CN116342011B - Intelligent matching method and system for vehicles and goods in whole vehicle transportation - Google Patents

Intelligent matching method and system for vehicles and goods in whole vehicle transportation Download PDF

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CN116342011B
CN116342011B CN202310580457.3A CN202310580457A CN116342011B CN 116342011 B CN116342011 B CN 116342011B CN 202310580457 A CN202310580457 A CN 202310580457A CN 116342011 B CN116342011 B CN 116342011B
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周志军
柴亚春
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Wanlian Yida Logistics Technology Co ltd
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Abstract

The invention provides a vehicle-cargo intelligent matching method and system for whole vehicle transportation. The intelligent matching method for the vehicles and the cargoes comprises the steps of collecting vehicle information and cargo information in a whole vehicle transportation order, and constructing an intelligent matching model for the vehicles and the cargoes by utilizing the vehicle information and the cargo information; embedding the intelligent matching model of the vehicle and the goods into an intelligent matching system, and inputting the goods information and the vehicle information into the intelligent matching model of the vehicle and the goods to obtain a matching result when receiving a whole vehicle transportation order; and the retraining judgment of the intelligent matching model of the vehicle and the cargo is carried out by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and the model retraining execution operation is carried out according to the retraining judgment result. The system comprises modules corresponding to the method steps.

Description

Intelligent matching method and system for vehicles and goods in whole vehicle transportation
Technical Field
The invention provides an intelligent matching method and system for vehicles and goods in whole vehicle transportation, and belongs to the technical field of intelligent matching of vehicles and goods.
Background
The whole vehicle transportation refers to loading cargoes into a complete transportation vehicle for transportation. Such transportation is commonly used for long distance transportation of large volumes of goods, such as logistics, freight and other industries.
However, during whole car transportation, the trucks may not be fully loaded, or some trucks may be fully loaded but need to transport more cargo. This requires matching of the cargo, i.e. the cargo to be transported and the cargo not fully loaded, so as to fully utilize the loading capacity of the cargo, reduce the empty rate and reduce the transportation cost.
Disclosure of Invention
The invention provides an intelligent matching method and system for vehicles and goods in whole vehicle transportation, which are used for solving the problems of low vehicle utilization rate and low transportation cost in whole vehicle transportation in the prior art, and the adopted technical scheme is as follows:
an intelligent matching method for vehicles and cargoes in whole vehicle transportation, which comprises the following steps:
collecting vehicle information and cargo information in a whole vehicle transportation order, and constructing a vehicle cargo intelligent matching model by utilizing the vehicle information and the cargo information;
embedding the intelligent matching model of the vehicle and the goods into an intelligent matching system, and inputting the goods information and the vehicle information into the intelligent matching model of the vehicle and the goods to obtain a matching result when receiving a whole vehicle transportation order; and the retraining judgment of the intelligent matching model of the vehicle and the cargo is carried out by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and the model retraining execution operation is carried out according to the retraining judgment result.
Further, collecting vehicle information and cargo information in a whole vehicle transportation order, and constructing a vehicle cargo intelligent matching model by using the vehicle information and the cargo information, wherein the vehicle cargo intelligent matching model comprises the following steps:
collecting vehicle information and cargo information in a whole vehicle transportation order, wherein the vehicle information comprises vehicle types, corresponding capacity volumes and corresponding quantity of each vehicle type, and the cargo information comprises cargo types, cargo quantity, cargo weight and the actual occupied capacity volume of cargoes after the whole vehicle is matched;
extracting vehicle characteristics from the vehicle information, and converting the vehicle characteristics into vehicle characteristic vectors;
extracting the cargo characteristics of the cargo information, and converting the cargo characteristics into a cargo characteristic matrix;
establishing a corresponding relation between a vehicle feature vector corresponding to each whole vehicle transportation order and the cargo feature matrix according to the whole vehicle transportation order;
constructing an intelligent matching model of the vehicle and the cargo by utilizing a neural network model structure, and dividing the whole vehicle transportation order into a training set and a testing set;
and training the intelligent matching model of the vehicle and the cargo by taking the corresponding relation among the vehicle feature vectors, the cargo feature matrix and the vehicle feature vectors corresponding to the whole vehicle transportation orders in the training set and the testing set and the cargo feature matrix as input quantity until the training is completed, and obtaining the intelligent matching model of the vehicle and the cargo after the training is completed.
Further, the setting ratio of the training set to the test set is 8.3:1.7.
Further, the retraining judgment of the intelligent matching model of the vehicle and the cargo is performed by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and the model retraining execution operation is performed according to the retraining judgment result, including:
the matching result is sent to a goods distribution terminal, wherein the goods distribution terminal corresponds to a worker for executing goods distribution;
the staff carries out cargo allocation according to the matching result displayed by the cargo allocation terminal to obtain a vehicle assembly result;
the vehicle assembly result is sent to an intelligent matching system through the goods distribution terminal; the vehicle assembly result comprises the cargo loading capacity volume of the truck, the actual occupied capacity volume of the cargo assembled into the truck when the assembly is completed, the vehicle-mounted rated weight and the actual weight of the cargo when the assembly is completed;
and judging the matching accuracy evaluation parameters of the intelligent matching model of the vehicle and the cargo by utilizing the vehicle assembly result, and retraining the intelligent matching model of the vehicle and the cargo when the accuracy evaluation parameters of the intelligent matching model of the vehicle and the cargo are lower than a preset accuracy evaluation parameter threshold.
Further, the process for obtaining the accuracy evaluation parameter includes:
extracting the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled into the freight car when the freight car is assembled;
acquiring a first parameter index by utilizing the cargo loading capacity volume of the truck and the actual occupied capacity volume of the cargo assembled into the truck when the cargo is assembled; the first parameter index is obtained through the following formula:
wherein,,Y 1 the first parameter index is indicated to be the first parameter index,Vrepresenting the cargo capacity volume of a truck;V 0 representing the actual occupied capacity volume of the cargo assembled into the truck upon completion of the assembly;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
extracting the vehicle-mounted rated weight and the actual weight of goods when the assembly is completed, wherein the vehicle-mounted rated weight and the actual weight of goods are contained in the vehicle assembly result;
acquiring a second parameter index by utilizing the vehicle-mounted rated weight and the actual weight of the goods when the assembly is completed; the second parameter index is obtained through the following formula:
wherein,,Y 2 the index of the second parameter is indicated,Wrepresenting the vehicle-mounted rated weight;W 0 representing the actual weight of the cargo when the assembly is completed;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
and acquiring accuracy evaluation parameters by combining the first parameter index and the second parameter index with a comprehensive index model, wherein the comprehensive index model is as follows:
wherein Y is an accuracy evaluation parameter;A 1 andA 2 respectively are provided withIs a preset volume weight parameter and a weight parameter, and,A 1 +A 2 =1。
an intelligent matching system for a vehicle cargo for a complete vehicle transportation, the intelligent matching system comprising:
the modeling module is used for collecting vehicle information and cargo information in the whole vehicle transportation order and constructing a vehicle-cargo intelligent matching model by utilizing the vehicle information and the cargo information;
the matching module is used for embedding the intelligent matching model of the vehicle and the goods into the intelligent matching system, and when receiving a whole vehicle transportation order, the matching module takes goods information and vehicle information as input quantity and inputs the goods information and the vehicle information into the intelligent matching model of the vehicle and the goods to obtain a matching result; and the retraining judgment of the intelligent matching model of the vehicle and the cargo is carried out by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and the model retraining execution operation is carried out according to the retraining judgment result.
Further, the modeling module includes:
the information collection module is used for collecting vehicle information and cargo information in the whole vehicle transportation order, wherein the vehicle information comprises vehicle types, corresponding capacity volumes and corresponding quantity of each vehicle type, and the cargo information comprises cargo types, cargo quantity, cargo weight and the actual occupied capacity volume of cargoes after the whole vehicle is matched;
the feature vector module is used for extracting vehicle features from the vehicle information and converting the vehicle features into vehicle feature vectors;
the feature matrix module is used for extracting the cargo features of the cargo information and converting the cargo features into a cargo feature matrix;
the corresponding relation module is used for establishing a corresponding relation between the vehicle characteristic vector corresponding to each whole vehicle transportation order and the cargo characteristic matrix according to the whole vehicle transportation order;
the sample set acquisition module is used for constructing an intelligent matching model of the vehicle and the cargo by utilizing a neural network model structure and dividing the transportation order of the whole vehicle into a training set and a testing set;
and the model training module is used for training the intelligent matching model of the vehicle and the cargo by taking the corresponding relation among the vehicle feature vectors, the cargo feature matrix and the vehicle feature vectors corresponding to the whole vehicle transportation orders in the training set and the testing set and the cargo feature matrix as input quantity until the training is completed, and obtaining the intelligent matching model of the vehicle and the cargo after the training is completed.
Further, the setting ratio of the training set to the test set is 8.3:1.7.
Further, the matching module includes:
the first result sending module is used for sending the matching result to a goods distributing terminal, wherein the goods distributing terminal corresponds to a worker for executing goods distribution;
the second vehicle assembly result acquisition module is used for carrying out cargo allocation by the staff according to the matching result displayed by the cargo allocation terminal to acquire a vehicle assembly result;
the second result sending module is used for sending the vehicle assembly result to the intelligent matching system through the cargo allocation terminal; the vehicle assembly result comprises the cargo loading capacity volume of the truck, the actual occupied capacity volume of the cargo assembled into the truck when the assembly is completed, the vehicle-mounted rated weight and the actual weight of the cargo when the assembly is completed;
and the retraining judgment module is used for judging the matching accuracy evaluation parameters of the intelligent vehicle-cargo matching model by utilizing the vehicle assembly result, and retraining the intelligent vehicle-cargo matching model when the accuracy evaluation parameters of the intelligent vehicle-cargo matching model are lower than a preset accuracy evaluation parameter threshold.
Further, the retraining judgment module includes:
the volume extraction module is used for extracting the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled to the freight car when the freight car is assembled;
the first parameter index acquisition module is used for acquiring a first parameter index by utilizing the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled into the freight car after assembly is completed; the first parameter index is obtained through the following formula:
wherein,,Y 1 the first parameter index is indicated to be the first parameter index,Vrepresenting the cargo capacity volume of a truck;V 0 representing the actual occupied capacity volume of the cargo assembled into the truck upon completion of the assembly;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
the weight acquisition module is used for extracting the vehicle-mounted rated weight contained in the vehicle assembly result and the actual weight of the goods when the assembly is completed;
the second parameter index acquisition module is used for acquiring a second parameter index by utilizing the vehicle-mounted rated weight and the actual weight of the goods when the assembly is completed; the second parameter index is obtained through the following formula:
wherein,,Y 2 the index of the second parameter is indicated,Wrepresenting the vehicle-mounted rated weight;W 0 representing the actual weight of the cargo when the assembly is completed;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
the comprehensive index obtaining module is used for obtaining accuracy evaluation parameters by combining the first parameter index and the second parameter index with a comprehensive index model, wherein the comprehensive index model is as follows:
wherein Y is an accuracy evaluation parameter;A 1 andA 2 respectively a preset volume weight parameter and a preset weight parameter,A 1 +A 2 =1。
the invention has the beneficial effects that:
according to the intelligent matching method and system for the vehicle and the cargo, disclosed by the invention, the matching accuracy and the matching efficiency of the vehicle and the cargo can be effectively improved by establishing the intelligent vehicle and cargo matching model, meanwhile, the intelligent matching method and system for the vehicle and the cargo are used for carrying out model matching detection accuracy self-checking through a vehicle and cargo matching result by setting an accuracy self-checking model, carrying out model retraining judgment through the self-checking result, and improving the self-checking performance and the model training accuracy of the intelligent vehicle and cargo matching model in a self-checking mode. The problems that the model accuracy needs to be considered to be detected and a model training mechanism needs to be manually restarted, so that the self-correction efficiency of the intelligent car accident model is reduced and the self-correction timeliness is poor are prevented.
Drawings
FIG. 1 is a flow chart of the intelligent matching method for vehicles and cargoes according to the invention;
FIG. 2 is a system block diagram of the intelligent matching system for vehicles and goods according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The embodiment of the invention provides a vehicle-cargo intelligent matching method for whole vehicle transportation, which comprises the following steps as shown in fig. 1:
s1, collecting vehicle information and cargo information in a whole vehicle transportation order, and constructing a vehicle-cargo intelligent matching model by utilizing the vehicle information and the cargo information;
s2, embedding the intelligent matching model of the vehicle and the cargo into an intelligent matching system, and inputting cargo information and vehicle information into the intelligent matching model of the vehicle and the cargo to obtain a matching result when receiving a whole vehicle transportation order; and the retraining judgment of the intelligent matching model of the vehicle and the cargo is carried out by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and the model retraining execution operation is carried out according to the retraining judgment result.
The technical scheme has the effects that: according to the intelligent matching method for the vehicle and the cargo, which are provided by the embodiment, the intelligent matching model is built, so that the matching accuracy and the matching efficiency of the vehicle and the cargo can be effectively improved, meanwhile, the intelligent matching method and the intelligent matching system for the vehicle and the cargo are used for carrying out model matching detection accuracy self-checking through a vehicle and cargo matching result by setting an accuracy self-checking model, carrying out model retraining judgment through the self-checking result, and improving the self-checking performance and the model training accuracy of the intelligent vehicle and cargo matching model in a self-checking mode. The problems that the model accuracy needs to be considered to be detected and a model training mechanism needs to be manually restarted, so that the self-correction efficiency of the intelligent car accident model is reduced and the self-correction timeliness is poor are prevented.
In one embodiment of the present invention, collecting vehicle information and cargo information in a complete vehicle transportation order, and constructing a vehicle cargo intelligent matching model using the vehicle information and the cargo information, includes:
s101, collecting vehicle information and cargo information in a whole vehicle transportation order, wherein the vehicle information comprises vehicle types, corresponding capacity volumes and corresponding quantity of each vehicle type, and the cargo information comprises cargo types, cargo quantity, cargo weight and the actual occupied capacity volume of cargoes after the whole vehicle is matched;
s102, extracting vehicle characteristics of the vehicle information, and converting the vehicle characteristics into vehicle characteristic vectors;
s103, extracting the cargo characteristics of the cargo information, and converting the cargo characteristics into a cargo characteristic matrix;
s104, establishing a corresponding relation between a vehicle feature vector corresponding to each whole vehicle transportation order and the cargo feature matrix according to the whole vehicle transportation order;
s105, constructing an intelligent matching model of the vehicle and the cargo by utilizing a neural network model structure, and dividing the transportation order of the whole vehicle into a training set and a testing set;
and S106, training the intelligent matching model of the vehicle and the cargo by taking the corresponding relation among the vehicle feature vectors, the cargo feature matrix and the vehicle feature vectors corresponding to the whole vehicle transportation orders in the training set and the testing set and the cargo feature matrix as input quantity until the training is completed, and obtaining the intelligent matching model of the vehicle and the cargo after the training is completed.
The setting ratio of the training set to the testing set is 8.3:1.7.
The technical scheme has the effects that: through the mode, the acquisition efficiency of the intelligent matching model of the vehicle and the goods and the model accuracy of the intelligent matching model of the vehicle and the goods can be effectively improved. Meanwhile, the training efficiency of the intelligent matching model of the vehicle and the goods and the accuracy of the model after training can be further improved through the setting that the setting proportion of the training set to the testing set is 8.3:1.7.
According to one embodiment of the invention, the retraining judgment of the intelligent matching model of the vehicle and the cargo is carried out by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and the model retraining execution operation is carried out according to the retraining judgment result, and the method comprises the following steps:
s501, sending the matching result to a distribution terminal, wherein the distribution terminal corresponds to a worker for executing distribution;
s502, the staff carries out cargo allocation according to the matching result displayed by the cargo allocation terminal to obtain a vehicle assembly result;
s503, sending the vehicle assembly result to an intelligent matching system through the cargo allocation terminal; the vehicle assembly result comprises the cargo loading capacity volume of the truck, the actual occupied capacity volume of the cargo assembled into the truck when the assembly is completed, the vehicle-mounted rated weight and the actual weight of the cargo when the assembly is completed;
s504, judging matching accuracy evaluation parameters of the intelligent vehicle-cargo matching model by using the vehicle assembly result, and retraining the intelligent vehicle-cargo matching model when the accuracy evaluation parameters of the intelligent vehicle-cargo matching model are lower than a preset accuracy evaluation parameter threshold.
The accuracy evaluation parameter obtaining process comprises the following steps:
s5041, extracting the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled to the freight car when the assembly is completed, wherein the freight capacity volume is contained in the vehicle assembly result;
s5042, acquiring a first parameter index by utilizing the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled into the freight car after assembly is completed; the first parameter index is obtained through the following formula:
wherein,,Y 1 the first parameter index is indicated to be the first parameter index,Vrepresenting the cargo capacity volume of a truck;V 0 representing the actual occupied capacity volume of the cargo assembled into the truck upon completion of the assembly;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
s5043, extracting the vehicle-mounted rated weight and the actual weight of cargoes when the assembly is completed, wherein the vehicle-mounted rated weight and the actual weight of cargoes are contained in the vehicle assembly result;
s5044, acquiring a second parameter index by using the vehicle-mounted rated weight and the actual weight of the goods when assembly is completed; the second parameter index is obtained through the following formula:
wherein,,Y 2 the index of the second parameter is indicated,Wrepresenting the vehicle-mounted rated weight;W 0 representing the actual weight of the cargo when the assembly is completed;λ 1 andλ 2 respectively representThe preset volume weight coefficient is used for controlling the volume weight coefficient,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
s5045, acquiring accuracy evaluation parameters by combining the first parameter index and the second parameter index with a comprehensive index model, wherein the comprehensive index model is as follows:
wherein Y is an accuracy evaluation parameter;A 1 andA 2 respectively a preset volume weight parameter and a preset weight parameter,A 1 +A 2 =1。
the technical scheme has the effects that: the accuracy self-checking of the model matching detection is carried out through the vehicle-cargo matching result by setting the accuracy self-checking model, the model retraining judgment is carried out through the self-checking result, and the self-checking performance and the model training accuracy of the intelligent vehicle-cargo matching model can be improved through a self-checking mode. The problems that the model accuracy needs to be considered to be detected and a model training mechanism needs to be manually restarted, so that the self-correction efficiency of the intelligent car accident model is reduced and the self-correction timeliness is poor are prevented.
In addition, because the matching result of the intelligent matching model of the vehicle and the cargo can deviate along with the change of the application of the actual vehicle for a long time, the first parameter index and the second parameter index are set, and the accuracy of the intelligent matching model of the vehicle and the cargo is evaluated at regular time in a mode of acquiring comprehensive accuracy evaluation parameters by utilizing the first parameter index and the second parameter index, the finding timeliness of errors of the intelligent matching model of the vehicle and the timeliness of model adjustment of the intelligent matching model of the vehicle and the cargo can be effectively improved, the finding timeliness of errors of the intelligent matching model of the vehicle and the timeliness of model adjustment are prevented from being poor, so that the finding time delay of errors is caused, and the errors generated by model matching are embodied in the matching result of the vehicle, so that the problems of cost increase and cargo allocation efficiency reduction occur. Meanwhile, the structural arrangement of the first parameter index, the second parameter index and the comprehensive index model can improve index evaluation accuracy under the condition of reducing the calculation amount and the calculation complexity of the model. And further, by means of reducing the calculation amount and the calculation complexity of the model and improving the accuracy of model evaluation, the response time consumed by model evaluation is reduced to the maximum extent, the evaluation speed is improved, and the timeliness of model adjustment and self-correction is further improved.
The embodiment of the invention provides a vehicle-cargo intelligent matching system for whole vehicle transportation, as shown in fig. 2, the vehicle-cargo intelligent matching system comprises:
the modeling module is used for collecting vehicle information and cargo information in the whole vehicle transportation order and constructing a vehicle-cargo intelligent matching model by utilizing the vehicle information and the cargo information;
the matching module is used for embedding the intelligent matching model of the vehicle and the goods into the intelligent matching system, and when receiving a whole vehicle transportation order, the matching module takes goods information and vehicle information as input quantity and inputs the goods information and the vehicle information into the intelligent matching model of the vehicle and the goods to obtain a matching result; and the retraining judgment of the intelligent matching model of the vehicle and the cargo is carried out by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and the model retraining execution operation is carried out according to the retraining judgment result.
The technical scheme has the effects that: according to the intelligent matching method for the vehicle and the cargo, which are provided by the embodiment, the intelligent matching model is built, so that the matching accuracy and the matching efficiency of the vehicle and the cargo can be effectively improved, meanwhile, the intelligent matching method and the intelligent matching system for the vehicle and the cargo are used for carrying out model matching detection accuracy self-checking through a vehicle and cargo matching result by setting an accuracy self-checking model, carrying out model retraining judgment through the self-checking result, and improving the self-checking performance and the model training accuracy of the intelligent vehicle and cargo matching model in a self-checking mode. The problems that the model accuracy needs to be considered to be detected and a model training mechanism needs to be manually restarted, so that the self-correction efficiency of the intelligent car accident model is reduced and the self-correction timeliness is poor are prevented.
In one embodiment of the invention, the modeling module includes:
the information collection module is used for collecting vehicle information and cargo information in the whole vehicle transportation order, wherein the vehicle information comprises vehicle types, corresponding capacity volumes and corresponding quantity of each vehicle type, and the cargo information comprises cargo types, cargo quantity, cargo weight and the actual occupied capacity volume of cargoes after the whole vehicle is matched;
the feature vector module is used for extracting vehicle features from the vehicle information and converting the vehicle features into vehicle feature vectors;
the feature matrix module is used for extracting the cargo features of the cargo information and converting the cargo features into a cargo feature matrix;
the corresponding relation module is used for establishing a corresponding relation between the vehicle characteristic vector corresponding to each whole vehicle transportation order and the cargo characteristic matrix according to the whole vehicle transportation order;
the sample set acquisition module is used for constructing an intelligent matching model of the vehicle and the cargo by utilizing a neural network model structure and dividing the transportation order of the whole vehicle into a training set and a testing set;
and the model training module is used for training the intelligent matching model of the vehicle and the cargo by taking the corresponding relation among the vehicle feature vectors, the cargo feature matrix and the vehicle feature vectors corresponding to the whole vehicle transportation orders in the training set and the testing set and the cargo feature matrix as input quantity until the training is completed, and obtaining the intelligent matching model of the vehicle and the cargo after the training is completed.
The setting ratio of the training set to the testing set is 8.3:1.7.
The technical scheme has the effects that: through the mode, the acquisition efficiency of the intelligent matching model of the vehicle and the goods and the model accuracy of the intelligent matching model of the vehicle and the goods can be effectively improved. Meanwhile, the training efficiency of the intelligent matching model of the vehicle and the goods and the accuracy of the model after training can be further improved through the setting that the setting proportion of the training set to the testing set is 8.3:1.7.
In one embodiment of the present invention, the matching module includes:
the first result sending module is used for sending the matching result to a goods distributing terminal, wherein the goods distributing terminal corresponds to a worker for executing goods distribution;
the second vehicle assembly result acquisition module is used for carrying out cargo allocation by the staff according to the matching result displayed by the cargo allocation terminal to acquire a vehicle assembly result;
the second result sending module is used for sending the vehicle assembly result to the intelligent matching system through the cargo allocation terminal; the vehicle assembly result comprises the cargo loading capacity volume of the truck, the actual occupied capacity volume of the cargo assembled into the truck when the assembly is completed, the vehicle-mounted rated weight and the actual weight of the cargo when the assembly is completed;
and the retraining judgment module is used for judging the matching accuracy evaluation parameters of the intelligent vehicle-cargo matching model by utilizing the vehicle assembly result, and retraining the intelligent vehicle-cargo matching model when the accuracy evaluation parameters of the intelligent vehicle-cargo matching model are lower than a preset accuracy evaluation parameter threshold.
Specifically, the retraining judgment module includes:
the volume extraction module is used for extracting the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled to the freight car when the freight car is assembled;
the first parameter index acquisition module is used for acquiring a first parameter index by utilizing the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled into the freight car after assembly is completed; the first parameter index is obtained through the following formula:
wherein,,Y 1 the first parameter index is indicated to be the first parameter index,Vrepresenting the cargo capacity volume of a truck;V 0 representing the actual occupied capacity volume of the cargo assembled into the truck upon completion of the assembly;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
the weight acquisition module is used for extracting the vehicle-mounted rated weight contained in the vehicle assembly result and the actual weight of the goods when the assembly is completed;
the second parameter index acquisition module is used for acquiring a second parameter index by utilizing the vehicle-mounted rated weight and the actual weight of the goods when the assembly is completed; the second parameter index is obtained through the following formula:
wherein,,Y 2 the index of the second parameter is indicated,Wrepresenting the vehicle-mounted rated weight;W 0 representing the actual weight of the cargo when the assembly is completed;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
the comprehensive index obtaining module is used for obtaining accuracy evaluation parameters by combining the first parameter index and the second parameter index with a comprehensive index model, wherein the comprehensive index model is as follows:
wherein Y is an accuracy evaluation parameter;A 1 andA 2 respectively a preset volume weight parameter and a preset weight parameter,A 1 +A 2 =1。
the technical scheme has the effects that: the accuracy self-checking of the model matching detection is carried out through the vehicle-cargo matching result by setting the accuracy self-checking model, the model retraining judgment is carried out through the self-checking result, and the self-checking performance and the model training accuracy of the intelligent vehicle-cargo matching model can be improved through a self-checking mode. The problems that the model accuracy needs to be considered to be detected and a model training mechanism needs to be manually restarted, so that the self-correction efficiency of the intelligent car accident model is reduced and the self-correction timeliness is poor are prevented.
In addition, because the matching result of the intelligent matching model of the vehicle and the cargo can deviate along with the change of the application of the actual vehicle for a long time, the first parameter index and the second parameter index are set, and the accuracy of the intelligent matching model of the vehicle and the cargo is evaluated at regular time in a mode of acquiring comprehensive accuracy evaluation parameters by utilizing the first parameter index and the second parameter index, the finding timeliness of errors of the intelligent matching model of the vehicle and the timeliness of model adjustment of the intelligent matching model of the vehicle and the cargo can be effectively improved, the finding timeliness of errors of the intelligent matching model of the vehicle and the timeliness of model adjustment are prevented from being poor, so that the finding time delay of errors is caused, and the errors generated by model matching are embodied in the matching result of the vehicle, so that the problems of cost increase and cargo allocation efficiency reduction occur. Meanwhile, the structural arrangement of the first parameter index, the second parameter index and the comprehensive index model can improve index evaluation accuracy under the condition of reducing the calculation amount and the calculation complexity of the model. And further, by means of reducing the calculation amount and the calculation complexity of the model and improving the accuracy of model evaluation, the response time consumed by model evaluation is reduced to the maximum extent, the evaluation speed is improved, and the timeliness of model adjustment and self-correction is further improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (4)

1. The intelligent matching method for the vehicles and the cargoes for the whole vehicle transportation is characterized by comprising the following steps of:
collecting vehicle information and cargo information in a whole vehicle transportation order, and constructing a vehicle cargo intelligent matching model by utilizing the vehicle information and the cargo information;
embedding the intelligent matching model of the vehicle and the goods into an intelligent matching system, and inputting the goods information and the vehicle information into the intelligent matching model of the vehicle and the goods to obtain a matching result when receiving a whole vehicle transportation order; and the retraining judgment of the intelligent matching model of the vehicle and the cargo is carried out by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and the model retraining execution operation is carried out according to the retraining judgment result;
the method for constructing the intelligent matching model of the vehicle and the goods comprises the steps of:
collecting vehicle information and cargo information in a whole vehicle transportation order, wherein the vehicle information comprises vehicle types, corresponding capacity volumes and corresponding quantity of each vehicle type, and the cargo information comprises cargo types, cargo quantity, cargo weight and the actual occupied capacity volume of cargoes after the whole vehicle is matched;
extracting vehicle characteristics from the vehicle information, and converting the vehicle characteristics into vehicle characteristic vectors;
extracting the cargo characteristics of the cargo information, and converting the cargo characteristics into a cargo characteristic matrix;
establishing a corresponding relation between a vehicle feature vector corresponding to each whole vehicle transportation order and the cargo feature matrix according to the whole vehicle transportation order;
constructing an intelligent matching model of the vehicle and the cargo by utilizing a neural network model structure, and dividing the whole vehicle transportation order into a training set and a testing set;
training the intelligent matching model of the vehicle and the cargo by taking the vehicle feature vectors, the cargo feature matrix and the corresponding relation between the vehicle feature vectors and the cargo feature matrix corresponding to the whole vehicle transportation orders in the training set and the testing set as input quantity until the training is completed, and obtaining the intelligent matching model of the vehicle and the cargo after the training is completed;
the method for performing the retraining judgment of the intelligent matching model of the vehicle and the cargo by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and performing model retraining execution operation according to the retraining judgment result comprises the following steps:
the matching result is sent to a goods distribution terminal, wherein the goods distribution terminal corresponds to a worker for executing goods distribution;
the staff carries out cargo allocation according to the matching result displayed by the cargo allocation terminal to obtain a vehicle assembly result;
the vehicle assembly result is sent to an intelligent matching system through the goods distribution terminal; the vehicle assembly result comprises the cargo loading capacity volume of the truck, the actual occupied capacity volume of the cargo assembled into the truck when the assembly is completed, the vehicle-mounted rated weight and the actual weight of the cargo when the assembly is completed;
judging matching accuracy evaluation parameters of the intelligent vehicle-cargo matching model by using the vehicle assembly result, and retraining the intelligent vehicle-cargo matching model when the accuracy evaluation parameters of the intelligent vehicle-cargo matching model are lower than a preset accuracy evaluation parameter threshold;
the accuracy evaluation parameter obtaining process comprises the following steps:
extracting the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled into the freight car when the freight car is assembled;
acquiring a first parameter index by utilizing the cargo loading capacity volume of the truck and the actual occupied capacity volume of the cargo assembled into the truck when the cargo is assembled; the first parameter index is obtained through the following formula:
wherein,,Y 1 the first parameter index is indicated to be the first parameter index,Vrepresenting the cargo capacity volume of a truck;V 0 representing the actual occupied capacity volume of the cargo assembled into the truck upon completion of the assembly;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
extracting the vehicle-mounted rated weight and the actual weight of goods when the assembly is completed, wherein the vehicle-mounted rated weight and the actual weight of goods are contained in the vehicle assembly result;
acquiring a second parameter index by utilizing the vehicle-mounted rated weight and the actual weight of the goods when the assembly is completed; the second parameter index is obtained through the following formula:
wherein,,Y 2 the index of the second parameter is indicated,Wrepresenting the vehicle-mounted rated weight;W 0 representing the actual weight of the cargo when the assembly is completed;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
and acquiring accuracy evaluation parameters by combining the first parameter index and the second parameter index with a comprehensive index model, wherein the comprehensive index model is as follows:
wherein Y is an accuracy evaluation parameter;A 1 andA 2 respectively a preset volume weight parameter and a preset weight parameter,A 1 +A 2 =1。
2. the intelligent matching method according to claim 1, wherein the setting ratio of the training set to the test set is 8.3:1.7.
3. The utility model provides a car goods intelligence matching system of whole car transportation, its characterized in that, car goods intelligence matching system includes:
the modeling module is used for collecting vehicle information and cargo information in the whole vehicle transportation order and constructing a vehicle-cargo intelligent matching model by utilizing the vehicle information and the cargo information;
the matching module is used for embedding the intelligent matching model of the vehicle and the goods into the intelligent matching system, and when receiving a whole vehicle transportation order, the matching module takes goods information and vehicle information as input quantity and inputs the goods information and the vehicle information into the intelligent matching model of the vehicle and the goods to obtain a matching result; and the retraining judgment of the intelligent matching model of the vehicle and the cargo is carried out by utilizing the vehicle assembly result corresponding to the matching result fed back by the cargo distribution terminal, and the model retraining execution operation is carried out according to the retraining judgment result;
wherein the modeling module comprises:
the information collection module is used for collecting vehicle information and cargo information in the whole vehicle transportation order, wherein the vehicle information comprises vehicle types, corresponding capacity volumes and corresponding quantity of each vehicle type, and the cargo information comprises cargo types, cargo quantity, cargo weight and the actual occupied capacity volume of cargoes after the whole vehicle is matched;
the feature vector module is used for extracting vehicle features from the vehicle information and converting the vehicle features into vehicle feature vectors;
the feature matrix module is used for extracting the cargo features of the cargo information and converting the cargo features into a cargo feature matrix;
the corresponding relation module is used for establishing a corresponding relation between the vehicle characteristic vector corresponding to each whole vehicle transportation order and the cargo characteristic matrix according to the whole vehicle transportation order;
the sample set acquisition module is used for constructing an intelligent matching model of the vehicle and the cargo by utilizing a neural network model structure and dividing the transportation order of the whole vehicle into a training set and a testing set;
the model training module is used for training the intelligent matching model of the vehicle and the cargo by taking the corresponding relation among the vehicle feature vectors, the cargo feature matrix and the vehicle feature vectors corresponding to the whole vehicle transportation orders in the training set and the testing set as input quantity until the training is completed, and obtaining the intelligent matching model of the vehicle and the cargo after the training is completed;
wherein, the matching module includes:
the first result sending module is used for sending the matching result to a goods distributing terminal, wherein the goods distributing terminal corresponds to a worker for executing goods distribution;
the second vehicle assembly result acquisition module is used for carrying out cargo allocation by the staff according to the matching result displayed by the cargo allocation terminal to acquire a vehicle assembly result;
the result sending module is used for sending the vehicle assembly result to an intelligent matching system through the goods distribution terminal; the vehicle assembly result comprises the cargo loading capacity volume of the truck, the actual occupied capacity volume of the cargo assembled into the truck when the assembly is completed, the vehicle-mounted rated weight and the actual weight of the cargo when the assembly is completed;
the retraining judgment module is used for judging matching accuracy evaluation parameters of the intelligent vehicle-cargo matching model by utilizing the vehicle assembly result, and retraining the intelligent vehicle-cargo matching model when the accuracy evaluation parameters of the intelligent vehicle-cargo matching model are lower than a preset accuracy evaluation parameter threshold;
wherein, retraining judging module includes:
the volume extraction module is used for extracting the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled to the freight car when the freight car is assembled;
the first parameter index acquisition module is used for acquiring a first parameter index by utilizing the freight capacity volume of the freight car and the actual occupied capacity volume of the freight car assembled into the freight car after assembly is completed; the first parameter index is obtained through the following formula:
wherein,,Y 1 representing the first parameter meansThe number of the mark is set to be equal to the number of the mark,Vrepresenting the cargo capacity volume of a truck;V 0 representing the actual occupied capacity volume of the cargo assembled into the truck upon completion of the assembly;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
the weight acquisition module is used for extracting the vehicle-mounted rated weight contained in the vehicle assembly result and the actual weight of the goods when the assembly is completed;
the second parameter index acquisition module is used for acquiring a second parameter index by utilizing the vehicle-mounted rated weight and the actual weight of the goods when the assembly is completed; the second parameter index is obtained through the following formula:
wherein,,Y 2 the index of the second parameter is indicated,Wrepresenting the vehicle-mounted rated weight;W 0 representing the actual weight of the cargo when the assembly is completed;λ 1 andλ 2 respectively representing preset volume weight coefficients,λ 1 +λ 2 <1, and,λ 2 >1.8λ 1 ；Y 0 representing preset reference parameters;
the comprehensive index obtaining module is used for obtaining accuracy evaluation parameters by combining the first parameter index and the second parameter index with a comprehensive index model, wherein the comprehensive index model is as follows:
wherein Y is an accuracy evaluation parameter;A 1 andA 2 respectively a preset volume weight parameter and a preset weight parameter,A 1 +A 2 =1。
4. the intelligent matching system according to claim 3, wherein said training set and test set are arranged in a ratio of 8.3:1.7.
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