CN114418417A - Vehicle and goods matching method, and transport capacity prediction method, device and electronic equipment based on vehicle and goods matching method - Google Patents

Vehicle and goods matching method, and transport capacity prediction method, device and electronic equipment based on vehicle and goods matching method Download PDF

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CN114418417A
CN114418417A CN202210082924.5A CN202210082924A CN114418417A CN 114418417 A CN114418417 A CN 114418417A CN 202210082924 A CN202210082924 A CN 202210082924A CN 114418417 A CN114418417 A CN 114418417A
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陈朝晖
孙晨鹏
罗竞佳
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Jiangsu Manyun Software Technology Co Ltd
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Abstract

The application provides a vehicle and goods matching method, and a transport capacity prediction method, a transport capacity prediction device and an electronic device based on the vehicle and goods matching method, wherein a specific implementation mode of the vehicle and goods matching method comprises the following steps: acquiring historical carrying information of a target vehicle; the historical carrying information comprises vehicle information and historical carrying source information; determining characteristic preference information of the target vehicle for the historical shipments according to the historical shipments source information; and determining a matching result of the target vehicle and the goods to be carried according to the characteristic preference information and the vehicle information. The method can improve the matching accuracy between the target vehicle and the goods to be carried so as to better meet the actual freight requirement.

Description

Vehicle and goods matching method, and transport capacity prediction method, device and electronic equipment based on vehicle and goods matching method
Technical Field
The application relates to the field of information processing, in particular to a vehicle and goods matching method, a transport capacity prediction method and device based on the vehicle and goods matching method, and electronic equipment.
Background
Freight transportation, i.e. freight transportation. Cargo transportation is a derivative demand of economic and social development and a support for physical circulation of commodities, and along with the continuous increase of economy, the freight demand is larger and larger.
In the freight market, a shipper can serve as a shipper to search for a carrier to carry goods through a network platform; specifically, the shipper may publish the shipment information on the network platform, and the network platform may search for a suitable carrier according to the shipment information. When the shipper and carrier match is successful, the carrier may use the carrier vehicle to carry the goods to transport the goods from the origin to the destination.
In the related art, when matching a carrier vehicle and goods to be carried, the network platform generally performs matching based on the number of carrier vehicles and the number of goods orders. Thus, the matching accuracy between the carrier vehicle and the goods to be carried is low and the actual freight requirements cannot be met well because the situation that the carrier vehicle cannot load the goods to be carried (for example, the carrier vehicle loaded with coal cannot be used for loading flour) due to the fact that the situations such as the cargo volume, the cargo stacking requirement, the packaging mode, the loading and unloading mode, the transportation route and the like are not considered.
Disclosure of Invention
The embodiment of the application aims to provide a vehicle and goods matching method, a capacity prediction method based on the vehicle and goods matching method, a capacity prediction device based on the vehicle and goods matching method and electronic equipment, so that the matching accuracy between a target vehicle and goods to be carried is improved, and the actual goods transportation requirements are better met.
In a first aspect, an embodiment of the present application provides a vehicle and goods matching method, where the method includes: acquiring historical carrying information of a target vehicle; the historical carrying information comprises vehicle information and historical carrying source information; determining characteristic preference information of the target vehicle for the historical shipments according to the historical shipments source information; and determining a matching result of the target vehicle and the goods to be carried according to the characteristic preference information and the vehicle information. In this way, the matching accuracy between the target vehicle and the goods to be carried can be improved, so as to better meet the actual freight requirement.
Optionally, the determining the characteristic preference information of the target vehicle for the historical shipments according to the historical shipments source information includes: determining the cargo characteristics of each historical shipper according to the plurality of historical shipper source information; counting the historical carrying times of the target vehicle for the goods with the goods characteristics aiming at each goods characteristic; and when any one of the historical shipments is detected to exceed the threshold value of the shipments, determining the goods characteristic corresponding to the historical shipments as the preference characteristic. Therefore, whether the goods characteristic is a preference characteristic or not can be determined by judging whether the historical carrying times corresponding to the goods characteristic exceeds the threshold value of the times, and the goods characteristic is convenient and quick.
Optionally, the determining the characteristic preference information of the target vehicle for the historical shipments according to the historical shipments source information includes: determining the cargo characteristics of each historical shipper according to the plurality of historical shipper source information; vectorizing the cargo characteristics of each historical shipper according to a preset rule; determining the cargo features corresponding to the vector with the maximum feature value in the vectors of the same type as preference features; or determining the goods characteristics corresponding to the vectors with the characteristic values being the characteristic mean values in the vectors of the same type as the preference characteristics. Like this, can be through the eigenvalue of the vector that judges a certain goods characteristic corresponds, just can determine whether this goods characteristic is preference characteristic, convenient quick.
Optionally, the determining, according to the feature preference information and the vehicle information, a matching result between the target vehicle and goods to be carried includes: vectorizing the vehicle characteristics corresponding to the vehicle information and the characteristics of the goods to be carried corresponding to the goods to be carried respectively to obtain vehicle characteristic vectors and the characteristic vectors of the goods to be carried; and determining the matching result according to the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried. Therefore, the vectorization vehicle characteristics and the characteristics of goods to be carried can be digitalized, and the convenience of determining the matching result can be improved to a certain extent.
Optionally, the determining the matching result according to the vector corresponding to the preference feature, the vehicle feature vector, and the feature vector of the goods to be transported includes: vector corresponding to the preference feature, the vehicle feature vector andthe characteristic vector of the goods to be carried is used as the input of a logistic regression model, and the matching result is determined based on the output of the logistic regression model; the regression algorithm corresponding to the logistic regression model comprises the following steps:
Figure BDA0003486641710000031
wherein the content of the first and second substances,
Figure BDA0003486641710000032
a probability parameter representing that the target vehicle can load goods to be carried, and w represents an importance degree parameter of the characteristics of the goods to be carried; b represents a correction parameter. Therefore, the matching result between the target vehicle and the goods to be carried can be determined based on the logistic regression model, and the purpose of improving the matching accuracy can be achieved to a certain extent.
Optionally, the preset rule includes: determining whether the cargo feature can be quantified numerically; if the cargo features can be quantified numerically, determining the characteristic numerical values of the cargo features as the characteristic values of the vectors; and if the cargo features cannot be quantified numerically, determining the characteristic value of the vector corresponding to the cargo features according to a preset coding rule. In this way, individual cargo features may be vectorized to determine preferred features based on vector feature values.
In a second aspect, an embodiment of the present application provides a capacity prediction method, where the method includes: acquiring an order to be processed in a target area; the order to be processed comprises information of goods to be carried; predicting an order receiving result of the order to be processed according to a matching result of the target vehicle and the goods to be carried indicated by the information of the goods to be carried; wherein the matching result is determined according to the steps in the method as provided in the first aspect above; and predicting the transport capacity information corresponding to the target area according to the order to be processed and the order receiving result. Therefore, the cargo owner can know the cargo carrying probability according to the transport capacity information, and the cargo owner can go to a target area to pick up orders autonomously or according to the instruction of the server, so that the aim of improving the cargo carrying efficiency is fulfilled.
In a third aspect, an embodiment of the present application provides a vehicle and goods matching device, including: the acquisition module is used for acquiring historical carrying information of the target vehicle; the historical carrying information comprises vehicle information and historical carrying source information; the first determining module is used for determining characteristic preference information of the target vehicle for the historical shipments according to the historical shipments source information; and the second determining module is used for determining the matching result of the target vehicle and the goods to be carried according to the characteristic preference information and the vehicle information.
In a fourth aspect, an embodiment of the present application provides a capacity prediction apparatus, including: the order acquisition module is used for acquiring the order to be processed in the target area; the order to be processed comprises information of goods to be carried; the first prediction module is used for predicting the order receiving result of the order to be processed according to the matching result of the target vehicle and the goods to be carried indicated by the information of the goods to be carried; wherein the matching result is determined according to the steps in the method as provided in the first aspect above; and the second prediction module is used for predicting the transport capacity information corresponding to the target area according to the order to be processed and the order receiving result.
In a fifth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, where the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the electronic device executes the steps in the method as provided in the first aspect or the second aspect.
In a sixth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the steps in the method as provided in the first or second aspect.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a vehicle-cargo matching method according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a capacity prediction method according to an embodiment of the present disclosure;
fig. 3 is a block diagram illustrating a structure of a vehicle-cargo matching device according to an embodiment of the present disclosure;
fig. 4 is a block diagram illustrating a structure of a capacity prediction apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device for executing a vehicle-cargo matching method or a transportation capacity prediction method according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
It should be noted that the embodiments or technical features of the embodiments in the present application may be combined without conflict.
In the related technology, the matching accuracy between the carrying vehicle and the goods to be carried is low, and the actual freight requirement cannot be well met; in order to solve the problem, the application provides a vehicle and goods matching method; further, the characteristic preference information of the target driver for goods to be carried is determined, and then the characteristic preference information is combined with the attribute information of the target vehicle to judge whether the target vehicle can carry the goods to be carried or not. Therefore, the matching accuracy between the carrying vehicle and the goods to be carried can be improved, and the actual freight requirements can be well met.
In some application scenarios, the vehicle and goods matching method can be applied to a server side for providing vehicle and goods matching service, and the server side can determine a matching result between goods to be carried and a target vehicle according to the acquired characteristic preference information and the attribute information of the target vehicle. In other application scenarios, the vehicle and goods matching method can also be applied to terminal equipment capable of realizing vehicle and goods matching. The terminal device may, for example, acquire the feature preference information and the attribute information of the target vehicle from the corresponding server, and then determine a matching result between the target vehicle and the goods to be carried. For example, the application is exemplified by being applied to a server.
The above solutions in the related art are all the results of practical and careful study of the inventor, and therefore, the discovery process of the above problems and the solutions proposed by the following embodiments of the present invention to the above problems should be the contribution of the inventor to the present invention in the course of the present invention.
Please refer to fig. 1, which shows a flowchart of a vehicle-cargo matching method according to an embodiment of the present application. As shown in fig. 1, the vehicle-cargo matching method includes the following steps 101 to 103.
Step 101, obtaining historical carrying information of a target vehicle; the historical carrying information comprises vehicle information and historical carrying source information;
the above-described vehicle information may be regarded as the own attribute information of the target vehicle. That is, the vehicle information may include, for example, the vehicle length information, the vehicle type information, the authorized load information, and the like of the target vehicle.
The historical shipper information may be considered as the shipper information that the target vehicle has shipped in the completed historical shipper order. Here, the historical shipper information may include, for example, information on the manner of loading and unloading of the historical shipper, the departure city, the destination city, and the like.
In some application scenarios, the server may obtain the historical carrying information of the target vehicle by obtaining the historical order information of the target vehicle. Here, the historical order information may include personal information such as name, sex, contact address, etc. of the target driver corresponding to the target vehicle, vehicle information of the target vehicle and historical source information corresponding to the historical order information, and status information of the historical order information. The status information here can be used to characterize whether the historical order was cancelled, committed, completed, etc. After the server acquires the historical order information, the server can screen out the historical orders with the state information representing the completion, and then acquire the historical carrying information of the target vehicle from the historical orders.
Step 102, determining characteristic preference information of the target vehicle for the historical shipments according to the historical shipments source information;
the characteristic preference information may be regarded as cargo characteristic information that can be accepted by the target vehicle. For example, the target vehicle may accept cargo characteristics such as dirty vehicles, short haul, long haul, etc.
After the server acquires the historical shipper information, the server can determine the characteristic preference information of the target vehicle for the historical shipper from the historical shipper information. For example, the historical shipper cargo a has cargo characteristics that may make the target vehicle dirty, and in this case, the dirty vehicle characteristics may be considered as the preferred cargo characteristics for the target vehicle.
And 103, determining a matching result of the target vehicle and the goods to be carried according to the characteristic preference information and the vehicle information.
After the server acquires the characteristic preference information and the vehicle information of the target vehicle, the server can determine whether the target vehicle is matched with the goods to be carried by combining the characteristic preference information and the vehicle information. For example, the server may first determine whether a dirty vehicle characteristic exists for the cargo to be carried, and if so, may further determine whether characteristic preference information indicates a target vehicle that is capable of receiving the dirty vehicle characteristic. If the target vehicle exists, the determined target vehicle can be considered to be matched with goods to be carried. Then, the owner of the target vehicle and the owner of the goods to be carried can be informed, so that the goods to be carried can be conveniently transported.
In this embodiment, through the above steps 101 to 103, a matching result between the target vehicle and the goods to be carried can be determined based on the characteristic preference information and the vehicle information of the target vehicle. In this way, the matching accuracy between the target vehicle and the goods to be carried can be improved, so as to better meet the actual freight requirement.
In some optional implementations, the step 102 may include the following sub-steps:
a substep 1021a, determining the cargo characteristics of each historical shipper according to a plurality of historical shipper source information;
in some application scenarios, the server may obtain a plurality of historical shipper information. These historical shipper source information may each correspond to a historical shipper. The server may then determine the shipment characteristics for each of the historical shipments. For example, for historical shipments a that the target vehicle a has shipped, the server may determine characteristics of the historical shipments a, such as dirty vehicle characteristics, short-distance transportation characteristics, etc.; for historical shipper B, the server may determine characteristics of the historical shipper B, such as dirty vehicle characteristics, long distance transport characteristics, etc.; for historical shipments C, the server may determine characteristics of the historical shipments C such as building material attributes, dirty car characteristics, short distance transportation characteristics, etc.
A substep 1022a of counting, for each cargo characteristic, the historical carrying times of the target vehicle for the cargo with the cargo characteristic;
after determining the cargo characteristics of each historical cargo, the server can count the historical cargo carrying times of the target vehicle for the cargo with the cargo characteristics for each cargo characteristic. For example, after determining the cargo characteristics of the historical transported cargo a, the historical transported cargo B, and the historical transported cargo C, the server may count the historical number of times of transportation of the cargo having the cargo characteristics with respect to the dirty vehicle characteristics, the short-distance transportation characteristics, the long-distance transportation characteristics, and the characteristics of the building materials. For example, the historical carrying times of goods with dirty vehicle characteristics can be counted to be 3 times; the historical carrying times of the goods with the short-distance transportation characteristics is 2 times; the historical carrying times of the goods with the long-distance transportation characteristics is 1; the number of historical shipments of goods that are characteristic of building materials is 1.
And a substep 1023a, when any one of the historical shipments is detected to exceed the threshold, determining the goods characteristic corresponding to the historical shipments as the preference characteristic.
After the server counts the historical carrying times corresponding to each goods characteristic, the preference characteristic can be further determined according to the historical carrying times. Specifically, the determination may be made based on whether or not the historical shipment count exceeds a count threshold. The number threshold here may include, for example, a preset threshold such as 10 times, 20 times, and the like. A threshold value may also be included that is determined according to certain rules based on the total number of shipments of the target vehicle. Certain rules herein may include, for example, a threshold number of times that is half of the total number of shipments. For example, when the number threshold is determined to be 2 times, the dirty vehicle feature may be determined as a preference feature corresponding to the target vehicle a.
In this implementation manner, through the sub-step 1021a to the sub-step 1023a, it can be determined whether a certain cargo feature is a preference feature by determining whether the historical shipping frequency corresponding to the cargo feature exceeds a frequency threshold, which is convenient and fast.
In some optional implementations, the step 102 may include the following sub-steps:
a substep 1021b, determining the cargo characteristics of each historical shipper according to the plurality of historical shipper source information;
the implementation process and the achieved technical effect of the sub-step 1021b can be the same as or similar to the sub-step 1021a, and are not described herein again.
Substep 1022b, vectorizing the cargo characteristics of each historical shipper respectively according to preset rules;
after determining the cargo characteristics of each historical shipper, the server may vectorize each cargo characteristic according to a preset rule.
In some optional implementations, the preset rule may include the following steps:
step 1, judging whether the cargo features can be quantified by numerical values;
in some application scenarios, the server may determine a vector of the cargo feature by determining whether the cargo feature can be quantified numerically. For example, when the weight of the cargo is 10 tons and the transport distance is 50 km, it can be considered that the weight characteristic and the distance characteristic can be numerically quantified; when the goods belong to the category of building materials, the characteristics belonging to the building materials cannot be quantified numerically.
Step 2, if the cargo features can be quantified numerically, determining the characteristic numerical values of the cargo features as the characteristic values of the vectors;
if it is determined that the cargo feature can be numerically quantified, the cargo feature may be vectorized and the feature value of the cargo feature may be determined as a feature value of the vector. For example, after it is determined that a weight feature can be numerically quantified, the weight feature may be vectorized, and a eigenvalue "10" of the weight feature may be determined as an eigenvalue of the vector. Note that, the weight unit may be described at an appropriate position.
And 3, if the cargo features cannot be quantified numerically, determining the feature values of the vectors corresponding to the cargo features according to a preset coding rule.
If the cargo features cannot be quantified numerically, the cargo features can be vectorized, and the feature values of the vectors corresponding to the cargo features can be determined according to the preset codes. The preset encoding rule may include a one-hot encoding (one-hot encoding) rule, for example. For example, after it is determined that the feature belonging to the building material cannot be numerically quantized, the feature belonging to the building material may be vectorized, and the value "1" may be determined as a feature value of a vector based on one-hot encoding.
Through the steps 1 to 3, the characteristics of each cargo can be vectorized, so that the preference characteristics can be determined based on the characteristic values of the vectors.
The substep 1023b is used for determining the cargo feature corresponding to the vector with the maximum feature value in the vectors of the same type as the preference feature; or determining the goods characteristics corresponding to the vectors with the characteristic values being the characteristic mean values in the vectors of the same type as the preference characteristics.
In some application scenarios, after vectorizing the cargo features, the type of vector may be determined. Specifically, multiple vectors corresponding to the same cargo feature may be determined to be of the same type. For example, after the weight feature a corresponding to the historical transported item a, the weight feature corresponding to the historical transported item B, and the weight feature C corresponding to the historical transported item C are vectorized, the obtained vector a ', the vector B ', and the vector C ' may be determined to be of the same type.
In some application scenarios, vectors corresponding to cargo features of a plurality of historical shipments may be normalized to a feature vector matrix. The feature vector matrix may be, for example, by TmnRepresents:
Figure BDA0003486641710000101
wherein a represents the historical number of shipments of the target vehicle; b represents the length of the feature vector of the historical shipments. In this way, vectors in the same column can be determined as the same type of vectors. It should be noted that, for the same eigenvector matrix, the unit information of the cargo features can be unified, so as to more concisely and clearly characterize the cargo features. For example, regarding the weight characteristics, a weight characteristic having a weight of "10 tons" may be described, and only the characteristic numerical value "10" may be used as the characteristic value, and the unit information thereof may be described as tons at the adaptive position.
In some application scenarios, after determining the vector type, the server may determine, as the preference feature, the cargo feature corresponding to the vector with the largest feature value in the vectors of the same type. For example, in the vectors a ', B ', and C ' of the same type, if the feature value of the vector a ' is 10, the feature value of the vector B ' is 8, and the feature value of the vector C ' is 7, the weight feature a corresponding to the vector a ' having the largest feature value may be determined as the preference feature.
In other application scenarios, after determining the vector type, the server may determine, as the preference feature, the cargo feature corresponding to the vector having the feature value as the feature average value in the same type of vector. For example, in the same type of vectors a ', B ', and C ', if the feature value of the vector a ' is 10, the feature value of the vector B ' is 8, and the feature value of the vector C ' is 6, the feature average value may be determined to be 8, and the weight feature B corresponding to the vector B ' having the feature value of 8 may be determined to be the preference feature. In these application scenarios, if there is no vector matching the feature average in the same type of vectors, the vector corresponding to the feature average may also be directly determined as the preference feature. For example, if the feature value of the vector a ' is 10, the feature value of the vector B ' is 7, and the feature value of the vector C ' is 10, the determined feature average value may be 9, and the weight feature having the vectorized feature value of 9 may be determined as the preference feature.
In this implementation, through substep 1021b to substep 1023b, whether this goods characteristic is preference characteristic, convenient quick can be determined through judging the eigenvalue of the vector that a certain goods characteristic corresponds.
In some optional implementations, the step 103 may include the following sub-steps:
a substep 1031, which is used for respectively vectorizing the vehicle characteristics corresponding to the vehicle information and the characteristics of the goods to be carried corresponding to the goods to be carried to obtain vehicle characteristic vectors and the characteristic vectors of the goods to be carried;
in some application scenarios, the vehicle characteristics and the characteristics of goods to be carried can be vectorized to determine the matching result more conveniently. In these application scenarios, for example, one-hot encoding may also be used for vectorization.
And a sub-step 1032 of determining the matching result according to the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried.
After the vehicle characteristic vector and the characteristic vector of the goods to be carried are determined, the vectorized preference characteristic vector can be combined to determine a matching result between the target vehicle and the goods to be carried. Therefore, the vectorization vehicle characteristics and the characteristics of goods to be carried can be digitalized, and the convenience of determining the matching result can be improved to a certain extent.
In some optional implementations, the sub-step 1032 may include: taking the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried as the input of a logistic regression model, and determining the matching result based on the output of the logistic regression model; the regression algorithm corresponding to the logistic regression model comprises the following steps:
Figure BDA0003486641710000121
wherein the content of the first and second substances,
Figure BDA0003486641710000122
a probability parameter representing that the target vehicle can load goods to be carried, and w represents an importance degree parameter of the characteristics of the goods to be carried; b represents a correction parameter。
In some application scenarios, the matching result may be determined by the logistic regression model. Specifically, the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried can be input into the logistic regression model, so that the logistic regression module determines the probability parameter that the target vehicle can load the goods to be carried by using the regression algorithm. A matching result can then be determined from the probability parameter. For example, if the probability parameter is greater than a preset probability threshold (e.g., 95%), it may be considered that the target vehicle matches the cargo to be carried. That is, the target vehicle can load the cargo to be carried.
In these application scenarios, before the vector corresponding to the preference feature, the vehicle feature vector, and the feature vector of the goods to be shipped are input into the logistic regression model, the vector corresponding to the preference feature, the vehicle feature vector, and the feature vector of the goods to be shipped may be spliced into a vector that can meet the input requirements of the logistic regression model. For example, the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried can be sequentially spliced to obtain a vector meeting the input requirement of the logistic regression model. For example, if the preferred features correspond to vectors: dc＝{ti1,ti2,ti3...tim}; wherein m represents the vector length of the vector corresponding to the preference feature; the vehicle feature vector is: dq＝{q1,q2,q3...qw}; wherein w represents the length of the vehicle feature vector; the characteristic vectors of the goods to be carried are as follows: dt＝{t1,t2,t3...tn}; wherein n represents the vector length of the characteristic vector of the goods to be carried. Thus, the spliced vector may be: x ═ ti1,ti2,ti3...tim，q1，q2，q3...qw，t1，t2，t3...ti...tn}。
In some application scenarios, the unconverged logistic regression model may be trained in advance through historical shipping information to obtain a logistic regression module that can be used to determine the matching result between the target vehicle and the goods to be shipped.
In these application scenarios, the logistic regression model may be trained, for example, by a preset computational formula. The preset calculation formula here may be:
Figure BDA0003486641710000123
wherein the content of the first and second substances,
Figure BDA0003486641710000124
representing a loss between a probability of actually loading the historical shipments and a predicted probability that the target vehicle is able to load the shipments to be shipped; y represents the probability that the target vehicle is actually loaded with historical shipments.
In these application scenarios, the logistic regression model may be considered to converge when the loss reaches a predetermined loss requirement (e.g., a loss value less than 0.1).
Therefore, the matching result between the target vehicle and the goods to be carried can be determined based on the logistic regression model, and the purpose of improving the matching accuracy can be achieved to a certain extent.
Please refer to fig. 2, which shows a flowchart of a capacity prediction method according to an embodiment of the present application. As shown in fig. 2, the capacity prediction method includes the following steps 201 to 203.
Step 201, obtaining an order to be processed in a target area; the order to be processed comprises information of goods to be carried;
the above-mentioned target area may be regarded as an area in which the capacity is to be predicted.
In some application scenarios, the server may obtain the pending orders in the target area. These pending orders may be sent by the shipper based on the information on the goods to be shipped. The information on the goods to be transported may include, for example, volume information, weight information, transportation information, and the like of the goods to be transported. It should be noted that in order to predict capacity conditions within the target area, all pending orders within the target area may be obtained.
Step 202, predicting an order receiving result of the order to be processed according to a matching result of the target vehicle and the goods to be carried indicated by the information of the goods to be carried; the matching result is determined according to any implementation mode in the vehicle and goods matching method;
in some application scenarios, the server may obtain a matching result between the target vehicle and the goods to be carried. Here, the process of determining the matching result and the obtained technical effect may refer to the content of any implementation manner in the vehicle and cargo matching method, which is not described herein again.
After the server side obtains the matching result, the order taking condition of the order to be processed can be predicted through the matching result. For example, if the matching result is that the target vehicle a is matched with the goods a to be transported, it can be predicted that the order to be processed corresponding to the goods a to be transported can be accepted; and if the matching result is that the target vehicle B is not matched with the goods B to be carried, the fact that the order to be processed corresponding to the goods B to be carried cannot be accepted can be predicted.
Step 203, predicting the transportation capacity information corresponding to the target area according to the order to be processed and the order receiving result.
After the server acquires the to-be-processed orders in the target area and the order receiving results respectively corresponding to the to-be-processed orders, the transportation capacity information corresponding to the target area can be predicted. The capacity information may include, for example, quantity information indicating that pending orders within the target area can be successfully picked up, specific location information where goods are to be shipped, and the like. In this way, the server can send reply information which can be ordered or can not be ordered to the corresponding cargo owner according to the transportation capacity information, and can also send position information of the cargo to be carried and the like to a target driver corresponding to the target vehicle, so as to realize transportation capacity scheduling of the target area.
In some application scenarios, after predicting the transportation capacity information corresponding to the target area, the server may further generate a thermodynamic diagram based on the transportation capacity information, so as to more vividly represent the transportation capacity condition of the target area. For example, there are 100 coal orders to be processed for a target area with 100 target vehicles. The server predicts that only 20 target vehicles can carry (can be regarded as transport capacity information) according to the order to be processed and the order receiving result. Thus, the coal transportation capacity gap of 80 units can be obtained through the capacity information of the target area. Thus, the target region may belong to a coal transportation hot zone when generating the thermodynamic diagram. In this way, other vehicles capable of carrying coal can go to the target area through the thermodynamic diagram to receive orders, and the freight efficiency can be improved.
In this embodiment, the transportation capacity information in the target area can be predicted based on the matching result between the target vehicle and the goods to be transported through the above steps 201 to 203, so that the shipper can know the probability that the goods are transported according to the transportation capacity information, and the shipper can go to the target area to pick up the order autonomously or according to the instruction of the server, thereby achieving the purpose of improving the shipping efficiency.
Referring to fig. 3, a block diagram of a vehicle and cargo matching device provided in an embodiment of the present application is shown, where the vehicle and cargo matching device may be a module, a program segment, or a code on an electronic device. It should be understood that the apparatus corresponds to the above-mentioned embodiment of the method of fig. 1, and can perform various steps related to the embodiment of the method of fig. 1, and the specific functions of the apparatus can be referred to the description above, and the detailed description is appropriately omitted here to avoid redundancy.
Optionally, the vehicle-cargo matching device includes an obtaining module 301, a first determining module 302, and a second determining module 303. The acquiring module 301 is configured to acquire historical carrying information of a target vehicle; the historical carrying information comprises vehicle information and historical carrying source information; a first determining module 302, configured to determine characteristic preference information of the target vehicle for historical shipments according to the historical shipments source information; and the second determining module 303 is configured to determine a matching result between the target vehicle and the goods to be carried according to the feature preference information and the vehicle information.
Optionally, the first determining module 302 is further configured to: determining the cargo characteristics of each historical shipper according to the plurality of historical shipper source information; counting the historical carrying times of the target vehicle for the goods with the goods characteristics aiming at each goods characteristic; and when any one of the historical shipments is detected to exceed the threshold value of the shipments, determining the goods characteristic corresponding to the historical shipments as the preference characteristic.
Optionally, the first determining module 302 is further configured to: determining the cargo characteristics of each historical shipper according to the plurality of historical shipper source information; vectorizing the cargo characteristics of each historical shipper according to a preset rule; determining the cargo features corresponding to the vector with the maximum feature value in the vectors of the same type as preference features; or determining the goods characteristics corresponding to the vectors with the characteristic values being the characteristic mean values in the vectors of the same type as the preference characteristics.
Optionally, the second determining module 303 is further configured to: vectorizing the vehicle characteristics corresponding to the vehicle information and the characteristics of the goods to be carried corresponding to the goods to be carried respectively to obtain vehicle characteristic vectors and the characteristic vectors of the goods to be carried; and determining the matching result according to the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried.
Optionally, the second determining module 303 is further configured to: taking the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried as the input of a logistic regression model, and determining the matching result based on the output of the logistic regression model; the regression algorithm corresponding to the logistic regression model comprises the following steps:
Figure BDA0003486641710000151
wherein the content of the first and second substances,
Figure BDA0003486641710000152
a probability parameter representing that the target vehicle can load goods to be carried, and w represents an importance degree parameter of the characteristics of the goods to be carried; b represents a correction parameter.
Optionally, the preset rule includes: determining whether the cargo feature can be quantified numerically; if the cargo features can be quantified numerically, determining the characteristic numerical values of the cargo features as the characteristic values of the vectors; and if the cargo features cannot be quantified numerically, determining the characteristic value of the vector corresponding to the cargo features according to a preset coding rule.
It should be noted that, for the convenience and brevity of description, the specific working procedure of the above-described apparatus may refer to the corresponding procedure in the foregoing method embodiment, and the description is not repeated herein.
Referring to fig. 4, a block diagram of a traffic prediction apparatus provided in an embodiment of the present application is shown, where the traffic prediction apparatus may be a module, a program segment, or code on an electronic device. It should be understood that the apparatus corresponds to the above-mentioned embodiment of the method of fig. 2, and can perform various steps related to the embodiment of the method of fig. 2, and the specific functions of the apparatus can be referred to the description above, and the detailed description is appropriately omitted here to avoid redundancy.
Optionally, the transportation capability prediction apparatus includes an order obtaining module 401, a first prediction module 402, and a second prediction module 403. The order obtaining module 401 is configured to obtain an order to be processed in a target area; the order to be processed comprises information of goods to be carried; the first prediction module 402 is used for predicting an order receiving result of the order to be processed according to a matching result of the target vehicle and the goods to be carried indicated by the information of the goods to be carried; wherein the matching result is determined according to the steps in the method as provided in the first aspect above; a second prediction module 403, configured to predict, according to the to-be-processed order and the order receiving result, transportation capacity information corresponding to the target area.
It should be noted that, for the convenience and brevity of description, the specific working procedure of the above-described apparatus may refer to the corresponding procedure in the foregoing method embodiment, and the description is not repeated herein.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device for executing a vehicle-cargo matching method or a transportation capacity prediction method according to an embodiment of the present disclosure, where the electronic device may include: at least one processor 501, such as a CPU, at least one communication interface 502, at least one memory 503, and at least one communication bus 504. Wherein the communication bus 504 is used to enable direct connection communication of these components. The communication interface 502 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. The memory 503 may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 503 may optionally be at least one storage device located remotely from the aforementioned processor. The memory 503 stores computer readable instructions, and when the computer readable instructions are executed by the processor 501, the electronic device can execute the method process as shown in fig. 1 or fig. 2.
It will be appreciated that the configuration shown in fig. 5 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 5 or may have a different configuration than shown in fig. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
Embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, may perform the method processes performed by an electronic device in the method embodiments shown in fig. 1 or fig. 2.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments, for example, the method may comprise: acquiring historical carrying information of a target vehicle; the historical carrying information comprises vehicle information and historical carrying source information; determining characteristic preference information of the target vehicle for the historical shipments according to the historical shipments source information; and determining a matching result of the target vehicle and the goods to be carried according to the characteristic preference information and the vehicle information.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. A vehicle and goods matching method is characterized by comprising the following steps:
acquiring historical carrying information of a target vehicle; the historical carrying information comprises vehicle information and historical carrying source information;
determining characteristic preference information of the target vehicle for the historical shipments according to the historical shipments source information;
and determining a matching result of the target vehicle and the goods to be carried according to the characteristic preference information and the vehicle information.
2. The method of claim 1, wherein said determining characteristic preference information for historical shipments for said target vehicle based on said historical shipper information comprises:
determining the cargo characteristics of each historical shipper according to the plurality of historical shipper source information;
counting the historical carrying times of the target vehicle for the goods with the goods characteristics aiming at each goods characteristic;
and when any one of the historical shipments is detected to exceed the threshold value of the shipments, determining the goods characteristic corresponding to the historical shipments as the preference characteristic.
3. The method of claim 1, wherein said determining characteristic preference information for historical shipments for said target vehicle based on said historical shipper information comprises:
determining the cargo characteristics of each historical shipper according to the plurality of historical shipper source information;
vectorizing the cargo characteristics of each historical shipper according to a preset rule;
determining the cargo features corresponding to the vector with the maximum feature value in the vectors of the same type as preference features; or
And determining the cargo features corresponding to the vectors with the feature values being the feature average values in the same type of vectors as preference features.
4. The method of claim 3, wherein determining a match between the target vehicle and the cargo to be carried based on the characteristic preference information and the vehicle information comprises:
vectorizing the vehicle characteristics corresponding to the vehicle information and the characteristics of the goods to be carried corresponding to the goods to be carried respectively to obtain vehicle characteristic vectors and the characteristic vectors of the goods to be carried;
and determining the matching result according to the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried.
5. The method of claim 4, wherein the determining the matching result according to the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried comprises:
taking the vector corresponding to the preference feature, the vehicle feature vector and the feature vector of the goods to be carried as the input of a logistic regression model, and determining the matching result based on the output of the logistic regression model; the regression algorithm corresponding to the logistic regression model comprises the following steps:
Figure FDA0003486641700000021
wherein the content of the first and second substances,
Figure FDA0003486641700000022
a probability parameter representing that the target vehicle can load goods to be carried, and w represents an importance degree parameter of the characteristics of the goods to be carried; b represents a correction parameter.
6. The method of claim 3, wherein the preset rules comprise:
determining whether the cargo feature can be quantified numerically;
if the cargo features can be quantified numerically, determining the characteristic numerical values of the cargo features as the characteristic values of the vectors;
and if the cargo features cannot be quantified numerically, determining the characteristic value of the vector corresponding to the cargo features according to a preset coding rule.
7. A capacity prediction method, comprising:
acquiring an order to be processed in a target area; the order to be processed comprises information of goods to be carried;
predicting an order receiving result of the order to be processed according to a matching result of the target vehicle and the goods to be carried indicated by the information of the goods to be carried; wherein the match result is determined according to the method of any one of claims 1-6;
and predicting the transport capacity information corresponding to the target area according to the order to be processed and the order receiving result.
8. A vehicle-cargo matching device, comprising:
the acquisition module is used for acquiring historical carrying information of the target vehicle; the historical carrying information comprises vehicle information and historical carrying source information;
the first determining module is used for determining characteristic preference information of the target vehicle for the historical shipments according to the historical shipments source information;
and the second determining module is used for determining the matching result of the target vehicle and the goods to be carried according to the characteristic preference information and the vehicle information.
9. An electronic device comprising a processor and a memory, the memory storing computer readable instructions that, when executed by the processor, perform the method of any of claims 1-6 or claim 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6 or 7.
CN202210082924.5A 2022-01-25 2022-01-25 Vehicle and goods matching method, and transport capacity prediction method, device and electronic equipment based on vehicle and goods matching method Pending CN114418417A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115099665A (en) * 2022-07-06 2022-09-23 江苏满运物流信息有限公司 Vehicle recommendation method and device, electronic equipment and storage medium
CN116342011A (en) * 2023-05-23 2023-06-27 万联易达物流科技有限公司 Intelligent matching method and system for vehicles and goods in whole vehicle transportation

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115099665A (en) * 2022-07-06 2022-09-23 江苏满运物流信息有限公司 Vehicle recommendation method and device, electronic equipment and storage medium
CN116342011A (en) * 2023-05-23 2023-06-27 万联易达物流科技有限公司 Intelligent matching method and system for vehicles and goods in whole vehicle transportation
CN116342011B (en) * 2023-05-23 2023-07-21 万联易达物流科技有限公司 Intelligent matching method and system for vehicles and goods in whole vehicle transportation

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