparison result; Determine the target distribution strategy corresponding to the target logistics cluster from the target mapping table according to the target comparison result; Perform logistics processing on the target logistics cluster according to the target distribution strategy to obtain a logistics processing result corresponding to the target logistics cluster.

7. A smart logistics management system based on big data, characterized in that: include: A data classification module, used to obtain first logistics information corresponding to a target logistics vehicle, and to perform data classification on the first logistics information to obtain a target cargo type corresponding to the first logistics information; A data screening module, used for screening the first logistics information according to the target cargo type to obtain the second logistics information corresponding to the target logistics vehicle; A data acquisition module, used to obtain target space information corresponding to the second logistics information according to the target three-dimensional data, and to measure the internal environment information of the target logistics vehicle according to the target sensor to obtain the corresponding initial internal environment parameters; a parameter adjustment module, configured to obtain peripheral logistics information corresponding to the second logistics information according to the target spatial information, and obtain first spatial information corresponding to the peripheral logistics information and second spatial information corresponding to the second logistics information from a database; Superimposing the surrounding logistics information on the first spatial information to obtain third spatial information; Obtaining current spatial information corresponding to the surrounding logistics information from the target three-dimensional data, calculating the similarity between the current spatial information and the third spatial information, and obtaining first extrusion information corresponding to the surrounding logistics information; Calculating the similarity between the second spatial information and the target spatial information to obtain second extrusion information corresponding to the second logistics information; The first extrusion information and the second extrusion information are integrated to determine the target sealing information corresponding to the second logistics information; the initial internal environment parameters are adjusted according to the target sealing information to obtain the target internal environment parameters corresponding to the second logistics information; A data clustering module, used for performing data clustering on the second logistics information according to the target internal environment parameters to obtain a target logistics cluster; A state prediction module is used to predict the state of the target logistics cluster according to the target internal environmental parameters to obtain the target logistics state corresponding to the target logistics cluster, wherein the target logistics cluster includes at least one target sub-cluster, and the target sub-cluster includes at least third sub-logistics information and fourth sub-logistics information, and the state prediction of the target logistics cluster according to the target internal environmental parameters to obtain the target logistics state corresponding to the target logistics cluster includes: obtaining third attribute information corresponding to the third sub-logistics information and fourth attribute information corresponding to the fourth sub-logistics information; obtaining the third environmental parameters corresponding to the third sub-logistics information and the fourth environmental parameters corresponding to the fourth sub-logistics information from the target internal environmental parameters; predicting the third attribute information and the third environmental parameters according to the state prediction model to obtain a first prediction result corresponding to the third sub-logistics information; predicting the third attribute information and the third environmental parameters according to the state prediction model The fourth attribute information and the fourth environmental parameter are used for information prediction to obtain a second prediction result corresponding to the fourth sub-logistics information; the third environmental parameter is quantified to obtain a first quantification result, and a reliability calculation is performed based on the first quantification result to obtain first reliable information corresponding to the third environmental parameter; first weight information corresponding to the first prediction result is determined based on the first reliable information; the fourth environmental parameter is quantified to obtain a second quantification result, and a reliability calculation is performed based on the second quantification result to obtain second reliable information corresponding to the fourth environmental parameter; second weight information corresponding to the second prediction result is determined based on the second reliable information; the first prediction result and the second prediction result are predicted and fused based on the first weight information and the second weight information to obtain a target logistics state corresponding to the target logistics cluster; wherein the first weight information and the second weight information are obtained based on the following formula: ； ； ； ； in, represents the i-th first quantization result corresponding to the j-th third environmental parameter, represents the first reliable information corresponding to the j-th third environmental parameter, m represents the quantity information corresponding to the first quantization result or the second quantization result, and n represents the number of parameter types corresponding to the third environmental parameter or the fourth environmental parameter; represents the i-th second quantization result corresponding to the j-th fourth environmental parameter, represents the second reliable information corresponding to the j-th fourth environmental parameter; represents the first weight information, represents the second weight information, and ln represents the natural logarithm; The logistics management module is used to adjust the logistics strategy of the target logistics vehicle according to the target logistics status to obtain the target logistics strategy corresponding to the target logistics vehicle.

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