Intelligent positioning system and positioning method for warehouse logistics

Abstract

The invention relates to the technical field of deep learning, and discloses an intelligent positioning system and method for warehouse logistics, and the method comprises the following steps: collecting cargo information through a wireless communication network, the cargo information comprising the attribute information of cargoes and the information of a transportation unit where the cargoes are located; obtaining first two-dimensional structure data based on historically collected cargo information, wherein the first two-dimensional structure data comprises a first data matrix and a first relation matrix; inputting the first two-dimensional structure data into the intelligent positioning model, and outputting a result representing the ID of the transportation unit where the goods are located; the cargo information is updated through the ID, output by the intelligent positioning model, of the transportation unit where the cargo is located; according to the method, the position information of the missing goods is predicted through the deep learning model, the emergency situation of the electronic tag can be flexibly dealt with, and only the necessary information of the position information of the goods is predicted.

Description

Technical Field

[0001] The present invention relates to the field of deep learning technology, and more specifically, to an intelligent positioning system and positioning method for warehousing logistics. Background Art

[0002] The transit transportation management of logistics goods needs to achieve the goal of maximizing transportation failure as much as possible, which requires relying on the positioning of cargo logistics. Although the use of electronic tags in conjunction with cargo pallets can be used as a means of cargo positioning, the actual logistics environment is extremely dynamic. Some cargo electronic tags may be blocked or damaged, resulting in communication interruption. Missing information will make it impossible to achieve the optimal transportation allocation goal. Summary of the invention

[0003] The present invention provides an intelligent positioning system and positioning method for warehousing logistics, which solves the technical problem in the related technology that communication interruption caused by shielding or damage of electronic tags of some goods and the missing information will lead to the inability to achieve the optimal goal of transportation allocation.

[0004] The present invention provides an intelligent positioning system and positioning method for warehousing logistics, comprising the following steps:

[0005] Step 100, collecting cargo information through a wireless communication network, the cargo information including attribute information of the cargo and information of the transport unit where the cargo is located;

[0006] The cargo information is stored through the electronic tags on the storage and logistics pallets carrying the cargo, and then transmitted and collected through the wireless communication network;

[0007] Step 200, based on the historically collected cargo information, obtaining No. 1 two-dimensional structure data including No. 1 data matrix and No. 1 relationship matrix, wherein one unit of the No. 1 data matrix represents No. 1 one-dimensional structure data of a cargo; the element of the No. 1 relationship matrix in the i-th row and the j-th column represents the association between the cargo represented by the i-th unit and the j-th unit of the No. 1 data matrix, if there is an association, the value of the element of the No. 1 relationship matrix is ​​1, otherwise it is 0;

[0008] The association between goods means that the goods have the same attributes and are located in the same transport unit;

[0009] The one-dimensional structure data includes n data items sorted by time, and the tth data item represents the cargo information collected at the tth moment;

[0010] Step 300, inputting the No. 1 two-dimensional structure data into the intelligent positioning model, the intelligent positioning model includes a first intermediate layer, a second intermediate layer and a first output layer, wherein the first intermediate layer inputs the No. 1 one-dimensional structure data, outputs the first intermediate representation data to the second intermediate layer, the second intermediate layer also inputs the No. 1 relationship matrix, the second intermediate layer outputs the second intermediate representation data to the first output layer, and the first output layer outputs the result indicating the ID of the transport unit where the goods are located;

[0011] Step 400, updating the cargo information through the ID of the transport unit where the cargo is located output by the intelligent positioning model.

[0012] Furthermore, the updating method is to replace the transport unit where the goods are located in the goods information with the ID of the transport unit where the goods are located output by the intelligent positioning model.

[0013] Furthermore, the calculation formula of the first intermediate layer is as follows:

[0014] h t =tanh(W hh h t-1 +W xh x t +b h )

[0015] where h t represents the tth first intermediate representation data output by the first intermediate layer, h t-1 represents the t-1th first intermediate representation data, x t represents the tth data item of the one-dimensional structure data, W hh and W xh are the first and second weight parameters, b h is the first bias parameter and tanh is the hyperbolic tangent function.

[0016] Furthermore, the calculation formula of the second intermediate layer is as follows:

[0017]

[0018] where k v The second intermediate representation data of the vth unit of the data matrix No. 1, M (v) is the set of cells associated with the vth cell of data matrix 1, The nth first intermediate representation data output when the first structural data of the vth unit of the first data matrix is ​​input into the first intermediate layer, c v is a normalization constant, σ is the sigmoid function, W k Represents the third weight parameter.

[0019] Furthermore, the calculation formula of the first output layer is as follows:

[0020] y v =σ(W y k v +b y )

[0021] where y v represents the first output vector, whose cth component value represents the probability value of the ID of the cth transport unit, and the ID of the transport unit with the largest probability value is selected as the ID of the transport unit where the goods represented by the vth unit are located, k v The second intermediate representation data of the vth unit of the data matrix No. 1, W y is the out weight parameter, b y is the out bias parameter, and σ represents the sigmoid function.

[0022] Furthermore, an intelligent positioning system and positioning method for warehousing logistics also include the following steps:

[0023] Step 500, the information of the transport unit where the cargo is located includes: the transport capacity of the transport unit, the used capacity of the transport unit, the speed of the transport unit, the location of the transport unit, the weather in the area where the transport unit is located, and the remaining fuel of the transport unit;

[0024] The second one-dimensional structure data is obtained by sorting the updated cargo information; the second one-dimensional structure data includes n data items sorted by time, and the t-th data item represents the second two-dimensional structure data generated by the cargo information collected at the t-th time;

[0025] The second two-dimensional structure data includes a second data matrix and a second relationship matrix. A unit of the second data matrix represents the cargo information of a cargo. The element of the i-th row and j-th column of the second relationship matrix represents the association between the cargoes represented by the i-th unit and the j-th unit of the second data matrix. If there is an association, the value of the element of the second relationship matrix is ​​1, otherwise it is 0.

[0026] Step 600, input the second one-dimensional structure data into the transportation strategy model, the transportation strategy model includes a third intermediate layer, a fourth intermediate layer and a second output layer, wherein the third intermediate layer inputs the second two-dimensional structure data, outputs the third intermediate representation data to the fourth intermediate layer, the fourth intermediate layer outputs the fourth intermediate representation data to the second output layer, and the second output layer outputs a structure representing the transportation strategy of the goods at the next moment, the transportation strategy includes the transportation unit to which each cargo needs to be moved when it arrives at the goods transfer station next time.

[0027] Furthermore, the calculation formula of the third intermediate layer is as follows:

[0028]

[0029] Where Q i The third intermediate representation data of the ith unit of the second relationship matrix, W Q Represents the state weight parameter, in i and in j Respectively represent the cargo information of the cargo represented by the i-th unit and the j-th unit of the second data matrix, T i represents the set of vertices directly connected to the i-th vertex, M represents T i The total number of vertices in R i，j =tanh(δ*in i T *in j +1), tanh represents the hyperbolic tangent function, and δ represents an adjustable parameter; the default value of δ is 25.

[0030] Furthermore, the calculation formula of the fourth intermediate layer is as follows:

[0031] u (t) =σ(W u X (t) +W u H (t-1) +b u )

[0032] r (t) =σ(W r X (t) +W r H (t-1) +b r )

[0033] C (t) =tanh(W c X (t) +W c r (t) ⊙H (t-1) +b c )

[0034] H (t) =(1-u (t) )⊙C (t) +u (t) ⊙H (t-1)

[0035]

[0036] Among them, W u , W r , W c represents the fourth, fifth, and sixth weight parameters, b u 、b r 、b crepresents the fourth, fifth, and sixth bias parameters, ⊙ represents the dot product, u (t) 、r (t) and C (t) Represent the first, second, and third intermediate states respectively, where X (t) represents the tth input of the fourth intermediate layer, The third intermediate representation data of the i-th unit of the second relationship matrix output when the t-th data item is input into the third intermediate layer, H 9t) and H (t-1) They represent the t-th and t-1-th fourth intermediate representation data, n≥t≥1, n represents the number of data items of the second one-dimensional structure data, and when t=1, H (t-1) =X (t) , tanh is the hyperbolic tangent function, and σ represents the sigmoid function.

[0037] Furthermore, the calculation formula of the second output layer is as follows:

[0038] G=σ(W G H (n) +b G )

[0039] Where G represents the second output vector. A component value of the second output vector represents the probability value of a transportation strategy. The transportation strategy belongs to the transportation strategy set. The transportation strategy set contains all the transportation strategies that can be executed. (n) Indicates the nth fourth intermediate representation data, W G is the second output weight parameter, b G is the second output bias parameter, and σ represents the sigmoid function.

[0040] The present invention provides a computer storage medium for storing computer-readable instructions, which can execute the aforementioned intelligent positioning system and positioning method for warehousing logistics when the computer-readable instructions are read.

[0041] The beneficial effects of the present invention are:

[0042] The present invention predicts missing cargo location information through a deep learning model, can flexibly respond to emergencies of electronic tags, and only predicts the essential information of cargo location information. Subsequently, the cargo can be generated into two-dimensional structure data through a transportation strategy model, and the original characteristics of the cargo can be supplemented by filling, and the generation of the transportation strategy of the cargo can be supported through the information transmission of the two-dimensional structure data.

[0043] In addition, the present invention adds weather information of the transport unit, and can adapt to emergencies such as temporary route interruption, vehicle failure, weather changes, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of an intelligent positioning system and positioning method for warehousing logistics of the present invention. DETAILED DESCRIPTION

[0045] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the present specification. Various examples may omit, replace, or add various processes or components as needed. In addition, the features described in some examples may also be combined in other examples.

[0046] At least one embodiment of the present invention discloses an intelligent positioning system and positioning method for warehousing logistics, such as Figure 1 As shown, the following steps are included:

[0047] Step 100, collecting cargo information through a wireless communication network, the cargo information including attribute information of the cargo and information of the transport unit where the cargo is located;

[0048] The cargo information is stored through the electronic tags on the storage and logistics pallets carrying the cargo, and then transmitted and collected through the wireless communication network;

[0049] Step 200, based on the historically collected cargo information, obtaining No. 1 two-dimensional structure data including No. 1 data matrix and No. 1 relationship matrix, wherein one unit of the No. 1 data matrix represents No. 1 one-dimensional structure data of a cargo; the element of the No. 1 relationship matrix in the i-th row and the j-th column represents the association between the cargo represented by the i-th unit and the j-th unit of the No. 1 data matrix, if there is an association, the value of the element of the No. 1 relationship matrix is ​​1, otherwise it is 0;

[0050] The association between goods means that the goods have the same attributes and are located in the same transport unit;

[0051] The one-dimensional structure data includes n data items sorted by time, and the tth data item represents the cargo information collected at the tth moment;

[0052] Step 300, inputting the No. 1 two-dimensional structure data into the intelligent positioning model, the intelligent positioning model includes a first intermediate layer, a second intermediate layer and a first output layer, wherein the first intermediate layer inputs the No. 1 one-dimensional structure data, outputs the first intermediate representation data to the second intermediate layer, the second intermediate layer also inputs the No. 1 relationship matrix, the second intermediate layer outputs the second intermediate representation data to the first output layer, and the first output layer outputs the result indicating the ID of the transport unit where the goods are located;

[0053] Step 400, updating cargo information by using the ID of the transport unit where the cargo is located output by the intelligent positioning model;

[0054] The specific updating method is to replace the transport unit where the goods are located in the goods information with the ID of the transport unit where the goods are located output by the intelligent positioning model.

[0055] In one embodiment of the present invention, the calculation formula of the first intermediate layer is as follows:

[0056] h t =tanh(W hh h t-1 +W xh x t +b h )

[0057] where h t represents the tth first intermediate representation data output by the first intermediate layer, h t-1 represents the t-1th first intermediate representation data, x t represents the tth data item of the one-dimensional structure data, W hh and W xh are the first and second weight parameters, b h is the first bias parameter and tanh is the hyperbolic tangent function.

[0058] In one embodiment of the present invention, the calculation formula of the second intermediate layer is as follows:

[0059]

[0060] where k v The second intermediate representation data of the vth unit of the data matrix No. 1, M (v) is the set of cells associated with the vth cell of data matrix 1, The nth first intermediate representation data output when the first structural data of the vth unit of the first data matrix is ​​input into the first intermediate layer, c v is a normalization constant (such as node degree), σ is the sigmoid function, W k Represents the third weight parameter.

[0061] In one embodiment of the present invention, the calculation formula of the first output layer is as follows:

[0062] y v =σ(W y k v +b y )

[0063] where y vrepresents the first output vector, whose cth component value represents the probability value of the ID of the cth transport unit, and the ID of the transport unit with the largest probability value is selected as the ID of the transport unit where the goods represented by the vth unit are located, k v The second intermediate representation data of the vth unit of the data matrix No. 1, W y is the out weight parameter, b y is the out bias parameter, and σ represents the sigmoid function.

[0064] In one embodiment of the present invention, an intelligent positioning system and positioning method for warehousing logistics further includes the following steps:

[0065] Step 500, the information of the transport unit where the cargo is located includes: the transport capacity of the transport unit, the used capacity of the transport unit, the speed of the transport unit, the location of the transport unit, the weather in the area where the transport unit is located, and the remaining fuel of the transport unit;

[0066] The second one-dimensional structure data is obtained by sorting the updated cargo information; the second one-dimensional structure data includes n data items sorted by time, and the t-th data item represents the second two-dimensional structure data generated by the cargo information collected at the t-th time;

[0067] The second two-dimensional structure data includes a second data matrix and a second relationship matrix. A unit of the second data matrix represents the cargo information of a cargo. The element of the i-th row and j-th column of the second relationship matrix represents the association between the cargoes represented by the i-th unit and the j-th unit of the second data matrix. If there is an association, the value of the element of the second relationship matrix is ​​1, otherwise it is 0.

[0068] Step 600, input the second one-dimensional structure data into the transportation strategy model, the transportation strategy model includes a third intermediate layer, a fourth intermediate layer and a second output layer, wherein the third intermediate layer inputs the second two-dimensional structure data, outputs the third intermediate representation data to the fourth intermediate layer, the fourth intermediate layer outputs the fourth intermediate representation data to the second output layer, and the second output layer outputs a structure representing the transportation strategy of the goods at the next moment, the transportation strategy includes the transportation unit to which each cargo needs to be moved when it arrives at the cargo transfer station next time (if the transportation unit to be moved to is the transportation unit where it is currently located, there is no need to move the cargo).

[0069] In one embodiment of the present invention, the calculation formula of the third intermediate layer is as follows:

[0070]

[0071] Where Q i The third intermediate representation data of the ith unit of the second relationship matrix, W QRepresents the state weight parameter, in i and in j Respectively represent the cargo information of the cargo represented by the i-th unit and the j-th unit of the second data matrix, T i represents the set of vertices directly connected to the i-th vertex, M represents T i The total number of vertices in R i,j =tanh(δ*in i T *in j +1), tanh represents the hyperbolic tangent function, and δ represents an adjustable parameter; the default value of δ is 25.

[0072] In one embodiment of the present invention, the calculation formula of the fourth intermediate layer is as follows:

[0073] u (t) =σ(W u X (t) +W u H (t-1) +b u )

[0074] r (t) =σ(W r X (t) +W r H (t-1) +b r )

[0075] C (t) =tanh(W c X (t) +W c r (t) ⊙H (t-1) +b c )

[0076] H (t) =(1-u (t) )⊙C (t) +u (t) ⊙H (t-1)

[0077]

[0078] Among them, W u , W r , W c represents the fourth, fifth, and sixth weight parameters, b u 、b r 、b c represents the fourth, fifth, and sixth bias parameters, ⊙ represents the dot product, u (t) 、r (t) and C (t)Represent the first, second, and third intermediate states respectively, where X (t) represents the tth input of the fourth intermediate layer, The third intermediate representation data of the i-th unit of the second relationship matrix output when the t-th data item is input into the third intermediate layer, H (t) and H (t-1) They represent the t-th and t-1-th fourth intermediate representation data, n≥t≥1, n represents the number of data items of the second one-dimensional structure data, and when t=1, H (t-1) =X (t) , tanh is the hyperbolic tangent function, and σ represents the sigmoid function.

[0079] In one embodiment of the present invention, the calculation formula of the second output layer is as follows:

[0080] G=σ(W G H (n) +b G )

[0081] Where G represents the second output vector. A component value of the second output vector represents the probability value of a transportation strategy. The transportation strategy belongs to the transportation strategy set. The transportation strategy set contains all the transportation strategies that can be executed. (n) Indicates the nth fourth intermediate representation data, W G is the second output weight parameter, b G is the second output bias parameter, and σ represents the sigmoid function.

[0082] In at least one embodiment of the present invention, a computer storage medium is provided for storing computer-readable instructions, which, when read, can execute the aforementioned intelligent positioning system and positioning method for warehousing logistics.

[0083] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation mode. The above-mentioned specific implementation mode is merely illustrative and not restrictive. Under the guidance of this embodiment, ordinary technicians in this field can also make more forms of equivalent embodiments, all of which are within the protection of this embodiment.

Claims

1. An intelligent positioning system and positioning method for warehousing logistics, characterized in that: The following steps are involved: Step 100, collecting cargo information through a wireless communication network, the cargo information including attribute information of the cargo and information of the transport unit where the cargo is located; The cargo information is stored through the electronic tags on the storage and logistics pallets carrying the cargo, and then transmitted and collected through the wireless communication network; Step 200, based on the historically collected cargo information, obtaining No. 1 two-dimensional structure data including No. 1 data matrix and No. 1 relationship matrix, wherein one unit of the No. 1 data matrix represents No. 1 one-dimensional structure data of a cargo; the element of the No. 1 relationship matrix in the i-th row and the j-th column represents the association between the cargo represented by the i-th unit and the j-th unit of the No. 1 data matrix, if there is an association, the value of the element of the No. 1 relationship matrix is ​​1, otherwise it is 0; The association between goods means that the goods have the same attributes and are located in the same transport unit; The one-dimensional structure data includes n data items sorted by time, and the tth data item represents the cargo information collected at the tth moment; Step 300, inputting the No. 1 two-dimensional structure data into the intelligent positioning model, the intelligent positioning model includes a first intermediate layer, a second intermediate layer and a first output layer, wherein the first intermediate layer inputs the No. 1 one-dimensional structure data, outputs the first intermediate representation data to the second intermediate layer, the second intermediate layer also inputs the No. 1 relationship matrix, the second intermediate layer outputs the second intermediate representation data to the first output layer, and the first output layer outputs the result indicating the ID of the transport unit where the goods are located; Step 400, updating the cargo information through the ID of the transport unit where the cargo is located output by the intelligent positioning model.

2. The intelligent positioning system and positioning method for warehousing logistics according to claim 1, characterized in that: The updating method is to replace the transport unit where the goods are located in the goods information with the ID of the transport unit where the goods are located output by the intelligent positioning model.

3. The intelligent positioning system and positioning method for warehousing logistics according to claim 1, characterized in that: The calculation formula for the first intermediate layer is as follows: h t =tanh(W hh h t-1 +W xh x t +b h ) where h t represents the tth first intermediate representation data output by the first intermediate layer, h t-1 represents the t-1th first intermediate representation data, x t represents the tth data item of the one-dimensional structure data, W hh and W xh are the first and second weight parameters, b h is the first bias parameter and tanh is the hyperbolic tangent function.

4. The intelligent positioning system and positioning method for warehousing logistics according to claim 3, characterized in that: The calculation formula for the second intermediate layer is as follows: where k v The second intermediate representation data of the vth unit of the data matrix No. 1, M (v) is the set of cells associated with the vth cell of data matrix 1, The nth first intermediate representation data output when the first structural data of the vth unit of the first data matrix is ​​input into the first intermediate layer, c v is a normalization constant, σ is the sigmoid function, W k Represents the third weight parameter.

5. The intelligent positioning system and positioning method for warehousing logistics according to claim 4, characterized in that: The calculation formula of the first output layer is as follows: y v =σ(W y k v +b y ) where y v represents the first output vector, whose cth component value represents the probability value of the ID of the cth transport unit, and the ID of the transport unit with the largest probability value is selected as the ID of the transport unit where the goods represented by the vth unit are located, k v The second intermediate representation data of the vth unit of the data matrix No. 1, W y is the out weight parameter, b y is the out bias parameter, and σ represents the sigmoid function.

6. The intelligent positioning system and positioning method for warehousing logistics according to claim 1, characterized in that: The following steps are also included: Step 500, the information of the transport unit where the cargo is located includes: the transport capacity of the transport unit, the used capacity of the transport unit, the speed of the transport unit, the location of the transport unit, the weather in the area where the transport unit is located, and the remaining fuel of the transport unit; The second one-dimensional structure data is obtained by sorting the updated cargo information; the second one-dimensional structure data includes n data items sorted by time, and the t-th data item represents the second two-dimensional structure data generated by the cargo information collected at the t-th time; The second two-dimensional structure data includes a second data matrix and a second relationship matrix. A unit of the second data matrix represents the cargo information of a cargo. The element of the i-th row and j-th column of the second relationship matrix represents the association between the cargoes represented by the i-th unit and the j-th unit of the second data matrix. If there is an association, the value of the element of the second relationship matrix is ​​1, otherwise it is 0. Step 600, input the second one-dimensional structure data into the transportation strategy model, the transportation strategy model includes a third intermediate layer, a fourth intermediate layer and a second output layer, wherein the third intermediate layer inputs the second two-dimensional structure data, outputs the third intermediate representation data to the fourth intermediate layer, the fourth intermediate layer outputs the fourth intermediate representation data to the second output layer, and the second output layer outputs a structure representing the transportation strategy of the goods at the next moment, the transportation strategy includes the transportation unit to which each cargo needs to be moved when it arrives at the goods transfer station next time.

7. The intelligent positioning system and positioning method for warehousing logistics according to claim 6, characterized in that: The calculation formula for the third intermediate layer is as follows: Where Q i The third intermediate representation data of the ith unit of the second relationship matrix, W Q Represents the state weight parameter, in j and in j Respectively represent the cargo information of the cargo represented by the i-th unit and the j-th unit of the second data matrix, T j represents the set of vertices directly connected to the i-th vertex, M represents T j The total number of vertices in R i，j =tanh(δ*in i T *in j +1), tanh represents the hyperbolic tangent function, and δ represents an adjustable parameter; the default value of δ is 25.

8. The intelligent positioning system and positioning method for warehousing logistics according to claim 7, characterized in that: The calculation formula for the fourth intermediate layer is as follows: u (t) =σ(W u X (t) +W u H (t-1) +b u ) r (t) =σ(W r X (t) +W r H (t-1) +b r ) C (t) =tanh(W c X (t) +W c r (t) ⊙H (t-1) +b c ) H (t) =(1-u (t) )⊙C (t) +u (t) ⊙H (t-1) Among them, W u , W r , W c represents the fourth, fifth, and sixth weight parameters, b u 、b r 、b c represents the fourth, fifth, and sixth bias parameters, ⊙ represents the dot product, u (t) 、r (t) and C (t) Represent the first, second, and third intermediate states respectively, where X (t) represents the tth input of the fourth intermediate layer, The third intermediate representation data of the i-th unit of the second relationship matrix output when the t-th data item is input into the third intermediate layer, H (t) and H (t-1) They represent the t-th and t-1-th fourth intermediate representation data, n≥t≥1, n represents the number of data items of the second one-dimensional structure data, and when t=1, H (t-1) =X (t) , tanh is the hyperbolic tangent function, and σ represents the sigmoid function.

9. The intelligent positioning system and positioning method for warehousing logistics according to claim 8, characterized in that: The calculation formula of the second output layer is as follows: G=σ(W G H (n) +b G ) Where G represents the second output vector, a component value of the second output vector represents the probability value of a transportation strategy, the transportation strategy belongs to the transportation strategy set, and the transportation strategy set contains all the transportation strategies that can be executed, H(n) represents the nth fourth intermediate representation data, W G is the second output weight parameter, b G is the second output bias parameter, and σ represents the sigmoid function.

10. A computer storage medium, characterized in that: It is used to store computer-readable instructions, which, when read, can execute an intelligent positioning system and positioning method for warehousing logistics as described in any one of claims 1-9.

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