Article transportation planning method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses an article transportation planning method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring a first article circulation data set; generating average circulation data; obtaining an average circulation data sequence; obtaining each first abnormal circulation data set; obtaining each second abnormal circulation data set; obtaining each mark circulation data set; performing data modification processing on each marked circulation data set to obtain each target circulation data set; storing each target circulation data set in a preset database; and based on each target circulation data set, executing an article transportation task corresponding to each target circulation data set. According to the embodiment of the invention, the reliability of the modified article circulation data can be improved, so that the probability of occurrence of errors of transported articles during article transportation caused by errors of the modified article circulation data is reduced, and the waste of transportation resources is reduced.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to an item transportation planning method, device, electronic device, and computer-readable medium. Background Art

[0002] Item transportation planning is a method that uses item-related information to plan the corresponding transportation mode of the item, so as to minimize the transportation resources consumed during transportation. In terms of item transportation, the items to be transported are often determined through item flow data, and the corresponding transportation mode of the items is planned. At present, when identifying and modifying abnormal data, the method usually adopted is: first obtain the item flow data set corresponding to each item. Then, through the item flow data set corresponding to each item, determine the threshold corresponding to each item. Then, determine each item flow data set that is greater than the corresponding threshold in each item flow data set as each item flow data set that needs to be modified, and modify it. Finally, transport the items according to the modified item flow data sets.

[0003] However, when using the above method to identify and modify abnormal data, the following technical problems often occur:

[0004] Determining whether the item flow data is abnormal by only one method can easily identify the correct data as abnormal data, resulting in low reliability of abnormal data identification, and further resulting in low reliability of the modified item flow data. This results in the transportation of wrong items when transporting items according to the modified item flow data, resulting in a waste of transportation resources. Summary of the invention

[0005] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present disclosure propose an item transportation planning method, apparatus, electronic device, and computer-readable medium to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide an article transportation planning method, the method comprising: for each of the article transfer stations, performing the following steps: for each of the article information corresponding to the article transfer station, obtaining an article flow data set corresponding to the article information within a first preset time range as a first article flow data set; based on each of the first article flow data sets obtained, generating average flow data corresponding to the first article flow data set; sorting the generated average flow data to obtain an average flow data sequence; sorting the first article flow data based on the average flow data sequence; The method comprises the following steps: performing a first abnormal data identification process on the data set to obtain each first abnormal circulation data set; performing a second abnormal data identification process on each first article circulation data set based on the average circulation data sequence to obtain each second abnormal circulation data set; performing a marking process on each first article circulation data set based on each first abnormal circulation data set and each second abnormal circulation data set to obtain each marked circulation data set; performing a data modification process on each marked circulation data set to obtain each target circulation data set; storing each target circulation data set in a preset database; and executing an article transportation task corresponding to each target circulation data set based on each target circulation data set.

[0008] In a second aspect, some embodiments of the present disclosure provide an article transportation planning device, the device comprising: an execution unit, configured to perform the following steps for each of the article circulation stations: for each of the article information corresponding to the above-mentioned article circulation station, obtaining an article circulation data set corresponding to the above-mentioned article information within a first preset time range as a first article circulation data set; based on each of the first article circulation data sets obtained, generating average circulation data corresponding to the above-mentioned first article circulation data sets; sorting the generated average circulation data to obtain an average circulation data sequence; based on the above-mentioned average circulation data sequence, sorting the above-mentioned first article circulation data sets. The first abnormal data identification process is performed on each of the above-mentioned article circulation data sets to obtain each first abnormal circulation data set; based on the above-mentioned average circulation data sequence, the second abnormal data identification process is performed on each of the above-mentioned first article circulation data sets to obtain each second abnormal circulation data set; based on the above-mentioned each first abnormal circulation data set and the above-mentioned each second abnormal circulation data set, the above-mentioned each first article circulation data set is marked to obtain each marked circulation data set; the above-mentioned each marked circulation data set is modified to obtain each target circulation data set; the above-mentioned each target circulation data set is stored in a preset database; based on the above-mentioned each target circulation data set, the article transportation task corresponding to the above-mentioned each target circulation data set is executed.

[0009] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and 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 manner of the above-mentioned first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0011] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the article transportation planning method of some embodiments of the present disclosure, the reliability of the modified article flow data can be improved and the waste of transportation resources can be reduced. Specifically, the reason for the low reliability of the modified article flow data and the waste of transportation resources is that it is easy to identify the correct data as abnormal data by only one way to determine whether the article flow data is abnormal, resulting in low reliability of the identification of abnormal data, and then resulting in low reliability according to the modified article flow data. As a result, the transported articles are wrong when the articles are transported according to the modified article flow data, resulting in waste of transportation resources. Based on this, the article transportation planning method of some embodiments of the present disclosure, for each of the article flow stations in each article flow station, performs the following steps: First, for each of the article information corresponding to the above-mentioned article flow station, obtain the article flow data set corresponding to the above-mentioned article information within the first preset time range as the first article flow data set. Thus, the original data to be processed can be obtained. Secondly, based on each of the first article flow data sets in the obtained first article flow data sets, the average flow data corresponding to the above-mentioned first article flow data set is generated. Thus, the average flow data of each object within the above-mentioned first preset time range can be obtained. Then, the generated average circulation data are sorted to obtain an average circulation data sequence. Thus, the sequence of average circulation data corresponding to each item can be obtained. Then, based on the average circulation data sequence, the first abnormal data identification process is performed on the first item circulation data set to obtain each first abnormal circulation data set. Thus, the abnormal data in each first item circulation data set can be identified by the first abnormal data identification method. Then, based on the average circulation data sequence, the second abnormal data identification process is performed on the first item circulation data set to obtain each second abnormal circulation data set. Thus, the abnormal data in each first item circulation data set can be identified by the second abnormal data identification method. Then, based on the first abnormal circulation data set and the second abnormal circulation data set, the first item circulation data set is marked to obtain each marked circulation data set. Thus, all abnormal data identified in each first item circulation data set can be marked. Then, the data modification process is performed on each marked circulation data set to obtain each target circulation data set. Thus, the identified abnormal data can be modified accordingly by marking the abnormal data, thereby improving the reliability of the modified item flow data. Then, the above-mentioned target flow data sets are stored in a preset database. Finally, based on the above-mentioned target flow data sets, the item transportation tasks corresponding to the above-mentioned target flow data sets are executed. Thus, the corresponding item transportation tasks can be executed through the modified item flow data.Because the abnormal data in the item flow data can be identified by two identification methods, the accuracy of identifying the abnormal data in the logistics flow data can be improved. Also, because the abnormal data in the item flow data can be modified accordingly by the marking information corresponding to the two identification methods, the reliability of the modified item flow data can be improved, thereby reducing the probability of the transport of the items being transported incorrectly due to the error in the modified item flow data, thereby reducing the waste of transportation resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0013] Figure 1 is a flow chart of some embodiments of the method for item transportation planning according to the present disclosure;

[0014] Figure 2 is a schematic diagram of the structure of some embodiments of the article transportation planning device according to the present disclosure;

[0015] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0017] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0018] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0019] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0020] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0021] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0022] Figure 1 The process 100 of some embodiments of the method for item transportation planning according to the present disclosure is shown. The method for item transportation planning comprises the following steps:

[0023] Step 101: for each of the article transfer stations, perform the following steps:

[0024] In some embodiments, the execution subject (eg, computing device) of the item transportation planning method may be a server. Each of the above-mentioned item transfer stations may be a place for storing and transporting items.

[0025] Step 1011: for each item information in each item information corresponding to the item circulation station, an item circulation data set corresponding to the item information within a first preset time range is obtained as a first item circulation data set.

[0026] In some embodiments, the execution subject may obtain, for each of the various types of item information corresponding to the item transfer station, an item flow data set corresponding to the item information within a first preset time range as a first item flow data set. The item information may be the name corresponding to the object in the item transfer station. The first preset time range may be a time range from one year ago to the present. The item flow data in the item flow data set may be the flow volume corresponding to the item information. In practice, the execution subject may obtain, from a preset item flow database, an item flow data set corresponding to the item information within a first preset time range as a first item flow data set. The item flow database may be a database for storing item flow data.

[0027] Step 1012: Based on each of the first article circulation data sets among the acquired first article circulation data sets, generate average circulation data of the corresponding first article circulation data set.

[0028] In some embodiments, the execution entity may generate average circulation data corresponding to the first item circulation data set based on each first item circulation data set in each first item circulation data set obtained. The average circulation data may be the average value of each first item circulation data set in the first item circulation data set. In practice, for each first item circulation data set in each first item circulation data set, first, the execution entity may determine the sum of each first item circulation data set in the first item circulation data set as the sum of the first item circulation data. Then, the number of each first item circulation data set in the first item circulation data set may be determined as the data volume. Then, the ratio of the sum of the first item circulation data to the data volume may be determined as the average circulation data corresponding to the first item circulation data set.

[0029] Step 1013, sorting the generated average flow data to obtain an average flow data sequence.

[0030] In some embodiments, the execution subject may sort the generated average circulation data to obtain an average circulation data sequence. Each average circulation data in the average circulation data sequence corresponds to item information. In practice, the execution subject may sort the generated average circulation data in ascending order to obtain an average circulation data sequence.

[0031] Step 1014: Based on the average circulation data sequence, first abnormal data identification processing is performed on each first item circulation data set to obtain each first abnormal circulation data set.

[0032] In some embodiments, the execution subject may perform first abnormal data identification processing on each of the first item flow data sets based on the average flow data sequence to obtain each first abnormal flow data set. Each of the first abnormal flow data in each of the first abnormal flow data sets may be abnormal item flow data obtained through the first abnormal data identification processing.

[0033] In some optional implementations of some embodiments, the execution subject may perform first abnormal data identification processing on each of the first item circulation data sets based on the average circulation data sequence through the following steps to obtain each of the first abnormal circulation data sets:

[0034] In the first step, each average flow data satisfying the first preset data condition in the above average flow data sequence is determined as each first average flow data. The above first preset data condition may be that the average flow data is within a preset percentage in the above average flow data sequence. The above preset percentage may be a pre-set percentage. For example, the above preset percentage may be 20%.

[0035] In the second step, each average flow data satisfying the second preset data condition in the above average flow data sequence is determined as each second average flow data. The above second preset data condition may be that the average flow data is within a first preset ratio range of the above average flow data sequence. The above first preset ratio range may be a preset ratio range. For example, the above first preset ratio range may be [21%, 40%].

[0036] In the third step, each average flow data satisfying the third preset data condition in the above average flow data sequence is determined as each third average flow data. The third preset data condition may be that the average flow data is within a second preset ratio range of the above average flow data sequence. The second preset ratio range may be a preset ratio range. For example, the second preset ratio range may be [41%, 100%].

[0037] Step 4: for each item information corresponding to the item transfer station, perform the following steps:

[0038] The first sub-step is to obtain an item circulation data set corresponding to the item information within a second preset time range as the second item circulation data set. The second preset time range may be a preset time range. For example, the second preset time range may be from three months before the current time to the current time. In practice, the execution subject may obtain an item circulation data set corresponding to the item information within the second preset time range from the item circulation database as the second item circulation data set.

[0039] The second sub-step, in response to determining that the second item circulation data set does not meet the preset data sample condition, obtains the item circulation data set corresponding to the item information within a third preset time range as the second item circulation data set to update the second item circulation data set. Among them, the preset data sample condition may be that the number of each second item circulation data that is not 0 in the second item circulation data set is greater than a preset effective threshold. The preset effective threshold may be a preset value. Here, the specific setting of the preset effective threshold is not limited. The third preset time range may be a time range greater than the second preset time range. For example, the third preset time range may be from six months before the current time to the current time. In practice, the execution subject may obtain the item circulation data set corresponding to the item information within the third preset time range from the item circulation database as the second item circulation data set.

[0040] The fifth step is to generate mean data and standard data corresponding to the second item circulation data set based on each of the second item circulation data sets obtained. Among them, the above-mentioned mean data can be the average value of each second item circulation data in the second item circulation data set. The above-mentioned standard data can be the standard deviation of each second item circulation data in the second item circulation data set. In practice, for each of the second item circulation data sets in the above-mentioned second item circulation data sets, the above-mentioned execution subject can obtain the arithmetic mean corresponding to each second item circulation data in the above-mentioned second item circulation data set through the formula of the arithmetic mean as the mean data. In practice, for each of the second item circulation data sets in the above-mentioned second item circulation data sets, the above-mentioned execution subject can obtain the standard deviation of each second item circulation data in the above-mentioned second item circulation data set through the formula of the standard deviation as the standard data.

[0041] The sixth step is to perform first abnormal data identification processing on each of the first item circulation data sets based on the above-mentioned first average circulation data, the above-mentioned second average circulation data, the above-mentioned third average circulation data, the generated mean data and the generated standard data to obtain each first abnormal circulation data set.

[0042] In the process of adopting technical solutions to solve the above technical problems, the following problems are often accompanied:

[0043] Using the same method to generate the circulation thresholds of different items can easily lead to low reliability of the generated circulation thresholds, resulting in low accuracy in identifying abnormal data in the item circulation data through the generated circulation thresholds, and thus low reliability of the modified item circulation data. This leads to a high probability of transportation errors when transporting items according to the modified item circulation data, resulting in a waste of transportation resources.

[0044] Faced with the above technical problems, we decided to adopt the following solutions:

[0045] In some optional implementations of some embodiments, the execution subject may perform first abnormal data identification processing on each of the first item circulation data sets based on each of the first average circulation data, each of the second average circulation data, each of the third average circulation data, each of the generated mean data, and each of the generated standard data through the following steps to obtain each of the first abnormal circulation data sets:

[0046] In the first step, for each of the first average flow data, the following steps are performed:

[0047] The first sub-step is to determine the first mean data and the first standard data corresponding to the first average circulation data based on the above-mentioned individual mean data and the above-mentioned individual standard data. The above-mentioned first mean data may be the mean data of the above-mentioned first average circulation data. The above-mentioned first standard data may be the standard data of the above-mentioned first average circulation data. In practice, the above-mentioned execution subject may determine the mean data corresponding to the above-mentioned first average circulation data among the above-mentioned individual mean data as the first mean data. The standard data corresponding to the above-mentioned first average circulation data among the above-mentioned individual standard data may be determined as the first standard data.

[0048] The second sub-step is to determine the product of the first standard data and the first preset number as the first target standard data.

[0049] The third sub-step is to determine the sum of the first mean data and the first target standard data as the first threshold data.

[0050] The fourth sub-step is to determine the first item circulation data set corresponding to the first average circulation data among the first item circulation data sets as the first data set to be processed.

[0051] The fifth sub-step is to determine the first abnormal flow data set corresponding to the first data set to be processed based on the first threshold data. In practice, the execution subject may determine each first data set to be processed that is greater than the first threshold data in the first data set to be processed as the first abnormal flow data set corresponding to the first data set to be processed.

[0052] In the second step, for each of the second average flow data, the following steps are performed:

[0053] The first sub-step is to determine the second mean data and the second standard data corresponding to the second average flow data based on the above-mentioned individual mean data and the above-mentioned individual standard data. The above-mentioned second mean data may be the mean data of the above-mentioned second average flow data. The above-mentioned second standard data may be the standard data of the above-mentioned second average flow data. In practice, the above-mentioned execution subject may determine the mean data corresponding to the above-mentioned second average flow data among the above-mentioned individual mean data as the second mean data. The standard data corresponding to the above-mentioned second average flow data among the above-mentioned individual standard data may be determined as the second standard data.

[0054] The second sub-step is to determine the product of the second standard data and the second preset number as the second target standard data.

[0055] The third sub-step is to determine the sum of the second mean data and the second target standard data as the second threshold data.

[0056] The fourth sub-step is to determine the first item circulation data set corresponding to the second average circulation data among the first item circulation data sets as the second data set to be processed.

[0057] The fifth sub-step is to determine the first abnormal flow data set corresponding to the second data set to be processed based on the second threshold data. In practice, the execution subject may determine each second data set to be processed that is greater than the second threshold data in the second data set to be processed as the first abnormal flow data set corresponding to the second data set to be processed.

[0058] Step 3: for each of the third average flow data, perform the following steps:

[0059] The first sub-step is to determine the third mean data and the third standard data corresponding to the third average circulation data based on the above-mentioned respective mean data and the above-mentioned respective standard data. The above-mentioned third mean data may be the mean data of the above-mentioned third average circulation data. The above-mentioned third standard data may be the standard data of the above-mentioned third average circulation data. In practice, the above-mentioned execution subject may determine the mean data corresponding to the above-mentioned third average circulation data among the above-mentioned respective mean data as the third mean data. The standard data corresponding to the above-mentioned third average circulation data among the above-mentioned respective standard data may be determined as the third standard data.

[0060] The second sub-step is to determine the product of the third standard data and the third preset number as the third target standard data.

[0061] The third sub-step is to determine the sum of the third mean data and the third target standard data as the third threshold data.

[0062] The fourth sub-step is to determine the first item circulation data set corresponding to the third average circulation data among the first item circulation data sets as the third data set to be processed.

[0063] The fifth sub-step is to determine the first abnormal flow data set corresponding to the third data set to be processed based on the third threshold data. In practice, the execution subject may determine each third data set to be processed that is greater than the third threshold data in the third data set to be processed as the first abnormal flow data set corresponding to the third data set to be processed.

[0064] The above technical solution and its related contents, combined with steps 1017 to 1019, serve as an inventive point of an embodiment of the present disclosure, and solve the problem of "waste of transportation resources". The factors leading to waste of transportation resources are often as follows: using the same method to generate the circulation thresholds of different items easily leads to low reliability of the generated circulation thresholds, resulting in low accuracy of identifying abnormal data in the item circulation data through the generated circulation thresholds, and then resulting in low reliability of the modified item circulation data. As a result, the probability of transportation errors when transporting items according to the modified item circulation data is high, resulting in waste of transportation resources. If the above factors are solved, the waste of transportation resources can be reduced. In order to achieve this effect, the present disclosure firstly performs the following steps for each of the above-mentioned first average circulation data: Secondly, based on the above-mentioned each mean data and the above-mentioned each standard data, determine the first mean data and the first standard data corresponding to the above-mentioned first average circulation data. Thus, the mean data and the standard data corresponding to the above-mentioned first average circulation data can be determined. Then, the product of the above-mentioned first standard data and the first preset number is determined as the first target standard data. Then, the sum of the first mean value data and the first target standard data is determined as the first threshold value data. Thus, the threshold value corresponding to the first average circulation data can be determined. Then, the first item circulation data set corresponding to the first average circulation data in each of the first item circulation data sets is determined as the first data set to be processed. Thus, the first item circulation data set corresponding to the first average circulation data can be determined. Then, based on the first threshold value data, the first abnormal circulation data set corresponding to the first data set to be processed is determined. Thus, each abnormal data in the first data set to be processed can be determined according to the first threshold value data. Then, for each of the second average circulation data in each of the second average circulation data, the following steps are performed: First, based on each of the mean value data and each of the standard data, the second mean value data and the second standard data corresponding to the second average circulation data are determined. Thus, the mean value data and the standard data corresponding to the second average circulation data can be determined. Secondly, the product of the second standard data and the second preset number is determined as the second target standard data. Then, the sum of the second mean value data and the second target standard data is determined as the second threshold value data. Thus, the threshold corresponding to the second average circulation data can be determined. Then, the first item circulation data set corresponding to the second average circulation data among the first item circulation data sets is determined as the second data set to be processed. Thus, the item circulation data set corresponding to the second average circulation data can be determined. Then, based on the second threshold data, the first abnormal circulation data set corresponding to the second data set to be processed is determined. Thus, each abnormal data in the second data set to be processed can be determined according to the generated second threshold data.Then, for each of the third average circulation data in the above-mentioned third average circulation data, the following steps are performed: First, based on the above-mentioned mean data and the above-mentioned standard data, the third mean data and the third standard data corresponding to the above-mentioned third average circulation data are determined. Thus, the mean data and the standard data corresponding to the above-mentioned third average circulation data can be determined. Secondly, the product of the above-mentioned third standard data and the third preset number is determined as the third target standard data. Then, the sum of the above-mentioned third mean data and the above-mentioned third target standard data is determined as the third threshold data. Thus, the threshold corresponding to the above-mentioned third average circulation data can be determined. Then, the first item circulation data set corresponding to the above-mentioned third average circulation data in the above-mentioned first item circulation data sets is determined as the third data set to be processed. Thus, the first item circulation data set corresponding to the above-mentioned third average circulation data can be determined. Finally, based on the above-mentioned third threshold data, the first abnormal circulation data set corresponding to the above-mentioned third data set to be processed is determined. Thus, each abnormal data in the above-mentioned third data set to be processed can be determined according to the above-mentioned third threshold data. Because the corresponding thresholds can be generated according to different methods based on the corresponding gradients of different items in the average flow data sequence, the reliability of the generated thresholds can be improved, and the accuracy of identifying abnormal data in the item flow data according to the thresholds can be improved, thereby increasing the reliability of the modified item flow data. In addition, the probability of transportation errors when transporting items according to the modified item flow data can be reduced, thereby reducing the waste of transportation resources.

[0065] Step 1015: Based on the average circulation data sequence, perform second abnormal data identification processing on each first item circulation data set to obtain each second abnormal circulation data set.

[0066] In some embodiments, the execution subject may perform second abnormal data identification processing on each of the first item flow data sets based on the average flow data sequence to obtain each second abnormal flow data set. Each second abnormal flow data in each of the second abnormal flow data sets may be abnormal item flow data obtained after performing second abnormal data identification processing on each of the first item flow data sets.

[0067] In some optional implementations of some embodiments, the execution subject may perform second abnormal data identification processing on each of the first item circulation data sets based on the average circulation data sequence to obtain each second abnormal circulation data set through the following steps:

[0068] The first step is to generate each quantile flow data based on the preset interval data, the preset quantile range information and the above-mentioned average flow data sequence. Among them, the above-mentioned interval data can be used to characterize the size of the interval between each two adjacent quantiles. For example, the above-mentioned interval data can be 0.5%. The above-mentioned quantile range information can characterize the range of quantiles that need to be processed for the above-mentioned average flow data sequence. For example, the above-mentioned quantile range information can be 90% to 99.5%. Each quantile flow data in the above-mentioned quantile flow data can be the average flow data of the corresponding quantile in the above-mentioned average flow data sequence. In practice, first, the above-mentioned execution subject can determine the various quantiles that need to be processed through the above-mentioned interval data and the above-mentioned quantile range information. For example, when the above-mentioned interval data is 0.5% and the above-mentioned quantile range information is 90% to 92%, the determined quantiles are 90%, 90.5%, 91%, 91.5%, and 92%. Secondly, the number of each average flow data in the above-mentioned average flow data sequence can be determined as the total amount of data. Then, for each of the above-mentioned quantiles, the product of the above-mentioned total amount of data and the above-mentioned quantile can be determined as the initial index value. Then, when the above-mentioned initial index value is an integer, the above-mentioned initial index value can be determined as the index value. When the above-mentioned initial index value is a decimal, the smallest integer greater than the above-mentioned initial index value is determined as the index value. Then, the average circulation data corresponding to the above-mentioned index value in the above-mentioned average circulation data sequence can be determined as the quantile circulation data corresponding to the above-mentioned quantile. As an example, when the average circulation data sequence is {2, 5, 9, 12, 13} and the quantile is 90%, the total amount of data in the average circulation data sequence is 5, and the product of the total amount of data 5 and the quantile 90% is the initial index value 4.5, and because the initial index value 4.5 is a decimal, the index value is the smallest integer 5 greater than the initial index value. Finally, the fifth average circulation data 13 in the average circulation data sequence can be determined as the quantile circulation data corresponding to the quantile 90%.

[0069] The second step is to generate various differential data based on the above-mentioned quantile flow data. Among them, each differential data in the above-mentioned differential data can be used to characterize the difference between two adjacent quantile flow data. In practice, for each two adjacent quantiles in the above-mentioned quantiles, the difference between the two quantile flow data corresponding to the above-mentioned two quantiles can be determined as the differential data corresponding to the target quantile. Among them, the above-mentioned target quantile can be the larger quantile of the above-mentioned two adjacent quantiles. As an example, when there are two adjacent quantiles 90% and 90.5%, and the two quantile flow data corresponding to the two quantiles are 5 and 10 respectively, the difference of 5 between 10 and 5 can be determined as the differential data corresponding to the quantile 90.5%.

[0070] The third step is to determine the average turnover data corresponding to the above-mentioned percentile data in the above-mentioned average turnover data sequence as the target average turnover data based on the preset percentile data. The above-mentioned percentile data may be a preset percentile. For example, the above-mentioned percentile data may be 75%. In practice, the above-mentioned execution entity may determine the average turnover data corresponding to the above-mentioned percentile data in the above-mentioned average turnover data sequence as the target average turnover data.

[0071] In the fourth step, the product of the target average flow data and the preset flow data is determined as the threshold data. The preset flow data may be a preset value. Here, the specific setting of the preset flow data is not limited.

[0072] Step 5: For each of the above differential data, perform the following steps:

[0073] The first sub-step is to compare the differential data with the threshold data to obtain a comparison result. The comparison result can be used to characterize the size of the differential data and the threshold data. For example, the comparison result can be "the differential data is greater than the threshold data" or "the differential data is less than the threshold data".

[0074] In the second sub-step, in response to determining that the comparison result does not meet the preset comparison result condition, the quantile flow data corresponding to the differential data in each of the quantile flow data is determined as the target quantile flow data. The preset comparison result condition may be that the comparison result is "the differential data is less than the threshold data".

[0075] In a third sub-step, for each of the first item circulation data sets in the above-mentioned first item circulation data sets, each of the first item circulation data sets in the above-mentioned first item circulation data sets that is greater than the above-mentioned target quantile circulation data is determined as a second abnormal circulation data set.

[0076] Step 1016: Based on each first abnormal circulation data set and each second abnormal circulation data set, mark each first item circulation data set to obtain each marked circulation data set.

[0077] In some embodiments, the execution subject may mark each of the first item flow data sets based on each of the first abnormal flow data sets and each of the second abnormal flow data sets to obtain each marked flow data set, wherein each marked flow data in each of the marked flow data sets may be marked item flow data.

[0078] In some optional implementations of some embodiments, the execution subject may perform marking processing on each of the first item flow data sets based on each of the first abnormal flow data sets and each of the second abnormal flow data sets through the following steps to obtain each marked flow data set:

[0079] In the first step, for each of the first item flow data sets, the following steps are performed:

[0080] In the first sub-step, each first item flow data set corresponding to each of the first abnormal flow data sets in the first item flow data set is determined as the first target data set. In practice, the execution subject may determine each first abnormal flow data set whose corresponding item information in each of the first abnormal flow data sets is the same as the item information corresponding to the first item flow data set as the first target data set.

[0081] In the second sub-step, each first item flow data set corresponding to each second abnormal flow data set in the first item flow data set is determined as the second target data set. In practice, the execution subject may determine each second abnormal flow data set whose corresponding item information in each second abnormal flow data set is the same as the item information corresponding to the first item flow data set as the second target data set.

[0082] The third sub-step is to mark the first item circulation data set based on the preset first marking information, the preset second marking information, the determined first target data set and the determined second target data set to obtain a marked circulation data set. The first marking information may be a label used to mark the first target data set in the first item circulation data set. For example, the first marking information may be "O1". The second marking information may be a label used to mark the second target data set in the first item circulation data set. For example, the second marking information may be "O2". The marked circulation data set may be the marked first item circulation data set. In practice, for each first item circulation data set in the first item circulation data set that is the same as the first target data set, the execution subject may determine the first marking information as the marking information corresponding to each first item circulation data set. Then, for each first item circulation data set in the first item circulation data set that is the same as the second target data set, the second marking information may be determined as the marking information corresponding to each first item circulation data set. Finally, for each first item circulation data for which the first marking information and the second marking information are not determined in the first item circulation data set, a null value may be determined as the marking information of each first item circulation data.

[0083] Step 1017, performing data modification processing on each marked flow data set to obtain each target flow data set.

[0084] In some embodiments, the execution subject may perform data modification processing on each of the marked flow data sets to obtain each of the target flow data sets, wherein the target flow data in each of the target flow data sets may be modified item flow data.

[0085] In some optional implementations of some embodiments, the execution subject may perform data modification processing on each of the marked flow data sets to obtain each target flow data set through the following steps:

[0086] In the first step, based on each of the above-mentioned marked flow data sets, perform the following steps:

[0087] In the first sub-step, for each marked flow data in the marked flow data set, the following steps are performed:

[0088] Sub-step 1: in response to determining that the marking information corresponding to the marked flow data meets the preset marking condition, executing the following steps:

[0089] First, based on the marking information corresponding to the above-mentioned marked flow data, the target modification data corresponding to the above-mentioned marked flow data is determined. Among them, the above-mentioned marking information can be a label corresponding to the marked flow data. The above-mentioned preset marking condition can be that the marking information corresponding to the marked flow data is not empty. In practice, first, the above-mentioned execution subject can determine the item information corresponding to the above-mentioned marked flow data as the target item information. Then, the average flow data whose corresponding item information in the above-mentioned average flow data sequence is the same as the above-mentioned target item information can be determined as the target average flow data. When the marking information corresponding to the above-mentioned marked flow data is the first marking information, the target threshold data corresponding to the above-mentioned target average flow data can be determined as the first modification data. Among them, the above-mentioned target threshold data can be the first threshold data or the second threshold data or the third threshold data corresponding to each average flow data in the average flow data sequence. The method of confirming the target threshold data corresponding to the target average flow data can refer to the specific implementation method of step 1014, which will not be repeated here. Then, the above-mentioned first modification data can be determined as the target modification data corresponding to the above-mentioned marked flow data. When the tag information corresponding to the above-mentioned marked flow data is the second tag information, the above-mentioned execution subject can determine the quantile of the above-mentioned target average flow data in the above-mentioned average flow data sequence through the quantile formula. Then, based on the average flow data sequence and the above-mentioned quantile, the quantile flow data corresponding to the above-mentioned quantile can be generated as the second modification data. Among them, the method of generating the quantile flow data can refer to the specific implementation method of step 1015, which will not be repeated here. Then, the above-mentioned second modification data is determined as the target modification data. When the tag information corresponding to the above-mentioned marked flow data contains the first tag information and the second tag information, the above-mentioned execution subject can generate the first modification data and the second modification data corresponding to the above-mentioned marked flow data. Then, the larger data of the above-mentioned first modification data and the second modification data can be determined as the target modification data.

[0090] Then, based on the target modification data, the marked flow data is modified to obtain the target flow data. In practice, the execution subject may determine the target modification data as the marked flow data to update the marked flow data and obtain the updated marked flow data as the target flow data.

[0091] Sub-step two: in response to determining that the marking information corresponding to the marked flow data does not satisfy the preset marking condition, determining the marked flow data as target flow data.

[0092] The second sub-step is to determine each obtained target flow data as a target flow data set.

[0093] Step 1018, storing each target flow data set into a preset database.

[0094] In some embodiments, the execution entity may store each target flow data set in a preset database, wherein the preset database may be a pre-set database for storing each target flow data set.

[0095] Step 1019, based on each target circulation data set, execute the item transportation task corresponding to each target circulation data set.

[0096] In some embodiments, the execution entity may execute the item transportation task corresponding to each of the target circulation data sets based on the each of the target circulation data sets.

[0097] In the process of adopting technical solutions to solve the above technical problems, the following problems are often accompanied:

[0098] When transporting goods, only relying on the modified goods flow data to schedule the subsequent transportation of goods has poor avoidance of transportation risks, resulting in low transportation efficiency of goods transportation.

[0099] Faced with the above technical problems, we decided to adopt the following solutions:

[0100] In some optional implementations of some embodiments, the execution subject may execute the item transportation task corresponding to each of the target circulation data sets based on the target circulation data sets through the following steps:

[0101] The first step is to generate target average data based on each target flow data set in the above target flow data sets and based on the above target flow data sets. The above target average data may be the average value of each target flow data set in the above target flow data sets. In practice, based on each target flow data set in the above target flow data sets, the above execution entity may determine the average value of the above target flow data sets as the corresponding target average data.

[0102] The second step is to generate a target average data sequence based on the generated target average data. In practice, the execution subject may sort the target average data in descending order to obtain the target average data sequence.

[0103] In the third step, each target average data satisfying the preset average data condition in the target average data sequence is determined as each average data to be processed. The preset average data condition may be that the target average data is greater than a preset threshold to be processed. The threshold to be processed may be a preset value. Here, the specific setting of the threshold to be processed is not limited.

[0104] Step 4: For each of the above average data to be processed, perform the following steps:

[0105] In the first sub-step, the target flow data set corresponding to the above-mentioned average data to be processed among the above-mentioned various target flow data sets is determined as the target flow data set to be processed.

[0106] In the second sub-step, the sum of each target flow data to be processed included in the target flow data set to be processed is determined as the quantity information of the items to be transported.

[0107] In the third sub-step, the item information corresponding to the target flow data set to be processed is determined as the item information to be transported.

[0108] The fourth sub-step is to generate the item transportation information corresponding to the above-mentioned average data to be processed based on the above-mentioned information on the quantity of items to be transported and the above-mentioned information on the items to be transported. The above-mentioned item transportation information can represent the information on the items to be transported and the quantity corresponding to the item information. In practice, the above-mentioned execution entity can combine the above-mentioned information on the quantity of items to be transported and the above-mentioned information on the items to be transported into the item transportation information.

[0109] The fifth step is to generate route planning information based on the generated transportation information of each item, the preset location information, the weather information and traffic information obtained in real time. Among them, the above-mentioned location information can be a location that represents the location where the item needs to be transported. The above-mentioned weather information can represent the weather conditions of the location corresponding to the above-mentioned location information. The above-mentioned traffic information can represent the congestion of each road. The above-mentioned route planning information can represent the route that the item transportation equipment needs to pass when transporting items. In practice, the above-mentioned execution subject can call the weather forecast API to obtain weather information in real time. The above-mentioned execution subject can obtain traffic information from the traffic data storage server. Among them, the above-mentioned traffic data storage server can be a server for managing and storing traffic information. In practice, the above-mentioned execution subject can input the above-mentioned location information, the weather information and traffic information obtained in real time into the route planning system to obtain each route. Then, the above-mentioned transportation information of each item and the above-mentioned routes can be sent to the terminal device corresponding to the technician. In response to detecting the selection operation of the above-mentioned routes by the technician through the above-mentioned terminal device, the route corresponding to the selection operation is determined as the route planning information. Among them, the above-mentioned route planning system can be a system that can automatically generate the arrival path according to the real-time weather conditions and traffic conditions through the input arrival location. For example, the route planning system may be a path planning API.

[0110] Step 6: Based on the above-mentioned item transportation information and the above-mentioned route planning information, control the corresponding item transportation equipment to transport the items corresponding to the above-mentioned item transportation information to the location corresponding to the above-mentioned location information. The above-mentioned item transportation equipment can be used to transport items. For example, the above-mentioned item transportation equipment can be an unmanned vehicle. In practice, the above-mentioned execution subject can control the above-mentioned item transportation equipment to transport the items corresponding to the above-mentioned item transportation information to the location corresponding to the above-mentioned location information according to the route planned by the above-mentioned route planning information.

[0111] The above technical solution and its related contents, as an inventive point of an embodiment of the present disclosure, solve the problem of "waste of transportation resources". The factors that lead to waste of transportation resources are often as follows: when transporting items, only relying on the modified item circulation data to schedule the subsequent transportation of items has poor avoidance of transportation risks, resulting in low transportation efficiency of item transportation. If the above factors are solved, the waste of transportation resources can be reduced. In order to achieve this effect, the present disclosure first generates target average data based on each target circulation data set in the above target circulation data sets. Thus, the average value of the circulation volume of each item can be obtained. Secondly, based on the generated target average data, a target average data sequence is generated. Thus, items with a large circulation volume can be determined. Then, each target average data in the above target average data sequence that meets the preset average data condition is determined as each average data to be processed. Thus, each item circulation data corresponding to each item that needs to be transported can be determined. Then, for each of the above-mentioned average data to be processed, the following steps are performed: first, the target flow data set corresponding to the above-mentioned average data to be processed in the above-mentioned target flow data sets is determined as the target flow data set to be processed. Secondly, the sum of the target flow data to be processed included in the above-mentioned target flow data set to be processed is determined as the quantity information of the items to be transported. Then, the item information corresponding to the above-mentioned target flow data set to be processed is determined as the item information to be transported. Then, based on the above-mentioned quantity information of the items to be transported and the above-mentioned item information to be transported, the item transportation information corresponding to the above-mentioned average data to be processed is generated. Thus, the items to be transported and the quantity of the items can be determined. Then, based on the generated transportation information of each item, the preset location information, the weather information and the traffic information obtained in real time, the route planning information is generated. Thus, according to the items to be transported, the real-time weather conditions and the traffic conditions, the route with the highest transportation efficiency can be planned. Finally, based on the above-mentioned transportation information of each item and the above-mentioned route planning information, the corresponding item transportation equipment is controlled to transport the items corresponding to the above-mentioned transportation information of each item to the position corresponding to the above-mentioned location information. Thus, the items can be transported according to the planned route. Also, because the most efficient route can be planned based on the items to be transported, real-time weather information and traffic information, the efficiency of transporting items can be improved and the waste of transportation resources can be reduced.

[0112] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the article transportation planning method of some embodiments of the present disclosure, the reliability of the modified article flow data can be improved and the waste of transportation resources can be reduced. Specifically, the reason for the low reliability of the modified article flow data and the waste of transportation resources is that it is easy to identify the correct data as abnormal data by only one way to determine whether the article flow data is abnormal, resulting in low reliability of the identification of abnormal data, and then resulting in low reliability according to the modified article flow data. As a result, the transported articles are wrong when the articles are transported according to the modified article flow data, resulting in waste of transportation resources. Based on this, the article transportation planning method of some embodiments of the present disclosure, for each of the article flow stations in each article flow station, performs the following steps: First, for each of the article information corresponding to the above-mentioned article flow station, obtain the article flow data set corresponding to the above-mentioned article information within the first preset time range as the first article flow data set. Thus, the original data to be processed can be obtained. Secondly, based on each of the first article flow data sets in the obtained first article flow data sets, the average flow data corresponding to the above-mentioned first article flow data set is generated. Thus, the average flow data of each object within the above-mentioned first preset time range can be obtained. Then, the generated average circulation data are sorted to obtain an average circulation data sequence. Thus, the sequence of average circulation data corresponding to each item can be obtained. Then, based on the average circulation data sequence, the first abnormal data identification process is performed on the first item circulation data set to obtain each first abnormal circulation data set. Thus, the abnormal data in each first item circulation data set can be identified by the first abnormal data identification method. Then, based on the average circulation data sequence, the second abnormal data identification process is performed on the first item circulation data set to obtain each second abnormal circulation data set. Thus, the abnormal data in each first item circulation data set can be identified by the second abnormal data identification method. Then, based on the first abnormal circulation data set and the second abnormal circulation data set, the first item circulation data set is marked to obtain each marked circulation data set. Thus, all abnormal data identified in each first item circulation data set can be marked. Then, the data modification process is performed on each marked circulation data set to obtain each target circulation data set. Thus, the identified abnormal data can be modified accordingly by marking the abnormal data, thereby improving the reliability of the modified item flow data. Then, the above-mentioned target flow data sets are stored in a preset database. Finally, based on the above-mentioned target flow data sets, the item transportation tasks corresponding to the above-mentioned target flow data sets are executed. Thus, the corresponding item transportation tasks can be executed through the modified item flow data.Because the abnormal data in the item flow data can be identified by two identification methods, the accuracy of identifying the abnormal data in the logistics flow data can be improved. Also, because the abnormal data in the item flow data can be modified accordingly by the marking information corresponding to the two identification methods, the reliability of the modified item flow data can be improved, thereby reducing the probability of the transport of the items being transported incorrectly due to the error in the modified item flow data, thereby reducing the waste of transportation resources.

[0113] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an item transportation planning device, and these device embodiments are Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0114] like Figure 2 As shown, the article transportation planning device 200 of some embodiments includes: an execution unit 201. The execution unit 201 is configured to perform the following steps for each of the article transfer stations: for each of the article information corresponding to the above-mentioned article transfer station, obtain an article flow data set corresponding to the above-mentioned article information within a first preset time range as a first article flow data set; based on each of the first article flow data sets obtained, generate average flow data corresponding to the above-mentioned first article flow data sets; sort the generated average flow data to obtain an average flow data sequence; based on the above-mentioned average flow data sequence, perform first abnormal data on the above-mentioned first article flow data sets; Identify and process to obtain each first abnormal circulation data set; based on the above average circulation data sequence, perform second abnormal data identification processing on each of the above first item circulation data sets to obtain each second abnormal circulation data set; based on each of the above first abnormal circulation data sets and each of the above second abnormal circulation data sets, perform marking processing on each of the above first item circulation data sets to obtain each marked circulation data set; perform data modification processing on each of the above marked circulation data sets to obtain each target circulation data set; store each of the above target circulation data sets in a preset database; based on each of the above target circulation data sets, execute the item transportation task corresponding to each of the above target circulation data sets.

[0115] It is understood that the units described in the device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 200 and the units included therein, and will not be described in detail here.

[0116] Reference below Figure 3, which shows a schematic structural diagram of an electronic device (such as a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0117] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0118] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0119] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0120] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0121] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0122] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: performs the following steps for each of the various item circulation stations: for each of the various item information corresponding to the above-mentioned item circulation stations, obtain an item circulation data set corresponding to the above-mentioned item information within a first preset time range as a first item circulation data set; based on each of the various first item circulation data sets obtained, generate average circulation data corresponding to the above-mentioned first item circulation data sets; sort the generated average circulation data to obtain an average circulation data sequence; based on the above-mentioned average circulation data sequence, sort the above-mentioned items. Perform first abnormal data identification processing on each first item flow data set to obtain each first abnormal flow data set; based on the above average flow data sequence, perform second abnormal data identification processing on each first item flow data set to obtain each second abnormal flow data set; based on each first abnormal flow data set and each second abnormal flow data set, perform marking processing on each first item flow data set to obtain each marked flow data set; perform data modification processing on each marked flow data set to obtain each target flow data set; store each target flow data set in a preset database; based on each target flow data set, execute the item transportation task corresponding to each target flow data set.

[0123] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The units described may also be provided in a processor, for example, may be described as: a processor includes an execution unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the execution unit may also be described as a "unit that performs the following steps".

[0126] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0127] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for item transportation planning, comprising: For each item transfer station in each item transfer station, perform the following steps: For each item information in each item information corresponding to the item circulation station, obtaining an item circulation data set corresponding to the item information within a first preset time range as a first item circulation data set; Based on each of the first item circulation data sets among the acquired first item circulation data sets, generating average circulation data corresponding to the first item circulation data set; Sorting the generated average flow data to obtain an average flow data sequence; Based on the average circulation data sequence, performing first abnormal data identification processing on each of the first item circulation data sets to obtain each first abnormal circulation data set; Based on the average circulation data sequence, performing second abnormal data identification processing on each of the first item circulation data sets to obtain each second abnormal circulation data set; Based on the first abnormal circulation data sets and the second abnormal circulation data sets, marking the first item circulation data sets to obtain marked circulation data sets; Performing data modification processing on each of the marked flow data sets to obtain each target flow data set; Storing each target flow data set in a preset database; Based on the target flow data sets, the item transportation tasks corresponding to the target flow data sets are executed.

2. The method according to claim 1, wherein: The marking process of each first item flow data set based on each first abnormal flow data set and each second abnormal flow data set to obtain each marked flow data set includes: For each of the first item flow data sets, the following steps are performed: determining each first item circulation data corresponding to each first abnormal circulation data set in the first item circulation data set as a first target data set; Determine each first item circulation data corresponding to each second abnormal circulation data set in the first item circulation data set as a second target data set; Based on the preset first marking information, the preset second marking information, the determined first target data set and the determined second target data set, the first item circulation data set is marked to obtain a marked circulation data set.

3. The method according to claim 1, wherein: Each of the marked flow data in each of the marked flow data sets has corresponding marking information; And the data modification processing is performed on each of the marked flow data sets to obtain each target flow data set, including: Based on each of the marked flow data sets, the following steps are performed: For each marked flow data in the marked flow data set, the following steps are performed: In response to determining that the marking information corresponding to the marked flow data meets the preset marking condition, the following steps are performed: Determining target modification data corresponding to the marked flow data based on the mark information corresponding to the marked flow data; Based on the target modification data, modify the marked flow data to obtain target flow data; In response to determining that the marking information corresponding to the marked flow data does not satisfy the preset marking condition, determining the marked flow data as target flow data; The obtained target flow data are determined as the target flow data set.

4. The method according to claim 1, wherein: The step of performing second abnormal data identification processing on each of the first item circulation data sets based on the average circulation data sequence to obtain each second abnormal circulation data set includes: Generate each quantile flow data based on the preset interval data, the preset quantile range information and the average flow data sequence; Based on the respective quantile flow data, generating respective differential data; Based on the preset percentile value data, the average flow data corresponding to the percentile value data in the average flow data sequence is determined as the target average flow data; The product of the target average flow data and the preset flow data is determined as the threshold data; For each differential data in the differential data, the following steps are performed: Comparing the differential data with the threshold data to obtain a comparison result; In response to determining that the comparison result does not satisfy a preset comparison result condition, determining the quantile flow data corresponding to the differential data among the respective quantile flow data as the target quantile flow data; For each of the first item circulation data sets, each of the first item circulation data sets that is greater than the target quantile circulation data in the first item circulation data sets is determined as a second abnormal circulation data set.

5. The method according to claim 1, wherein: The step of performing first abnormal data identification processing on each of the first item circulation data sets based on the average circulation data sequence to obtain each of the first abnormal circulation data sets includes: Determine each average circulation data satisfying a first preset data condition in the average circulation data sequence as each first average circulation data; Determine each average circulation data satisfying a second preset data condition in the average circulation data sequence as each second average circulation data; Determine each average circulation data satisfying a third preset data condition in the average circulation data sequence as each third average circulation data; For each item information in each item information corresponding to the item transfer station, the following steps are performed: Acquire an item circulation data set corresponding to the item information within a second preset time range as a second item circulation data set; In response to determining that the second item circulation data set does not meet the preset data sample condition, obtaining an item circulation data set corresponding to the item information within a third preset time range as the second item circulation data set, so as to update the second item circulation data set; Based on each of the obtained second article circulation data sets, generating mean data and standard data corresponding to the second article circulation data set; Based on the first average circulation data, the second average circulation data, the third average circulation data, the generated mean data and the generated standard data, the first abnormal data identification processing is performed on the first item circulation data sets to obtain the first abnormal circulation data sets.

6. An article transportation planning device, comprising: The execution unit is configured to perform the following steps for each of the item circulation stations: for each of the item information corresponding to the item circulation station, obtain an item circulation data set corresponding to the item information within a first preset time range as a first item circulation data set; based on each of the first item circulation data sets obtained, generate average circulation data corresponding to the first item circulation data set; Sorting the generated average circulation data to obtain an average circulation data sequence; performing first abnormal data identification processing on the first item circulation data sets based on the average circulation data sequence to obtain first abnormal circulation data sets; Based on the average circulation data sequence, performing second abnormal data identification processing on each of the first item circulation data sets to obtain each second abnormal circulation data set; based on each of the first abnormal circulation data sets and each of the second abnormal circulation data sets, performing marking processing on each of the first item circulation data sets to obtain each marked circulation data set; Performing data modification processing on each of the marked flow data sets to obtain each target flow data set; Storing each target flow data set in a preset database; Based on the target flow data sets, the item transportation tasks corresponding to the target flow data sets are executed.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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