A logistics data analysis and processing method and platform based on big data

Abstract

The present invention provides a logistics data analysis and processing method and platform based on big data, which belongs to the field of big data technology, and specifically includes: treating logistics materials whose update abnormality probability does not meet the requirements as suspected abnormal materials, determining the consumption abnormality probability of the suspected abnormal materials and the consumption abnormal materials based on the deviation between the material consumption data and the historical consumption data, determining the material management risk of the logistics management system based on different update abnormality probabilities of the suspected abnormal materials and different consumption abnormality probabilities of the consumption abnormal materials, when the material management risk does not meet the requirements, performing inventory processing on the logistics materials of the logistics management system, and updating the status of the logistics materials of the logistics management system according to the inventory results, thereby ensuring the reliability of the guarantee of the logistics materials.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data, and in particular relates to a logistics data analysis and processing method and platform based on big data. Background Art

[0002] The construction of the logistics information system has greatly improved the reliability of logistics support and the order of logistics management. However, at the same time, the logistics information system generates a large amount of logistics data during operation. Therefore, if the logistics data cannot be analyzed and processed, the reliability requirements of logistics support cannot be met.

[0003] In order to solve the above technical problems, the existing technical solution in the invention patent application CN201810471951.5 "A method for evaluating the health of a hospital logistics system" proposes to evaluate the health of the hospital logistics system based on the analysis of hospital logistics data from three indicators: transportation task execution, logistics transportation task allocation, and transportation task saturation. However, it is not difficult to find through analysis that there are the following technical problems:

[0004] Since the logistics management system manages a large number of materials, there is a high possibility that abnormal situations may occur, such as the material consumption status not being updated or the update being abnormal, which makes it impossible for logistics management personnel to accurately determine the actual material usage status, and thus unable to ensure the reliability of logistics material support.

[0005] In response to the above technical problems, the present invention provides a logistics data analysis and processing method and platform based on big data. Summary of the invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a logistics data analysis and processing method based on big data is provided.

[0008] A logistics data analysis and processing method based on big data, characterized by specifically comprising:

[0009] S1 determines the status update data of different types of logistics materials according to the logistics management system, and determines the update abnormality probability of the logistics materials according to the deviation between the status update data and the historical update data of the logistics materials. When the update abnormality probability of the logistics materials does not meet the requirements, proceed to the next step;

[0010] S2: The logistics materials whose update abnormality probability does not meet the requirements are regarded as suspected abnormal materials, and the material consumption data of the suspected abnormal materials is determined through the status update data of the suspected abnormal materials, and the consumption abnormality probability of the suspected abnormal materials and the consumption abnormal materials are determined based on the deviation between the material consumption data and the historical consumption data;

[0011] S3 determines the material management risk of the logistics management system based on the abnormal update probabilities of different suspected abnormal materials and the abnormal consumption probabilities of different abnormal consumption materials. When the material management risk does not meet the requirements, proceed to the next step;

[0012] S4 performs an inventory process on the logistics materials of the logistics management system, and updates the status of the logistics materials of the logistics management system according to the inventory result.

[0013] The beneficial effects of the present invention are:

[0014] 1. The probability of abnormal update of logistics materials is determined based on the deviation between the status update data and the historical update data of logistics materials, thereby realizing the screening of logistics materials with abnormal status update data from the perspective of the number and frequency of status updates of logistics materials, avoiding the technical problem of being unable to accurately grasp the true situation of logistics materials due to long-term non-update of status, and also laying the foundation for the determination of logistics materials for further inventory processing.

[0015] 2. The material management risk of the logistics management system is determined based on the abnormal update probabilities of different suspected abnormal materials and the abnormal consumption probabilities of different abnormal consumption materials. It not only considers the impact of the number of suspected abnormal materials with abnormal status update data and the size of the abnormal probability on the material management risk of the overall logistics management system, but also further combines the comprehensive consideration of abnormal consumption materials with abnormal consumption data to achieve accurate assessment of material management risks from multiple dimensions, and also provides data basis for the generation of differentiated inventory processing strategies for logistics materials.

[0016] A further technical solution is that the status update data of the logistical materials includes the number of status updates of the logistical materials within a recent preset time period and the time intervals between different status update numbers.

[0017] A further technical solution is that the method for determining the probability of abnormal update of the logistics materials is:

[0018] Determine the number of status updates of the logistics materials on different dates within a recent preset time period based on the status update data, and determine the similarity of status updates of different dates with different historical dates in combination with the intervals between the number of status updates on different dates;

[0019] The abnormal probability of the updated status on different dates is determined by the similarity with the status updates on different historical dates, and the abnormal probability of the updated status of the logistics materials is determined based on the abnormal probability of the updated status on different dates within the most recent preset time period.

[0020] A further technical solution is that the update abnormality probability of the logistics materials ranges from 0 to 1, wherein when the update abnormality probability of the logistics materials is greater than a preset probability threshold, it is determined that the update abnormality probability of the logistics materials does not meet the requirements.

[0021] A further technical solution is that, when the material management risk meets the requirements, there is no need to conduct an inventory of all logistical materials, and only suspected abnormal materials and abnormally consumed materials need to be counted.

[0022] A further technical solution is that the value range of the material management risk of the logistics management system is between 0 and 1, wherein when the material management risk of the logistics management system is greater than a preset management risk threshold, it is determined that the material management risk of the logistics management system does not meet the requirements.

[0023] In a second aspect, the present invention provides a logistics data analysis and processing platform based on big data, which adopts the above-mentioned logistics data analysis and processing method based on big data, and is characterized in that it specifically includes:

[0024] Update the abnormal assessment module, abnormal consumption assessment module, management risk assessment module, and status update processing module;

[0025] The update anomaly assessment module is responsible for determining the status update data of different types of logistics materials according to the logistics management system, and determining the probability of update anomaly of the logistics materials according to the deviation between the status update data and the historical update data of the logistics materials;

[0026] The abnormal consumption assessment module is responsible for treating the logistics materials whose update abnormal probability does not meet the requirements as suspected abnormal materials, determining the material consumption data of the suspected abnormal materials through the status update data of the suspected abnormal materials, and determining the abnormal consumption probability of the suspected abnormal materials and the abnormal consumption materials based on the deviation between the material consumption data and the historical consumption data;

[0027] The management risk assessment module is responsible for determining the material management risk of the logistics management system based on the abnormal update probabilities of different suspected abnormal materials and the abnormal consumption probabilities of different abnormal consumption materials;

[0028] The status update processing module is responsible for performing an inventory process on the logistics materials of the logistics management system, and updating the status of the logistics materials of the logistics management system according to the inventory result.

[0029] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0032] Figure 1 It is a flow chart of a logistics data analysis and processing method based on big data;

[0033] Figure 2 It is a flow chart of a method for determining the probability of abnormal update of logistic materials;

[0034] Figure 3 The present invention is a flowchart of a method for determining the probability of abnormal consumption of suspected abnormal materials. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0036] The following will further elaborate from two perspectives: method-based embodiments and system-based embodiments.

[0037] Method Example

[0038] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a logistics data analysis and processing method based on big data is provided, which is characterized by specifically including:

[0039] S1 determines the status update data of different types of logistics materials according to the logistics management system, and determines the update abnormality probability of the logistics materials according to the deviation between the status update data and the historical update data of the logistics materials. When the update abnormality probability of the logistics materials does not meet the requirements, proceed to the next step;

[0040] Furthermore, the status update data of the logistical materials include the number of status updates of the logistical materials within a recent preset time period and the time intervals between different status update numbers.

[0041] Specifically, Figure 2 As shown, the method for determining the probability of abnormal update of the logistics materials is:

[0042] Determine the number of status updates of the logistics materials on different dates within a recent preset time period based on the status update data, and determine the similarity of status updates of different dates with different historical dates in combination with the intervals between the number of status updates on different dates;

[0043] The abnormal probability of the updated status on different dates is determined by the similarity with the status updates on different historical dates, and the abnormal probability of the updated status of the logistics materials is determined based on the abnormal probability of the updated status on different dates within the most recent preset time period.

[0044] Furthermore, the update abnormality probability of the logistics materials ranges from 0 to 1, wherein when the update abnormality probability of the logistics materials is greater than a preset probability threshold, it is determined that the update abnormality probability of the logistics materials does not meet the requirements.

[0045] In another embodiment, the method for determining the probability of abnormal update of the logistics materials is:

[0046] S11 determines the average daily update times of the logistics materials and the average intervals of different status update times through the historical update data of the logistics materials, and uses them as the benchmark update times and benchmark intervals;

[0047] S12 determines the number of status updates of the logistics materials on different dates within the most recent preset time period based on the status update data, and determines whether there is a date on which the deviation between the number of status updates and the benchmark number of updates does not meet the requirement, if not, proceeds to the next step, if yes, proceeds to step S13;

[0048] S13 determines the number of status updates whose interval deviation does not meet the requirement according to the reference interval and the interval deviation of the number of status updates of the logistics materials in the most recent preset time period, and uses it as the interval abnormality number to determine whether the interval abnormality number of the logistics materials in the most recent preset time meets the requirement. If so, it is determined that the update abnormality probability of the logistics materials meets the requirement. If not, proceed to the next step.

[0049] S14 determines the abnormal update status values ​​of different dates based on the deviation between the status update times of different dates and the benchmark update times, and the deviation between the intervals of different status update times and the benchmark intervals, and determines whether there is a date on which the abnormal update status value does not meet the requirements. If so, proceed to the next step; if not, determine whether the abnormal update probability of the logistics materials meets the requirements;

[0050] S15 obtains the number of abnormal intervals of the logistics materials within a preset time, and determines the abnormal probability of updating the logistics materials in combination with abnormal values ​​of the update status on different dates.

[0051] In another embodiment, the method for determining the probability of abnormal update of the logistics materials is:

[0052] Determine the average daily update times of the logistics materials and the average intervals of different status update times through the historical update data of the logistics materials, and use them as the benchmark update times and benchmark intervals;

[0053] Determine abnormal values ​​of update status on different dates based on deviations between the number of status updates on different dates and the benchmark number of updates, and deviations between the intervals between different number of status updates and the benchmark interval;

[0054] The abnormal probability of updating the logistics materials is determined by abnormal values ​​of the updating status on different dates.

[0055] In another embodiment, the method for determining the probability of abnormal update of the logistics materials is:

[0056] S21 determines the average daily update times of the logistics materials and the average intervals of different status update times through the historical update data of the logistics materials, and uses them as the benchmark update times and benchmark intervals, and determines the update times interval of the logistics materials in the most recent preset time period based on the benchmark update times, and judges whether the update times of the logistics materials in the most recent preset time period are within the update times interval, if yes, proceeds to the next step, if not, proceeds to step S23;

[0057] S22 determines the number of status updates that do not meet the requirements according to the reference interval and the interval deviation of the number of status updates of the logistics materials in the most recent preset time period, and uses it as the number of interval abnormalities to determine whether the number of interval abnormalities of the logistics materials in the most recent preset time meets the requirements. If so, it is determined that the probability of the update abnormality of the logistics materials meets the requirements. If not, proceed to the next step.

[0058] S23 determines the update status evaluation amount of the logistics materials according to the update times, interval abnormal times, and the interval time between different interval abnormal times and the adjacent status update times of the logistics materials in the latest preset time period, and judges whether the update status evaluation amount of the logistics materials meets the requirements. If yes, proceed to the next step; if not, proceed to step S25;

[0059] S24 determines the number of status updates of the logistics materials on different dates within a recent preset time period based on the status update data, determines the update status abnormality values ​​of different dates based on the deviations between the status update numbers on different dates and the benchmark update numbers, and the deviations between the intervals between different status update numbers and the benchmark intervals, determines the suspected abnormal update dates based on the update status abnormality values, and determines whether the number of the suspected abnormal update dates meets the requirements, if so, determines that the update abnormality probability of the logistics materials meets the requirements, if not, proceeds to the next step;

[0060] S25 determines the update abnormality probability of the logistics material based on the update status abnormal values ​​of different dates and the update status evaluation amount of the logistics material.

[0061] S2: The logistics materials whose update abnormality probability does not meet the requirements are regarded as suspected abnormal materials, and the material consumption data of the suspected abnormal materials is determined through the status update data of the suspected abnormal materials, and the consumption abnormality probability of the suspected abnormal materials and the consumption abnormal materials are determined based on the deviation between the material consumption data and the historical consumption data;

[0062] Furthermore, the material consumption data of the suspected abnormal material includes the material consumption of the suspected abnormal material on different dates within a recent preset time period and the matching time period of the material consumption.

[0063] Specifically, Figure 3 As shown, the method for determining the abnormal probability of consumption of the suspected abnormal materials is:

[0064] Determine the average daily consumption of the suspected abnormal materials and the average consumption in different time periods through the historical consumption data;

[0065] Based on the material consumption data, determine the total material consumption of the suspected abnormal material on different dates and the material consumption in different time periods within the most recent preset time period, and determine the abnormal material consumption values ​​on different dates in combination with the average daily consumption of the suspected abnormal material and the average consumption in different time periods;

[0066] The abnormal consumption probability of the suspected abnormal materials is determined according to the abnormal values ​​of material consumption on different dates.

[0067] Furthermore, when the probability of abnormal consumption of the suspected abnormal material is not less than a preset abnormal probability limit value, the suspected abnormal material is determined to be abnormal consumption material.

[0068] In another embodiment, the method for determining the abnormal probability of consumption of the suspected abnormal materials is:

[0069] S41 determines the average daily consumption of the suspected abnormal material and the average consumption in different time periods through the historical consumption data, and determines whether the total material consumption of the suspected abnormal material in the most recent preset time period meets the requirements based on the average daily consumption. If so, proceed to the next step; if not, proceed to step S43;

[0070] S42 determines the total material consumption of the suspected abnormal material on different dates within the most recent preset time period based on the material consumption data, and determines whether there is a date on which the total material consumption does not meet the requirement based on the average daily consumption. If so, proceed to the next step; if not, determine that the suspected abnormal material does not belong to the material with abnormal consumption;

[0071] S43 determines the total material consumption of the suspected abnormal material on different dates and the material consumption in different time periods within the most recent preset time period based on the material consumption data, and determines the abnormal material consumption values ​​on different dates in combination with the average daily consumption of the suspected abnormal material and the average consumption in different time periods, and determines whether there is a date on which the abnormal material consumption value does not meet the requirements. If so, proceed to the next step; if not, determine that the suspected abnormal material does not belong to the abnormal consumption material;

[0072] S44 obtains the total material consumption of the suspected abnormal material in the most recent preset time period, and determines the abnormal consumption probability of the suspected abnormal material in combination with the abnormal material consumption values ​​on different dates.

[0073] In another embodiment, the method for determining the abnormal probability of consumption of the suspected abnormal materials is:

[0074] Determine the average daily consumption of the suspected abnormal materials and the average consumption in different time periods through the historical consumption data, and determine the total material consumption of the suspected abnormal materials on different dates within the most recent preset time period based on the material consumption data;

[0075] Determine whether there is a deviation consumption date in the most recent preset time period through the total material consumption and the average daily consumption. If yes, proceed to the next step. If not, determine that the suspected abnormal material does not belong to the abnormal consumption material;

[0076] The deviation between the material consumption on the deviation consumption date and the daily average consumption is used as the material consumption deviation, and whether the sum of the material consumption deviation on the deviation consumption date meets the requirements is determined. If so, proceed to the next step; if not, determine that the suspected abnormal material does not belong to the material with abnormal consumption;

[0077] Based on the material consumption data, determine the total material consumption of the suspected abnormal material on different dates and the material consumption in different time periods within the most recent preset time period, and determine the abnormal material consumption values ​​on different dates in combination with the average daily consumption of the suspected abnormal material and the average consumption in different time periods;

[0078] The total material consumption of the suspected abnormal material in the most recent preset time period is obtained, and the abnormal consumption probability of the suspected abnormal material is determined in combination with the abnormal material consumption values ​​on different dates.

[0079] S3 determines the material management risk of the logistics management system based on the abnormal update probabilities of different suspected abnormal materials and the abnormal consumption probabilities of different abnormal consumption materials. When the material management risk does not meet the requirements, proceed to the next step;

[0080] Furthermore, the method for determining the material management risk of the logistics management system is:

[0081] Determine different basic weight values ​​of suspected abnormal materials according to different types of suspected abnormal materials, and determine the comprehensive update abnormality probability of the suspected abnormal materials in combination with the update abnormality probabilities of different suspected abnormal materials;

[0082] Determine different basic weight values ​​of abnormally consumed materials according to different types of abnormally consumed materials, and determine the comprehensive abnormal consumption probability of the abnormally consumed materials in combination with the abnormal consumption probabilities of the different abnormally consumed materials;

[0083] The quantity ratio of the suspected abnormal materials and the abnormally consumed materials in the logistics management system is obtained, and the material management risk of the logistics management system is determined in combination with the comprehensive abnormal update probability of the suspected abnormal materials and the comprehensive abnormal consumption probability of the abnormally consumed materials.

[0084] Specifically, when the material management risk meets the requirements, there is no need to conduct an inventory of all logistical materials, and only suspected abnormal materials and abnormally consumed materials need to be counted.

[0085] It should be noted that the value range of the material management risk of the logistics management system is between 0 and 1. When the material management risk of the logistics management system is greater than the preset management risk threshold, it is determined that the material management risk of the logistics management system does not meet the requirements.

[0086] In another embodiment, the method for determining the material management risk of the logistics management system is:

[0087] Obtaining the quantity ratio of the suspected abnormal materials and the abnormally consumed materials in the logistics management system, and determining whether the quantity ratio meets the requirements, if so, proceeding to the next step, if not, determining that the material management risk of the logistics management system does not meet the requirements;

[0088] Determine the basic weight values ​​of different logistics materials according to the types of logistics materials, and judge whether the proportion of the basic weight values ​​of the suspected abnormal materials and the abnormally consumed materials in the logistics management system meets the requirements. If so, proceed to the next step; if not, determine that the material management risk of the logistics management system does not meet the requirements;

[0089] Determine the comprehensive update abnormality probability of the suspected abnormal material according to the basic weight values ​​and update abnormality probabilities of different suspected abnormal materials, and judge whether the comprehensive update abnormality probability of the suspected abnormal material meets the requirements. If so, proceed to the next step; if not, determine that the material management risk of the logistics management system does not meet the requirements;

[0090] Determine the comprehensive abnormal consumption probability of the abnormal consumption materials according to the basic weight values ​​and abnormal consumption probabilities of different abnormal consumption materials, and judge whether the comprehensive abnormal consumption probability of the suspected abnormal materials meets the requirements. If so, proceed to the next step; if not, determine that the material management risk of the logistics management system does not meet the requirements;

[0091] The quantity ratio of the suspected abnormal materials and the abnormally consumed materials in the logistics management system is obtained, and the material management risk of the logistics management system is determined in combination with the comprehensive abnormal update probability of the suspected abnormal materials and the comprehensive abnormal consumption probability of the abnormally consumed materials.

[0092] S4 performs an inventory process on the logistics materials of the logistics management system, and updates the status of the logistics materials of the logistics management system according to the inventory result.

[0093] System Example

[0094] On the other hand, the present invention provides a logistics data analysis and processing platform based on big data, which adopts the above-mentioned logistics data analysis and processing method based on big data, and is characterized in that it specifically includes:

[0095] Update the abnormal assessment module, abnormal consumption assessment module, management risk assessment module, and status update processing module;

[0096] The update anomaly assessment module is responsible for determining the status update data of different types of logistics materials according to the logistics management system, and determining the probability of update anomaly of the logistics materials according to the deviation between the status update data and the historical update data of the logistics materials;

[0097] The abnormal consumption assessment module is responsible for treating the logistics materials whose update abnormal probability does not meet the requirements as suspected abnormal materials, determining the material consumption data of the suspected abnormal materials through the status update data of the suspected abnormal materials, and determining the abnormal consumption probability of the suspected abnormal materials and the abnormal consumption materials based on the deviation between the material consumption data and the historical consumption data;

[0098] The management risk assessment module is responsible for determining the material management risk of the logistics management system based on the abnormal update probabilities of different suspected abnormal materials and the abnormal consumption probabilities of different abnormal consumption materials;

[0099] The status update processing module is responsible for performing an inventory process on the logistics materials of the logistics management system, and updating the status of the logistics materials of the logistics management system according to the inventory result.

[0100] Through the above embodiments, the present invention achieves the following beneficial effects:

[0101] 1. The probability of abnormal update of logistics materials is determined based on the deviation between the status update data and the historical update data of logistics materials, thereby realizing the screening of logistics materials with abnormal status update data from the perspective of the number and frequency of status updates of logistics materials, avoiding the technical problem of being unable to accurately grasp the true situation of logistics materials due to long-term non-update of status, and also laying the foundation for the determination of logistics materials for further inventory processing.

[0102] 2. The material management risk of the logistics management system is determined based on the abnormal update probabilities of different suspected abnormal materials and the abnormal consumption probabilities of different abnormal consumption materials. It not only considers the impact of the number of suspected abnormal materials with abnormal status update data and the size of the abnormal probability on the material management risk of the overall logistics management system, but also further combines the comprehensive consideration of abnormal consumption materials with abnormal consumption data to achieve accurate assessment of material management risks from multiple dimensions, and also provides data basis for the generation of differentiated inventory processing strategies for logistics materials.

[0103] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0104] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A logistics data analysis and processing method based on big data, characterized in that: Specifically include: Determine the status update data of different types of logistics materials according to the logistics management system, and determine the probability of abnormal update of the logistics materials according to the deviation between the status update data and the historical update data of the logistics materials. When the probability of abnormal update of the logistics materials does not meet the requirements, proceed to the next step; The logistics materials whose update abnormality probability does not meet the requirements are regarded as suspected abnormal materials, and the material consumption data of the suspected abnormal materials is determined through the status update data of the suspected abnormal materials, and the consumption abnormality probability of the suspected abnormal materials and the consumption abnormal materials are determined based on the deviation between the material consumption data and the historical consumption data; Determine the material management risk of the logistics management system based on different abnormal update probabilities of suspected abnormal materials and different abnormal consumption probabilities of abnormal consumption materials, and when the material management risk does not meet the requirements, proceed to the next step; Carry out an inventory of the logistics materials in the logistics management system, and update the status of the logistics materials in the logistics management system according to the inventory result; The method for determining the abnormal probability of updating the logistics materials is as follows: Determine the number of status updates of the logistics materials on different dates within a recent preset time period based on the status update data, and determine the similarity of status updates of different dates with different historical dates in combination with the intervals between the number of status updates on different dates; Determine the abnormal probability of the updated status of different dates by the similarity of the status updates with different historical dates, and determine the abnormal probability of the updated status of the logistics materials based on the abnormal probability of the updated status of different dates within the most recent preset time period; The method for determining the abnormal probability of consumption of the suspected abnormal materials is: Determine the average daily consumption of the suspected abnormal materials and the average consumption in different time periods through the historical consumption data; Based on the material consumption data, determine the total material consumption of the suspected abnormal material on different dates and the material consumption in different time periods within the most recent preset time period, and determine the abnormal material consumption values ​​on different dates in combination with the average daily consumption of the suspected abnormal material and the average consumption in different time periods; The abnormal consumption probability of the suspected abnormal materials is determined according to the abnormal values ​​of material consumption on different dates.

2. The logistics data analysis and processing method based on big data according to claim 1, characterized in that: The status update data of the logistics materials include the number of status updates of the logistics materials in the most recent preset time period and the time intervals between different status update numbers.

3. The logistics data analysis and processing method based on big data according to claim 1, characterized in that: The value range of the abnormal update probability of the logistics materials is between 0 and 1, wherein when the abnormal update probability of the logistics materials is greater than a preset probability threshold, it is determined that the abnormal update probability of the logistics materials does not meet the requirements.

4. The logistics data analysis and processing method based on big data according to claim 1, characterized in that: The material consumption data of the suspected abnormal material includes the material consumption of the suspected abnormal material on different dates within a recent preset time period and the matching time period of the material consumption.

5. The method for analyzing and processing logistics data based on big data according to claim 4, characterized in that: When the consumption abnormality probability of the suspected abnormal material is not less than the preset abnormal probability limit value, the suspected abnormal material is determined to be the consumption abnormality material.

6. The logistic data analysis and processing method based on big data according to claim 1, characterized in that: When the material management risk meets the requirements, there is no need to conduct an inventory of all logistics materials. Only suspected abnormal materials and abnormally consumed materials need to be counted.

7. A logistics data analysis and processing platform based on big data, using a logistics data analysis and processing method based on big data according to any one of claims 1 to 6, characterized in that: Specifically include: Update the abnormal assessment module, abnormal consumption assessment module, management risk assessment module, and status update processing module; The update anomaly assessment module is responsible for determining the status update data of different types of logistics materials according to the logistics management system, and determining the probability of update anomaly of the logistics materials according to the deviation between the status update data and the historical update data of the logistics materials; The abnormal consumption assessment module is responsible for treating the logistics materials whose update abnormal probability does not meet the requirements as suspected abnormal materials, determining the material consumption data of the suspected abnormal materials through the status update data of the suspected abnormal materials, and determining the abnormal consumption probability of the suspected abnormal materials and the abnormal consumption materials based on the deviation between the material consumption data and the historical consumption data; The management risk assessment module is responsible for determining the material management risk of the logistics management system based on the abnormal update probabilities of different suspected abnormal materials and the abnormal consumption probabilities of different abnormal consumption materials; The status update processing module is responsible for performing an inventory process on the logistics materials of the logistics management system, and updating the status of the logistics materials of the logistics management system according to the inventory result.

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