A supply chain shared cold chain warehousing service management platform and method

Abstract

The present application relates to the field of warehouse management technology, and discloses a supply chain shared cold chain warehousing service management platform and method. The method includes: performing data governance on historical outbound data and historical storage data to obtain first complete data and second complete data; calculating a first score for each commodity based on the forecast time period and the first complete data, and calculating a second score for each storage area based on the second complete data; dividing the commodities into hot-selling commodities and non-hot-selling commodities based on the first score, and dividing the storage area into a fast outbound area and a normal outbound area based on the second score; matching the first score and the second score to determine a warehousing plan; obtaining the stage outbound data of each commodity in the warehouse at each preset time interval, generating a future outbound trend based on the stage outbound data, and updating the warehousing plan based on the future outbound trend. The present invention comprehensively considers the popularity of commodities, outbound convenience and commodity storage requirements, and ultimately selects the most suitable storage location for the commodities.

Description

Technical Field

[0001] The present application relates to the field of warehouse management technology, and in particular to a supply chain shared cold chain warehousing service management platform and method. Background Art

[0002] Modern warehouse management systems (WMS) are gradually being used in most companies. However, although these systems can improve item access efficiency and reduce inventory errors, they still face many challenges in large-scale and complex supply chain environments, especially in resource scheduling, cargo distribution, and dynamic optimization of storage space. The system's response speed and management accuracy still need to be further improved.

[0003] To solve the above problems, the following methods have been proposed in the prior art. For example, the Chinese patent document with publication number CN117132196A discloses a warehouse management method, device, equipment and storage medium. The method first classifies the popularity of warehouse goods based on historical order data and predicts the popularity type of the goods based on their sales volume. Then, the heat frequency value of each storage location is calculated based on the type of storage location and the initial heat frequency value. Finally, according to the heat frequency value and inventory of the storage location, a suitable storage location is selected to transfer the goods to be transferred to the target storage location. For another example, the Chinese patent document with publication number CN114862294A discloses a recommendation management method for small and medium-sized warehouses based on big data analysis. The method improves the turnover rate of goods by optimizing the warehousing and outbound processes, and by comparing historical data and product sales, high-sales goods are preferentially allocated to storage locations close to the distribution area, while low-sales goods are allocated to storage locations farther away, thereby improving warehouse management efficiency.

[0004] However, in cold chain warehouse management, since most of the goods involved are sensitive to environmental conditions such as temperature and humidity, in addition to data such as goods and sales, environmental data such as temperature and humidity also need to be included in the recommendation system for comprehensive consideration to avoid goods being placed in locations with large temperature fluctuations, which may cause damage to the goods. Summary of the Invention

[0005] This application provides a supply chain shared cold chain warehousing service management platform and method to solve the problems existing in the above-mentioned background technology.

[0006] In order to achieve the above-mentioned purpose of the invention, the present invention proposes a supply chain shared cold chain warehousing service management method, comprising:

[0007] Collect historical shipment data of goods and historical storage data of each storage area of the warehouse, and perform data management on the historical shipment data and the historical storage data to obtain first complete data and second complete data;

[0008] Calculate a first score for each commodity based on the forecast period and the first complete data, and calculate a second score for each storage area based on the second complete data;

[0009] Based on the first score, the products are divided into hot-selling products and non-hot-selling products; based on the second score, the storage area is divided into a fast delivery area and a normal delivery area;

[0010] Obtaining storage parameters of each storage area in the fast delivery area and storage requirements of the hot-selling products;

[0011] Under the constraints of the storage requirements and the storage parameters, matching the first score and the second score, and determining a storage solution for each of the hot-selling products based on the matching results;

[0012] The phased outbound data of each commodity in the warehouse is obtained at preset intervals, the future outbound trend is generated based on the phased outbound data, and the warehousing plan is updated based on the future outbound trend to continuously optimize the warehousing layout.

[0013] Furthermore, data governance of the historical outbound data includes the following steps:

[0014] Arrange each first sub-data in the historical outbound data into a first time series based on its timestamp, divide the first time series into multiple sub-sequences based on the timestamp, set a corresponding adjustment weight for each sub-sequence, modify the first sub-data in the sub-sequence based on the adjustment weight to obtain second sub-data, and define the adjusted first time series as an initial sequence;

[0015] Calculate the mean and standard deviation of the initial sequence, generate a first numerical range and a second numerical range based on the mean and the standard deviation, set the second sub-data as a normal value if the second sub-data is within the first numerical range, and set the second sub-data as an abnormal value if the second sub-data is outside the second numerical range. Correct the abnormal value, and define the corrected initial sequence as a standard sequence.

[0016] Furthermore, correcting the abnormal value includes the following steps:

[0017] The upper limit and lower limit of the second numerical range are located, and the abnormal value smaller than the lower limit is corrected to the lower limit, and the abnormal value larger than the upper limit is corrected to the upper limit.

[0018] Furthermore, calculating the first score includes the following steps:

[0019] Generate a trend component sequence, a seasonal component sequence, and a residual component sequence for each commodity corresponding to the standard sequence based on a seasonal decomposition algorithm, extract sequences corresponding to the forecast time period from the trend component sequence, seasonal component sequence, and residual component sequence as the first, second, and third sequences, and perform weighted summation on the first, second, and third sequences to calculate an initial score;

[0020] The correlation scores between the commodities are calculated based on the standard sequence of each commodity, and the initial scores are revised based on the correlation scores to obtain the first score.

[0021] Furthermore, calculating the relevance score includes the following steps:

[0022] The product to be calculated is defined as a target product, and the standard sequences of the target product and the remaining products are combined to obtain multiple sequence groups. Two of the standard sequences in the sequence groups respectively define a first analysis sequence and a second analysis sequence. The first analysis sequence and the second analysis sequence are analyzed based on linear regression to obtain a correlation coefficient between the target product and the remaining products. The correlation coefficient with the target product that is greater than a judgment threshold is used as an influence coefficient, and the product corresponding to the influence coefficient is defined as an associated product. The relevance score of the target product is calculated based on the influence coefficient and the initial score of the associated product.

[0023] Furthermore, calculating the second score includes the following steps:

[0024] Based on the straight-line distance from the storage area to the picking area, a fuzzy theory algorithm is applied to calculate the distance convenience score of the storage area. Based on the position of the storage area in the shelf, the hierarchical analysis method is used to calculate the accessibility score of the storage area. Based on the actual transportation path from the storage area to the picking area, a linear regression algorithm is used to calculate the path complexity score of the storage area. Based on the Bayesian model algorithm, the influence weights of all scores on the outbound efficiency are analyzed. Based on the influence weights, a weighted sum of all scores is taken to obtain a comprehensive score, and the comprehensive score is defined as the second score.

[0025] Furthermore, generating future outbound trends includes the following steps:

[0026] Acquire actual shipment data of goods within the forecast time period, convert the actual shipment data into an actual shipment sequence, segment the actual shipment sequence into multiple interval sequences, define the last interval sequence as a first target sequence, define the interval sequence with the highest similarity to the first target sequence as a second target sequence, and define the interval sequence after the second target sequence as a third target sequence;

[0027] The actual outbound sequence is fitted based on the least squares method to obtain a linear regression function, and the third target sequence is corrected based on the linear regression function to obtain the future outbound trend.

[0028] Furthermore, matching the first score and the second score includes the following steps:

[0029] The hot-selling product with the highest first score is obtained, a storage area that meets the storage requirements is screened out in the fast delivery area, and the product with the highest second score is located in the screened out storage area as the storage location of the hot-selling product.

[0030] Furthermore, the division into the fast warehouse-out area and the ordinary warehouse-out area comprises the following steps:

[0031] A first threshold and a second threshold are set, and the commodities whose first score is greater than the first threshold are classified as the hot-selling commodities, and the storage area whose second score is greater than the second threshold is classified as the fast delivery area.

[0032] Furthermore, the present application provides a supply chain shared cold chain warehousing service management platform for implementing the above-mentioned supply chain shared cold chain warehousing service management method, the platform comprising:

[0033] A collection module, configured to collect historical shipment data of commodities and historical storage data of each storage area of the warehouse, and perform data management on the historical shipment data and the historical storage data to obtain first complete data and second complete data;

[0034] a classification module, configured to calculate a first score for each product based on the predicted time period and the first complete data, calculate a second score for each storage area based on the second complete data, classify the products into hot-selling products and non-hot-selling products based on the first score, and classify the storage areas into fast-delivery areas and normal-delivery areas based on the second score;

[0035] a matching module configured to obtain storage parameters of each storage area within the fast delivery area and the storage requirements of the hot-selling products, match the first score with the second score within the constraints of the storage requirements and the storage parameters, and determine a storage solution for each of the hot-selling products based on the matching results;

[0036] The update module is used to obtain the stage outbound data of each commodity in the warehouse at every preset time interval, generate future outbound trends based on the stage outbound data, and update the warehousing plan based on the future outbound trends to continuously optimize the warehousing layout.

[0037] This invention ensures data accuracy through comprehensive management of historical shipment and storage data, providing a reliable foundation for subsequent warehouse optimization. Using the first and second scores, it quickly divides popular products into fast-delivery zones and only matches these zones. This reduces data computation and avoids large-scale adjustments to the current warehouse layout. By matching the storage requirements of each product with the storage parameters of the storage zone, the most appropriate storage area is selected for each product, preventing product damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 A schematic diagram of a supply chain shared cold chain warehousing service management method for this application;

[0040] Figure 2 A schematic diagram predicting future outbound trends for this application;

[0041] Figure 3 This is a schematic diagram of a supply chain shared cold chain warehousing service management platform for this application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0043] It is understood that the terms "first," "second," etc., used herein may be used to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script without departing from the scope of this application.

[0044] like Figure 1 As shown, a supply chain shared cold chain warehousing service management platform includes:

[0045] S1: Collect the historical shipment data of the goods and the historical storage data of each storage area of the warehouse, and perform data governance on the historical shipment data and historical storage data to obtain the first complete data and the second complete data.

[0046] Specifically, historical outbound data and historical storage data can be exported from the warehouse management system (WMS). Historical outbound data includes information such as the product's outbound time, quantity, customer information, outbound destination, order type, and shipping date. Historical storage data includes inventory status, product placement time, product removal time, shelf ID, and physical location. Data governance includes operations such as deduplication, missing value processing, timestamp normalization, and outlier detection to ensure data accuracy. Governed historical outbound data is defined as first-level complete data, and historical storage data is defined as second-level complete data.

[0047] S2: Calculate a first score for each commodity based on the predicted time period and the first complete data, and calculate a second score for each storage area based on the second complete data.

[0048] Before calculating the score, this embodiment first sets the current time point and the future period to be predicted, that is, the preset time period. Since some products are not hot-selling throughout the year but are hot-selling seasonally, it is necessary to determine the preset time period. For example, if the warehouse layout is to be optimized in the next month, the popularity of the products in the next month is calculated based on historical records.

[0049] During the calculation process, if the product's outbound shipment is not affected by other products, the first score is calculated based on its own outbound shipment frequency over a certain period of time. If the product's outbound shipment is affected by other products, for example, if product A is often shipped with product B, the first score for product A is recalculated based on the outbound shipment frequency of both products A and B. For the second score, this embodiment combines factors such as the distance between the storage area and the picking area, the convenience of picking up goods, and the convenience of transportation to calculate the second score. The specific methods for calculating the first and second scores will be described later. A higher first score indicates a higher outbound shipment popularity for the product, and a higher second score indicates a higher outbound shipment speed and convenience for the storage area.

[0050] S3: Based on the first score, the products are divided into hot-selling products and non-hot-selling products. Based on the second score, the storage area is divided into a fast delivery area and a normal delivery area.

[0051] Specifically in this embodiment, the division into the fast delivery area and the normal delivery area includes the following steps:

[0052] A first threshold and a second threshold are set, and commodities with a first score greater than the first threshold are classified as hot-selling commodities, and storage areas with a second score greater than the second threshold are classified as fast-export areas.

[0053] By setting a first threshold and a second threshold, when the first score is greater than the first threshold, the product is classified as a hot seller, otherwise it is classified as a non-hot seller. When the second score is greater than the second threshold, the storage area is classified as a fast delivery area, otherwise it is classified as a standard delivery area. Storage areas closer to the picking area are more convenient and more likely to be classified as fast delivery areas. For example, the first threshold is 70 and the second threshold is 60.

[0054] S4: Obtain the storage parameters of each storage area in the fast delivery area, as well as the storage requirements of hot-selling products.

[0055] S5: Under the constraints of storage requirements and storage parameters, the first score and the second score are matched, and a storage solution for each hot-selling product is determined based on the matching results.

[0056] Matching the first score and the second score includes the following steps:

[0057] Obtain the best-selling product with the highest first score, filter out a storage area that meets its storage requirements in the fast delivery area, and locate the second highest score in the filtered storage area as the storage location for the best-selling product.

[0058] Storage parameters include the temperature changes in each storage area. The temperature fluctuation range and stability of the storage are determined by the temperature changes. Storage requirements include the storage temperature range of the goods. The closer to the center of the entire warehouse, the higher the corresponding ambient temperature. Therefore, it is necessary to match a reasonable storage location according to the product storage requirements.

[0059] Before generating a storage plan, products are first classified into hot-selling and non-hot-selling products, and storage areas are divided into fast-delivery areas and standard-delivery areas. Subsequent matching is performed only on hot-selling products and fast-delivery areas, reducing matching complexity, improving matching speed, and avoiding large-scale warehouse layout adjustments. When matching hot-selling products, the first product with the highest first score is obtained. Based on the storage requirements of the hot-selling products, suitable storage areas are selected from the fast-delivery areas. The second-highest-scoring storage area is located within these selected areas as the product's storage location. Matching is then performed on subsequent products with lower first scores. If any product does not find a matching storage area, it is stored in the standard-delivery area.

[0060] S6: Obtain the phased outbound data of each commodity in the warehouse at preset intervals, generate future outbound trends based on the phased outbound data, and update the warehousing plan based on the future outbound trends to continuously optimize the warehousing layout.

[0061] The preset time is, for example, 15 days. After the warehousing plan is generated, the product outbound data will continue to be obtained in the future. After 15 days, the future outbound trend of the product will be determined based on the outbound data within 15 days. If it is determined based on the future outbound trend that the product is no longer hot-selling, for example, if the sales volume of the product is predicted to start to decline, the product will be transferred to the general outbound area, thereby continuously optimizing the warehousing layout and ensuring the efficient operation of the warehouse.

[0062] This invention ensures data accuracy through comprehensive management of historical shipment and storage data, providing a reliable foundation for subsequent warehouse optimization. Using the first and second scores, it quickly divides popular products into fast-delivery zones and only matches these zones. This reduces data computation and avoids large-scale adjustments to the current warehouse layout. By matching the storage requirements of each product with the storage parameters of the storage zone, the most appropriate storage area is selected for each product, preventing product damage.

[0063] It is particularly noteworthy that the present invention comprehensively considers the popularity of the goods, the convenience of outbound transportation and the storage requirements of the goods, and ultimately selects the most suitable storage location for the goods.

[0064] In this embodiment, data governance of historical outbound data includes the following steps:

[0065] Based on the timestamp of each first sub-data in the historical outbound data, it is organized into a first time series, and the first time series is divided into multiple sub-sequences according to the timestamp. A corresponding adjustment weight is set for each sub-sequence. Based on the adjustment weight, the first sub-data in the sub-sequence is modified to obtain the second sub-data. The adjusted first time series is defined as the initial sequence.

[0066] In this embodiment, outliers in historical outbound data are located and corrected based on the following steps. The historical outbound data includes first sub-data corresponding to different product types. The first sub-data has a timestamp, such as Outbound Time: January 1, 3:00 PM, Type: Frozen Food A. The first sub-data corresponding to the same product type are then organized into a first time series based on the order of the timestamps. The first time series is then divided into multiple sub-sequences. The division method can be manually specified, and the adjustment weights range from 0 to 1. For example, the sequence within three months prior to the current time point is segmented and designated as Subsequence 1, and the sequence from the previous three to six months is designated as Subsequence 2. The adjustment weights set for Subsequence 1 are 0.9, and for Subsequence 2 are 0.8. Each first sub-data in Subsequence 1 is multiplied by 0.9 to obtain the second sub-data. Each first sub-data in Subsequence 2 is multiplied by 0.8 to obtain the second sub-data. Finally, the adjusted first time series is defined as the initial sequence.

[0067] The further away the historical shipment data is from the current time point, the lower its reference significance. Therefore, this step can reduce the historical data that is farther away from the current time point to avoid affecting the calculation of the subsequent first score.

[0068] The mean and standard deviation of the initial sequence are calculated, and a first numerical range and a second numerical range are generated based on the mean and standard deviation. If the second sub-data is within the first numerical range, the second sub-data is set as a normal value. If the second sub-data is outside the second numerical range, the second sub-data is set as an abnormal value. The abnormal value is corrected, and the corrected initial sequence is defined as the standard sequence.

[0069] In this embodiment, the lower limit of the first numerical range is μ-g1σ, and the upper limit of the first numerical range is μ+g1σ, where μ is the average value of the initial sequence, σ is the standard deviation of the initial sequence, and g1 is the first correction coefficient. The value of the first correction coefficient is set within a numerical range less than 1 according to actual needs. For example, for the initial sequence: 3, 20, 18, 21, 25, 18, 26, 23, 22, 40, its average value is 21.6 and its standard deviation is 8.64. If the first correction coefficient is 1, then the first numerical range is 12.93-30.24, and data within this range is normal data. For the second numerical range, its corresponding lower limit is μ-g2σ, and the upper limit of the first numerical range is μ+g2σ, where g2 is the second correction coefficient. The value of the second correction coefficient is set within a numerical range greater than 1 according to actual needs. Similarly, for the above initial sequence, if the second correction coefficient is set to 2, then the second numerical range is 4.32-38.88, and the second sub-data outside the second numerical range is defined as an abnormal value.

[0070] Specifically, the present invention corrects the abnormal value including the following steps:

[0071] The upper limit and lower limit of the second numerical range are located, and abnormal values smaller than the lower limit are corrected to the lower limit, and abnormal values greater than the upper limit are corrected to the upper limit.

[0072] In the above example, the second sub-data with values of 3 and 40 are outliers. Since 3 is less than the lower limit of the second numerical range, the value 3 is corrected to 4.32. Since 40 is greater than the upper limit of the second numerical range, the value 40 is corrected to 38.88. This method can quickly locate outliers in the initial sequence and correct them, thereby ensuring the accuracy of subsequent data analysis.

[0073] Calculating the first score includes the following steps:

[0074] Based on the seasonal decomposition algorithm, the trend component sequence, seasonal component sequence and residual component sequence of the standard sequence corresponding to each commodity are generated. The sequences corresponding to the forecast time period are intercepted from the trend component sequence, seasonal component sequence and residual component sequence as the first sequence, second sequence and third sequence. The weighted sum of the first sequence, second sequence and third sequence is used to calculate the initial score.

[0075] The correlation scores between the commodities are calculated based on the standard sequence of each commodity, and the initial scores are revised based on the correlation scores to obtain a first score.

[0076] Specifically, an additive model is used here to perform seasonal decomposition. First, a sliding window is applied to the standard series to calculate the trend component, obtaining the trend value of the data corresponding to each time point in the standard series. The sliding window size is three months. The centralized moving average algorithm is used to calculate the trend component of the data corresponding to each time point. The seasonal component and residual component are then calculated based on the trend component, using existing methods. A larger trend component indicates a greater volume of goods shipped, independent of seasonality. A larger seasonal component indicates a greater impact of seasonality on the volume of goods shipped. A larger residual component indicates greater volatility, or in other words, greater randomness, in the volume of goods shipped.

[0077] Next, a trend sequence, seasonal sequence, and residual sequence are constructed based on the trend component, seasonal component, and residual component, respectively. Data from the trend, seasonal, and residual sequences corresponding to the forecast period are intercepted. For example, if the forecast period is March, the sequence data corresponding to March in the trend, seasonal, and residual sequences are intercepted as the first, second, and third sequences, respectively. The sum of all values in the first sequence is used as the first value. Similarly, the sum of the values in the second and third sequences is used as the second and third values. The first, second, and third values are weighted and summed to obtain an initial score. Specifically, the weight of the first value is 0.5, the weight of the second value is 0.2, and the weight of the third value is 0.2. After obtaining the initial score, the correlation score between the products is calculated and added to the initial score to obtain the result as the first score.

[0078] In this embodiment, calculating the relevance score includes the following steps:

[0079] The product to be calculated is defined as the target product. The standard sequences of the target product and the remaining products are combined to obtain multiple sequence groups. The two standard sequences in the sequence group define the first analysis sequence and the second analysis sequence respectively. The first analysis sequence and the second analysis sequence are analyzed based on linear regression to obtain the correlation coefficient between the target product and the remaining products. The correlation coefficient with the target product that is greater than the judgment threshold is used as the influence coefficient. The product corresponding to the influence coefficient is defined as the associated product. The relevance score of the target product is calculated based on the influence coefficient and the initial score of the associated product.

[0080] To calculate the relevance score for a target product, the target product's standard series and the standard series of the remaining products are obtained. The target product's standard series are time-aligned with each of the remaining products' standard series. Correlation analysis is performed on the aligned data to obtain the Pearson correlation coefficient (R) between the target product's standard series and the remaining products' standard series. The correlation coefficient (R) ranges from -1 to 1. A correlation coefficient closer to 1 indicates a positive correlation between the two time series, meaning that an increase in one time series data will simultaneously cause an increase in the other. Next, an initial score is obtained for each product using a seasonal decomposition algorithm. The initial score for each product is multiplied by the influence coefficient to obtain a first value. The first values of all associated products are summed to obtain a second value, which is used as the relevance score for the target product. A higher relevance score for the target product indicates that the target product's shipment volume is more easily affected by other products, and this influence results in an increase in the product's shipment volume.

[0081] In this embodiment, calculating the second score includes the following steps:

[0082] Based on the straight-line distance from the storage area to the picking area, the fuzzy theory algorithm is applied to calculate the distance convenience score of the storage area. Based on the location of the storage area on the shelf, the hierarchical analysis method is used to calculate the accessibility score of the storage area. Based on the actual transportation path from the storage area to the picking area, the linear regression algorithm is used to calculate the path complexity score of the storage area. The Bayesian model algorithm is used to analyze the influence weights of all scores on the outbound efficiency. Based on the influence weights, the weighted sum of all scores is used to obtain the comprehensive score, which is defined as the second score.

[0083] For the convenience score, three fuzzy sets are first defined, namely the close distance set, the medium distance set, and the long distance set. Each distance set defines a corresponding membership function, and the membership functions include trigonometric functions. Trapezoidal function and Gaussian function. This embodiment uses trigonometric function as the membership function of the close distance set. The trigonometric function includes three sub-functions, namely u=1, (d≤d1), u=(d2-d) / (d2-d1), (d1<d≤d2), u=0, (d>d2), where u is the membership degree, d is the straight-line distance from the storage area to the picking area, d1 and d2 are preset critical distances, respectively. The value of d1 is 10 meters, and the value of d2 is 50 meters. For example, the straight-line distance from the storage area to the picking area is 30, then its membership degree with the close distance set is 0.4. No more examples are given for the membership functions of the medium distance set and the long distance set. Those skilled in the art can set them according to actual conditions. Finally, the convenience score is calculated by taking the weighted sum of the membership of each type. The membership of the three types is 0.4, 0.4, and 0, and the corresponding weights are 0.3, 0.3, and 0.4, respectively. The convenience score is 0.4*0.3+0.4*0.3+0*0.4=0.24.

[0084] When calculating a storage area's accessibility score using the Analytic Hierarchy Process (AHP), we first identify all factors influencing shelf accessibility, such as shelf location, the need for specific equipment, ease of operation, presence of obstacles, and ease of operator access. These factors are then scored using values ranging from 1 to 9. A judgment matrix is then constructed based on the values of each factor (the construction method is not detailed here). The judgment matrix is then normalized, and finally, eigenvalue decomposition is used to calculate the eigenvalues and eigenvectors of the normalized matrix. The eigenvectors contain the weights of each factor. Each storage area is then scored based on these criteria. For example, if a storage area has a shelf location score of 9, equipment compatibility (whether specific equipment is required, ease of operation) of 6, access difficulty (whether obstacles are present, etc.) of 8, and operability (whether operator access is easy) of 9, and the corresponding factor weights are 0.4, 0.3, 0.2, and 0.1, respectively, the storage area's accessibility score is 7.4.

[0085] For the path complexity score, two influencing factors are first set: the number of turns from the storage area to the picking area and the total length of the path. Then, a linear regression function is constructed to calculate the path complexity score. For example, the linear regression function is S=N·ω1+L·ω2, where S is the path complexity score, N is the number of turns, L is the total length of the path, and ω1 and ω2 are the weights of the number of turns and the total length of the path, respectively.

[0086] Finally, the transportation time of goods from the shelves to the picking area in the storage area is obtained from the historical storage data. Then, based on these transportation times, the conditional probability of each score is calculated in combination with the Bayesian algorithm to obtain the impact weight on transportation efficiency. Finally, based on the impact weight, the distance convenience score, accessibility score, and path complexity score calculated above are weighted and summed, and the final comprehensive score is used as the first score of each storage area.

[0087] In this embodiment, generating future delivery trends includes the following steps:

[0088] Obtain the actual shipment data of goods within a preset time period, convert the actual shipment data into an actual shipment time series, fit the actual shipment time series based on the least squares method to obtain a linear regression function, and generate an ideal time series and growth trend based on the linear regression function;

[0089] The actual outbound sequence is divided into multiple subsequences. The last subsequence is defined as the first target sequence. The subsequence with the highest similarity to the first target sequence is defined as the second target sequence. The subsequence after the second target sequence is defined as the third target sequence. The third target sequence is modified based on the growth trend and defined as the future outbound trend.

[0090] In this embodiment, generating future delivery trends includes the following steps:

[0091] The actual shipment data of the goods within the forecast period is obtained, and the actual shipment data is converted into an actual shipment sequence. The actual shipment sequence is divided into multiple interval sequences. The last interval sequence is defined as the first target sequence, the interval sequence with the highest similarity to the first target sequence is defined as the second target sequence, and the interval sequence after the second target sequence is defined as the third target sequence.

[0092] The actual outbound sequence is fitted based on the least squares method to obtain a linear regression function. Based on the linear regression function, the third target sequence is corrected to obtain the future outbound trend.

[0093] For example, if the forecast period is 5 days and the warehouse layout has been adjusted for 30 days, the actual shipment sequence is constructed based on the actual daily shipment data for the 30 days. The actual shipment sequence is then split into multiple interval sequences, such as data from days 1-5 is divided into an interval sequence, and data from days 5-10 is divided into an interval sequence. The actual shipment sequence is then fitted using the least squares method to obtain a linear regression function of the form y = kx + b, where y is the daily shipment volume, k and b are the fitted coefficients and intercept, and x is time. For example, the linear regression function is y = 8.3x + 105.

[0094] The last interval sequence is located as the first target sequence, that is, the interval sequence corresponding to 25-30 days. Figure 2 The prediction process is schematically explained. First, locate Figure 2 Q1 in , obtain the interval sequence that is most similar to this interval, that is, the second target sequence, such as Figure 2 In Q2, the Pearson correlation coefficient can be used as the similarity, and the interval sequence after the second target sequence is the third target sequence, such as Figure 2 If the k value in the linear regression function is positive, it means that the actual outbound sequence is increasing. Therefore, the third target sequence is appropriately increased to obtain the future outbound trend. Figure 2 Take the correction process as an example: substitute the last time point t of the actual delivery sequence into the linear regression function to obtain the third value. After moving the third target sequence to interval Q4, use the third value as the starting point and keep its fluctuation characteristics unchanged. The specific numerical calculation process will not be described here.

[0095] like Figure 3 As shown, this application provides a supply chain shared cold chain warehousing service management platform for implementing the above-mentioned supply chain shared cold chain warehousing service management method, and the platform includes:

[0096] The acquisition module is used to collect the historical outbound data of the goods and the historical storage data of each storage area of the warehouse, and perform data management on the historical outbound data and historical storage data to obtain the first complete data and the second complete data.

[0097] The division module is used to calculate a first score for each product based on the predicted time period and the first complete data, calculate a second score for each storage area based on the second complete data, divide the products into hot-selling products and non-hot-selling products based on the first score, and divide the storage area into a fast delivery area and a normal delivery area based on the second score.

[0098] The matching module is used to obtain the storage parameters of each storage area in the fast delivery area and the storage requirements of hot-selling products. Under the constraints of storage requirements and storage parameters, the first score and the second score are matched, and the warehousing plan for each hot-selling product is determined based on the matching results.

[0099] The update module is used to obtain the stage outbound data of each commodity in the warehouse at preset intervals, generate future outbound trends based on the stage outbound data, and update the warehousing plan based on the future outbound trends to continuously optimize the warehousing layout.

[0100] It should be understood that the various technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0101] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included outside the scope of protection of the present invention.

Claims

1. A supply chain shared cold chain warehousing service management method, characterized in that: Collect historical shipment data of goods and historical storage data of each storage area of the warehouse, and perform data management on the historical shipment data and the historical storage data to obtain first complete data and second complete data; Calculate a first score for each commodity based on the forecast period and the first complete data, and calculate a second score for each storage area based on the second complete data; Based on the first score, the products are divided into hot-selling products and non-hot-selling products; based on the second score, the storage area is divided into a fast delivery area and a normal delivery area; Obtaining storage parameters of each storage area in the fast delivery area and storage requirements of the hot-selling products; Under the constraints of the storage requirements and the storage parameters, matching the first score and the second score, and determining a storage solution for each of the hot-selling products based on the matching results; Obtaining the phased shipment data of each commodity in the warehouse at preset intervals, generating future shipment trends based on the phased shipment data, and updating the warehousing plan based on the future shipment trends to continuously optimize the warehouse layout; Data governance of the historical outbound data includes the following steps: Arrange each first sub-data in the historical outbound data into a first time series based on the timestamp of each sub-data, divide the first time series into multiple sub-sequences based on the timestamp, set a corresponding adjustment weight for each sub-sequence, and modify the first sub-data in the sub-sequence based on the adjustment weight to obtain second sub-data, so as to reduce the historical outbound data that is farther from the current time point. The adjusted first time series is defined as an initial sequence; Calculating the mean and standard deviation of the initial sequence, generating a first value range and a second value range based on the mean and the standard deviation, setting the second sub-data as a normal value if the second sub-data is within the first value range, and setting the second sub-data as an abnormal value if the second sub-data is outside the second value range, correcting the abnormal value, and defining the corrected initial sequence as a standard sequence; Calculating the first score includes the following steps: Generate a trend component sequence, a seasonal component sequence, and a residual component sequence for each commodity corresponding to the standard sequence based on a seasonal decomposition algorithm; extract sequences corresponding to the forecast time period from the trend component sequence, seasonal component sequence, and residual component sequence as a first sequence, a second sequence, and a third sequence; sum the values in the first sequence as a first value, sum the values in the second sequence as a second value, and sum the values in the third sequence as a third value; and calculate an initial score by performing a weighted sum of the first, second, and third values; Calculating a correlation score between each commodity based on the standard sequence of each commodity, wherein a higher correlation score of a commodity indicates that the shipment volume of the commodity is more easily affected by other commodities, and revising the initial score based on the correlation score to obtain the first score; Matching the first score and the second score includes the following steps: The hot-selling product with the highest first score is obtained, a storage area that meets the storage requirements is screened out in the fast delivery area, and the product with the highest second score is located in the screened out storage area as the storage location of the hot-selling product.

2. The method according to claim 1, characterized in that Correcting the outliers includes the following steps: The upper limit and lower limit of the second numerical range are located, and the abnormal value smaller than the lower limit is corrected to the lower limit, and the abnormal value larger than the upper limit is corrected to the upper limit.

3. The method according to claim 1, characterized in that Calculating the relevance score includes the following steps: The product to be calculated is defined as a target product, and the standard sequences of the target product and the remaining products are combined to obtain multiple sequence groups. Two of the standard sequences in the sequence groups respectively define a first analysis sequence and a second analysis sequence. The first analysis sequence and the second analysis sequence are analyzed based on linear regression to obtain a correlation coefficient between the target product and the remaining products. The correlation coefficient with the target product that is greater than a judgment threshold is used as an influence coefficient, and the product corresponding to the influence coefficient is defined as an associated product. The relevance score of the target product is calculated based on the influence coefficient and the initial score of the associated product.

4. The method according to claim 1, wherein Calculating the second score includes the following steps: Based on the straight-line distance from the storage area to the picking area, a fuzzy theory algorithm is applied to calculate the distance convenience score of the storage area. Based on the position of the storage area in the shelf, the hierarchical analysis method is used to calculate the accessibility score of the storage area. Based on the actual transportation path from the storage area to the picking area, a linear regression algorithm is used to calculate the path complexity score of the storage area. Based on the Bayesian model algorithm, the influence weights of all scores on the outbound efficiency are analyzed. Based on the influence weights, a weighted sum of all scores is taken to obtain a comprehensive score, and the comprehensive score is defined as the second score.

5. The method according to claim 1, wherein Generating future outbound trends involves the following steps: Acquire actual shipment data of goods within the forecast time period, convert the actual shipment data into an actual shipment sequence, segment the actual shipment sequence into multiple interval sequences, define the last interval sequence as a first target sequence, define the interval sequence with the highest similarity to the first target sequence as a second target sequence, and define the interval sequence after the second target sequence as a third target sequence; The actual outbound sequence is fitted based on the least squares method to obtain a linear regression function, and the third target sequence is corrected based on the linear regression function to obtain the future outbound trend.

6. The method according to claim 1, characterized in that The division into the fast delivery area and the ordinary delivery area includes the following steps: A first threshold and a second threshold are set, and the commodities whose first score is greater than the first threshold are classified as the hot-selling commodities, and the storage area whose second score is greater than the second threshold is classified as the fast delivery area.

7. A supply chain shared cold chain warehousing service management platform, used to implement the supply chain shared cold chain warehousing service management method according to any one of claims 1 to 6, characterized in that: include: A collection module is used to collect historical shipment data of commodities and historical storage data of each storage area of the warehouse, and perform data management on the historical shipment data and the historical storage data to obtain first complete data and second complete data; a classification module, configured to calculate a first score for each product based on the predicted time period and the first complete data, calculate a second score for each storage area based on the second complete data, classify the products into hot-selling products and non-hot-selling products based on the first score, and classify the storage areas into fast-delivery areas and normal-delivery areas based on the second score; a matching module configured to obtain storage parameters of each storage area within the fast delivery area and the storage requirements of the hot-selling products, match the first score with the second score within the constraints of the storage requirements and the storage parameters, and determine a storage solution for each of the hot-selling products based on the matching results; The update module is used to obtain the stage outbound data of each commodity in the warehouse at every preset time interval, generate future outbound trends based on the stage outbound data, and update the warehousing plan based on the future outbound trends to continuously optimize the warehousing layout.

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