CN108256753B - Emergency material allocation method and device - Google Patents
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Abstract
The invention discloses an emergency material allocation method and device, which comprises the steps of obtaining the correlation factors with material network points; screening the association factors according to a preset rule to obtain a characteristic attribute of which the association with the deployment result exceeds a preset value; inputting the characteristic attributes serving as sample characteristic vectors into a logistic regression model to obtain corresponding logistic functions; obtaining each coefficient in the logistic regression model by adopting a maximum likelihood estimation method; and acquiring the current characteristic attribute of the current material network point, and bringing the attribute into a logistic regression model to obtain a blending result. The method predicts the availability of the current material network points by adopting a logistic regression model, can reasonably select the material network points under the unsupervised condition, and in addition, due to the screening of the associated factors, the obtained characteristic attributes have larger influence on the allocation result, so the method is more effective and reasonable.
Description
Technical Field
The invention relates to the technical field of emergency logistics, in particular to an emergency material allocation method and device.
Background
The demand on the timeliness of logistics is higher and higher at present, especially on the aspect of emergency logistics. If the material allocation efficiency is higher, the user experience of the acquiring party can be directly influenced.
In order to achieve efficient distribution, a large number of material supply points are generally provided, and which material supply point is activated is determined according to a distribution destination. Because the materials stored in each material network are different and the demands of the delivery destinations are different, the materials are often blindly delivered in the delivery process, which not only causes the problem of the delivery time process, but also often causes high transportation cost and sometimes enables more material networks.
Based on the above problems, how to reasonably implement material allocation to optimize the distribution process is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide an emergency material allocation method and device, which are used for reasonably realizing material allocation and optimizing the distribution process.
In order to solve the technical problem, the invention provides an emergency material allocation method, which comprises the following steps:
acquiring the association factors with the material network points;
screening the association factors according to a preset rule to obtain a characteristic attribute of which the association with the deployment result exceeds a preset value;
inputting the characteristic attributes serving as sample characteristic vectors into a logistic regression model to obtain corresponding logistic functions;
obtaining each coefficient in the logistic regression model by adopting a maximum likelihood estimation method;
and acquiring the current characteristic attribute of the current material network point, and bringing the current characteristic attribute into the logistic regression model to obtain a blending result.
Preferably, the predetermined rule is a statistical analysis method.
Preferably, the association factors specifically include: the distance between the vehicle and other network points within a preset range, the material type, the material storage amount, the number of material transportation vehicles, the number of traffic lights in a transportation path and the distance between the vehicle and a delivery destination.
Preferably, before inputting the feature attributes as sample feature vectors into a logistic regression model, the method further includes:
performing data preprocessing on the characteristic attributes;
the data preprocessing comprises value complementing processing, non-numerical characteristic factorization processing, normalization processing and discretization processing.
Preferably, the method further comprises the following steps:
and optimizing the logistic regression model by adopting a random gradient descent method.
Preferably, the storage format of the association factor is specifically a Data Frame format.
Preferably, when the allocation result is 1, the path planning information of the current material network point and the delivery destination is fetched and sent to a preset terminal device;
wherein, the blending result is 0 to indicate that the selection is not performed, and 1 to indicate that the selection is performed.
In order to solve the above technical problem, the present invention provides an emergency material allocation device, including:
the acquisition unit is used for acquiring the association factors with the material network points;
the screening unit is used for screening the association factors according to a preset rule to obtain a characteristic attribute of which the association with the allocation result exceeds a preset value;
the training unit is used for inputting the characteristic attributes serving as sample characteristic vectors into a logistic regression model to obtain corresponding logistic functions;
the calculation unit is used for obtaining each coefficient in the logistic regression model by adopting a maximum likelihood estimation method;
and the allocation result output unit is used for acquiring the current characteristic attribute of the current material network point and bringing the current characteristic attribute into the logistic regression model to obtain an allocation result.
Preferably, the predetermined rule is a statistical analysis method.
Preferably, the association factors specifically include: the distance between the vehicle and other network points within a preset range, the material type, the material storage amount, the number of material transportation vehicles, the number of traffic lights in a transportation path and the distance between the vehicle and a delivery destination.
The emergency material allocation method provided by the invention comprises the steps of obtaining the correlation factors with material network points; screening the association factors according to a preset rule to obtain a characteristic attribute of which the association with the deployment result exceeds a preset value; inputting the characteristic attributes serving as sample characteristic vectors into a logistic regression model to obtain corresponding logistic functions; obtaining each coefficient in the logistic regression model by adopting a maximum likelihood estimation method; and acquiring the current characteristic attribute of the current material network point, and bringing the attribute into a logistic regression model to obtain a blending result. The method predicts the availability of the current material network points by adopting a logistic regression model, can reasonably select the material network points under the unsupervised condition, and in addition, due to the screening of the associated factors, the obtained characteristic attributes have larger influence on the allocation result, so the method is more effective and reasonable.
In addition, the invention also provides an emergency material allocation device corresponding to the method, and the effect is as described above.
Drawings
In order to illustrate the embodiments of the present invention more clearly, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of an emergency material allocation method according to an embodiment of the present invention;
fig. 2 is a flowchart of another emergency material allocation method according to an embodiment of the present invention;
fig. 3 is a structural diagram of an emergency material allocating device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without any creative work belong to the protection scope of the present invention.
The core of the invention is to provide an emergency material allocation method and device, which are used for reasonably realizing material allocation and optimizing the distribution process.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flowchart of an emergency material allocation method according to an embodiment of the present invention. As shown in fig. 1, the method includes:
s10: and acquiring the association factors with the material network points.
S11: and screening the association factors according to a preset rule to obtain the characteristic attribute of which the association with the deployment result exceeds a preset value.
S12: and inputting the characteristic attributes serving as sample characteristic vectors into the logistic regression model to obtain corresponding logistic functions.
S13: and obtaining each coefficient in the logistic regression model by adopting a maximum likelihood estimation method.
S14: and acquiring the current characteristic attribute of the current material network point, and bringing the attribute into a logistic regression model to obtain a blending result.
It is understood that the relevant factors in step S10 need to be obtained from the historical data, and as a preferred embodiment, the relevant factors specifically include: the distance between the vehicle and other network points within a preset range, the material type, the material storage amount, the number of material transportation vehicles, the number of traffic lights in a transportation path and the distance between the vehicle and a delivery destination. In a specific implementation, a data table can be established for each material network point, and the table contains the relevant factors. The storage format of the associated factor is specifically a Data Frame format. It should be noted that the types of the above-mentioned related factors are only a part, and other types of factors may be included according to actual situations, and the present invention is not limited.
The predetermined rule in step S11 may be a statistical analysis method, but certainly, except that the statistical analysis method is only one of the statistical analysis methods, the method may determine which factors of the associated factors have a large association with the deployment result and which factors have a small association with the deployment result, and then ignore the factors having a small association, thereby obtaining the feature attributes. The characteristic attributes obtained after screening are used as samples of the logistic regression model, and the logistic regression model can be trained through the characteristic attributes.
Because the allocation result has only two possibilities, one is that the material network point is selected, and the other is not selected, the adjustment result corresponds to a two-classification problem. The two-classification problem means that the predicted y value is only two values (0 or 1), and it can be understood that there are many corresponding solutions to the two-classification problem, and the two-classification problem is realized by using a logistic regression model in the invention. A function used in the logistic regression model is used to normalize the value of y so that the value of y is within the interval (0,1), and this function is called logistic function (also called Sigmoid function). The description of the specific logic function can be referred to in the prior art, and the description of the invention is not repeated. After the logistic function is obtained, the maximum likelihood estimation method is adopted for calculation, each coefficient in the corresponding logistic regression model when the value of the likelihood function is maximum is the coefficient to be finally obtained, and the logistic regression model is equivalent to the completion of training.
And inputting the obtained current characteristic attribute of the current material network point into the logistic regression model to obtain a blending result. Whether the current material network point can be selected or not can be determined through the allocation result.
The emergency material allocation method provided by the embodiment comprises the steps of obtaining correlation factors with material network points; screening the association factors according to a preset rule to obtain a characteristic attribute of which the association with the deployment result exceeds a preset value; inputting the characteristic attributes serving as sample characteristic vectors into a logistic regression model to obtain corresponding logistic functions; obtaining each coefficient in the logistic regression model by adopting a maximum likelihood estimation method; and acquiring the current characteristic attribute of the current material network point, and bringing the attribute into a logistic regression model to obtain a blending result. The method predicts the availability of the current material network points by adopting a logistic regression model, can reasonably select the material network points under the unsupervised condition, and in addition, due to the screening of the associated factors, the obtained characteristic attributes have larger influence on the allocation result, so the method is more effective and reasonable.
Fig. 2 is a flowchart of another emergency material allocation method according to an embodiment of the present invention. As shown in fig. 2, on the basis of the foregoing embodiment, as a preferred implementation, before inputting the feature attributes as sample feature vectors into the logistic regression model, the method further includes:
s20: and carrying out data preprocessing on the characteristic attributes.
The data preprocessing comprises value complementing processing, non-numerical characteristic factorization processing, normalization processing and discretization processing.
In a specific implementation, for historical data, some data may not be complete, or some data may be erroneous, and therefore, data preprocessing needs to be performed on the characteristic attributes to ensure that the obtained data is usable.
As shown in fig. 2, on the basis of the above embodiment, as a preferred implementation, the method further includes:
s21: and optimizing the logistic regression model by adopting a random gradient descent method.
The random gradient descent is updated iteratively once by each sample, if the sample size is large (for example, hundreds of thousands), the parameter θ may be already iterated to the optimal solution by using only tens of thousands or thousands of samples, and compared to the batch gradient descent method, one iteration requires hundreds of thousands of training samples, one iteration cannot be optimal, and if 10 iterations are performed, 10 times of traversing the training samples is required. However, one problem associated with the stochastic gradient descent method is that it is more noisy than the batch gradient descent method, so that the stochastic gradient descent method does not go towards global optimization every iteration. And (3) minimizing the loss function of each sample, wherein although the loss function obtained in each iteration is not towards the global optimal direction, the direction of a large whole is towards the global optimal solution, and the final result is often near the global optimal solution.
On the basis of the above embodiment, as a preferred embodiment, when the allocation result is 1, the path planning information of the current material network point and the delivery destination is fetched and sent to the preset terminal device;
wherein, the blending result is 0 to indicate that the selection is not performed, and 1 to indicate that the selection is performed.
It can be understood that after the allocation result is calculated, the staff needs to know the transportation path between the current material supply point and the delivery destination, so as to successfully transport the material to the delivery destination. Based on this, in this embodiment, the path planning information is sent to a preset terminal device, for example, the terminal device may be a mobile phone, and the path planning information may be a map and voice, so that the staff may transport the material to the destination according to the information. In conclusion, the staff does not need to select a path, so that the time for transporting materials is saved.
In the above, the embodiment corresponding to the emergency material allocation method is described in detail, and the invention further provides a device corresponding to the method. Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here. Fig. 3 is a structural diagram of an emergency material allocating device according to an embodiment of the present invention. As shown in fig. 3, the apparatus includes:
an obtaining unit 10, configured to obtain a factor associated with a material website;
the screening unit 11 is configured to screen the association factors according to a predetermined rule to obtain a characteristic attribute of which the association with the deployment result exceeds a preset value;
the training unit 12 is configured to input the feature attributes as sample feature vectors into a logistic regression model to obtain corresponding logistic functions;
a calculating unit 13, configured to obtain each coefficient in the logistic regression model by using a maximum likelihood estimation method;
and the allocation result output unit 14 is used for acquiring the current characteristic attribute of the current material website and bringing the current characteristic attribute into the logistic regression model to obtain an allocation result.
The emergency material allocation device provided by the embodiment comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring the association factors with material outlets; the screening unit is used for screening the association factors according to a preset rule to obtain a characteristic attribute of which the association with the allocation result exceeds a preset value; the training unit is used for inputting the characteristic attributes serving as sample characteristic vectors into the logistic regression model to obtain corresponding logistic functions; the calculation unit is used for obtaining each coefficient in the logistic regression model by adopting a maximum likelihood estimation method; and the allocation result output unit is used for acquiring the current characteristic attribute of the current material network point and bringing the current characteristic attribute into the logistic regression model to obtain an allocation result. The method predicts the availability of the current material network points by adopting a logistic regression model, can reasonably select the material network points under the unsupervised condition, and in addition, due to the screening of the associated factors, the obtained characteristic attributes have larger influence on the allocation result, so the method is more effective and reasonable.
As a preferred embodiment, the predetermined rule is in particular a statistical analysis method.
As a preferred embodiment, the association factors specifically include: the distance between the vehicle and other network points within a preset range, the material type, the material storage amount, the number of material transportation vehicles, the number of traffic lights in a transportation path and the distance between the vehicle and a delivery destination.
The emergency material allocation method and device provided by the invention are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
It is further noted that, in the present specification, relational terms such as first and second, and the like are 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. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (8)
1. An emergency material allocation method is characterized by comprising the following steps:
acquiring the association factors with the material network points;
screening the association factors according to a preset rule to obtain a characteristic attribute of which the association with the deployment result exceeds a preset value;
inputting the characteristic attributes serving as sample characteristic vectors into a logistic regression model to obtain corresponding logistic functions;
obtaining each coefficient in the logistic regression model by adopting a maximum likelihood estimation method;
acquiring the current characteristic attribute of the current material network point, and bringing the current characteristic attribute into the logistic regression model to obtain a blending result;
the related factors specifically include: the distance between the vehicle and other network points within a preset range, the material type, the material storage amount, the number of material transportation vehicles, the number of traffic lights in a transportation path and the distance between the vehicle and a delivery destination.
2. The method of claim 1, wherein the predetermined rule is a statistical analysis method.
3. The method of claim 1, wherein before inputting the feature attributes as sample feature vectors into a logistic regression model, the method further comprises:
performing data preprocessing on the characteristic attributes;
the data preprocessing comprises value complementing processing, non-numerical characteristic factorization processing, normalization processing and discretization processing.
4. The method of claim 1, further comprising:
and optimizing the logistic regression model by adopting a random gradient descent method.
5. The method of claim 1, wherein the storage format of the correlation factor is a Data Frame format.
6. The emergency material allocation method according to claim 1, wherein when the allocation result is 1, the path planning information of the current material network point and the delivery destination is fetched and sent to a preset terminal device;
wherein, the blending result is 0 to indicate that the selection is not performed, and 1 to indicate that the selection is performed.
7. An emergency material deployment device, comprising:
the acquisition unit is used for acquiring the association factors with the material network points;
the screening unit is used for screening the association factors according to a preset rule to obtain a characteristic attribute of which the association with the allocation result exceeds a preset value;
the training unit is used for inputting the characteristic attributes serving as sample characteristic vectors into a logistic regression model to obtain corresponding logistic functions;
the calculation unit is used for obtaining each coefficient in the logistic regression model by adopting a maximum likelihood estimation method;
the allocation result output unit is used for acquiring the current characteristic attribute of the current material network point and bringing the current characteristic attribute into the logistic regression model to obtain an allocation result;
the related factors specifically include: the distance between the vehicle and other network points within a preset range, the material type, the material storage amount, the number of material transportation vehicles, the number of traffic lights in a transportation path and the distance between the vehicle and a delivery destination.
8. The emergency material deployment device of claim 7, wherein the predetermined rule is a statistical analysis method.
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