Logistics freight prediction method, system and equipment based on Internet of Things AI technology

Abstract

The invention relates to the technical field of business management information processing, and provides a logistics freight prediction method and system based on an Internet of Things AI technology, a computer readable medium and electronic equipment. The method comprises the steps of obtaining economic development data of a target area through an Internet of Things architecture, performing quantitative evaluation on a target industry according to industrial data to determine industrial parameters, and constructing an industrial index model; performing quantitative evaluation on the target cargo according to the cargo data to determine a cargo attribute vector; on the basis of the static features corresponding to the cargo attribute vectors, in combination with dynamic features in real-time cargo information acquired by an Internet of Things architecture, performing grading processing on the cargos in the target area to generate a cargo grading result; and constructing a freight prediction model based on the industrial index model and the cargo grading result. The industrial and cargo conditions of the target area can be comprehensively reflected in real time, the constructed freight prediction model can comprehensively consider the industrial development trend and the cargo market demand, and the prediction accuracy and reliability are improved.

Description

Technical Field

[0001] The present application relates to the field of business management information processing technology, and more specifically, to a logistics freight forecasting method, system, computer-readable medium and electronic device based on the Internet of Things AI technology. Background Art

[0002] With the booming development of e-commerce and the accelerated advancement of globalization, the logistics industry has ushered in unprecedented development opportunities. The substantial increase in cargo transportation volume has made logistics companies face a more complex transportation environment and higher management requirements. In order to improve transportation efficiency, reduce transportation costs and enhance competitiveness, logistics companies need to use advanced technical means to achieve intelligent management and decision-making.

[0003] In the current logistics and transportation management applications, commercial vehicle freight forecasting mainly relies on traditional factors such as transportation distance, cargo weight and volume. However, this method often ignores the important impact of regional industries and cargo conditions on freight, which leads to the problem that freight forecasting in the commercial vehicle logistics transportation process is not objective and accurate enough. Summary of the invention

[0004] The present application provides a logistics freight forecasting method, system, computer-readable medium and electronic device based on IoT AI technology, which can at least to some extent solve the problem that freight forecasting in the process of commercial vehicle logistics transportation is not objective and accurate enough.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to one aspect of the present application, a logistics freight prediction method based on IoT AI technology is provided, including: obtaining economic development data of a target area through an IoT architecture; quantitatively evaluating a target industry based on industry data in the economic development data, determining industry parameters representing the commercial attributes of the target industry, and constructing an industry index model based on the industry parameters; quantitatively evaluating a target cargo based on cargo data in the economic development data, and determining a cargo attribute vector representing the commercial attributes of the target cargo; based on static features corresponding to the cargo attribute vector and in combination with dynamic features in real-time cargo information obtained by the IoT architecture, grading cargo in the target area to generate a cargo grading result; and constructing a freight prediction model based on the industry index model and the cargo grading result. In the present application, based on the aforementioned scheme, the target industry is quantitatively evaluated according to the industrial data in the economic development data to determine the industrial parameters representing the commercial attributes of the target industry, including: determining the regional factor corresponding to the target area according to the population data in the economic development data; determining the distance factor between the enterprises in the target area according to the location information of each enterprise in the economic development data; determining the competition factor of the target area according to the scale data of each enterprise in the economic development data; and using the regional factor, the distance factor and the competition factor as the industrial parameters.

[0007] In the present application, based on the aforementioned solution, the industry index model is constructed based on the industry parameters, including: extracting features from the industry parameters according to the spatial correlation between the industry parameters to construct the industry index model.

[0008] In the present application, based on the aforementioned scheme, the target goods are quantitatively evaluated according to the goods data in the economic development data to determine the goods attribute vector representing the commercial attributes of the target goods, including: extracting feature information according to the goods data in the economic development data; quantitatively evaluating the feature information to determine the goods attribute vector representing the commercial attributes of the target goods.

[0009] In the present application, based on the aforementioned scheme, the static features corresponding to the cargo attribute vectors are combined with the dynamic features in the real-time cargo information obtained by the Internet of Things architecture to grade the cargo in the target area and generate cargo grading results, including: reducing the dimension of the cargo attribute vectors to generate static features; generating dynamic features based on the real-time cargo information obtained by the Internet of Things architecture; splicing the static features and the dynamic features, classifying the splicing results, and generating cargo grading results.

[0010] In the present application, based on the aforementioned scheme, a freight prediction model is constructed based on the industry index model and the cargo grading results, including: linearly processing the industry index model and the cargo grading results to generate linear results; performing deep cross network-based splicing processing on the industry index model and the cargo grading results to generate nonlinear results; and generating a commercial vehicle freight prediction model based on the linear results and the nonlinear results.

[0011] In the present application, based on the aforementioned scheme, after constructing the freight prediction model based on the industry index model and the cargo classification results, it also includes: displaying the commercial vehicle freight output by the commercial vehicle freight prediction model on the terminal interface.

[0012] According to one aspect of the present application, a logistics freight forecasting system based on the Internet of Things AI technology is provided, comprising: An acquisition unit, used for acquiring economic development data of a target area through an Internet of Things architecture; An industry unit, used to quantitatively evaluate the target industry according to the industry data in the economic development data, determine industry parameters representing the commercial attributes of the target industry, and construct an industry index model based on the industry parameters; A cargo unit, performing a quantitative evaluation on a target cargo according to the cargo data in the economic development data, and determining a cargo attribute vector representing a commercial attribute of the target cargo; A grading unit, configured to grade the goods in the target area based on the static features corresponding to the goods attribute vector and in combination with the dynamic features in the real-time goods information acquired by the Internet of Things architecture, and generate a goods grading result; A prediction unit is used to construct a freight prediction model based on the industry index model and the cargo classification result.

[0013] In the present application, based on the aforementioned scheme, the target industry is quantitatively evaluated according to the industrial data in the economic development data to determine the industrial parameters representing the commercial attributes of the target industry, including: determining the regional factor corresponding to the target area according to the population data in the economic development data; determining the distance factor between the enterprises in the target area according to the location information of each enterprise in the economic development data; determining the competition factor of the target area according to the scale data of each enterprise in the economic development data; and using the regional factor, the distance factor and the competition factor as the industrial parameters.

[0014] In the present application, based on the aforementioned solution, the industry index model is constructed based on the industry parameters, including: extracting features from the industry parameters according to the spatial correlation between the industry parameters to construct the industry index model.

[0015] In the present application, based on the aforementioned scheme, the target goods are quantitatively evaluated according to the goods data in the economic development data to determine the goods attribute vector representing the commercial attributes of the target goods, including: extracting feature information according to the goods data in the economic development data; quantitatively evaluating the feature information to determine the goods attribute vector representing the commercial attributes of the target goods.

[0016] In the present application, based on the aforementioned scheme, the static features corresponding to the cargo attribute vectors are combined with the dynamic features in the real-time cargo information obtained by the Internet of Things architecture to grade the cargo in the target area and generate cargo grading results, including: reducing the dimension of the cargo attribute vectors to generate static features; generating dynamic features based on the real-time cargo information obtained by the Internet of Things architecture; splicing the static features and the dynamic features, classifying the splicing results, and generating cargo grading results.

[0017] In the present application, based on the aforementioned scheme, a freight prediction model is constructed based on the industry index model and the cargo grading results, including: linearly processing the industry index model and the cargo grading results to generate linear results; performing deep cross network-based splicing processing on the industry index model and the cargo grading results to generate nonlinear results; and generating a commercial vehicle freight prediction model based on the linear results and the nonlinear results.

[0018] In the present application, based on the aforementioned scheme, after constructing the freight prediction model based on the industry index model and the cargo classification results, it also includes: displaying the commercial vehicle freight output by the commercial vehicle freight prediction model on the terminal interface.

[0019] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the logistics freight prediction method based on the Internet of Things AI technology as described in the above embodiment is implemented.

[0020] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the logistics freight prediction method based on the Internet of Things AI technology as described in the above embodiments.

[0021] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the logistics freight prediction method based on the Internet of Things AI technology provided in the above various optional implementations.

[0022] In the above process, the economic development data of the target area is obtained through the Internet of Things architecture, and the target industry is quantitatively evaluated according to the industrial data in the economic development data, and the industrial parameters representing the commercial attributes of the target industry are determined. Based on the industrial parameters, an industrial index model is constructed; the target goods are quantitatively evaluated according to the cargo data in the economic development data, and the cargo attribute vector representing the commercial attributes of the target goods is determined; based on the static characteristics corresponding to the cargo attribute vector and the dynamic characteristics in the real-time cargo information obtained by the Internet of Things architecture, the cargo in the target area is graded and a cargo grading result is generated; based on the industrial index model and the cargo grading result, a commercial vehicle freight forecasting model is constructed. By integrating economic development data through the Internet of Things technology and classifying and quantitatively evaluating it, the industrial and cargo conditions of the target area can be reflected in real time and comprehensively, providing an accurate data basis for freight forecasting. Combined with the industrial index model and the cargo grading results, the freight forecasting model constructed can comprehensively consider the industrial development trend and cargo market demand, and improve the accuracy and reliability of the forecast.

[0023] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 The flowchart of a logistics freight rate prediction method based on the Internet of Things AI technology in one embodiment of the present application is schematically shown.

[0026] Figure 2 The flowchart for generating cargo classification results in one embodiment of the present application is schematically shown.

[0027] Figure 3 A schematic diagram of a logistics freight forecasting system based on IoT AI technology in one embodiment of the present application is schematically shown.

[0028] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0030] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, systems, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0032] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0033] The implementation details of the technical solution of this application are described in detail below: Figure 1 A flow chart of a method for predicting logistics freight rates based on IoT AI technology according to an embodiment of the present application is shown. Figure 1 As shown, the logistics freight prediction method based on the Internet of Things AI technology includes at least steps S110 to S150, which are described in detail as follows: In step S110, the economic development data of the target area is obtained through the Internet of Things architecture.

[0034] In practical applications, the IoT architecture includes perception layer, network layer, platform layer and application layer.

[0035] The perception layer is the bottom layer of the IoT architecture, like "touch" and "senses". It is composed of various sensors, such as temperature and humidity sensors. It interacts with the physical world to collect environmental or physical data, and converts non-electrical signals into digital signals. Its data collection characteristics are crucial to the effectiveness of the IoT system. Specifically, in this embodiment, in the IoT data acquisition module corresponding to the perception layer, IoT devices such as Global Positioning System (GPS) locators and sensors are deployed on commercial vehicles to collect information such as vehicle location, speed, and status in real time. The GPS / Beidou positioning terminal with anti-disassembly function is bound to the transport vehicle one by one to collect vehicle operation data in real time. At the same time, the association and matching of the positioning terminal identifier and the frame number of the transport vehicle is realized through the IoT platform.

[0036] The network layer is the "nerve center" that connects the upper and lower layers. Data is transmitted by building a wireless communication device network, such as setting up routers and gateways. To ensure the accuracy, integrity and timeliness of data transmission, a variety of communication technologies are used to meet the needs of different scenarios. Specifically, in this embodiment, the data transmission and processing module corresponding to the network layer transmits the collected data to the cloud server through a mobile communication network or satellite communication.

[0037] The platform layer is the "brain" of the Internet of Things. The data processing center is at this layer and is responsible for data storage, processing (cleaning, denoising, etc.), conversion and distribution. It may also have equipment and security management functions to ensure system stability and data security. Specifically, the data reported by the positioning terminal is cleaned through the Internet of Things platform to ensure data accuracy, and the physical model is converted to pre-process the original data of the device into business attribute indicators. After that, map matching is performed, and the longitude and latitude reported by the positioning terminal are used to draw the path to determine the actual driving path of the vehicle.

[0038] The application layer is the top layer of the IoT architecture, where users interact and value is reflected. Various applications are developed based on platform layer data, such as smart homes and related applications in the industrial field, to provide intelligent solutions for life and industry. In this embodiment, in the mileage calculation module corresponding to the application layer, a map matching algorithm or a path planning algorithm is used to build a freight calculation model based on multi-dimensional factors to calculate the precise mileage within the vehicle cycle based on real-time location information, input the current transportation parameters into the freight calculation model, and the freight calculation model outputs the freight in real time; at the same time, the prediction model is trained using historical data to output future freight predictions. Then, based on the prediction results, strategic suggestions such as logistics path optimization and cost savings are proposed.

[0039] This embodiment builds an Internet of Things architecture in the target area, including deploying sensors, building a wireless communication network, and data processing center and other facilities. Specifically, when deploying sensors, various sensor devices are configured and managed to ensure that they can accurately collect environmental or physical data in the target area. When building a wireless communication device network, wireless communication devices such as routers and gateways are set up to ensure that the data collected by the sensors can be efficiently and stably transmitted to the data processing center. The data processing center processes and analyzes the received data and is responsible for the storage, processing, conversion and distribution of the data, so as to ultimately support the needs of various Internet of Things application layers.

[0040] Specifically, in this embodiment, the target area may include a production and distribution area corresponding to a certain product, or a production and business coverage area of ​​a certain type of enterprise, etc.

[0041] Among them, the sensor network is responsible for collecting physical quantities related to various economic data, such as traffic flow, cargo transportation volume, storage facility usage, etc.; wireless communication equipment is responsible for transmitting this data to the data processing center in real time; after the data processing center receives the data from the sensor network, it will go through a preliminary processing process and an integration process. Among them, the processing process includes steps such as data cleaning, data format unification, and data standardization. The integration process is to classify and organize the data based on the data source and data type, so as to integrate data from different sources and different formats into a unified data set for subsequent analysis.

[0042] From the integrated data set, data related to economic development are extracted as economic development data, which may specifically include indicator data such as industrial added value, fixed asset investment, and total retail sales of consumer goods in the target area. It may also include data indirectly related to economic development and directly related to the transportation industry, such as road freight volume and cargo turnover.

[0043] Through the implementation of this step, the economic development data of the target area can be effectively collected. These data not only reflect the economic status and development trend of the target area, but also provide rich input features for the commercial vehicle freight forecasting model, which helps to improve the accuracy and reliability of freight forecasting.

[0044] In step S120, a quantitative assessment is performed on the target industry based on the industry data in the economic development data, industry parameters representing the commercial attributes of the target industry are determined, and an industry index model is constructed based on the industry parameters.

[0045] In this embodiment, based on the industrial data in the economic development data, the target industry is deeply analyzed through a quantitative evaluation algorithm to determine the industrial parameters that can accurately represent the commercial attributes of the target industry. Subsequently, based on these industrial parameters, an industrial index model is constructed that can dynamically reflect the development status and trends of the target industry. This industrial index model can not only be updated in real time to adapt to market changes, but also provide strong data support and decision-making basis for subsequent commercial vehicle freight forecasts.

[0046] In one embodiment of the present application, a quantitative assessment is performed on the target industry according to the industry data in the economic development data to determine the industry parameters representing the commercial attributes of the target industry, including: Determining a regional factor corresponding to the target area according to the population data in the economic development data; Determining distance factors between enterprises in the target area according to location information of each enterprise in the economic development data; Determining the competition factor of the target area according to the scale data of each enterprise in the economic development data; The regional factor, the distance factor and the competition factor are used as the industry parameters.

[0047] In one embodiment of the present application, in the process of determining the regional factor, first, according to the population data in the economic development data, the first number of employees corresponding to the target industry in the target area is obtained, and the total number of employees in the target industry nationwide is obtained; then, the quotient between the first number of employees and the total number of employees is used as the first parameter, the quotient between the total number of employees in the target area and the total number of employees nationwide is used as the second parameter, and the quotient between the first parameter and the second parameter is used as the regional factor. In this embodiment, the regional factor is used to measure the relationship between the target industry and the population of the target area.

[0048] In one embodiment of the present application, in the process of determining the distance factor, firstly, according to the location information of each enterprise in the economic development data, the first distance between the enterprise in the target industry and other similar enterprises in the target area is obtained, and the average distance between enterprises in the target industry nationwide is determined, and the quotient between the first distance and the average distance is used as the third parameter, and then the mean of the third parameter of all enterprises in the target industry is determined, and the reciprocal of the mean is used as the distance factor. In this embodiment, the distance factor is used to characterize the distance attribute between each enterprise in the same industry in the target area.

[0049] In one embodiment of the present application, in the process of determining the competition factor, the Herfindahl index is determined based on the scale data of each enterprise in the economic development data. The calculation of the Herfindahl index is an existing public technology and is not described in detail in this application. The logarithm of the Herfindahl index is then taken as the competition factor.

[0050] Finally, the regional factor, the distance factor and the competition factor are used as the industry parameters. The above process is suitable for comparing the industrial agglomeration potential of regions of different scales and economic levels, such as the emerging industrial clusters in the central and western regions. It can more comprehensively evaluate the regional industrial attributes and capture their spatiotemporal dynamic characteristics.

[0051] After obtaining the industry parameters, the features in the industry parameters are extracted according to the spatial correlation between the industry parameters to construct an industry index model. Specifically, the spatial correlation between industry parameters includes the degree of mutual influence and dependence between different industries in space. Specifically, the degree of association between industry parameters (that is, spatial correlation) can be determined by calculating the distance between the corresponding vectors of industry parameters. This spatial correlation is derived from a variety of factors, such as resource sharing, market demand, and technology spillover effects.

[0052] Features that can reflect spatial correlation are extracted from industry parameters, including but not limited to: transaction volume between industries, which reflects the economic connection and degree of dependence between industries; technological similarity, which measures the technological connection between industries through indicators such as technology citations and R&D investment; geographical proximity, which is related to factors such as resource sharing and personnel flow; supply chain relationships, which are the upstream and downstream relationships of industries in the supply chain, and the impact of such relationships on the industry index.

[0053] After extracting the features, these features are used to build an industry index model to predict or explain the changes in the industry index. Specifically, multiple linear regression or logistic regression methods can be used to take industry parameters as independent variables and industry index as dependent variables to build an industry index model through regression analysis.

[0054] The above process uses industrial data in economic development data to conduct quantitative assessment of the target industry, and constructs an industrial index model by extracting the spatial correlation characteristics between industrial parameters, thereby enhancing the ability of the industrial index model to capture industrial development trends and market dynamics.

[0055] In step S130, a quantitative evaluation is performed on the target goods according to the goods data in the economic development data to determine a goods attribute vector representing the commercial attributes of the target goods.

[0056] In this embodiment, a quantitative assessment is performed based on the cargo data in the economic development data, and an in-depth analysis is performed on the target cargo to determine a cargo attribute vector that can fully reflect its commercial attributes. This vector will cover multiple key indicators of the cargo to more accurately describe the performance and potential value of the target cargo in the market.

[0057] In one embodiment of the present application, a quantitative evaluation is performed on the target goods according to the goods data in the economic development data to determine a goods attribute vector representing the commercial attributes of the target goods, including: Extracting characteristic information based on the goods data in the economic development data; The characteristic information is quantitatively evaluated to determine a cargo attribute vector representing the commercial attribute of the target cargo.

[0058] In one embodiment of the present application, the cargo data includes data such as the volume, weight, and material of the cargo. The attributes of the cargo include physical attributes (such as volume, weight, density, etc.), chemical attributes (such as perishability, dangerous goods level, etc.), and energy consumption attributes during transportation (such as energy consumption coefficient), etc. Cargo attribute vectorization is the process of converting various attributes of the cargo into quantifiable mathematical indicators or feature vectors. The cargo attribute vector obtained through this process may include feature information such as volume density ratio, weight distribution entropy, perishability index, dangerous goods level, and energy consumption coefficient.

[0059] Among them, the volume density ratio is the ratio of the volume of the cargo to its weight. It reflects the compactness or density of the cargo. For transportation, high-density cargo means higher transportation efficiency because more weight can be transported in a limited space.

[0060] Weight distribution entropy is an indicator that measures the uniformity of cargo weight distribution. If the weight distribution of cargo is uneven, it may cause instability or safety hazards during transportation. By quantifying this unevenness, weight distribution entropy provides an important reference for transportation planning.

[0061] The perishability index is an indicator that measures the ease with which goods are likely to deteriorate or be damaged during transportation. For perishable goods, such as food and medicine, the perishability index is directly related to the preservation and safety issues during transportation.

[0062] Dangerous goods are classified into different levels according to their dangerousness. The classification of dangerous goods helps to take corresponding safety measures during transportation to ensure the safety of people and goods.

[0063] Energy consumption coefficient is an indicator to measure the energy consumption efficiency in the process of cargo transportation. It reflects the energy consumption required to transport a unit weight or volume of cargo. For the pursuit of green and energy-saving transportation methods, energy consumption coefficient is an important consideration.

[0064] After determining the characteristic information, a quantitative evaluation is performed based on the characteristic information to determine the characteristic vector corresponding to each vector index of the characteristic information as the cargo attribute vector representing the commercial attributes of the target cargo. By quantifying the characteristic information, a cargo attribute vector is constructed accordingly. This vector is a multi-dimensional data set that comprehensively and accurately depicts the commercial attributes of the target cargo. Such a quantitative evaluation provides a solid data foundation and scientific basis for subsequent decision-making, market strategy planning, and commercial vehicle freight rate forecasting.

[0065] In step S140, based on the static features corresponding to the cargo attribute vector and in combination with the dynamic features in the real-time cargo information acquired by the IoT architecture, the cargo in the target area is graded to generate a cargo classification result.

[0066] In this embodiment, a comprehensive analysis and evaluation is performed based on the static features in the cargo attribute vector, such as size, weight, and material, combined with the dynamic features in the cargo information captured in real time by the IoT architecture, such as the current inventory level (temperature, humidity), real-time transportation status (location), etc. Subsequently, these goods are carefully graded using the preset grading rules to generate a clear cargo grading result. This result not only reflects the comprehensive performance of the goods in the current market environment, but also provides strong data support for subsequent decisions such as inventory management, logistics scheduling, and commercial vehicle freight pricing.

[0067] like Figure 2 As shown, in one embodiment of the present application, based on the static features corresponding to the cargo attribute vector and in combination with the dynamic features in the real-time cargo information acquired by the IoT architecture, the cargo in the target area is graded to generate a cargo classification result, including: S210, reducing the dimension of the cargo attribute vector to generate static features; S220, generating dynamic features based on the real-time cargo information obtained by the Internet of Things architecture; S230, performing splicing processing on the static features and the dynamic features, classifying the splicing results, and generating a cargo classification result.

[0068] In practical applications, under the Internet of Things architecture, the grading of goods needs to comprehensively consider their static characteristics and dynamic characteristics. In order to accurately grade the goods, this application classifies them by combining static characteristics and dynamic characteristics.

[0069] In one embodiment of the present application, the process of reducing the dimension of the cargo attribute vector and generating static features includes the following steps: determining the covariance matrix corresponding to the cargo attribute vector; performing eigenvalue decomposition on the covariance matrix to generate multiple eigenvalues ​​and their corresponding eigenvectors; selecting the previousk The eigenvalue vectors corresponding to the largest eigenvalues ​​constitute a projection matrix; and static features are generated based on the product between the cargo attribute vector and the projection matrix.

[0070] Furthermore, for highly nonlinear data, the first correlation parameter between the elements in the cargo attribute vector is calculated for: in, , as well as Represents the element value in the cargo attribute vector, m represents the number of elements in the cargo attribute vector, represents the vector factor obtained by training based on historical data, Represents an exponential function with the natural constant e as the base.

[0071] At the same time, in the low-dimensional space corresponding to the cargo attribute vector, the second correlation parameter between the elements in the cargo attribute vector is calculated. for: in, , as well as express , as well as The corresponding low-dimensional coordinates respectively.

[0072] Generate an objective function based on the first correlation parameter and the second correlation parameter for: Here, log represents logarithmic operation.

[0073] The static characteristics in the cargo attribute vector are determined by minimizing the objective function. Specifically, the objective function is calculated, and when the objective function value is the minimum, the corresponding and , as a static feature.

[0074] In one embodiment of the present application, the process of generating dynamic features based on the real-time cargo information obtained by the Internet of Things architecture includes the following steps: obtaining real-time cargo information based on the Internet of Things architecture, and representing the real-time cargo information as time series data, wherein the real-time cargo information includes temperature, humidity and location; extracting statistical features from the time series data, such as mean, variance, maximum value, minimum value, change trend, etc.; using time series to extract trend information from the statistical features as the dynamic features.

[0075] In one embodiment of the present application, the static features and the dynamic features are spliced ​​together, the splicing results are classified, and the process of generating a cargo grading result includes the following steps: splicing the static features and the dynamic features to form a complete feature vector, training the spliced ​​feature vector using a machine learning classification algorithm, and generating a cargo grading result.

[0076] The above process determines the commercial attribute vector of the target goods by quantitatively evaluating the goods data in the economic development data, and combines the real-time goods information obtained by the IoT architecture to fully consider the static and dynamic characteristics of the goods. The classification of goods not only improves the efficiency of logistics management, but also provides more refined data support for freight forecasting.

[0077] In step S150, a freight forecasting model is constructed based on the industry index model and the cargo classification result.

[0078] In one embodiment of the present application, based on the already constructed industry index model and cargo classification results, these two aspects of data are further integrated as key input features. Subsequently, a machine learning algorithm is used to conduct in-depth learning and pattern recognition on these input features to build an accurate freight forecasting model. This model can comprehensively consider multiple factors such as industry development trends, cargo market demand and supply conditions, and make efficient and accurate forecasts of commercial vehicle freight, providing strong decision-making support for logistics companies and transporters.

[0079] In one embodiment of the present application, a freight forecasting model is constructed based on the industry index model and the cargo classification result, including: Performing linear processing on the industry index model and the goods classification result to generate a linear result; Performing a splicing process based on a deep cross network on the industry index model and the goods classification result to generate a nonlinear result; A commercial vehicle freight rate prediction model is generated based on the linear result and the nonlinear result.

[0080] In one embodiment of the present application, the industry index model and cargo classification results are linearly processed, that is, the original data is converted into linear results using a linear function (such as a weighted sum) to capture the linear relationship in the data, that is, those freight rate changes that can be directly predicted by proportional changes.

[0081] The industry index model and cargo classification results are used as input and spliced ​​through a deep cross network. In the deep cross network, through multiple layers of nonlinear activation functions and cross layers, high-order feature interactions between input data are learned to generate nonlinear results to capture nonlinear relationships in the data, that is, those freight changes that cannot be predicted by simple linear models.

[0082] After that, the linear and nonlinear results are fused, that is, the linear and nonlinear results are written into the same training set, and the historical freight data is used as labels to train the fused model through the back propagation algorithm. During the training process, model parameters such as weights or biases can be adjusted to minimize the prediction error. Strategies such as regularization are used to prevent the model from overfitting, and methods such as cross-validation are used to evaluate the performance of the model. The performance of the model is evaluated on the validation set to ensure that the model maintains good predictive ability on unseen data. If the model performance is poor, it can be optimized by adjusting the model structure, increasing the amount of data, or improving feature engineering.

[0083] Deploy the trained commercial vehicle freight rate prediction model to actual application scenarios to predict freight rates in real time. At the same time, the performance of the model in actual operation can be monitored and regularly updated and maintained as needed.

[0084] Through the above steps, a commercial vehicle freight forecasting model combining linear and nonlinear characteristics can be constructed. This model can more accurately capture the law of freight changes, improve the prediction accuracy and generalization ability of the model, and provide more accurate freight forecasting services for the commercial vehicle transportation industry.

[0085] In one embodiment of the present application, after the commercial vehicle freight prediction model has been successfully run and can output the predicted freight results, check the data format of the prediction results to ensure that it can be correctly parsed and displayed by the terminal interface. If the model outputs raw data, it may need to be properly formatted, such as converted to a currency format. Bind the freight data output by the prediction model to the display elements (such as text boxes, labels, etc.) on the terminal interface, and deploy the designed interface and prediction model to the target device. Through the above steps, the freight information output by the commercial vehicle freight prediction model can be displayed on the terminal interface, providing users with an intuitive and easy-to-use freight query service.

[0086] In the above process, the economic development data of the target area is obtained through the Internet of Things architecture, and the target industry is quantitatively evaluated according to the industrial data in the economic development data, and the industrial parameters representing the commercial attributes of the target industry are determined. Based on the industrial parameters, an industrial index model is constructed; the target goods are quantitatively evaluated according to the goods data in the economic development data, and the goods attribute vector representing the commercial attributes of the target goods is determined; based on the static characteristics corresponding to the goods attribute vector and the dynamic characteristics in the real-time goods information obtained by the Internet of Things architecture, the goods in the target area are graded and the goods grading results are generated; based on the industrial index model and the goods grading results, a freight forecasting model is constructed. By integrating and processing economic development data through the Internet of Things technology, this method can reflect the industrial and cargo conditions of the target area in real time and comprehensively, and provide an accurate data basis for freight forecasting. Combined with the industrial index model and the goods grading results, the freight forecasting model constructed can comprehensively consider the industrial development trend and the goods market demand, and improve the accuracy and reliability of the forecast.

[0087] The following introduces the device embodiment of the present application, which can be used to execute the logistics freight prediction method based on the Internet of Things AI technology in the above-mentioned embodiment of the present application. It can be understood that the device can be a computer program (including program code) running in a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present application. For details not disclosed in the device embodiment of the present application, please refer to the embodiment of the logistics freight prediction method based on the Internet of Things AI technology mentioned above in the present application.

[0088] Figure 3 A block diagram of a logistics freight forecasting system based on IoT AI technology according to an embodiment of the present application is shown.

[0089] Reference Figure 3 As shown, according to an embodiment of the present application, a logistics freight forecasting system based on the Internet of Things AI technology includes: An acquisition unit 310 is used to acquire economic development data of a target area through an Internet of Things architecture; An industry unit 320 is used to quantitatively evaluate the target industry according to the industry data in the economic development data, determine industry parameters representing the commercial attributes of the target industry, and construct an industry index model based on the industry parameters; The cargo unit 330 performs quantitative evaluation on the target cargo according to the cargo data in the economic development data, and determines a cargo attribute vector representing the commercial attribute of the target cargo; A grading unit 340 is used to grade the goods in the target area based on the static features corresponding to the goods attribute vector and the dynamic features in the real-time goods information acquired by the Internet of Things architecture, and generate a goods grading result; The prediction unit 350 is used to construct a freight prediction model based on the industry index model and the cargo classification result.

[0090] In the present application, based on the aforementioned scheme, the target industry is quantitatively evaluated according to the industrial data in the economic development data to determine the industrial parameters representing the commercial attributes of the target industry, including: determining the regional factor corresponding to the target area according to the population data in the economic development data; determining the distance factor between the enterprises in the target area according to the location information of each enterprise in the economic development data; determining the competition factor of the target area according to the scale data of each enterprise in the economic development data; and using the regional factor, the distance factor and the competition factor as the industrial parameters.

[0091] In the present application, based on the aforementioned solution, the industry index model is constructed based on the industry parameters, including: extracting features from the industry parameters according to the spatial correlation between the industry parameters to construct the industry index model.

[0092] In the present application, based on the aforementioned scheme, the target goods are quantitatively evaluated according to the goods data in the economic development data to determine the goods attribute vector representing the commercial attributes of the target goods, including: extracting feature information according to the goods data in the economic development data; quantitatively evaluating the feature information to determine the goods attribute vector representing the commercial attributes of the target goods.

[0093] In the present application, based on the aforementioned scheme, the static features corresponding to the cargo attribute vectors are combined with the dynamic features in the real-time cargo information obtained by the Internet of Things architecture to grade the cargo in the target area and generate cargo grading results, including: reducing the dimension of the cargo attribute vectors to generate static features; generating dynamic features based on the real-time cargo information obtained by the Internet of Things architecture; splicing the static features and the dynamic features, classifying the splicing results, and generating cargo grading results.

[0094] In the present application, based on the aforementioned scheme, a freight prediction model is constructed based on the industry index model and the cargo grading results, including: linearly processing the industry index model and the cargo grading results to generate linear results; performing deep cross network-based splicing processing on the industry index model and the cargo grading results to generate nonlinear results; and generating a commercial vehicle freight prediction model based on the linear results and the nonlinear results.

[0095] In the present application, based on the aforementioned scheme, after constructing the freight prediction model based on the industry index model and the cargo classification results, it also includes: displaying the commercial vehicle freight output by the commercial vehicle freight prediction model on the terminal interface.

[0096] In the above process, the economic development data of the target area is obtained through the Internet of Things architecture, and the target industry is quantitatively evaluated according to the industrial data in the economic development data, and the industrial parameters representing the commercial attributes of the target industry are determined. Based on the industrial parameters, an industrial index model is constructed; the target goods are quantitatively evaluated according to the goods data in the economic development data, and the goods attribute vector representing the commercial attributes of the target goods is determined; based on the static characteristics corresponding to the goods attribute vector and the dynamic characteristics in the real-time goods information obtained by the Internet of Things architecture, the goods in the target area are graded and the goods grading results are generated; based on the industrial index model and the goods grading results, a freight forecasting model is constructed. By integrating and processing economic development data through the Internet of Things technology, this method can reflect the industrial and cargo conditions of the target area in real time and comprehensively, and provide an accurate data basis for freight forecasting. Combined with the industrial index model and the goods grading results, the freight forecasting model constructed can comprehensively consider the industrial development trend and the goods market demand, and improve the accuracy and reliability of the forecast.

[0097] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0098] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0099] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory 402 or the program loaded from the storage part 408 to the random access memory 403, such as executing the logistics freight prediction method based on the Internet of Things AI technology described in the above embodiment. In the random access memory 403, various programs and data required for system operation are also stored. The central processing unit 401, the read-only memory 402 and the random access memory 403 are connected to each other through the bus 404. The input / output interface 405 is also connected to the bus 404.

[0100] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0101] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit 401, various functions defined in the system of the present application are executed.

[0102] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0103] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0104] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0105] According to one aspect of the present application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations. By integrating economic development data through Internet of Things technology and classifying and quantitatively evaluating it, it is possible to reflect the industrial and cargo conditions in the target area in real time and comprehensively, and provide an accurate data basis for freight forecasting. Combined with the industry index model and the cargo classification results, the constructed freight forecasting model can comprehensively consider the industrial development trend and cargo market demand, and improve the accuracy and reliability of the forecast.

[0106] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or it may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by an electronic device, the electronic device implements the logistics freight forecasting method based on the Internet of Things AI technology described in the above embodiment. By integrating economic development data through the Internet of Things technology and classifying and quantitatively evaluating it, it can reflect the industry and cargo status of the target area in real time and comprehensively, and provide an accurate data basis for freight forecasting. Combined with the industry index model and cargo classification results, the constructed freight forecasting model can comprehensively consider the industry development trend and cargo market demand, and improve the accuracy and reliability of the forecast.

[0107] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0108] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0109] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0110] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A logistics freight forecasting method based on IoT AI technology, characterized in that: include: Obtain economic development data of target areas through IoT architecture; Conducting quantitative evaluation of the target industry according to the industrial data in the economic development data, determining industrial parameters representing the commercial attributes of the target industry, and constructing an industrial index model based on the industrial parameters; Performing a quantitative evaluation on the target goods according to the goods data in the economic development data, and determining a goods attribute vector representing the commercial attributes of the target goods; Based on the static features corresponding to the cargo attribute vector and in combination with the dynamic features in the real-time cargo information acquired by the Internet of Things architecture, the cargo in the target area is graded to generate a cargo classification result; Based on the industry index model and the cargo classification results, a commercial vehicle freight rate prediction model is constructed.

2. The method for predicting logistics freight based on the Internet of Things AI technology according to claim 1 is characterized in that: The quantitative evaluation of the target industry according to the industrial data in the economic development data to determine the industrial parameters representing the commercial attributes of the target industry includes: Determining a regional factor corresponding to the target area according to the population data in the economic development data; Determining distance factors between enterprises in the target area according to location information of each enterprise in the economic development data; Determining the competition factor of the target area according to the scale data of each enterprise in the economic development data; The regional factor, the distance factor and the competition factor are used as the industry parameters.

3. The method for predicting logistics freight based on the Internet of Things AI technology according to claim 2 is characterized in that: The constructing of an industry index model based on the industry parameters includes: According to the spatial correlation between the industrial parameters, the features in the industrial parameters are extracted to construct an industrial index model.

4. The method for predicting logistics freight based on the Internet of Things AI technology according to claim 1 is characterized in that: The step of quantitatively evaluating the target goods according to the goods data in the economic development data to determine a goods attribute vector representing the commercial attributes of the target goods includes: Extracting characteristic information based on the goods data in the economic development data; The characteristic information is quantitatively evaluated to determine a cargo attribute vector representing the commercial attribute of the target cargo.

5. The method for predicting logistics freight based on the Internet of Things AI technology according to claim 4 is characterized in that: The step of classifying the goods in the target area based on the static features corresponding to the goods attribute vector and combining the dynamic features in the real-time goods information acquired by the Internet of Things architecture to generate a goods classification result includes: Performing dimension reduction on the cargo attribute vector to generate static features; Generate dynamic features based on the real-time cargo information obtained by the IoT architecture; The static features and the dynamic features are spliced ​​together, and the splicing results are classified to generate a cargo classification result.

6. The method for predicting logistics freight based on the Internet of Things AI technology according to claim 1 is characterized in that: The constructing of a commercial vehicle freight rate prediction model based on the industry index model and the cargo classification result includes: Performing linear processing on the industry index model and the goods classification result to generate a linear result; Performing a splicing process based on a deep cross network on the industry index model and the goods classification result to generate a nonlinear result; A commercial vehicle freight rate prediction model is generated based on the linear result and the nonlinear result.

7. The method for predicting logistics freight based on the Internet of Things AI technology according to claim 1 is characterized in that: After constructing the freight forecasting model based on the industry index model and the cargo classification result, the method further includes: The commercial vehicle freight rate output by the commercial vehicle freight rate prediction model is displayed on the terminal interface.

8. A logistics freight forecasting system based on IoT AI technology, characterized in that: include: An acquisition unit for acquiring real-time cargo information and economic development data of a target area through an IoT architecture; An industry unit, used to quantitatively evaluate the target industry according to the industry data in the economic development data, determine industry parameters representing the commercial attributes of the target industry, and construct an industry index model based on the industry parameters; A cargo unit, performing a quantitative evaluation on a target cargo according to the cargo data in the economic development data, and determining a cargo attribute vector representing a commercial attribute of the target cargo; A grading unit, configured to grade the goods in the target area based on the static features corresponding to the goods attribute vector and in combination with the dynamic features in the real-time goods information acquired by the Internet of Things architecture, and generate a goods grading result; A prediction unit is used to build a commercial vehicle freight prediction model based on the industry index model and the cargo classification result.

9. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the logistics freight forecasting method based on the Internet of Things AI technology as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the logistics freight prediction method based on the Internet of Things AI technology as described in any one of claims 1 to 7.

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