Article flow volume information sending method, device and equipment and computer readable medium

Abstract

Embodiments of the present disclosure disclose an article turnover quantity information sending method, device, equipment and computer readable medium. A specific implementation of the method comprises: obtaining a target article single-day historical turnover quantity set and a target article historical single-day average value set; generating a target coefficient; generating a value label; marking each preset historical time corresponding to a preset historical day to obtain a historical time period label set; generating a period turnover quantity increment according to the historical time period label set, the target article single-day historical turnover quantity set and the preset historical day; generating article turnover quantity information according to the target article single-day historical turnover quantity set, the preset historical day and the period turnover quantity increment; and sending the article turnover quantity information to an associated inventory terminal. The implementation reduces the waste of warehouse space or the loss of fresh food, and reduces the consumption of computing resources when estimating the turnover quantity.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to methods, apparatus, devices, and computer-readable media for transmitting information on the volume of goods circulation. Background Technology

[0002] With the continuous advancement of logistics technology, determining the volume of goods movement is crucial for supply chain management. Currently, the common method for forecasting volume movement is manual calculation, relying on the subjective judgment of logistics station staff to estimate the volume. In addition, some logistics stations also use complex models to predict volume movement for goods with different categories and attributes.

[0003] However, when using the above method to predict turnover, the following technical problems often arise:

[0004] First, manual calculations of estimated turnover are highly subjective and lack concrete data support, resulting in low accuracy and wasted warehouse space or spoilage of fresh goods. Second, complex modeling requires business characteristic data specific to each logistics station. Different stations need separate models, and the turnover is then estimated using those models. The same model has poor portability between different logistics stations, and due to the complexity of the models, both modeling and runtime require significant computing resources.

[0005] Secondly, manual calculations of the estimated turnover have significant deviations. In addition, complex modeling relies on a lot of business characteristic data, and the cost of maintaining such business characteristic data is high in actual applications. Once the business characteristic data is incorrect, the turnover estimated through modeling will also be incorrect. The model has poor resistance to fluctuations, which further leads to the waste of warehouse space or the loss of fresh goods. Summary of the Invention

[0006] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0007] Some embodiments of this disclosure provide methods, apparatus, electronic devices, and computer-readable media for transmitting goods turnover information to solve one or more of the technical problems mentioned in the background section above.

[0008] In a first aspect, some embodiments of this disclosure provide a method for sending commodity turnover information. The method includes: obtaining a set of daily historical turnover of a target commodity and a set of daily historical average value of a target commodity based on a preset number of historical days corresponding to the target commodity; wherein the set of daily historical turnover of the target commodity includes the same number of daily historical turnovers as the preset number of historical days, and the set of daily historical average value of the target commodity includes the same number of daily historical average values ​​of the target commodity as the preset number of historical days; generating a target coefficient based on the preset number of historical days, the set of daily historical turnover of the target commodity, and the set of daily historical value of the target commodity; and generating a value marker based on the set of daily historical turnover of the target commodity, the preset number of historical days, and the target coefficient. In response to the value marker characterizing the target item as a value-sensitive item, based on the target item's historical daily value set and historical daily turnover set, low-value period markers are applied to each preset historical time corresponding to a preset number of historical days, resulting in a historical time period marker set; based on the historical time period marker set, the target item's historical daily turnover set, and the preset number of historical days, a period turnover increment is generated; based on the target item's historical daily turnover set, the preset number of historical days, and the period turnover increment, item turnover information is generated, including the predicted total item turnover; the item turnover information is sent to the associated inventory terminal, enabling the inventory terminal to display the predicted total item turnover included in the item turnover information.

[0009] Secondly, some embodiments of this disclosure provide a device for transmitting commodity circulation volume information. The device includes: an acquisition unit configured to acquire a set of daily historical circulation volume of a target commodity and a set of daily historical average value of a target commodity based on a preset number of historical days corresponding to the target commodity, wherein the number of daily historical circulation volumes of the target commodity included in the set of daily historical circulation volume of the target commodity is the same as the preset number of historical days, and the number of daily historical average values ​​of the target commodity included in the set of daily historical average value of the target commodity is the same as the preset number of historical days; a first generation unit configured to generate a target coefficient based on the preset number of historical days, the set of daily historical circulation volume of the target commodity, and the set of daily historical value of the target commodity; a second generation unit configured to generate a value marker based on the set of daily historical circulation volume of the target commodity, the preset number of historical days, and the target coefficient; and a schedule marker sheet. The first unit is configured to respond to the value marker characterizing the target item as a value-sensitive item, and to mark each preset historical time corresponding to a preset number of historical days as a low-value period based on the target item's historical daily value set and the target item's historical daily turnover set, thereby obtaining a historical time period marker set; the second unit is configured to generate a period turnover increment based on the historical time period marker set, the target item's historical daily turnover set, and the preset number of historical days; the third unit is configured to generate item turnover information based on the target item's historical daily turnover set, the preset number of historical days, and the period turnover increment, wherein the item turnover information includes the predicted total item turnover; and the fourth unit is configured to send the item turnover information to the associated inventory terminal, so that the inventory terminal displays the predicted total item turnover included in the item turnover information.

[0010] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0011] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0012] The above-described embodiments of this disclosure have the following beneficial effects: The method for sending goods turnover information according to some embodiments of this disclosure reduces the waste of warehouse space or the loss of fresh goods, and lowers the computational resource consumption when estimating turnover volume. Specifically, the reasons for the waste of warehouse space or the loss of fresh goods, and the high computational resource consumption when estimating turnover volume, are: manual calculation of the estimated turnover volume is highly subjective and lacks specific data support, resulting in low accuracy of the estimated turnover volume, leading to the waste of warehouse space or the loss of fresh goods. Complex modeling requires business feature data specific to a particular logistics station. Different logistics stations need to be modeled separately, and the turnover volume needs to be estimated through the model. The portability of the same model between different logistics stations is poor, and due to the complexity of the model, both modeling and model operation require a large amount of computational resources. Based on this, the method for sending goods turnover information according to some embodiments of this disclosure first obtains the daily historical turnover volume set and the historical daily average value set of the target item based on a preset number of historical days corresponding to the target item. Then, a target coefficient is generated based on the preset historical number of days, the set of historical daily turnover of the target item, and the set of historical daily value of the target item. This yields a target coefficient characterizing the correlation between the historical daily turnover and the historical daily value of the target item. Next, a value marker is generated based on the set of historical daily turnover of the target item, the preset historical number of days, and the target coefficient. This yields a value marker characterizing whether the target item is a value-sensitive item. Then, in response to the value marker characterizing the target item as a value-sensitive item, low-value time slots are marked for each preset historical time corresponding to the preset historical number of days, based on the set of historical daily value and the set of historical daily turnover of the target item, resulting in a set of historical time slot markers. This yields a set of historical time slot markers characterizing the time slots of the target item. Finally, based on the set of historical time slot markers, the set of historical daily turnover of the target item, and the preset historical number of days, a time slot turnover increment is generated. This yields a time slot turnover increment characterizing the increase in turnover due to the time slot effect. Then, based on the target item's daily historical turnover set, preset historical days, and periodic turnover increments, item turnover information is generated. This yields item turnover information including the predicted total turnover. Finally, the item turnover information is sent to the associated inventory terminal, allowing the terminal to display the predicted total turnover included in the turnover information. Because turnover prediction is based on two business characteristic data points—the target item's daily historical turnover set and the target item's historical daily average value set—the turnover prediction is supported by concrete data, thereby improving the accuracy of the projected turnover.Because the turnover prediction is based solely on two business characteristic data points—the daily historical turnover volume set and the daily historical average value set of the target item—and does not involve logistics station-related business data, the item turnover information transmission methods corresponding to the above embodiments of this disclosure can be applied to different logistics stations. This improves the portability of turnover prediction across different logistics stations. Furthermore, since the turnover prediction is based solely on two business characteristic data points—the daily historical turnover volume set and the daily historical average value set of the target item—the amount of data is significantly reduced compared to the business characteristic data relied upon by complex modeling, thereby saving the computing resources required for turnover prediction. Attached Figure Description

[0013] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0014] Figure 1 This is a schematic diagram of an application scenario of the method for sending item turnover information according to some embodiments of this disclosure;

[0015] Figure 2 This is a flowchart of some embodiments of the method for sending information on the volume of goods circulation according to this disclosure;

[0016] Figure 3 These are flowcharts of some other embodiments of the method for sending information on the circulation of goods according to this disclosure;

[0017] Figure 4 This is a schematic diagram of the structure of some embodiments of the item circulation volume information sending device according to the present disclosure;

[0018] Figure 5 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0019] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0020] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0021] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0022] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0023] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0024] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario of the method for sending information on the turnover of goods according to some embodiments of this disclosure.

[0026] exist Figure 1In the application scenario, firstly, the computing device 101 can obtain a set of daily historical turnover volume 103 and a set of daily historical average value 104 for the target item based on a preset historical number of days 102 corresponding to the target item. The set of daily historical turnover volume 103 includes the same number of daily historical turnover volumes as the preset historical number of days 102. The set of daily historical average value 104 includes the same number of daily historical average values ​​as the preset historical number of days 102. Then, the computing device 101 can generate a target coefficient 105 based on the preset historical number of days 102, the set of daily historical turnover volume 103, and the set of daily historical value 104. Finally, the computing device 101 can generate a value marker 106 based on the set of daily historical turnover volume 103, the preset historical number of days 102, and the target coefficient 105. Then, in response to the value marker 106 indicating that the target item is a value-sensitive item, the computing device 101 can mark each preset historical time corresponding to the preset number of historical days 102 as a low-value period based on the target item's historical daily value set 104 and the target item's historical daily turnover set 103, thus obtaining a historical time period marker set 107. Afterwards, the computing device 101 can generate a period turnover increment 108 based on the historical time period marker set 107, the target item's historical daily turnover set 103, and the preset number of historical days 102. Next, the computing device 101 can generate item turnover information 109 based on the target item's historical daily turnover set 103, the preset number of historical days 102, and the period turnover increment 108 (for example, item turnover information 109 could be: "The predicted item turnover is: 1000."), where item turnover information 109 includes the predicted total item turnover. Finally, the computing device 101 can send the item turnover information 109 to the associated inventory terminal 110, so that the inventory terminal 110 can display the predicted total item turnover included in the item turnover information 109.

[0027] It should be noted that the aforementioned computing device 101 can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed within the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0028] It should be understood that Figure 1 The number of computing devices shown is merely illustrative. Any number of computing devices can be used depending on implementation needs.

[0029] Continue to refer to Figure 2The flowchart 200 illustrates some embodiments of a method for sending goods circulation volume information according to the present disclosure. This method for sending goods circulation volume information includes the following steps:

[0030] Step 201: Based on the preset number of historical days corresponding to the target item, obtain the set of historical daily turnover volume of the target item and the set of historical daily average value of the target item.

[0031] In some embodiments, the execution entity of the method for sending information on the volume of goods circulation (e.g., Figure 1 The computing device 101 shown can obtain a set of historical daily turnover volume and a set of historical daily average value of a target item via a wired or wireless connection, based on a preset historical number of days corresponding to the target item. The target item can be a pre-specified item. The preset historical number of days can be a pre-defined number of historical days. For example, the preset historical number of days can be the past 40 days. The historical daily turnover volume of the target item in the set of historical daily turnover volume can be the total daily turnover volume (total daily sales) of the target item during a preset historical time corresponding to the preset historical number of days. The preset historical time can be one day within the time period corresponding to the preset historical number of days. For example, when the preset historical number of days is the past 40 days, the preset historical time can be one day within the past 40 days. The historical daily average value of the target item in the set of historical daily average value of the target item can be the item value (unit price) of the target item during a preset historical time corresponding to the preset historical number of days. The set of historical daily turnover volumes of the aforementioned target items includes the same number of historical daily turnover volumes as the aforementioned preset number of historical days. The set of historical daily average values ​​of the aforementioned target items includes the same number of historical daily average values ​​as the aforementioned preset number of historical days. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future known wireless connection methods.

[0032] Step 202: Generate the target coefficient based on the preset number of historical days, the set of historical daily turnover of the target item, and the set of historical daily value of the target item.

[0033] In some embodiments, the executing entity can generate a target coefficient based on a preset number of historical days, a set of historical daily turnover volumes of the target item, and a set of historical daily values ​​of the target item. The target coefficient can be a coefficient characterizing the correlation between the historical daily turnover volume and the historical daily value of the target item. In practice, firstly, the executing entity can determine the average of the historical daily turnover volumes of each target item included in the set of historical daily turnover volumes as the average historical daily turnover volume of the target item. Then, the executing entity can determine the average of the historical daily values ​​of each target item included in the set of historical daily values ​​of the target item as the average historical daily value of the target item. Secondly, the target coefficient can be generated using the following formula (the denominator of the following formula is not zero):

[0034]

[0035] in,

[0036] Where R[i] represents the target coefficient corresponding to target item f. T represents the preset historical number of days. For example, when T is 40, it can represent that the preset historical number of days is the past 40 days. t represents the t-th day of history. For example, when T is 40 and t is 1, it represents the first day of the past 40 days. S[i][t] represents the daily historical turnover of target item i corresponding to target item i on the t-th day of history. P[i][t] represents the historical daily average value of target item i corresponding to target item i on the t-th day of history. This represents the average daily historical turnover of the target item. This represents the historical average daily value of the target item.

[0037] Therefore, a target coefficient can be obtained to characterize the correlation between the daily historical turnover of the target item and the daily historical value of the target item.

[0038] Step 203: Generate a value tag based on the daily historical turnover set of the target item, the preset number of historical days, and the target coefficient.

[0039] In some embodiments, the executing entity can generate a value marker based on the daily historical turnover set of the target item, a preset number of historical days, and a target coefficient. This value marker can be a marker indicating whether the target item is a value-sensitive item. A value-sensitive item can be an item whose turnover is significantly affected by value fluctuations. For example, a value marker of 1 indicates that the target item is a value-sensitive item, while a value marker of 0 indicates that the target item is not a value-sensitive item.

[0040] In practice, the first step is for the aforementioned implementing entity to generate the historical valid days based on the daily historical turnover set of the target item and a preset historical number of days. The historical valid days can be defined as the number of days the target item has been in circulation within each preset historical time period corresponding to the preset historical number of days. Specifically, the historical valid days can be generated using the following formula:

[0041] Ts[i]=len({t|S[i][t]>0}).

[0042] Where Ts[i] represents the number of valid historical days. len() represents the operation of counting the elements in the set. S[f][t] represents the daily historical turnover of target item f corresponding to the target item on the t-th day of history.

[0043] The second step is to generate the item inspection statistic using the following formula (the denominator of the following formula is not zero) based on the target coefficient and the number of historical valid days:

[0044]

[0045] Where TR[i] represents the item inspection statistic. R[f] represents the target coefficient for the corresponding target item i. Ts[f] represents the number of historical valid days.

[0046] The third step is to generate the item confidence threshold using the following formula, based on the historical valid days and preset confidence level parameters:

[0047]

[0048] Where L[i] represents the item confidence threshold. Student represents the Student distribution. Ts[f] represents the number of valid historical days. X represents the preset confidence level parameter. For example, X can be 0.02.

[0049] The fourth step is to generate the value marker using the following formula, based on the item inspection statistics and item confidence limits:

[0050]

[0051] Where K[i] represents the value marker, TR[i] represents the item test statistic, and L[i] represents the item confidence limit.

[0052] Thus, a value marker can be obtained to characterize whether the target item is a value-sensitive item.

[0053] In some optional implementations of certain embodiments, the execution entity first generates a historical valid number of days based on the daily historical turnover set of the target item and a preset historical number of days. Second, it generates an item inspection statistic based on the target coefficient and the historical valid number of days. Third, it generates an item confidence limit based on the historical valid number of days and a preset confidence level parameter. Fourth, it generates a value marker based on the item inspection statistic, item confidence limit, target coefficient, preset correlation lower limit, historical valid number of days, and preset reference day lower limit. In practice, the execution entity can generate a value marker based on the following formula:

[0054]

[0055] Where K[i] represents the value marker. TR[i] represents the item test statistic. L[i] represents the item confidence limit. R[i] represents the target coefficient corresponding to item i. Rx represents the lower threshold of relevance. The default value of Rx is -0.3. Ts[i] represents the number of valid historical days. Tx is a preset lower limit of reference days. For example, Tx can be 30 days.

[0056] Therefore, by determining the number of valid historical days, the item inspection statistic, and the item confidence limit, a value marker characterizing whether a target item is a value-sensitive item can be obtained.

[0057] The formula for generating value markers and its related content, as an inventive point of this disclosure, solves the technical problem of: the inability to distinguish whether a target item is a value-sensitive item through value markers, resulting in low accuracy of estimated turnover, leading to wasted warehouse space or spoilage of fresh goods. Factors leading to wasted warehouse space or spoilage of fresh goods often include: a lack of value markers. Solving these factors can improve the accuracy of estimated turnover, thereby reducing wasted warehouse space or spoilage of fresh goods. To achieve this effect, this disclosure generates a target coefficient, historical valid days, item inspection statistics, and item confidence limits. When the item inspection statistics are greater than the item confidence limits, the target coefficient is less than the lower correlation threshold, and the historical valid days are greater than the lower reference days limit, a value marker of "1" is assigned to the target item. At this time, a value marker of "1" for the target item indicates that the target item is a value-sensitive item. This allows us to distinguish whether a target item is a value-sensitive item, and thus predict the turnover based on whether the target item is a value-sensitive item, thereby improving the accuracy of the predicted turnover and reducing the waste of warehouse space or the loss of fresh goods.

[0058] Step 204: In response to the value marker characterizing the target item as a value-sensitive item, based on the target item's historical daily value set and the target item's historical daily turnover set, mark each preset historical time corresponding to the preset number of historical days as a low-value period to obtain a historical time period marker set.

[0059] In some embodiments, the executing entity may, in response to the value marker indicating that the target item is a value-sensitive item, mark each preset historical time corresponding to a preset number of historical days as a low-value period based on the target item's historical daily value set and historical daily turnover set, thus obtaining a historical time period marker set. In practice, the executing entity may mark preset historical time markers where the target item's historical daily value exceeds a preset value threshold, and where the target item's historical daily turnover exceeds a preset turnover threshold, as low-value periods, thus obtaining a historical time period marker set. The preset value threshold and the preset turnover threshold can be set by relevant personnel. Thus, a historical time period marker set representing the period of the target item can be obtained.

[0060] In some optional implementations of certain embodiments, the first step is that the executing entity can perform dimensional unification processing on the daily historical turnover volume of each target item in the set of daily historical turnover volumes of target items, and use the dimensional unification processing of the daily historical turnover volume of target items as the unified daily historical turnover volume of items, thus obtaining the set of unified daily historical turnover volumes of items. In practice, firstly, the executing entity can determine the average of the daily historical turnover volumes of each target item included in the set of daily historical turnover volumes of target items as the average daily historical turnover volume of target items. Then, the executing entity can perform dimensional unification processing on the daily historical turnover volume of each target item in the set of daily historical turnover volumes of target items based on the historical average value of target items, the historical average value of target items, and the preset number of historical days, using the following formula (the denominator of the following formula is not 0):

[0061]

[0062] Where X1[i][t] represents the historical daily circulation volume of the unified item i corresponding to the target item on historical day t. P[i][t] represents the historical daily average value of the target item i corresponding to the target item on historical day t. This represents the average daily value of the target item throughout history. T represents the preset number of historical days.

[0063] The second step involves standardizing the historical daily value of each target item in the historical daily value set. This standardized historical daily value is then used as the standardized historical daily value, resulting in a standardized historical daily value set. In practice, the implementing entity can use the following formula to standardize the historical daily value of each target item in the historical daily value set, based on the target item's historical daily turnover volume, the average historical daily turnover volume, and a preset number of historical days (the denominator of the following formula is not zero):

[0064]

[0065] Where X2[i][t] represents the standardized daily value of target item i on historical day t. C represents the dimensional importance parameter. The default value of C is 0.5. S[i][t] represents the daily historical turnover of target item i on historical day t. This represents the average daily historical turnover of the target item. T represents the preset number of historical days.

[0066] The third step involves combining the daily historical turnover of each standardized item in the standardized item daily historical turnover set with the corresponding daily historical value of the standardized item daily historical turnover in the standardized item daily historical value set to form an item sample, thus obtaining an item sample set. For example, the above item sample set can be: {(X1[f][t], X2[i][t])|t=1,2,...,T}. Here, X1[i][t] represents the standardized item daily historical turnover of target item i on historical day t. X2[f][t] represents the standardized item daily historical value of target item i on historical day t.

[0067] The fourth step involves clustering the item sample set to determine two cluster center items. In practice, the k-means clustering algorithm can be used to determine these two cluster center items. For example, the two cluster center items mentioned above can be represented by the following formulas: (A1[i], A2[i]) and (B1[i], B2[i]). Here, A1[i] represents the daily historical turnover of the standardized items included in the first cluster center item sample. A2[i] represents the daily historical value of the standardized items included in the first cluster center item sample. B1[i] represents the daily historical turnover of the standardized items included in the second cluster center item sample. B2[i] represents the daily historical value of the standardized items included in the second cluster center item sample.

[0068] The fifth step involves labeling the preset historical time periods corresponding to the preset number of historical days as low-value time slots based on the two cluster center item samples, resulting in a set of historical time slot labels. These labels characterize whether the corresponding preset historical time belongs to a specific time slot. For example, a low-value time slot label of 1 indicates that the corresponding preset historical time belongs to a specific time slot, while a low-value time slot label of 0 indicates that the corresponding preset historical time belongs to a specific time slot. In practice, the segmentation boundary value can first be determined using the following formula based on the two cluster center item samples:

[0069]

[0070] in, The values ​​represent the dividing boundary. A1[i] represents the daily historical turnover of standardized items included in the first cluster center item sample. B1[i] represents the daily historical turnover of standardized items included in the second cluster center item sample.

[0071] Then, based on the above segmentation boundary values ​​and the above set of historical daily average values ​​of target items, each historical time period marker in the historical time period marker set can be determined using the following formula:

[0072]

[0073] Where Q[i][t] represents the historical time period marker corresponding to target item i on historical day t, included in the above historical time period marker set. P[i][t] represents the historical average daily value of target item i on historical day t. This represents the boundary value of the partition.

[0074] The formula for determining historical time slot markers and its related content, as an inventive point of this disclosure, solves the technical problem of: the inability to determine whether each preset historical time belongs to a slot through segmentation boundary values ​​and a set of historical time slot markers, resulting in low accuracy of estimated turnover, leading to wasted warehouse space or spoilage of fresh goods. Factors leading to wasted warehouse space or spoilage of fresh goods often include: a lack of segmentation boundary values ​​and a set of historical time slot markers. Solving these factors can improve the accuracy of estimated turnover, thereby reducing wasted warehouse space or spoilage of fresh goods. To achieve this effect, firstly, this disclosure performs dimensional unification processing on the daily historical turnover volume of each target item in the set of daily historical turnover volumes. Then, it performs dimensional unification processing on the daily historical value of each target item in the set of daily historical values. Secondly, the unified daily historical turnover set of each item, including the unified daily historical turnover volume of each item, and the unified daily historical value set of each item, are combined to form an item sample set. Then, the item sample set is clustered to determine two cluster center item samples. Finally, a split boundary value is generated based on the two cluster center item samples. When the historical average daily value of the target item is less than the split boundary value, the corresponding historical time period is marked as "1", thus obtaining a historical time period mark set. At this point, a historical time period marked as "1" indicates that the preset historical time belongs to a specific period. This allows for the differentiation of whether each preset historical time belongs to a specific period, enabling the prediction of turnover volume based on this distinction. This improves the accuracy of the predicted turnover volume and reduces warehouse space waste or spoilage of fresh produce.

[0075] Step 205: Generate the incremental turnover of the period based on the historical time period marker set, the daily historical turnover set of the target item, and the preset number of historical days.

[0076] In some embodiments, the executing entity can generate the incremental schedule turnover based on a set of historical schedule markers, a set of historical daily turnover volumes for the target item, and a preset number of historical days. In practice, firstly, the executing entity can receive a preset historical time selected by relevant staff. Specifically, the executing entity can receive the preset historical time selected by relevant staff before generating each incremental schedule turnover. Alternatively, the executing entity can receive the preset historical time selected by relevant staff before generating an incremental schedule turnover once, and use the selected preset historical time to generate the incremental schedule turnover for a period of time. Here, the specific setting of the aforementioned period of time can be set by the relevant staff. For example, the aforementioned period of time can be the next two months. Then, the executing entity can use the following formula to minimize the estimation error through iteration, thereby generating the incremental schedule turnover:

[0077]

[0078] Where Delta represents the estimation error. N[i] represents the increment of the schedule turnover. ta represents the selected preset historical time. Here, ta can be selected by relevant staff (the preset historical time represented by ta is at least 7 days away from the current day). Q[i][ta] represents the historical schedule marker corresponding to target item i on historical day ta. S[i][t] represents the daily historical turnover of target item i corresponding to target item i on historical day t. Q[i][t] represents the historical schedule markers corresponding to target item i on historical day t included in the above historical schedule marker set. S[i][ta] represents the daily historical turnover of target item i corresponding to target item i on historical day ta.

[0079] Therefore, we can obtain the increment of turnover due to the slot effect, which represents the increase in turnover.

[0080] Step 206: Generate item turnover information based on the target item's daily historical turnover set, preset historical days, and period turnover increment.

[0081] In some embodiments, the aforementioned executing entity can generate item turnover information based on the target item's daily historical turnover set, a preset number of historical days, and the turnover increment for specific time periods. This item turnover information includes the predicted total turnover of items. In practice, firstly, the executing entity can generate the predicted total turnover of items based on the target item's daily historical turnover set, a preset number of historical days, and the turnover increment for specific time periods. Then, the predicted total turnover of items and a preset corpus can be combined to form item turnover information. The preset corpus can be a pre-defined set of data. For example, the preset corpus could be: "The predicted turnover of items is:". In practice, the executing entity can generate item turnover information through various methods based on the target item's daily historical turnover set, a preset number of historical days, and the turnover increment for specific time periods. Thus, item turnover information including the predicted total turnover of items can be obtained.

[0082] Step 207: Send the item turnover information to the associated inventory terminal so that the inventory terminal can display the predicted total item turnover included in the item turnover information.

[0083] In some embodiments, the aforementioned executing entity can send the goods turnover information to an associated inventory terminal, enabling the inventory terminal to display the predicted total goods turnover included in the goods turnover information. The inventory terminal can be a tablet computer used by relevant staff. This allows the inventory terminal to display the predicted total goods turnover included in the goods turnover information.

[0084] Optionally, in the first step, the aforementioned executing entity can determine the replenishment quantity of goods based on the goods turnover information and warehouse inventory. The warehouse inventory can be the amount of the target goods currently in the warehouse. In practice, if the predicted total goods turnover information is greater than the warehouse inventory, the executing entity can determine the difference between the predicted total goods turnover and the warehouse inventory as the replenishment quantity. In the second step, the executing entity can control the associated vehicles to dispatch goods according to the replenishment quantity. In practice, the executing entity can control the associated vehicles to transport the target goods in quantities equal to the replenishment quantity to the relevant warehouse. This achieves the preparation of the target goods.

[0085] The above-described embodiments of this disclosure have the following beneficial effects: The method for sending goods turnover information according to some embodiments of this disclosure reduces the waste of warehouse space or the loss of fresh goods, and lowers the computational resource consumption when estimating turnover volume. Specifically, the reasons for the waste of warehouse space or the loss of fresh goods, and the high computational resource consumption when estimating turnover volume, are: manual calculation of the estimated turnover volume is highly subjective and lacks specific data support, resulting in low accuracy of the estimated turnover volume, leading to the waste of warehouse space or the loss of fresh goods. Complex modeling requires business feature data specific to a particular logistics station. Different logistics stations need to be modeled separately, and the turnover volume needs to be estimated through the model. The portability of the same model between different logistics stations is poor, and due to the complexity of the model, both modeling and model operation require a large amount of computational resources. Based on this, the method for sending goods turnover information according to some embodiments of this disclosure first obtains the daily historical turnover volume set and the historical daily average value set of the target item based on a preset number of historical days corresponding to the target item. Then, a target coefficient is generated based on the preset historical number of days, the set of historical daily turnover of the target item, and the set of historical daily value of the target item. This yields a target coefficient characterizing the correlation between the historical daily turnover and the historical daily value of the target item. Next, a value marker is generated based on the set of historical daily turnover of the target item, the preset historical number of days, and the target coefficient. This yields a value marker characterizing whether the target item is a value-sensitive item. Then, in response to the value marker characterizing the target item as a value-sensitive item, low-value time slots are marked for each preset historical time corresponding to the preset historical number of days, based on the set of historical daily value and the set of historical daily turnover of the target item, resulting in a set of historical time slot markers. This yields a set of historical time slot markers characterizing the time slots of the target item. Finally, based on the set of historical time slot markers, the set of historical daily turnover of the target item, and the preset historical number of days, a time slot turnover increment is generated. This yields a time slot turnover increment characterizing the increase in turnover due to the time slot effect. Then, based on the target item's daily historical turnover set, preset historical days, and periodic turnover increments, item turnover information is generated. This yields item turnover information including the predicted total turnover. Finally, the item turnover information is sent to the associated inventory terminal, allowing the terminal to display the predicted total turnover included in the turnover information. Because turnover prediction is based on two business characteristic data points—the target item's daily historical turnover set and the target item's historical daily average value set—the turnover prediction is supported by concrete data, thereby improving the accuracy of the projected turnover.Because the turnover prediction is based solely on two business characteristic data points—the daily historical turnover volume set and the daily historical average value set of the target item—and does not involve logistics station-related business data, the item turnover information transmission methods corresponding to the above embodiments of this disclosure can be applied to different logistics stations, improving the portability of turnover prediction between different logistics stations. Furthermore, since the turnover prediction is based solely on two business characteristic data points—the daily historical turnover volume set and the daily historical average value set of the target item—the amount of data is significantly reduced compared to the business characteristic data relied upon by complex modeling, thereby saving the computing resources occupied when performing turnover prediction.

[0086] Further reference Figure 3 This illustrates a flow 300 of another embodiment of the method for sending goods circulation volume information. Flow 300 of this method includes the following steps:

[0087] Step 301: Based on the preset number of historical days corresponding to the target item, obtain the set of historical daily turnover volume of the target item and the set of historical daily average value of the target item.

[0088] Step 302: Generate the target coefficient based on the preset number of historical days, the set of historical daily turnover of the target item, and the set of historical daily value of the target item.

[0089] Step 303: Generate a value tag based on the daily historical turnover set of the target item, the preset number of historical days, and the target coefficient.

[0090] Step 304: In response to the value marker characterizing the target item as a value-sensitive item, based on the target item's historical daily value set and the target item's historical daily turnover set, mark each preset historical time corresponding to the preset number of historical days as a low-value period to obtain a historical time period marker set.

[0091] Step 305: Generate the incremental turnover of the period based on the historical time period marker set, the daily historical turnover set of the target item, and the preset number of historical days.

[0092] In some embodiments, the specific implementation of steps 301-305 and their resulting technical effects can be found in [reference needed]. Figure 2 Steps 201-205 in the corresponding embodiments will not be repeated here.

[0093] Step 306: Mark the preset future time points as low-value time slots to obtain future time slot labels.

[0094] In some embodiments, the executing entity can mark a preset future time point as a low-value sales period to obtain a future sales period label. The preset future time point can be a future day on which sales volume needs to be estimated. In practice, the executing entity can use the following formula to mark the preset future time point as a low-value sales period:

[0095]

[0096] Where Qf[i][t′] represents the future time slot marker of target item i at the preset future time point t′. F[i][t′] represents the pre-set daily value of target item i at the preset future time point t′. This represents the boundary value of the partition.

[0097] Thus, a future time slot marker representing whether the target item is in a slot at a preset future time point can be obtained.

[0098] Step 307: Determine the number of days to be restored based on the preset historical number of days.

[0099] In some embodiments, the execution entity can determine the number of days to be restored based on a preset historical number of days. In practice, any value can be selected from those less than the preset historical number of days and a multiple of 7 as the number of days to be restored. For example, when the preset historical number of days is 40 days, the execution entity can select 14 days as the number of days to be restored. Thus, by determining the number of days to be restored, the amount of business data used to generate the expected basic throughput is controlled within a reasonable range, thereby avoiding the consumption of excessive computing resources.

[0100] Step 308: Determine the time period to be restored based on the number of days to be restored and the current time.

[0101] In some embodiments, the executing entity can determine the time period to be restored based on the number of days to be restored and the current time. In practice, the executing entity can determine the time period to be restored based on the number of days to be restored preceding the current time. For example, when the number of days to be restored is 14 days and the current time is December 15th, the executing entity can determine December 1st to December 14th as the time period to be restored. Thus, the time period to be restored corresponding to the aforementioned number of days to be restored can be obtained.

[0102] Step 309: Select the daily historical turnover of each target item corresponding to the time period to be restored from the set of daily historical turnover of the target item as the set of daily historical turnover of the item to be restored.

[0103] In some embodiments, the executing entity may select the daily historical turnover of each target item corresponding to the time period to be restored from the daily historical turnover set of the target item as the daily historical turnover set of the item to be restored. Thus, the obtained daily historical turnover set of the item to be restored represents the daily historical turnover set of the target item in the time period to be restored.

[0104] Step 310: Select the historical daily values ​​of each target item corresponding to the time period to be restored from the historical daily value set of the target item as the historical daily value set of the item to be restored.

[0105] In some embodiments, the executing entity may select the historical daily values ​​of each target item corresponding to the time period to be restored from the historical daily value set of the target item as the historical daily value set of the item to be restored. Thus, the obtained historical daily value set of the item to be restored represents the daily value set of the target item during the time period to be restored.

[0106] Step 311: Generate the estimated basic turnover volume based on the set of historical daily turnover volume of the items to be restored, the set of historical daily value of the items to be restored, the number of days to be restored, and the turnover volume increment during the period.

[0107] In some embodiments, the aforementioned executing entity can generate the estimated basic turnover volume based on the set of historical daily turnover volumes of the items to be restored, the set of historical daily values ​​of the items to be restored, the number of days to be restored, and the turnover volume increment for each period. In practice, the aforementioned executing entity can generate the estimated basic turnover volume using the following formula:

[0108]

[0109] Where M0 represents the expected basic turnover volume. th represents the number of days to be restored. T represents the preset historical number of days. S[i][t] represents the historical daily turnover volume of the item to be restored corresponding to target item i on historical day t. N[i] represents the increment of turnover volume for each period. Q[i][t] represents the historical time period marker for target item i on historical day t.

[0110] Thus, we can obtain the expected basic turnover volume representing the corresponding preset future time point.

[0111] Step 312: Generate item circulation information based on the expected basic circulation volume, the incremental circulation volume during the designated period, the future time period markers, and the preset item value set.

[0112] In some embodiments, the aforementioned executing entity can generate item turnover information based on the expected basic turnover volume, the incremental turnover volume during specific periods, future time period markers, and a preset set of item values. The item turnover information includes the predicted total item turnover volume. In practice, firstly, the aforementioned executing entity can generate the predicted total item turnover volume based on the expected basic turnover volume, the incremental turnover volume during specific periods, future time period markers, and a preset set of item values. Then, the predicted total item turnover volume and the preset corpus can be combined to form item turnover volume information. The predicted total item turnover volume can be generated using the following formula:

[0113] M[i][t′]=M0+N[i]×Qf[i][t′].

[0114] Where M[i][t′] represents the predicted total circulation volume of items. M0 represents the expected basic circulation volume. N[i] represents the incremental circulation volume during a specific time period. Qf[i][t′] represents the future time period marker of target item i at a preset future time point t′.

[0115] This allows us to obtain information on the total volume of goods circulation, including predictions of the total volume of goods circulation.

[0116] Steps 306-312, as an inventive point of this disclosure, address the second technical problem mentioned in the background: "Manual calculation of the estimated turnover volume has a large deviation. Furthermore, complex modeling relies on a large amount of business characteristic data, and the cost of maintaining this data is high in practical applications. Once the business characteristic data is incorrect, the estimated turnover volume will also be incorrect, and the model's resistance to fluctuations is poor, further leading to wasted warehouse space or spoilage of fresh goods." The factors leading to further waste of warehouse space or spoilage of fresh goods are as follows: large deviation in manually calculated turnover volume; and complex modeling relying on a large amount of business characteristic data. Solving these factors can improve the accuracy of the estimated turnover volume. To achieve this, after generating the incremental turnover volume for a given period, this disclosure first marks the preset future time points as low-value periods, obtaining future time period markers. This provides a future time period marker characterizing whether the target item is in a given period at the preset future time point. Then, based on the preset historical number of days, the number of days to be restored is determined. Therefore, by determining the number of days to be restored, the amount of business data used to generate the estimated basic turnover volume is controlled within a reasonable range, thereby avoiding excessive consumption of computing resources. Next, based on the number of days to be restored and the current time, the time period to be restored is determined. Thus, the time period to be restored corresponding to the aforementioned number of days to be restored can be obtained. Secondly, the daily historical turnover volume of each target item corresponding to the time period to be restored is selected from the target item's daily historical turnover volume set as the target item's daily historical turnover volume set. Thus, the obtained target item's daily historical turnover volume set represents the target item's daily historical turnover volume set within the target item's time period. Then, the historical daily value of each target item corresponding to the time period to be restored is selected from the target item's historical daily value set as the target item's historical daily value set. Thus, the obtained target item's historical daily value set represents the target item's daily value set within the target item's time period. Finally, based on the target item's daily historical turnover volume set, the target item's historical daily value set, the number of days to be restored, and the schedule turnover volume increment, the estimated basic turnover volume is generated. This allows us to obtain the projected basic turnover volume representing the corresponding preset future time point. Then, based on the projected basic turnover volume, the incremental turnover volume for each time slot, the future time slot markers, and the preset item value set, item turnover information is generated. This yields item turnover information including the predicted total item turnover volume. Therefore, it avoids item omissions and estimation errors caused by human judgment, improves the accuracy of projected turnover volume, and thus reduces warehouse space waste or spoilage of perishable goods. Furthermore, because the generation of item turnover information relies on relatively little business feature data and does not involve logistics station business data, it reduces the cost of maintaining the business feature data required to generate item turnover information in practical applications.When data in the business characteristic data that is unrelated to the business characteristic data on which the generated item turnover information depends is corrupted, it will not affect the generation of item turnover information. This avoids errors in the estimated turnover caused by errors in the business characteristic data, thereby reducing the waste of warehouse space or the loss of fresh goods.

[0117] Step 313: Send the item turnover information to the associated inventory terminal so that the inventory terminal can display the predicted total item turnover information.

[0118] In some embodiments, the specific implementation of step 313 and its resulting technical effects can be found in [reference needed]. Figure 2 Step 207 in the corresponding embodiments will not be repeated here.

[0119] from Figure 3 It can be seen from this that, with Figure 2 Compared to the description of some corresponding embodiments, Figure 3 The process 300 of the item turnover information sending method in some corresponding embodiments embodies steps that extend the item turnover information generation process. Therefore, the solutions described in these embodiments can generate item turnover information by predicting the basic turnover volume, the incremental turnover volume during a specific period, future time period markers, and a preset item value set. This avoids item omissions and estimation errors caused by human judgment, improves the accuracy of the predicted turnover volume, and thus reduces warehouse space waste or spoilage of fresh goods. Furthermore, because the generation of item turnover information relies on relatively little business feature data and does not involve logistics station business data, the cost of maintaining the business feature data on which the item turnover information depends in practical applications is reduced. When data in the business feature data that is unrelated to the business feature data on which the item turnover information depends is incorrect, it will not affect the generation of the item turnover information, avoiding errors in the predicted turnover volume caused by errors in the business feature data, thereby reducing warehouse space waste or spoilage of fresh goods.

[0120] Further reference Figure 4 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a device for transmitting information on the volume of goods circulation. These device embodiments are similar to... Figure 2 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0121] like Figure 4As shown, the item turnover information sending device 400 in some embodiments includes: an acquisition unit 401, a first generation unit 402, a second generation unit 403, a schedule marking unit 404, a third generation unit 405, a fourth generation unit 406, and a sending unit 407. The acquisition unit 401 is configured to acquire a set of historical daily turnover volumes and a set of historical daily average values ​​of the target item based on a preset number of historical days corresponding to the target item. The set of historical daily turnover volumes includes the same number of historical daily turnover volumes as the preset number of historical days, and the set of historical daily average values ​​includes the same number of historical daily average values ​​of the target item as the preset number of historical days. The first generation unit 402 is configured to generate a target coefficient based on the preset number of historical days, the set of historical daily turnover volumes, and the set of historical daily values ​​of the target item. The second generation unit 403 is configured to generate a value mark based on the set of historical daily turnover volumes, the preset number of historical days, and the target coefficient. The schedule marking unit 404 is configured to respond to the value mark representing the target item... The target item is a value-sensitive item. Based on the target item's historical daily value set and historical daily turnover set, low-value period markers are assigned to each preset historical time corresponding to a preset number of historical days, resulting in a historical time period marker set. The third generation unit 405 is configured to generate a period turnover increment based on the historical time period marker set, the target item's historical daily turnover set, and the preset number of historical days. The fourth generation unit 406 is configured to generate item turnover information based on the target item's historical daily turnover set, the preset number of historical days, and the period turnover increment, wherein the item turnover information includes the predicted total item turnover. The sending unit 407 is configured to send the item turnover information to the associated inventory terminal, so that the inventory terminal displays the predicted total item turnover included in the item turnover information.

[0122] It is understandable that the units described in the device 400 are related to the reference. Figure 2 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to device 400 and the units contained therein, and will not be repeated here.

[0123] The following is for reference. Figure 5 It illustrates electronic devices suitable for implementing some embodiments of the present disclosure (such as...). Figure 1 The diagram shows the structure of the computing device 101)500. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0124] like Figure 5As shown, the electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0125] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 5 Each box shown can represent a device or multiple devices as needed.

[0126] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 509, or installed from storage device 508, or installed from ROM 502. When the computer program is executed by processing device 501, it performs the functions defined in the methods of some embodiments of this disclosure.

[0127] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0128] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0129] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: obtain a set of daily historical turnover volume and a set of daily historical average value of the target item based on a preset historical number of days corresponding to the target item, wherein the set of daily historical turnover volume includes the same number of daily historical turnover volumes as the preset historical number of days, and the set of daily historical average value includes the same number of daily historical average values ​​of the target item as the preset historical number of days; generate a target coefficient based on the preset historical number of days, the set of daily historical turnover volume, and the set of daily historical value; and generate a target coefficient based on the set of daily historical turnover volume, the preset historical number of days, and the target coefficient. Generate value markers; in response to the value markers indicating that the target item is a value-sensitive item, mark each preset historical time corresponding to a preset number of historical days as a low-value period based on the target item's historical daily value set and historical daily turnover set, thus obtaining a historical time period marker set; generate a period turnover increment based on the historical time period marker set, the target item's historical daily turnover set, and the preset number of historical days; generate item turnover information based on the target item's historical daily turnover set, the preset number of historical days, and the period turnover increment, wherein the item turnover information includes the predicted total item turnover; send the item turnover information to the associated inventory terminal, so that the inventory terminal displays the predicted total item turnover included in the item turnover information.

[0130] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0132] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit, a first generation unit, a second generation unit, a schedule marking unit, a third generation unit, a fourth generation unit, and a sending unit. The names of these units do not necessarily limit the specific unit; for example, the sending unit may also be described as "a unit that sends goods turnover information to an associated inventory terminal."

[0133] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0134] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for sending information on the volume of goods circulation, comprising: Based on the preset number of historical days corresponding to the target item, obtain the set of daily historical turnover volume of the target item and the set of daily historical average value of the target item, wherein the number of daily historical turnover volumes of the target item included in the set of daily historical turnover volume of the target item is the same as the preset number of historical days, and the number of daily historical average values ​​of the target item included in the set of daily historical average value of the target item is the same as the preset number of historical days. Based on the preset number of historical days, the set of historical daily turnover of the target item, and the set of historical daily value of the target item, a target coefficient is generated, wherein the target coefficient is a coefficient that characterizes the correlation between the historical daily turnover of the target item and the historical daily value of the target item. Based on the daily historical turnover set of the target item, the preset number of historical days, and the target coefficient, a value tag is generated, including: Based on the set of daily historical turnover of the target item and the preset number of historical days, generate the number of valid historical days; Based on the target coefficient and the number of valid historical days, generate the item inspection statistics; Based on the historical valid days and the preset confidence level parameters, generate the item confidence limits; A value tag is generated based on the item inspection statistic, the item confidence limit, the target coefficient, the preset correlation lower limit threshold, the historical valid days, and the preset reference days lower limit. In response to the value marker indicating that the target item is a value-sensitive item, based on the historical daily value set and the historical daily turnover set of the target item, low-value period markers are applied to each preset historical time corresponding to the preset number of historical days, resulting in a historical time period marker set, including: The corresponding target item whose historical daily value is greater than a preset value threshold and whose historical daily turnover is greater than a preset turnover threshold are marked as low-value periods to obtain a set of historical time period markers. Based on the historical time slot marker set, the target item's daily historical turnover set, and the preset historical number of days, an incremental turnover for each slot is generated, including: Receive the preset historical time selected by relevant staff; The estimation error is minimized through iteration, thereby generating the incremental volume of slot turnover. Based on the set of historical daily turnover of the target item, the preset number of historical days, and the turnover increment of the period, the item turnover information is generated, wherein the item turnover information includes the predicted total turnover of the item; The item turnover information is sent to the associated inventory terminal, so that the inventory terminal can display the predicted total item turnover included in the item turnover information.

2. The method according to claim 1, wherein, The step of marking each preset historical time corresponding to the preset number of historical days as a low-value period includes: The daily historical turnover volume of each target item in the set of daily historical turnover volume of the target items is subjected to dimension unification processing, and the daily historical turnover volume of the target items after dimension unification processing is used as the unified daily historical turnover volume of the items, thus obtaining the set of unified daily historical turnover volume of the items. The historical daily value of each target item in the historical daily value set of the target items is subjected to dimension unification processing, and the dimension-unified historical daily value of the target items is used as the unified historical daily value of the items, thus obtaining the unified historical daily value set of the items. The unified item daily historical turnover volume of each unified item included in the unified item daily historical turnover volume set and the unified item daily historical value corresponding to the unified item daily historical turnover volume in the unified item daily value set are combined to form an item sample, thus obtaining an item sample set. The set of item samples is clustered to determine two cluster center item samples; Based on the item samples of the two cluster centers, the preset historical time corresponding to the preset number of historical days is marked as a low-value period, thus obtaining a set of historical time period marks.

3. The method according to claim 1, wherein, The generated item turnover information includes: Mark low-value time slots at preset future time points to obtain future time slot labels; Determining the number of days to be restored based on the preset historical number of days includes: arbitrarily selecting a value from a set of values ​​that are less than the preset historical number of days and are multiples of 7 as the number of days to be restored; Based on the number of days to be restored and the current time, determine the time period to be restored, including: determining the time period of the number of days to be restored before the current time as the time period to be restored; Select the daily historical turnover of each target item corresponding to the time period to be restored from the set of daily historical turnover of the target item as the set of daily historical turnover of the target item to be restored, wherein the set of daily historical turnover of the target item to be restored represents the set of daily historical turnover of the target item in the time period to be restored. Select the historical daily values ​​of each target item corresponding to the time period to be restored from the set of historical daily values ​​of the target items as the set of historical daily values ​​of the items to be restored; Based on the set of historical daily turnover of the items to be restored, the set of historical daily value of the items to be restored, the number of days to be restored, and the incremental turnover of the period, the estimated basic turnover is generated.

4. The method according to claim 3, wherein, The generated item turnover information also includes: Based on the estimated basic turnover volume, the incremental turnover volume during the specified period, the future time period marker, and the preset item value set, item turnover volume information is generated, wherein the item turnover volume information includes the predicted total item turnover volume.

5. The method according to claim 1, wherein, The method further includes: Based on the information on the volume of goods turnover and the warehouse inventory, determine the replenishment quantity of goods; Control the associated vehicles to dispatch goods according to the replenishment quantity of the goods.

6. A device for transmitting information on the volume of goods circulation, comprising: The acquisition unit is configured to acquire a set of historical daily turnover volume of the target item and a set of historical daily average value of the target item based on a preset number of historical days corresponding to the target item, wherein the number of historical daily turnover volumes of the target item included in the set of historical daily turnover volume of the target item is the same as the preset number of historical days, and the number of historical daily average values ​​of the target item included in the set of historical daily average value of the target item is the same as the preset number of historical days. The first generation unit is configured to generate a target coefficient based on the preset number of historical days, the set of historical daily turnover of the target item, and the set of historical daily value of the target item, wherein the target coefficient is a coefficient characterizing the correlation between the historical daily turnover of the target item and the historical daily value of the target item. The second generation unit is configured to generate a value tag based on the target item's daily historical turnover set, the preset number of historical days, and the target coefficient. This includes: generating a historical valid number of days based on the target item's daily historical turnover set and the preset number of historical days; generating an item verification statistic based on the target coefficient and the historical valid days; generating an item confidence limit based on the historical valid days and a preset confidence level parameter; and generating a value tag based on the item verification statistic, the item confidence limit, the target coefficient, a preset lower correlation threshold, the historical valid days, and a preset lower reference number of days. The scheduling unit is configured to, in response to the value marker characterizing the target item as a value-sensitive item, mark each preset historical time corresponding to the preset number of historical days as a low-value scheduling unit based on the target item's historical daily value set and the target item's historical daily turnover set, to obtain a historical time scheduling mark set, including: marking the preset historical time markers where the corresponding target item's historical daily value is greater than a preset value threshold and the corresponding target item's historical daily turnover is greater than a preset turnover threshold as low-value scheduling units, to obtain a historical time scheduling mark set; The third generation unit is configured to generate an incremental change in the turnover of the target item based on the set of historical time slot markers, the set of historical daily turnover of the target item, and the preset number of historical days. This includes: receiving a preset historical time selected by relevant staff; and minimizing the estimation error through iteration to generate the incremental change in the turnover of the target item. The fourth generation unit is configured to generate item turnover information based on the target item's daily historical turnover set, the preset number of historical days, and the period turnover increment, wherein the item turnover information includes the predicted total turnover of items; The sending unit is configured to send the item turnover information to an associated inventory terminal, so that the inventory terminal displays the predicted total item turnover included in the item turnover information.

7. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-5.

8. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

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