Intelligent Logistics Warehouse Data Processing Method and System, Electronic Device, Storage Medium

Abstract

The present disclosure provides a method and system for processing intelligent logistics warehousing data, an electronic device, and a storage medium, belonging to the technical field of warehousing data processing. The method includes: classifying first data based on a hierarchical clustering algorithm to obtain a plurality of first data subsets. The first data is warehousing data to be processed. Screening data from the plurality of first data subsets to select a plurality of target data subsets. In response to receiving a data processing instruction, processing the plurality of target data subsets based on the data processing instruction and an association rule library. The association rule library includes association rules between warehousing data. The method and system for processing intelligent logistics warehousing data, the electronic device, and the storage medium provided by the present disclosure can improve the quality of warehousing data processing.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of warehousing data processing, and more specifically, relates to a method and system for processing intelligent logistics warehousing data, an electronic device, and a storage medium. Background Art

[0002] In the field of logistics warehousing, with the wide application of technologies such as the Internet of Things and sensors, the scope and accuracy of data collection have been continuously improved, generating a large amount of warehousing data, including goods information, warehousing environment information, logistics operation information, etc. These data have characteristics such as multi-source, heterogeneous, and high-dimensional. Traditional data processing means based on manual experience or simple statistical analysis are difficult to effectively manage and analyze them, resulting in difficult-to-exploit data value and low data processing quality. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a method and system for processing intelligent logistics warehousing data, an electronic device, and a storage medium to improve the quality of warehousing data processing.

[0004] In the first aspect of the embodiments of the present disclosure, a method for processing intelligent logistics warehousing data is provided, including:

[0005] Classifying the first data based on a hierarchical clustering algorithm to obtain multiple first data subsets; the first data is the warehousing data to be processed;

[0006] Selecting multiple target data subsets from the multiple first data subsets;

[0007] In response to receiving a data processing instruction, processing the multiple target data subsets based on the data processing instruction and an association rule library; the association rule library includes association rules between warehousing data.

[0008] In the second aspect of the embodiments of the present disclosure, a system for processing intelligent logistics warehousing data is provided, including:

[0009] A clustering module for classifying the first data based on a hierarchical clustering algorithm to obtain multiple first data subsets; the first data is the warehousing data to be processed;

[0010] A data screening module for selecting multiple target data subsets from the multiple first data subsets;

[0011] A data processing module for processing the multiple target data subsets based on the data processing instruction and an association rule library in response to receiving a data processing instruction; the association rule library includes association rules between warehousing data.

[0012] In a third aspect of the embodiments of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned intelligent logistics warehousing data processing method are implemented.

[0013] In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned intelligent logistics warehousing data processing method are implemented.

[0014] The beneficial effects of the intelligent logistics warehousing data processing method, system, electronic device, and storage medium provided by the embodiments of the present disclosure are as follows:

[0015] By classifying the first data through a hierarchical clustering algorithm, complex warehousing data can be quickly divided into multiple subsets according to similar characteristics, reducing the complexity and workload of data processing and improving the processing speed. Screening data from multiple first data subsets to obtain a target data subset helps to remove noise data, outliers, and irrelevant data, ensuring that the data used for subsequent analysis has higher accuracy, integrity, and representativeness, improving the quality of data processing, and thus enhancing the reliability of decisions made based on these data. Processing the target data subset based on data processing instructions and an association rule library can make full use of the association rules between warehousing data, improving the quality and accuracy of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a flowchart of the intelligent logistics warehousing data processing method provided by an embodiment of the present disclosure;

[0018] Figure 2 It is a block diagram of the structure of the intelligent logistics warehousing data processing system provided by an embodiment of the present disclosure;

[0019] Figure 3 It is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present disclosure. However, those skilled in the art should clearly understand that the present disclosure can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present disclosure.

[0021] To make the objectives, technical solutions, and advantages of the present disclosure clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0022] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for processing intelligent logistics warehousing data provided by an embodiment of the present disclosure. The method may include S101 to S103.

[0023] S101: Classify the first data based on the hierarchical clustering algorithm to obtain multiple first data subsets. The first data is warehousing data to be processed.

[0024] In this embodiment, classifying the first data based on the hierarchical clustering algorithm to obtain multiple first data subsets includes:

[0025] Classify the first data based on the hierarchical clustering algorithm to obtain a multi-level classification result. Each level of the multi-level classification result includes multiple clusters.

[0026] Select the target-level classification result from the multi-level classification result based on the number of clusters in each level of the multi-level classification result and the data volume of each cluster.

[0027] Use the multiple clusters in the target-level classification result as multiple first data subsets.

[0028] In this embodiment, the first data may include the original data set of each link in the logistics warehousing, such as basic information of goods, warehousing operation records, logistics transportation details, and warehouse environment data, etc. The multi-level classification result represents the classification situation at different levels after being processed by the hierarchical clustering algorithm. Each level divides the data into multiple clusters with similar characteristics, reflecting the grouping of data at different granularities. A cluster is a set of data objects with high similarity during the clustering process, and these objects show consistency in certain attributes or behavior patterns.

[0029] In this embodiment, each data point in the first data is regarded as an initial separate cluster, and then the similarity between clusters is calculated according to the selected similarity measurement method. For example, for goods data, the similarity is first calculated based on the basic attributes of the goods for preliminary merging to form larger clusters; then other factors such as warehousing operations are considered to further subdivide or merge the clusters, gradually constructing a tree-shaped multi-level classification structure.

[0030] When selecting the classification result of the target layer, comprehensively consider the number of clusters in each layer and the amount of data in each cluster. If the number of clusters is too large, it may lead to over-fine classification, which is not conducive to grasping the overall data characteristics; if the number of clusters is too small, important details may be lost. At the same time, clusters with too small data volume may not be representative, while the internal differences in clusters that are too large may be significant. By weighing these factors, select a target layer that can reflect the main characteristics of the data and has an appropriate granularity, and determine multiple clusters in this layer as the first data subset for subsequent targeted data screening and analysis processing.

[0031] Exemplarily, in a large e-commerce warehouse, various types of goods are stored, and its warehousing data is used as the first data. Using the hierarchical clustering algorithm, the first-layer classification result is divided according to the large categories of goods, such as electronic products, clothing, food, etc., and each category forms a cluster. In the electronic product cluster, the second-layer classification result is divided according to the brand. The third-layer classification result is divided according to the inbound and outbound time pattern of electronic products. Suppose now we want to analyze the inventory management of each brand of electronic products at different sales stages. By evaluating the number of clusters and the amount of data in each layer, the second layer can be selected as the classification result of the target layer, and the corresponding clusters are used as the first data subset. Subsequently, data screening can be performed on these subsets. After removing outliers, the data can be further processed in combination with the association rule library.

[0032] S102: Perform data screening from multiple first data subsets to select multiple target data subsets.

[0033] In this embodiment, performing data screening from multiple first data subsets to select multiple target data subsets includes:

[0034] Calculate the number of data samples in each first data subset.

[0035] Determine the first threshold based on the number of data samples in multiple first data subsets.

[0036] Select the first data subset whose number of data samples is greater than or equal to the first threshold from multiple first data subsets, and use this first data subset as the target data subset.

[0037] In this embodiment, the number of data samples refers to the number of data records included in each subset. The first threshold is used to screen the first data subset. The determination of the first threshold needs to consider factors such as the overall data scale of the warehousing data and the requirements of the analysis purpose for data representativeness. For example, if the data volume is huge and data with broad representativeness is desired, the first threshold can be increased; if only specific fine-grained analysis is carried out and the data is limited, the first threshold can be appropriately reduced.

[0038] Exemplarily, the calculation formula for the first threshold is:

[0039]

[0040] Where, represents the first threshold, represents the proportionality coefficient, M represents the total number of data samples of all target data subsets, and N represents the number of target data subsets.

[0041] Through the strict screening of the first threshold, abnormal subsets with too few data samples can be removed. For example, the number of samples in most of the first data subsets is in the range of 200 - 1000, and the number of samples in an individual first data subset is 10, which is not in the same data magnitude, then the data of this first data subset may be incorrect data.

[0042] S103: In response to receiving a data processing instruction, process multiple target data subsets based on the data processing instruction and the association rule library. The association rule library includes the association rules between warehousing data.

[0043] In this embodiment, the data processing instruction may include an anomaly detection instruction and a data sampling instruction. The anomaly detection instruction is used to discover data points or patterns in the warehousing data that do not conform to the normal mode or expected range, and the data sampling instruction is used to extract representative data samples from the target data subsets. The association rules between warehousing data may include the association mapping relationship between data features extracted from the warehousing data and the dependency relationship between warehousing data, etc.

[0044] Exemplarily, when an anomaly detection instruction is received, the target data subset is scanned according to the rules in the association rule library regarding the normal inbound and outbound patterns of electronic components, inventory fluctuation ranges, environmental parameter standards, etc. For example, the association rule library stores a rule that "the inbound and outbound volume of a certain type of chip is positively correlated with the peak production season of electronic products, and the procurement volume from a specific supplier will increase by 20% during the peak season, and it is stored in a low-temperature and low-humidity area". When processing the target data subset, if it is found that the inbound and outbound volume of the chip does not increase during the peak production season, the procurement volume decreases, and the storage location is recorded in a high-temperature and high-humidity area, and the data relationship between these features does not conform to the association rule, then this data is classified as an outlier, prompting the management to check the data accuracy or investigate whether there are abnormal situations such as logistics link errors or supplier problems.

[0045] For the data sampling instruction, samples are extracted from the target data subset according to the sampling parameters in the instruction. For example, when using stratified sampling, stratification is performed according to the categories and suppliers of electronic components, and data is extracted within each layer at a set ratio for subsequent rapid analysis or model training, improving the processing efficiency while ensuring the representativeness of the data.

[0046] It can be concluded from the above that by finely classifying the first data through the hierarchical clustering algorithm and then screening out the target data subset based on the number of data samples, noise data and outliers can be effectively removed, ensuring the data quality for subsequent analysis, making the decisions based on these data more accurate and reliable, and reducing decision-making mistakes caused by data errors. The application of the association rule library provides a more comprehensive knowledge base for data processing. Whether it is anomaly detection or data sampling, operations can be carried out according to the internal association rules between warehousing data, improving the data processing quality.

[0047] Operations such as data screening and stratified sampling can reduce the unnecessary data processing volume, quickly locate key data, while ensuring the effectiveness of the data, improving the speed of data processing, enabling the enterprise to respond to market changes more timely, and providing strong support for the efficient operation of intelligent logistics warehousing.

[0048] In an embodiment of the present disclosure, the intelligent logistics warehousing data processing method further includes:

[0049] Based on the hierarchical clustering algorithm, data classification is performed on the second data to obtain multiple second data subsets. The second data is historical warehousing data.

[0050] Based on multiple second data subsets, an association rule library is determined.

[0051] In this embodiment, determining the association rule library based on multiple second data subsets includes:

[0052] Calculate the association features of the data in each second data subset, and establish initial association rules based on the association features corresponding to all second data subsets.

[0053] Calculate the support and confidence of the initial association rules based on all second data subsets.

[0054] Filter the initial association rules based on the support and confidence to obtain the association rule library.

[0055] In this embodiment, calculating the association features of the data in each second data subset includes:

[0056] Extract features from the second data subset to obtain a feature data set.

[0057] Calculate the correlation coefficient between the feature data sets, and determine the association features based on the correlation coefficient.

[0058] In this embodiment, the second data is historical warehousing data within a specific past time period. The association feature refers to an attribute or index that can reflect the internal relationship between data. In the goods data, if certain goods often enter and leave the warehouse simultaneously, this is an association feature; in the warehouse operation data, the relationship between the inbound and outbound frequency of goods in a specific storage area and the equipment maintenance situation in that area is also an association feature.

[0059] In this embodiment, the support refers to the proportion of the number of records that simultaneously satisfy the antecedent and consequent of a certain association rule in all second data subsets to the total number of records. The support can reflect the frequency of occurrence of this rule in the data set. For example, when analyzing the associated purchase relationship of goods, if the rule "buy a mobile phone case when buying a mobile phone" appears frequently in the historical sales data, that is, the support is high, it indicates that there is a strong association between these two goods.

[0060] The confidence is used to measure the probability that the consequent occurs under the condition that the antecedent of the rule occurs. Taking the above example, if a high proportion of the records of buying a mobile phone also buy a mobile phone case at the same time, then the confidence of the rule "buy a mobile phone case when buying a mobile phone" is high, which indicates that when a customer buys a mobile phone, it is very likely that they will also buy a mobile phone case, and this association relationship has a high reliability.

[0061] In this embodiment, filtering the initial association rules based on the support and confidence includes:

[0062] Calculate the evaluation value of the initial association rule based on the support, confidence, and rule filtering formula.

[0063] The rule filtering formula is:

[0064]

[0065] Among them, represents the evaluation value of the initial association rule, , and represent weight coefficients, represents support, represents confidence. n represents the number of data quality influencing factors, represents the evaluation value corresponding to the i-th data quality influencing factor, represents the weight coefficient of the i-th data quality influencing factor.

[0066] Filter the initial association rules based on the evaluation value of the initial association rules.

[0067] Exemplarily, the data quality influencing factors may include data timeliness and data integrity. When the evaluation values of the referenced data timeliness and data integrity are higher, the reliability of the initial association rules calculated based on this data is higher; conversely, the reliability of the calculated initial association rules is lower.

[0068] Exemplarily, in a certain electronic product storage center, the historical storage data of the past year is used as the second data. The hierarchical clustering algorithm is used to divide these data into second data subsets of different categories of goods, such as home appliances, clothing, digital products, etc. Feature extraction is performed on each subset. Taking the home appliance category as an example, features such as the inbound time, sales volume, and return rate of different brands of home appliances are extracted, and the correlation coefficient is calculated. It is found that when the inbound volume of a certain well-known brand of air conditioner increases significantly in summer, the inbound volumes of the supporting air conditioner brackets and drainage pipes will also increase accordingly, thus establishing corresponding initial association rules (Summer - Air conditioner - Inbound volume: Increase; Air conditioner bracket - Inbound volume: Increase; Drainage pipe - Inbound volume: Increase).

[0069] Calculate the support and confidence of these initial association rules. Assume that in all historical data records, the proportion of the cases where this association rule satisfies both the antecedent and the consequent is 40%, that is, the support is 0.4; among the records where the inbound volume of the air conditioner increases, in 70% of the cases, the supporting products also increase accordingly, that is, the confidence is 0.7.

[0070] Considering the data quality influencing factors, the weight coefficient of data timeliness is 0.3, the weight coefficient of data integrity is 0.7, the current data timeliness evaluation value is 0.8, and the data integrity evaluation value is 0.9. Calculate the evaluation value according to the rule screening formula. If this evaluation value reaches the set standard, this association rule will be included in the association rule library.

[0071] In this embodiment, by comprehensively considering various factors, high-value association rules can be more accurately screened, avoiding the omission of valuable rules or the misselection of low-quality rules caused by single-factor judgment. Considering the influencing factors of data quality enables the rule base to better adapt to business changes, ensuring that the association rules always reflect the current real and reliable warehousing laws, providing a solid basis for decision-making such as inventory management and logistics scheduling, and improving the overall efficiency and effectiveness of intelligent logistics warehousing operations.

[0072] In one embodiment of the present disclosure, the data processing instruction includes an anomaly detection instruction.

[0073] Processing multiple target data subsets based on the data processing instruction and the association rule base includes:

[0074] Determine the data type to which each target data subset belongs.

[0075] If the data processing instruction is an anomaly detection instruction:

[0076] Match target association rules from the association rule base based on the data type to which the target data subset belongs.

[0077] Perform outlier detection on the target data subset based on the target association rule.

[0078] In this embodiment, the anomaly detection instruction aims to find data points or patterns in the warehousing data that deviate from the normal mode. The parameters carried by the anomaly detection instruction may include detection sensitivity and time range, etc. The data type refers to the category of warehousing data represented by the target data subset, such as goods information category, warehousing operation category, etc.

[0079] In this embodiment, after receiving the anomaly detection instruction, analyze the parameters carried in the instruction. For example, the detection sensitivity determines the strictness of the anomaly judgment, and the time range defines the time period of the detected data. Determine the data type of each target data subset, and clarify which one of the goods information category, warehousing operation category, etc. it belongs to. Accurately match target association rules from the association rule base according to the data type. These rules are normal data patterns mined from historical data. Compare the actual data of the target data subset with the target association rules one by one. Once the data exceeds the normal range set by the rules, it is determined as an outlier, thus realizing the effective detection of abnormal situations in the warehousing data.

[0080] Exemplarily, when an anomaly detection instruction is received, the data type of each target data subset is discriminated. The target association rule that matches it is found from the association rule library according to the data type. For example, for the goods inbound and outbound data subset, the association rule regarding the relationship between the normal inbound and outbound quantity, frequency, and time of goods is found as the target association rule. The actual data of the target data subset is compared and analyzed with the target association rule. If the data violates the pattern set in the rule, these data are marked as outliers, and the detailed information of the anomaly is recorded, such as the abnormal data points, the degree of anomaly, and the business processes that may be affected, etc., for further investigation of the reasons and taking corresponding measures in the future.

[0081] Through the anomaly detection instruction in this embodiment, the anomalies in the warehouse data can be located flexibly and accurately. The detection is carried out by matching the association rules according to the data type, which has strong pertinence. Anomalies such as incorrect goods information and warehouse operation errors can be discovered in a timely manner, avoiding decision-making mistakes caused by data deviation. This helps to optimize the warehouse management process, improve the accuracy of inventory management, ensure the efficient and stable operation of the logistics warehouse, and reduce the operation risk.

[0082] In an embodiment of the present disclosure, the intelligent logistics warehouse data processing method further includes:

[0083] Determine the third data based on the target update frequency and the target time window.

[0084] Determine the association rule corresponding to the third data, and update the association rule library based on the association rule corresponding to the third data.

[0085] In this embodiment, the target update frequency refers to the preset time interval for updating the association rule library, and the target time window is the time range for determining the data used to update the association rule. The third data is a data set selected from all the warehouse data according to the target update frequency and the target time window for updating the association rule library.

[0086] In this embodiment, the update process is periodically started according to the set target update frequency, and the third data is extracted from the historical warehousing data according to the target time window. For example, if the target update frequency is once a quarter and the target time window is the past month, then the warehousing data of the past month is selected as the third data at the beginning of each quarter. Analyze the third data, and use the same method as establishing the initial association rule library to calculate the association characteristics of the data in the third data subset, and determine its corresponding association rules, such as finding that new combinations of goods frequently enter and leave the warehouse, the association between new logistics transportation methods and goods types, etc. Compare and integrate these new association rules with the original association rule library, add the new rules that meet certain conditions (such as the support and confidence reaching a certain threshold) to the association rule library, and at the same time eliminate the old rules that are no longer applicable (such as invalidated due to business adjustments or market changes), so as to realize the update of the association rule library and make it better reflect the actual situation and internal laws of the current warehousing business.

[0087] In this embodiment, the association rule library is updated regularly according to the target update frequency and time window, which can make the warehousing data processing closely fit the dynamic changes of the business. Timely incorporate new rules such as the association of goods and the association of logistics transportation that emerge, and eliminate invalid old rules to ensure the continuous improvement of the data processing accuracy. It helps to optimize inventory management, improve logistics efficiency, reduce costs, enhance the enterprise's adaptability and competitiveness to market changes, and ensure the efficient operation of the intelligent logistics warehousing system.

[0088] Corresponding to the intelligent logistics warehousing data processing method in the above embodiment, Figure 2 is a structural block diagram of an intelligent logistics warehousing data processing system provided by an embodiment of the present disclosure. For the sake of illustration, only the parts related to the embodiments of the present disclosure are shown. Refer to Figure 2 and the intelligent logistics warehousing data processing system 20 includes: a clustering module 21, a data screening module 22, and a data processing module 23.

[0089] Among them, the clustering module 21 is used to classify the first data based on the hierarchical clustering algorithm to obtain multiple first data subsets. The first data is the warehousing data to be processed.

[0090] The data screening module 22 is used to screen data from multiple first data subsets to select multiple target data subsets.

[0091] The data processing module 23 is used to process multiple target data subsets in response to receiving a data processing instruction, based on the data processing instruction and the association rule library. The association rule library includes the association rules between warehousing data.

[0092] In an embodiment of the present disclosure, the intelligent logistics warehousing data processing system 20 further includes:

[0093] An association calculation module is configured to classify the second data based on a hierarchical clustering algorithm to obtain multiple second data subsets. The second data is historical warehousing data.

[0094] Determine an association rule library based on the multiple second data subsets.

[0095] In an embodiment of the present disclosure, the association calculation module is specifically configured to calculate the association features of the data in each second data subset, and establish an initial association rule based on the association features corresponding to all the second data subsets.

[0096] Calculate the support and confidence of the initial association rule based on all the second data subsets.

[0097] Screen the initial association rule based on the support and confidence to obtain the association rule library.

[0098] In an embodiment of the present disclosure, the association calculation module is further specifically configured to calculate the evaluation value of the initial association rule based on the support, confidence, and rule screening formula.

[0099] The rule screening formula is:

[0100]

[0101] Wherein, represents the evaluation value of the initial association rule, , and represent weight coefficients, represents the support, represents the confidence, n represents the number of data quality influencing factors, represents the evaluation value corresponding to the i-th data quality influencing factor, represents the weight coefficient of the i-th data quality influencing factor.

[0102] Screen the initial association rule based on the evaluation value of the initial association rule.

[0103] In an embodiment of the present disclosure, the data screening module 22 is specifically configured to calculate the number of data samples in each first data subset.

[0104] Determine a first threshold based on the number of data samples in the multiple first data subsets.

[0105] Select the first data subsets with the number of data samples greater than or equal to the first threshold from the multiple first data subsets, and use the first data subset as the target data subset.

[0106] In an embodiment of the present disclosure, the data processing instruction includes an anomaly detection instruction. The data processing module 23 is specifically configured to determine the data type to which each target data subset belongs.

[0107] If the data processing instruction is an anomaly detection instruction:

[0108] Match the target association rule from the association rule library based on the data type to which the target data subset belongs.

[0109] Perform outlier detection on the target data subset based on the target association rule.

[0110] In an embodiment of the present disclosure, the intelligent logistics warehousing data processing system 20 further includes:

[0111] A rule update module, configured to determine the third data based on the target update frequency and the target time window.

[0112] Determine the association rule corresponding to the third data, and update the association rule library based on the association rule corresponding to the third data.

[0113] See Figure 3 , Figure 3 which is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. As Figure 3 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through a communication bus 305. The memory 304 is used to store a computer program, and the computer program includes program instructions. The processor 301 is configured to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module in the above system embodiments, such as Figure 2 the functions of the modules 21 to 23 shown.

[0114] It should be understood that in the embodiments of the present disclosure, the so-called processor 301 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0115] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0116] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may further include a non-volatile random access memory. For example, the memory 304 may further store information about the device type.

[0117] In a specific implementation, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present disclosure may implement the implementation manners described in the first embodiment and the second embodiment of the intelligent logistics warehousing data processing method provided by the embodiments of the present disclosure, and may also implement the implementation manner of the electronic device 300 described in the embodiments of the present disclosure, which will not be elaborated herein.

[0118] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the methods of the above embodiments are implemented. It may also be completed by instructing relevant hardware through the computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiments may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0119] A computer-readable storage medium may be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device. Further, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0120] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this disclosure.

[0121] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0122] In several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection to each other may be an indirect coupling or communication connection through some interfaces or units, or may also be an electrical, mechanical, or other form of connection.

[0123] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of this disclosure.

[0124] In addition, in each embodiment of the present disclosure, each functional unit may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0125] The above is only the specific implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present disclosure can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A smart logistics warehousing data processing method, characterized in that: include: Classifying the first data based on a hierarchical clustering algorithm to obtain a plurality of first data subsets; The first data is storage data to be processed; Performing data screening from the plurality of first data subsets to select a plurality of target data subsets; Classifying the second data based on a hierarchical clustering algorithm to obtain a plurality of second data subsets; the second data is historical warehouse data; Calculate the association features of the data in each second data subset, and establish an initial association rule based on the association features corresponding to all the second data subsets; Calculating the support and confidence of the initial association rule based on all second data subsets; Calculating the evaluation value of the initial association rule based on the support, the confidence and the rule screening formula; The rule screening formula is: in, represents the evaluation value of the initial association rule, , and represents the weight coefficient, Express support, represents the confidence level, n represents the number of factors affecting data quality, represents the evaluation value corresponding to the i-th data quality influencing factor, Represents the weight coefficient of the i-th data quality influencing factor; Screening the initial association rules based on the evaluation values ​​of the initial association rules to obtain an association rule library; In response to receiving the data processing instruction, the plurality of target data subsets are processed based on the data processing instruction and an association rule base; the association rule base includes association rules between warehoused data.

2. The method for processing intelligent logistics warehousing data according to claim 1, characterized in that: The step of screening data from the plurality of first data subsets to select a plurality of target data subsets includes: Calculate the number of data samples of each first data subset; Determine a first threshold based on the number of data samples of the plurality of first data subsets; A first data subset whose number of data samples is greater than or equal to a first threshold is selected from a plurality of first data subsets, and the first data subset is used as a target data subset.

3. The method for processing intelligent logistics warehousing data according to claim 1, characterized in that: The data processing instructions include anomaly detection instructions; The processing of the plurality of target data subsets based on the data processing instructions and the association rule base includes: Determine the data type to which each target data subset belongs; If the data processing instruction is an exception detection instruction: Matching a target association rule from the association rule library based on the data type to which the target data subset belongs; Outlier detection is performed on the target data subset based on the target association rule.

4. The method for processing intelligent logistics warehousing data according to claim 1, characterized in that: Also includes: determining third data based on the target update frequency and the target time window; An association rule corresponding to the third data is determined, and an association rule library is updated based on the association rule corresponding to the third data.

5. A smart logistics warehousing data processing system, characterized in that: include: A clustering module, used for classifying the first data based on a hierarchical clustering algorithm to obtain a plurality of first data subsets; The first data is storage data to be processed; A data screening module, used to screen data from the plurality of first data subsets to select a plurality of target data subsets; An association calculation module, used for classifying the second data based on a hierarchical clustering algorithm to obtain a plurality of second data subsets; the second data is historical warehouse data; Calculate the association features of the data in each second data subset, and establish an initial association rule based on the association features corresponding to all the second data subsets; Calculating the support and confidence of the initial association rule based on all second data subsets; Calculating the evaluation value of the initial association rule based on the support, the confidence and the rule screening formula; The rule screening formula is: in, represents the evaluation value of the initial association rule, , and represents the weight coefficient, Express support, represents the confidence level, n represents the number of factors affecting data quality, represents the evaluation value corresponding to the i-th data quality influencing factor, Represents the weight coefficient of the i-th data quality influencing factor; Screening the initial association rules based on the evaluation values ​​of the initial association rules to obtain an association rule library; The data processing module is used to process the multiple target data subsets based on the data processing instructions and an association rule base in response to receiving the data processing instructions; the association rule base includes association rules between warehouse data.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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