A warehouse logistics full-chain management system and method based on big data

Abstract

The present invention discloses a storage logistics full-chain management system and method based on big data, and relates to the technical field of storage information management. The present invention obtains historical cargo storage reports of cargo storage nodes in a monitored area, generates a historical cargo storage change set; constructs a storage statistics pair set according to the historical cargo storage change set; analyzes the storage quantity change trend of each cargo code in the historical cargo storage report corresponding to the cargo storage node in the monitored area, generates a first marked coordinate point set and a second marked coordinate point set; analyzes the cargo storage change rate of each cargo code in each detection cycle; constructs a storage ratio association group; according to the storage ratio association group, filters the temporary optimal product ratio from the association group arrangement set; and performs a real-time early warning and adjusts the product ratio according to the real-time storage ratio matching score threshold. The automatic management of commercial storage information is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse information management, and in particular to a warehouse logistics full-chain management system and method based on big data. Background Art

[0002] With the development of technology, warehouse management is transforming towards digitalization and intelligence. In the production and operation of enterprises, warehouse management is crucial, which is related to the capital occupation level and asset operation efficiency of enterprises. The application of intelligent warehouse management system can improve the efficiency and management level of warehouse operation.

[0003] In the daily management of warehousing and logistics, the ratio of commodity categories plays a vital role. In the actual warehousing management, enterprises do not further analyze and update the ratio of commodity categories based on the actual warehousing circulation data, which easily leads to the accumulation of inventory of certain commodities and insufficient supply of certain commodities, further resulting in adverse reactions such as limited sales performance, damaged customer experience, and reduced supply chain efficiency.

[0004] Therefore, the present invention discloses a warehouse logistics full-chain management system and method based on big data to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a warehouse logistics full-chain management system and method based on big data to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: a storage logistics full-chain management method based on big data, the method comprising the following steps:

[0007] S1: Obtain historical cargo storage reports of cargo storage nodes in the area to be monitored, integrate the data in the historical cargo storage reports according to cargo codes and detection cycles, and generate a historical cargo storage change set; construct a storage statistics pair set based on the historical cargo storage change set;

[0008] S2: Analyze the storage quantity change trend of each cargo code in the historical cargo storage report corresponding to the cargo storage node in the monitored area according to the storage statistics pair set, and generate a first marked coordinate point set and a second marked coordinate point set;

[0009] S3: Analyze the cargo storage change rate of each cargo code in each detection cycle according to the first marked coordinate point set, the second marked coordinate point set and the fitting curve generation; associate the cargo storage change rate of the cargo code of the same cargo storage node with the corresponding product ratio to generate a storage ratio association group;

[0010] S4: Analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association groups; arrange the storage ratio association groups according to the storage ratio matching scores to construct an association group arrangement set; and select the temporary optimal product ratio from the association group arrangement set;

[0011] S5: Monitor the cargo storage volume of the current cargo storage node in real time, calculate the real-time storage ratio matching score, and issue real-time warnings and adjust the product ratio according to the real-time storage ratio matching score threshold.

[0012] According to the above scheme, one cargo storage node corresponds to one historical cargo storage report; the historical cargo storage report includes the storage quantity of cargo corresponding to each code in the cargo storage node at the time of daily settlement; the daily settlement is the system preset time node for statistical time of cargo storage quantity.

[0013] According to the above scheme, S1 includes the following contents:

[0014] S101: Extract the cargo codes in the historical cargo storage reports corresponding to each cargo storage node in the monitored area, and generate a cargo code set, recorded as Code, Code={Code （i，j） |i∈[1,I],j∈[1,J]};where Code （i，j） represents the code of the jth type of goods in the i-th cargo storage node; I represents the total number of cargo storage nodes in the monitored area; J represents the total number of cargo codes in the i-th cargo storage node;

[0015] In the present invention, different codes are used for the same goods with different attributes; that is, if two goods have the same name but are not exactly the same in size, the two goods are encoded with two different goods; this method can more effectively separate goods with the same name but different attributes, and can provide effective data support for subsequent analysis;

[0016] S102: Extract the storage quantity information of the same coded goods at the time of daily settlement in the i-th goods storage node in the monitored area, split it according to the detection cycle, and form a historical goods storage change set, recorded as NCode, NCode={NCode （i，j） （m，n） |m∈[1, M], n∈[1, N]};

[0017] Where NCode （i，j） （m，n） Indicates the cargo code （i，j） The storage quantity at the daily settlement of the nth day in the mth detection cycle, where M represents the total number of detection cycles and N represents the total number of days in each detection cycle;

[0018] Extract the storage quantity of the jth cargo code of the i-th cargo storage node at the daily settlement in the m-th detection cycle from the historical cargo storage change set; combine the number of days in the corresponding detection cycle to generate a storage statistics pair set, recorded as NCode （i，j） m ,

[0019] NCode （i，j） m ={(n,NCode （i，j） （m，n） )|n∈[1,N]}.

[0020] The present invention extracts the cargo codes of the corresponding cargo storage nodes in the monitored area in the historical cargo storage report, bundles the cargo codes of the corresponding cargo storage nodes in the monitored area with the storage quantity at the time of daily settlement, and provides data reference for subsequent analysis of the changes in the storage quantity at the time of daily settlement.

[0021] According to the above scheme, S2 includes the following contents:

[0022] S201: Construct a plane rectangular coordinate system and set NCode for the storage statistics pair corresponding to the mth detection cycle （i，j） m The storage statistics in (n, NCode （i，j） （m，n） ) as coordinate points, where the number of days corresponds to the X-axis and the storage quantity at the time of daily settlement corresponds to the Y-axis; mark each coordinate point in the plane rectangular coordinate system;

[0023] S202: Compare the storage quantity at the time of daily settlement between two adjacent coordinate points:

[0024] If NCode exists （i，j） （m，n1） <NCode （i，j） （m，n2） In the case of NCode, the storage quantity for daily settlement is NCode （i，j） （m，n1） The corresponding coordinate point and the storage quantity at daily settlement is NCode （i，j） （m，n1） The corresponding coordinate points are marked and the coordinate points (n1, NCode （i，j） （m，n1） ) is recorded as the first marked coordinate point and as the last point of the previous fitting curve, and the coordinate point (n2, NCode （i，j） （m，n2） ) is recorded as the second marked coordinate point and used as the initial point of the next fitting curve, and the coordinate points are connected in sequence to generate the corresponding fitting curve; where 0 <n1<n2<N;

[0025] If NCode does not exist （i，j） （m，n1） <NCode （i，j） （m，n2） In the case of , two adjacent coordinate points are connected in sequence to generate a fitting curve;

[0026] S203: Set the warehouse statistics to NCode （i，j） m The first coordinate point in is recorded as the second marked coordinate point, and the warehouse statistics pair set NCode （i，j） m The last coordinate point in is recorded as the first marked coordinate point; the first marked coordinate point and the second marked coordinate point are extracted in sequence to generate a first marked coordinate point set and a second marked coordinate point set.

[0027] The present invention can effectively find the mutation point by analyzing the storage quantity at the time of daily settlement between two adjacent coordinate points, split according to the mutation point to form different coordinate point sets, and provide data support for subsequent analysis of the storage situation of the goods.

[0028] According to the above solution, S3 contains the following:

[0029] S301: Analyze the cargo code according to the first set of marked coordinate points, the second set of marked coordinate points and the generation of the fitting curve （i，j） Cargo storage change rate during the mth detection cycle:

[0030] ;

[0031] Among them SCode （i，j） m Indicates the cargo code （i，j） The cargo storage change rate in the mth detection cycle; the cargo storage change rate is used to describe the average change rate of the storage quantity in the detection cycle; Y 2 a represents the storage quantity at the time of daily settlement corresponding to the a-th coordinate point in the second set of marked coordinate points; Y 1 a represents the storage quantity corresponding to the ath coordinate point in the first set of marked coordinate points at the time of daily settlement; a∈[1, A]; A represents the number of coordinate points in the first set of marked coordinate points; Y 2 1 represents the storage quantity at the daily settlement corresponding to the first coordinate point in the second marked coordinate point set; Y 1 1 represents the storage quantity at the daily settlement corresponding to the first coordinate point in the first marked coordinate point set; TY aIndicates the number of days required to get from the ath coordinate point in the second set of marked coordinate points to the ath coordinate point in the first set of marked coordinate points;

[0032] S302: Calculate the cargo storage change rate corresponding to different cargo codes in each cargo storage node in the monitored area in each detection cycle, and generate a historical storage situation set recorded as SCode by calculating the cargo storage change rate corresponding to different cargo codes in the i-th cargo storage node in the monitored area in the m-th detection cycle. i m ; Extract the product ratio of different cargo codes in the i-th cargo storage node in the monitored area in the m-th detection cycle and record it as PCode i m ; Associate the historical storage situation set with the corresponding product ratio situation to generate a storage ratio association group recorded as (SCode i m , PCode i m ).

[0033] According to the above scheme, S4 includes the following contents:

[0034] S401: Analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association group:

[0035] ;

[0036] Among them, SRM i m PCode represents the storage ratio matching score of the i-th cargo storage node in the monitored area during the m-th detection cycle; （i，j） m Indicates the cargo code （i，j） The corresponding product ratio percentage in the mth detection cycle; S[] represents the sample standard deviation function;

[0037] By analyzing the storage ratio matching score based on the corresponding cargo storage change rate of different cargo codes in each cargo storage node in the monitored area during each detection cycle and the corresponding product ratio situation, it can effectively quantify the matching degree between the current product ratio situation and the cargo storage node, provide data support for the subsequent selection of the best product ratio situation, and improve accuracy.

[0038] S402: Arrange the storage ratio association groups in each detection period in the i-th cargo storage node in the monitored area in a descending order according to the storage ratio matching score; if the storage ratio matching scores of two or more storage ratio association groups are the same, arrange them in a descending order according to the average of the cargo storage change rate; and record the finally generated arrangement as the association group arrangement set;

[0039] S403: Use the associated group arrangement set as a solution selection list for the product ratio of the current cargo storage node, and use the product ratio corresponding to the maximum ratio matching score stored in the associated group arrangement set as the temporary optimal product ratio of the current cargo storage node.

[0040] According to the above scheme, S5 includes the following contents:

[0041] S501: Extract the storage quantity at the time of daily settlement of each cargo code in the cargo storage node within the current detection period, and map the storage quantity at the time of daily settlement to the plane rectangular coordinate system, generate a real-time fitting curve YS (x), and analyze the cargo storage change rate and storage ratio matching score of the cargo storage node within the current detection period according to the real-time fitting curve YS (x), which is recorded as the real-time storage ratio matching score SSRM i ;

[0042] S502: Determine the warning signal according to the real-time storage ratio matching score;

[0043] If 0≤SSRM i ≤θ, it indicates that the real-time storage ratio matching score does not meet the current cargo storage node, and an early warning signal is issued;

[0044] Extract the sequence replacement product ratio in the association group arrangement set, take the temporary best product ratio as the first replacement plan of the current cargo storage node, and replace it in sequence, repeat the real-time storage ratio matching score calculation and early warning signal judgment until the real-time storage ratio matching score meets the current cargo storage node; perform circulation changes of different cargo codes according to the cargo code logistics extraction plan corresponding to the product ratio; where θ is the real-time storage ratio matching score threshold;

[0045] θ = max{(SRM i max +SRM i min )÷2, SRM i max ×α}; where SRM i max It represents the maximum value of the storage ratio matching score of the i-th cargo storage node in each detection cycle in the monitored area; SRM i min represents the minimum value of the storage ratio matching score of the i-th cargo storage node in each detection cycle in the monitored area; α represents the threshold coefficient, which is a system preset constant; max{} represents the maximum value calculation function;

[0046] By calculating the storage ratio matching score threshold through the historical storage ratio matching score of the cargo storage node, the storage quantity change trend of the cargo storage node can be more effectively displayed, and the product ratio that meets the current cargo storage node can be found more accurately, thereby improving the accuracy of the system and realizing the automated management of commercial storage information.

[0047] If SSRM i >θ, it indicates that the real-time storage ratio matching score is consistent with the current cargo storage node and no warning signal is issued.

[0048] Another aspect of the present application provides a warehousing and logistics full-chain management system based on big data, which is applied to the above-mentioned warehousing and logistics full-chain management method based on big data, and the system includes a cargo storage data integration module, a storage change analysis module, a storage ratio association module, an optimal product ratio analysis module and a real-time warning module;

[0049] The cargo storage data integration module is used to obtain historical cargo storage reports of cargo storage nodes in the monitored area, integrate the data in the historical cargo storage reports according to the cargo codes and detection cycles, generate a historical cargo storage change set; and construct a storage statistics pair set according to the historical cargo storage change set;

[0050] The storage change analysis module is used to analyze the storage quantity change trend of each cargo code in the historical cargo storage report corresponding to the cargo storage node in the monitored area according to the storage statistical pair set, and generate a first marked coordinate point set and a second marked coordinate point set;

[0051] The storage ratio association module is used to analyze the cargo storage change rate of each cargo code in each detection cycle according to the first mark coordinate point set, the second mark coordinate point set and the fitting curve generation situation; associate the cargo storage change rate of the cargo code of the same cargo storage node with the corresponding product ratio situation to generate a storage ratio association group;

[0052] The optimal product ratio analysis module is used to analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association group; arrange the storage ratio association group according to the storage ratio matching score to construct an association group arrangement set; and filter the temporary optimal product ratio from the association group arrangement set;

[0053] The real-time warning module is used to monitor the cargo storage volume of the current cargo storage node in real time, calculate the real-time storage ratio matching score, and make real-time warnings and adjust the product ratio according to the real-time storage ratio matching score threshold.

[0054] The cargo storage data integration module includes a cargo code extraction unit and a storage statistics pair construction unit;

[0055] The cargo code extraction unit is used to extract cargo codes from historical cargo storage reports corresponding to each cargo storage node in the monitored area to generate a cargo code set;

[0056] The storage statistics pair construction unit is used to extract the storage quantity information of the same coded goods at the time of daily settlement in the goods storage nodes in the monitored area, split it according to the detection cycle, and form a historical goods storage change set; the storage quantity at the time of daily settlement is combined with the number of days in the corresponding detection cycle to generate a storage statistics pair set;

[0057] The storage change analysis module includes a curve fitting unit and a marking coordinate extraction unit;

[0058] The curve fitting unit is used to map the storage statistics pair to a plane rectangular coordinate system, compare the storage quantity at the daily settlement between two adjacent coordinate points, mark the coordinate points according to conditions, and connect the two adjacent coordinate points to generate a fitting curve;

[0059] The marked coordinate extraction unit is used to record the first coordinate point in the warehouse statistical pair set as the second marked coordinate point, and record the last coordinate point in the warehouse statistical pair set as the first marked coordinate point; and extract the first marked coordinate point and the second marked coordinate point in sequence to generate the first marked coordinate point set and the second marked coordinate point set.

[0060] The storage ratio association module includes a cargo storage analysis unit and an association group construction unit;

[0061] The storage ratio association module is used to analyze the cargo storage change rate of the cargo code in each detection cycle according to the first mark coordinate point set, the second mark coordinate point set and the fitting curve generation situation;

[0062] The association group construction unit is used to generate a historical storage situation set by comparing the storage change rate of goods corresponding to different goods codes in each goods storage node in the monitored area in each detection cycle; extracting the product ratios corresponding to different goods codes in each goods storage node in the monitored area in each detection cycle; and associating the historical storage situation set with the corresponding product ratio to generate a storage ratio association group;

[0063] The optimal product ratio analysis module includes a storage ratio matching calculation unit and an associated group arrangement unit;

[0064] The storage ratio matching calculation unit is used to analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association group;

[0065] The association group arrangement unit arranges the storage ratio association groups in each detection cycle in the i-th cargo storage node in the monitored area in a sequence from large to small according to the storage ratio matching score to generate an arrangement recorded as an association group arrangement set; and takes the product ratio corresponding to the maximum storage ratio matching score in the association group arrangement set as the temporary optimal product ratio of the current cargo storage node.

[0066] The real-time warning module includes a real-time monitoring calculation unit and a warning judgment unit;

[0067] The real-time monitoring calculation unit is used to extract the storage quantity at the time of daily settlement of each cargo code in the cargo storage node within the current detection period, and map the storage quantity at the time of daily settlement to a plane rectangular coordinate system, generate a real-time fitting curve, and analyze the cargo storage change rate and storage ratio matching score of the cargo storage node within the current detection period according to the real-time fitting curve;

[0068] The warning judgment unit determines the warning signal according to the real-time storage ratio matching score; extracts the sequence replacement product ratio in the associated group arrangement set, and replaces the temporary best product ratio in sequence as the first replacement plan of the current cargo storage node, repeats the real-time storage ratio matching score calculation and warning signal judgment until the real-time storage ratio matching score meets the current cargo storage node.

[0069] Compared with the prior art, the beneficial effects of the present invention are as follows: in the present invention, different codes are used for the same type of goods with different attributes; that is, if the names of two goods are the same, but the sizes of the two goods are not exactly the same, the two goods use two different goods codes; by adopting this method, the goods with the same name of different attributes can be more effectively split, and effective data support can be provided for subsequent analysis; the present invention extracts the goods code of the corresponding goods storage node in the monitored area in the historical goods storage report, and bundles the goods code of the corresponding goods storage node in the monitored area with the storage quantity at the daily settlement, so as to provide data reference for the subsequent analysis of the change of the storage quantity at the daily settlement. The present invention can effectively find the mutation point by analyzing the storage quantity at the daily settlement between two adjacent coordinate points, split according to the mutation point, form different coordinate point sets, and provide data support for the subsequent analysis of the storage situation of goods. According to the storage change rate of the corresponding goods storage of different goods codes in each goods storage node in the monitored area in each detection cycle and the corresponding product ratio, the storage ratio matching score is analyzed, which can effectively quantify the matching degree of the current product ratio and the goods storage node, and provide data support for the subsequent selection of the best product ratio, and improve the accuracy. By calculating the storage ratio matching score threshold through the historical storage ratio matching score of the cargo storage node, the storage quantity change trend of the cargo storage node can be more effectively displayed, and the product ratio that meets the current cargo storage node can be found more accurately, thereby improving the accuracy of the system and realizing the automated management of commercial storage information. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0071] Figure 1 It is a flow chart of a whole-chain management method of warehousing logistics based on big data of the present invention;

[0072] Figure 2 This is a schematic diagram of the structure of a full-chain warehousing logistics management system based on big data in the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Example 1: Please refer to Figure 1 The present invention provides a technical solution: a storage logistics full-chain management method based on big data, the method comprising the following steps:

[0075] S1: Obtain historical cargo storage reports of cargo storage nodes in the area to be monitored, integrate the data in the historical cargo storage reports according to cargo codes and detection cycles, and generate a historical cargo storage change set; construct a storage statistics pair set based on the historical cargo storage change set;

[0076] S2: Analyze the storage quantity change trend of each cargo code in the historical cargo storage report corresponding to the cargo storage node in the monitored area according to the storage statistics pair set, and generate a first marked coordinate point set and a second marked coordinate point set;

[0077] S3: Analyze the cargo storage change rate of each cargo code in each detection cycle according to the first marked coordinate point set, the second marked coordinate point set and the fitting curve generation; associate the cargo storage change rate of the cargo code of the same cargo storage node with the corresponding product ratio to generate a storage ratio association group;

[0078] S4: Analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association groups; arrange the storage ratio association groups according to the storage ratio matching scores to construct an association group arrangement set; and select the temporary optimal product ratio from the association group arrangement set;

[0079] S5: Monitor the cargo storage volume of the current cargo storage node in real time, calculate the real-time storage ratio matching score, and issue real-time warnings and adjust the product ratio according to the real-time storage ratio matching score threshold.

[0080] One cargo storage node corresponds to one historical cargo storage report; the historical cargo storage report contains the storage quantity of cargo corresponding to each code in the cargo storage node at the time of daily settlement; the daily settlement is the system preset statistical time node for the cargo storage quantity.

[0081] In S1, the following contents are included:

[0082] S101: Extract the cargo codes in the historical cargo storage reports corresponding to each cargo storage node in the monitored area, and generate a cargo code set, recorded as Code, Code={Code （i，j） |i∈[1,I],j∈[1,J]};where Code （i，j） represents the code of the jth type of goods in the i-th cargo storage node; I represents the total number of cargo storage nodes in the monitored area; J represents the total number of cargo codes in the i-th cargo storage node;

[0083] S102: Extract the storage quantity information of the same coded goods at the time of daily settlement in the i-th goods storage node in the monitored area, split it according to the detection cycle, and form a historical goods storage change set, recorded as NCode, NCode={NCode （i，j） （m，n） |m∈[1, M], n∈[1, N]};

[0084] Where NCode （i，j） （m，n） Indicates the cargo code （i，j） The storage quantity at the daily settlement of the nth day in the mth detection cycle, where M represents the total number of detection cycles and N represents the total number of days in each detection cycle;

[0085] Extract the storage quantity of the jth cargo code of the i-th cargo storage node at the daily settlement in the m-th detection cycle from the historical cargo storage change set; combine the number of days in the corresponding detection cycle to generate a storage statistics pair set, recorded as NCode （i，j） m ,

[0086] NCode （i，j） m ={(n,NCode （i，j） （m，n） )|n∈[1,N]}.

[0087] In S2, the following contents are included:

[0088] S201: Construct a plane rectangular coordinate system and set NCode for the storage statistics pair corresponding to the mth detection cycle （i，j） m The storage statistics in (n, NCode （i，j） （m，n） ) as coordinate points, where the number of days corresponds to the X-axis and the storage quantity at the time of daily settlement corresponds to the Y-axis; mark each coordinate point in the plane rectangular coordinate system;

[0089] S202: Compare the storage quantity at the time of daily settlement between two adjacent coordinate points:

[0090] If NCode exists （i，j） （m，n1） <NCode （i，j） （m，n2） In the case of NCode, the storage quantity for daily settlement is NCode （i，j） （m，n1） The corresponding coordinate point and the storage quantity at daily settlement is NCode （i，j） （m，n1） The corresponding coordinate points are marked and the coordinate points (n1, NCode （i，j）（m，n1） ) is recorded as the first marked coordinate point and as the last point of the previous fitting curve, and the coordinate point (n2, NCode （i，j） （m，n2） ) is recorded as the second marked coordinate point and used as the initial point of the next fitting curve, and the coordinate points are connected in sequence to generate the corresponding fitting curve; where 0 <n1<n2<N;

[0091] In this embodiment, the warehouse statistics pair set is {(1, 10), (2, 9), (3, 8), (4, 10), (5, 9)};

[0092] Where NCode （i，j） （m。3） <NCode （i，j） （m。4） ; Record the coordinate point (3, 8) as the first marked coordinate point and as the last point of the previous fitting curve, record the coordinate point (4, 10) as the second marked coordinate point and as the initial point of the next fitting curve, and connect the coordinate points in sequence to generate the corresponding fitting curve;

[0093] If NCode does not exist （i，j） （m，n1） <NCode （i，j） （m，n2） In the case of , two adjacent coordinate points are connected in sequence to generate a fitting curve;

[0094] S203: Set the warehouse statistics to NCode （i，j） m The first coordinate point in is recorded as the second marked coordinate point, and the warehouse statistics pair set NCode （i，j） m The last coordinate point in is recorded as the first marked coordinate point; the first marked coordinate point and the second marked coordinate point are extracted in sequence to generate a first marked coordinate point set and a second marked coordinate point set.

[0095] In S3, the following are included:

[0096] S301: Analyze the cargo code according to the first set of marked coordinate points, the second set of marked coordinate points and the generation of the fitting curve （i，j） Cargo storage change rate during the mth detection cycle:

[0097] ;

[0098] Among them SCode （i，j） m Indicates the cargo code （i，j） The change rate of cargo storage in the mth detection cycle; Y 2 arepresents the storage quantity at the time of daily settlement corresponding to the a-th coordinate point in the second set of marked coordinate points; Y 1 a represents the storage quantity corresponding to the ath coordinate point in the first set of marked coordinate points at the time of daily settlement; a∈[1, A]; A represents the number of coordinate points in the first set of marked coordinate points; Y 2 1 represents the storage quantity at the daily settlement corresponding to the first coordinate point in the second marked coordinate point set; Y 1 1 represents the storage quantity at the daily settlement corresponding to the first coordinate point in the first marked coordinate point set; TY a Indicates the number of days required to get from the ath coordinate point in the second set of marked coordinate points to the ath coordinate point in the first set of marked coordinate points;

[0099] S302: Calculate the cargo storage change rate corresponding to different cargo codes in each cargo storage node in the monitored area in each detection cycle, and generate a historical storage situation set recorded as SCode by calculating the cargo storage change rate corresponding to different cargo codes in the i-th cargo storage node in the monitored area in the m-th detection cycle. i m ; Extract the product ratio of different cargo codes in the i-th cargo storage node in the monitored area in the m-th detection cycle and record it as PCode i m ; Associate the historical storage situation set with the corresponding product ratio situation to generate a storage ratio association group recorded as (SCode i m , PCode i m ).

[0100] In S4, the following are included:

[0101] S401: Analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association group:

[0102] ;

[0103] Among them, SRM i m PCode represents the storage ratio matching score of the i-th cargo storage node in the monitored area during the m-th detection cycle; （i，j） m Indicates the cargo code （i，j） The corresponding product ratio percentage in the mth detection cycle; S[] represents the sample standard deviation function;

[0104] S402: Arrange the storage ratio association groups in each detection period in the i-th cargo storage node in the monitored area in a descending order according to the storage ratio matching score; if the storage ratio matching scores of two or more storage ratio association groups are the same, arrange them in a descending order according to the average of the cargo storage change rate; and record the finally generated arrangement as the association group arrangement set;

[0105] S403: Use the associated group arrangement set as a solution selection list for the product ratio of the current cargo storage node, and use the product ratio corresponding to the maximum ratio matching score stored in the associated group arrangement set as the temporary optimal product ratio of the current cargo storage node.

[0106] In S5, the following are included:

[0107] S501: Extract the storage quantity at the time of daily settlement of each cargo code in the cargo storage node within the current detection period, and map the storage quantity at the time of daily settlement to the plane rectangular coordinate system, generate a real-time fitting curve YS (x), and analyze the cargo storage change rate and storage ratio matching score of the cargo storage node within the current detection period according to the real-time fitting curve YS (x), which is recorded as the real-time storage ratio matching score SSRM i ;

[0108] S502: Determine the warning signal according to the real-time storage ratio matching score;

[0109] If 0≤SSRM i ≤θ, it indicates that the real-time storage ratio matching score does not meet the current cargo storage node, and an early warning signal is issued;

[0110] Extract the sequence replacement product ratio in the association group arrangement set, take the temporary best product ratio as the first replacement plan of the current cargo storage node, and replace it in sequence, repeat the real-time storage ratio matching score calculation and early warning signal judgment until the real-time storage ratio matching score meets the current cargo storage node; perform circulation changes of different cargo codes according to the cargo code logistics extraction plan corresponding to the product ratio; where θ is the real-time storage ratio matching score threshold;

[0111] θ = max{(SRM i max +SRM i min )÷2, SRM i max ×α}; where SRM i max It represents the maximum value of the storage ratio matching score of the i-th cargo storage node in each detection cycle in the monitored area; SRM i minIt represents the minimum value of the storage ratio matching score of the i-th cargo storage node in each detection cycle in the monitored area; α represents the threshold coefficient, which is a system preset constant; max{} represents the maximum value calculation function;

[0112] If SSRM i >θ, it indicates that the real-time storage ratio matching score is consistent with the current cargo storage node and no warning signal is issued.

[0113] Example 2: Please refer to Figure 2 , the present invention provides a technical solution: a full-chain warehousing logistics management system based on big data, the system includes a cargo storage data integration module, a storage change analysis module, a storage ratio association module, an optimal product ratio analysis module and a real-time warning module;

[0114] The cargo storage data integration module is used to obtain the historical cargo storage report of the cargo storage nodes in the monitored area, integrate the data in the historical cargo storage report according to the cargo code and the detection cycle, and generate a historical cargo storage change set; and construct a storage statistics pair set based on the historical cargo storage change set;

[0115] The storage change analysis module is used to analyze the storage quantity change trend of each cargo code in the historical cargo storage report corresponding to the cargo storage node in the monitored area according to the storage statistics pair set, and generate a first marked coordinate point set and a second marked coordinate point set;

[0116] The storage ratio association module is used to analyze the cargo storage change rate of each cargo code in each detection cycle according to the first mark coordinate point set, the second mark coordinate point set and the fitting curve generation situation; associate the cargo storage change rate of the cargo code of the same cargo storage node with the corresponding product ratio situation to generate a storage ratio association group;

[0117] The optimal product ratio analysis module is used to analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association group; arrange the storage ratio association groups according to the storage ratio matching scores to construct an association group arrangement set; and filter the temporary optimal product ratio from the association group arrangement set;

[0118] The real-time warning module is used to monitor the cargo storage volume of the current cargo storage node in real time, calculate the real-time storage ratio matching score, and issue real-time warnings and adjust the product ratio plan based on the real-time storage ratio matching score threshold.

[0119] The cargo storage data integration module includes a cargo code extraction unit and a storage statistics pair construction unit;

[0120] The cargo code extraction unit is used to extract the cargo codes in the historical cargo storage reports corresponding to each cargo storage node in the monitored area to generate a cargo code set;

[0121] The storage statistics pair construction unit is used to extract the storage quantity information of the same coded goods at the time of daily settlement in the goods storage nodes in the monitored area, split it according to the detection cycle, and form a historical goods storage change set; the storage quantity at the time of daily settlement is combined with the number of days in the corresponding detection cycle to generate a storage statistics pair set;

[0122] The storage change analysis module includes a curve fitting unit and a marking coordinate extraction unit;

[0123] The curve fitting unit is used to map the storage statistics pair to a plane rectangular coordinate system, compare the storage quantity at the daily settlement between two adjacent coordinate points, mark the coordinate points according to the conditions, and connect the two adjacent coordinate points to generate a fitting curve;

[0124] The marked coordinate extraction unit is used to record the first coordinate point in the warehouse statistical pair set as the second marked coordinate point, and record the last coordinate point in the warehouse statistical pair set as the first marked coordinate point; extract the first marked coordinate point and the second marked coordinate point in sequence to generate the first marked coordinate point set and the second marked coordinate point set.

[0125] The storage ratio association module includes a cargo storage analysis unit and an association group construction unit;

[0126] The storage ratio association module is used to analyze the cargo storage change rate of the cargo code in each detection cycle according to the first marking coordinate point set, the second marking coordinate point set and the fitting curve generation situation;

[0127] The association group construction unit is used to generate a historical storage situation set by comparing the storage change rate of goods corresponding to different goods codes in each goods storage node in the monitored area in each detection cycle; extracting the product ratio situation corresponding to different goods codes in each goods storage node in the monitored area in each detection cycle; associating the historical storage situation set with the corresponding product ratio situation to generate a storage ratio association group;

[0128] The optimal product ratio analysis module includes a storage ratio matching calculation unit and an associated group arrangement unit;

[0129] The storage ratio matching calculation unit is used to analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association group;

[0130] The association group arrangement unit arranges the storage ratio association groups in each detection cycle in the i-th cargo storage node in the monitored area in a sequence from large to small according to the storage ratio matching score to generate an arrangement recorded as an association group arrangement set; the product ratio corresponding to the maximum storage ratio matching score in the association group arrangement set is used as the temporary optimal product ratio of the current cargo storage node.

[0131] The real-time warning module includes a real-time monitoring calculation unit and a warning judgment unit;

[0132] The real-time monitoring calculation unit is used to extract the storage quantity at the time of daily settlement of each cargo code in the cargo storage node within the current detection period, and map the storage quantity at the time of daily settlement to a plane rectangular coordinate system, generate a real-time fitting curve, and analyze the cargo storage change rate and storage ratio matching score of the cargo storage node within the current detection period according to the real-time fitting curve;

[0133] The early warning judgment unit determines the early warning signal according to the real-time storage ratio matching score; extracts the sequence replacement product ratio in the associated group arrangement set, and replaces the temporary best product ratio as the first replacement plan of the current cargo storage node in sequence, repeats the real-time storage ratio matching score calculation and early warning signal judgment until the real-time storage ratio matching score meets the current cargo storage node.

[0134] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0135] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A full-chain management method for warehousing logistics based on big data, characterized in that: The method comprises the following steps: S1: Obtain historical cargo storage reports of cargo storage nodes in the area to be monitored, integrate the data in the historical cargo storage reports according to cargo codes and detection cycles, and generate a historical cargo storage change set; construct a storage statistics pair set based on the historical cargo storage change set; S2: Analyze the storage quantity change trend of each cargo code in the historical cargo storage report corresponding to the cargo storage node in the monitored area according to the storage statistics pair set, and generate a first marked coordinate point set and a second marked coordinate point set; S3: Analyze the cargo storage change rate of each cargo code in each detection cycle according to the first marked coordinate point set, the second marked coordinate point set and the fitting curve generation; associate the cargo storage change rate of the cargo code of the same cargo storage node with the corresponding product ratio to generate a storage ratio association group; S4: Analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association groups; arrange the storage ratio association groups according to the storage ratio matching scores to construct an association group arrangement set; and select the temporary optimal product ratio from the association group arrangement set; S5: Real-time monitoring of the storage volume of the current cargo storage node, calculation of the real-time storage ratio matching score, and real-time warning and adjustment of the product ratio according to the real-time storage ratio matching score threshold; S1 contains the following contents: S101: extract the cargo code corresponding to each cargo storage node in the monitored area in the historical cargo storage report, and generate a cargo code set, recorded as Code, Code = {Code (i，j) |i∈[1,I],j∈[1,J]};where Code (i，j) Indicates the code of the jth type of goods in the i-th goods storage node; I represents the total number of cargo storage nodes in the area to be monitored; J represents the total number of cargo codes in the i-th cargo storage node; S102: Extract the storage quantity information of the same coded goods at the time of daily settlement in the i-th goods storage node in the monitored area, split it according to the detection cycle, and form a historical goods storage change set, recorded as NCode, NCode = {NCode (i，j) (m，n) |m∈[1, M], n∈[1, N]}; Where NCode (i，j) (m ， n) Indicates the cargo code (i，j) The storage quantity at the daily settlement of the nth day in the mth detection cycle, where M represents the total number of detection cycles and N represents the total number of days in each detection cycle; Extract the storage quantity of the jth cargo code of the i-th cargo storage node at the daily settlement in the m-th detection cycle from the historical cargo storage change set; combine the number of days in the corresponding detection cycle to generate a storage statistics pair set, recorded as NCode (i，j) m , NCode (i，j) m ={(n,NCode (i，j) (m，n) )|n∈[1,N]}; In S2, the following contents are included: S201: Construct a plane rectangular coordinate system and set NCode for the storage statistics pair corresponding to the mth detection cycle (i，j) m The storage statistics in (n, NCode (i，j) (m ， n) ) as coordinate points, where the number of days corresponds to the X-axis and the storage quantity at the time of daily settlement corresponds to the Y-axis; mark each coordinate point in the plane rectangular coordinate system; S202: Compare the storage quantity at the time of daily settlement between two adjacent coordinate points: If NCode exists (i，j) (m ， n1) <NCode (i，j) (m ， n2) In the case of NCode, the storage quantity for daily settlement is NCode (i，j) (m ， n1) The corresponding coordinate point and the storage quantity at daily settlement is NCode (i，j) (m ， n1) The corresponding coordinate points are marked and the coordinate points (n1, NCode (i，j) (m ， n1) ) is recorded as the first marked coordinate point and as the last point of the previous fitting curve, and the coordinate point (n2, NCode (i，j) (m ， n2) ) is recorded as the second marked coordinate point and used as the initial point of the next fitting curve, and the coordinate points are connected in sequence to generate the corresponding fitting curve; where 0 <n1<n2<N; If NCode does not exist (i，j) (m ， n1) <NCode (i，j) (m ， n2) In the case of , two adjacent coordinate points are connected in sequence to generate a fitting curve; S203: Set the warehouse statistics to NCode (i，j) m The first coordinate point in is recorded as the second marked coordinate point, and the warehouse statistics pair set NCode (i，j) m The last coordinate point in is recorded as the first marked coordinate point; the first marked coordinate point and the second marked coordinate point are extracted in sequence to generate a first marked coordinate point set and a second marked coordinate point set; In S3, the following are included: S301: Analyze the cargo code according to the first set of marked coordinate points, the second set of marked coordinate points and the generation of the fitting curve (i，j) Cargo storage change rate during the mth detection cycle: Among them SCode (i，j) m Indicates the cargo code (i，j) The change rate of cargo storage in the mth detection cycle; Y 2 a represents the storage quantity at the time of daily settlement corresponding to the a-th coordinate point in the second set of marked coordinate points; Y 1 a represents the storage quantity corresponding to the ath coordinate point in the first set of marked coordinate points at the time of daily settlement; a∈[1, A]; A represents the number of coordinate points in the first set of marked coordinate points; Y 2 1 represents the storage quantity at the daily settlement corresponding to the first coordinate point in the second marked coordinate point set; Y 1 1 represents the storage quantity at the daily settlement corresponding to the first coordinate point in the first marked coordinate point set; TY a Indicates the number of days required to get from the ath coordinate point in the second set of marked coordinate points to the ath coordinate point in the first set of marked coordinate points; S302: Calculate the cargo storage change rate corresponding to different cargo codes in each cargo storage node in the monitored area in each detection cycle, and generate a historical storage situation set recorded as SCode by calculating the cargo storage change rate corresponding to different cargo codes in the i-th cargo storage node in the monitored area in the m-th detection cycle. i m ; Extract the product ratio of different cargo codes in the i-th cargo storage node in the monitored area in the m-th detection cycle and record it as PCode i m ; Associate the historical storage situation set with the corresponding product ratio situation to generate a storage ratio association group recorded as (SCode i m , PCode i m ); S5 contains the following contents: S501: Extract the storage quantity at the time of daily settlement of each cargo code in the cargo storage node within the current detection period, and map the storage quantity at the time of daily settlement to the plane rectangular coordinate system, generate a real-time fitting curve YS(x), and analyze the cargo storage change rate and storage ratio matching score of the cargo storage node within the current detection period according to the real-time fitting curve YS(x), which is recorded as the real-time storage ratio matching score SSRM i ; S502: Determine the warning signal according to the real-time storage ratio matching score; If 0≤SSRM i ≤θ, it indicates that the real-time storage ratio matching score does not meet the current cargo storage node, and an early warning signal is issued; Extract the sequence replacement product ratio in the association group arrangement set, take the temporary best product ratio as the first replacement plan of the current cargo storage node, and replace it in sequence, repeat the real-time storage ratio matching score calculation and early warning signal judgment until the real-time storage ratio matching score meets the current cargo storage node; perform circulation changes of different cargo codes according to the cargo code logistics extraction plan corresponding to the product ratio; where θ is the real-time storage ratio matching score threshold; θ=max{(SRM i max +SRM i min )÷2, SRM i max ×α}; where SRM i max It represents the maximum value of the storage ratio matching score of the i-th cargo storage node in each detection cycle in the monitored area; SRM i min Indicates the minimum value of the storage ratio matching score of the i-th cargo storage node in each detection cycle in the monitored area; α represents a threshold coefficient, which is a system preset constant; max{} represents a maximum value calculation function; If SSRM i >θ, it indicates that the real-time storage ratio matching score is consistent with the current cargo storage node and no warning signal is issued.

2. According to the big data-based warehousing logistics full-chain management method of claim 1, it is characterized by: One cargo storage node corresponds to one historical cargo storage report; the historical cargo storage report includes the storage quantity of cargo corresponding to each code in the cargo storage node at the time of daily settlement; the daily settlement is a system preset statistical time node for the storage quantity of cargo.

3. A warehouse logistics full-chain management method based on big data according to claim 2, characterized in that: In S4, the following are included: S401: Analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association group: Among them, SRM i m PCode represents the storage ratio matching score of the i-th cargo storage node in the monitored area during the m-th detection cycle; (i，j) m Indicates the cargo code (i，j) The corresponding product ratio percentage in the mth detection cycle; S[] represents the sample standard deviation function; S402: Arrange the storage ratio association groups in each detection period in the i-th cargo storage node in the monitored area in a descending order according to the storage ratio matching score; if the storage ratio matching scores of two or more storage ratio association groups are the same, arrange them in a descending order according to the average of the cargo storage change rate; and record the finally generated arrangement as the association group arrangement set; S403: Use the associated group arrangement set as a solution selection list for the product ratio of the current cargo storage node, and use the product ratio corresponding to the maximum ratio matching score stored in the associated group arrangement set as the temporary optimal product ratio of the current cargo storage node.

4. A warehouse logistics full-chain management system based on big data, the system is applied to the warehouse logistics full-chain management method based on big data as described in any one of claims 1-3, characterized in that: The system includes a cargo storage data integration module, a storage change analysis module, a storage ratio association module, an optimal product ratio analysis module and a real-time warning module; The cargo storage data integration module is used to obtain historical cargo storage reports of cargo storage nodes in the monitored area, integrate the data in the historical cargo storage reports according to the cargo codes and detection cycles, generate a historical cargo storage change set; and construct a storage statistics pair set according to the historical cargo storage change set; The storage change analysis module is used to analyze the storage quantity change trend of each cargo code in the historical cargo storage report corresponding to the cargo storage node in the monitored area according to the storage statistical pair set, and generate a first marked coordinate point set and a second marked coordinate point set; The storage ratio association module is used to analyze the cargo storage change rate of each cargo code in each detection cycle according to the first mark coordinate point set, the second mark coordinate point set and the fitting curve generation situation; associate the cargo storage change rate of the cargo code of the same cargo storage node with the corresponding product ratio situation to generate a storage ratio association group; The optimal product ratio analysis module is used to analyze the storage ratio matching scores of each cargo storage node in different detection cycles according to the storage ratio association group; arrange the storage ratio association group according to the storage ratio matching score to construct an association group arrangement set; and filter the temporary optimal product ratio from the association group arrangement set; The real-time warning module is used to monitor the cargo storage volume of the current cargo storage node in real time, calculate the real-time storage ratio matching score, and make real-time warnings and adjust the product ratio according to the real-time storage ratio matching score threshold.

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