File Management Method and System

Abstract

The present invention provides a file management method and system, belonging to the technical field of file management, specifically including: obtaining the original documents of different orders of teaching equipment, determining the sales data of different types of teaching equipment in different orders according to the recognition results of the original documents, automatically generating sales files corresponding to different orders, determining the sales popularity of different types of teaching equipment through the sales data of different types of teaching equipment in the sales files, determining the transportation damage data of different types of teaching equipment in different orders according to the recognition results of the original documents of different orders, determining the damage risk of different types of teaching equipment according to the transportation damage data, determining the minimum inventory of different types of teaching equipment through the sales popularity and the damage risk, and determining whether production management is required in combination with the inventory data of different types of teaching equipment, ensuring the accuracy of file data.

Description

Technical Field

[0001] The present invention belongs to the technical field of file management, and particularly relates to a file management method and system. Background Art

[0002] For enterprises mainly engaged in teaching equipment, not only are there a large number of customers, but also the types and quantities of teaching equipment purchased by different customers vary. Therefore, if the sales files of different teaching equipment cannot be managed targeted, it may not only lead to chaotic management of the sales records of teaching equipment within the enterprise, but also unable to conduct production management of different production lines based on the analysis results of the sales files of different teaching equipment, avoiding problems such as the inability to supply orders in a timely manner due to the mismatch between production and sales.

[0003] In view of the above technical problems, the present invention provides a file management method and system. Summary of the Invention

[0004] To achieve the object of the present invention, the present invention adopts the following technical solutions:

[0005] According to one aspect of the present invention, a file management method is provided.

[0006] A file management method, characterized in that it specifically includes:

[0007] S1 Obtain the original files of different orders of teaching equipment, determine the sales data of different types of teaching equipment for different orders according to the recognition results of the original files, and automatically generate sales files corresponding to different orders;

[0008] S2 Determine the sales unit prices of different types of teaching equipment for different orders according to the sales data, and combine the preset price range to determine the data anomaly probability of different orders. When it is determined that data verification is required according to the number of un-verified sales files and the data anomaly probability of the corresponding orders, proceed to the next step;

[0009] S3 Conduct data verification on the un-verified sales files based on the business data of other business systems within the enterprise, and when there is no problem with the data of the sales files, determine the sales popularity of different types of teaching equipment through the sales data of different types of teaching equipment in the sales files;

[0010] S4 Determine the transportation damage data of different types of teaching equipment for different orders through the recognition results of the original files of different orders, determine the damage risk of different types of teaching equipment according to the transportation damage data, determine the minimum inventory of different types of teaching equipment through the sales popularity and the damage risk, and combine the inventory data of different types of teaching equipment to determine whether production management is required.

[0011] A further technical solution lies in that the original documents include the order placement documents, shipping documents, and sales contract documents of the order.

[0012] A further technical solution lies in that the sales data of the teaching equipment includes the seller, sales volume, and unit sales price of the teaching equipment.

[0013] A further technical solution lies in automatically generating sales files corresponding to different orders, specifically including:

[0014] Automatically generating sales file documents based on the sales data of different types of teaching equipment in the order, and automatically generating sales files corresponding to different orders through the original documents and sales file documents.

[0015] A further technical solution lies in that the business data of other business systems includes the financial change data of the financial system and the inventory change data of the warehouse management system.

[0016] On the other hand, the present invention provides an archive management system that adopts an archive management method, which specifically includes:

[0017] An archive formation module; a verification and evaluation module; a popularity evaluation module; a production management module;

[0018] Among them, the archive formation module is responsible for obtaining the original documents of different orders of teaching equipment, determining the sales data of different types of teaching equipment in different orders according to the recognition results of the original documents, and automatically generating sales files corresponding to different orders;

[0019] The verification and evaluation module is responsible for determining the unit sales price of different types of teaching equipment in different orders according to the sales data, determining the data anomaly probability of different orders in combination with the preset price range, and determining whether data verification is required according to the number of un-verified sales files and the data anomaly probability of the corresponding orders;

[0020] The popularity evaluation module is responsible for verifying the data of un-verified sales files based on the business data of other business systems within the enterprise, and when there are no problems with the data of the sales files, determining the sales popularity of different types of teaching equipment through the sales data of different types of teaching equipment in the sales files;

[0021] The production management module is responsible for determining the transportation damage data of different types of teaching equipment in different orders according to the recognition results of the original documents of different orders, determining the damage risk of different types of teaching equipment according to the transportation damage data, determining the minimum inventory of different types of teaching equipment through the sales popularity and damage risk, and determining whether production management is required in combination with the inventory data of different types of teaching equipment.

[0022] The beneficial technical effects of the present invention are as follows:

[0023] 1. Determine whether data verification is required based on the number of un-verified sales files and the data anomaly probability of the corresponding orders. This not only takes into account the number of un-verified sales files but also evaluates the data anomaly probability of un-verified sales files through the verification of the data anomaly probability of orders. Thus, on the basis of ensuring data accuracy, it also avoids the technical problems of high data processing pressure or low data processing efficiency caused by frequent data verification.

[0024] 2. Determine the minimum inventory levels of different types of teaching equipment based on sales popularity and breakage risk, achieving an accurate assessment of the inventory requirements of teaching equipment from two perspectives: sales demand and breakage situation. This avoids the technical problem of being unable to process orders in a timely manner due to inventory not meeting requirements and realizes the efficient utilization of the production line.

[0025] Other features and advantages will be described in the following specification, and some of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the accompanying drawings.

[0026] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given in conjunction with the accompanying drawings and are described in detail as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0028] Figure 1 is a flowchart of a file management method;

[0029] Figure 2 is a flowchart of a method for determining the data anomaly probability of an order;

[0030] Figure 3 is a framework diagram of a file management system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] 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 accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0032] The following will illustrate the solution through Example 1 and Example 2. Example 1

[0033] To solve the above problems, according to one aspect of the present invention, as Figure 1 shown, according to one aspect of the present invention, a file management method is provided, which is characterized in that it specifically includes:

[0034] S1 Obtain the original files of different orders of teaching equipment, determine the sales data of different types of teaching equipment for different orders according to the recognition results of the original files, and automatically generate sales files corresponding to different orders;

[0035] S2 Determine the sales unit prices of different types of teaching equipment for different orders according to the sales data, and combine the preset price range to determine the data anomaly probability of different orders. When it is determined that data verification is required according to the number of un-verified sales files and the data anomaly probability of the corresponding orders, proceed to the next step;

[0036] S3 Perform data verification on the un-verified sales files based on the business data of other business systems within the enterprise, and when there are no problems with the data in the sales files, determine the sales popularity of different types of teaching equipment through the sales data of different types of teaching equipment in the sales files;

[0037] S4 Determine the transportation damage data of different types of teaching equipment for different orders through the recognition results of the original files of different orders, determine the damage risk of different types of teaching equipment according to the transportation damage data, determine the minimum inventory quantity of different types of teaching equipment through the sales popularity and the damage risk, and determine whether production management is required in combination with the inventory data of different types of teaching equipment.

[0038] Furthermore, the original files include the order placement files, transportation and shipping files, and sales contract files of the orders.

[0039] Specifically, the sales data of the teaching equipment includes the seller, sales volume, and sales unit price of the teaching equipment.

[0040] It can be understood that automatically generating the sales files corresponding to different orders specifically includes:

[0041] Automatically generate sales file documents according to the sales data of different types of teaching equipment of the orders, and automatically generate the sales files corresponding to different orders through the original files and the sales file documents.

[0042] In one possible embodiment, as Figure 2As shown, the method for determining the data anomaly probability of the order in the above step S1 is as follows:

[0043] Determine the anomaly probability of different types of teaching equipment according to the deviation amount between the selling price of different types of teaching equipment in the order and the preset price range, and the sales quantity of different types of teaching equipment;

[0044] Based on the anomaly probability of different types of teaching equipment and the quantity of types of teaching equipment in the order, determine the data anomaly probability of the order.

[0045] It should be noted that the deviation amount between the selling price of the teaching equipment and the preset price range is determined according to the end point value of the preset price range close to the selling price, specifically determined according to the deviation amount between the end point value and the selling price.

[0046] In another embodiment, the method for determining the data anomaly probability of the order in the above step S1 is as follows:

[0047] Judge whether there is any deviated teaching equipment according to the deviation amount between the selling price of different types of teaching equipment in the order and the preset price range. If so, proceed to the next step; if not, determine the data anomaly probability of the order based on the preset anomaly probability;

[0048] Judge whether the sales quantity of the deviated teaching equipment is greater than the preset equipment quantity. If so, determine the data anomaly probability of the order through the sales quantity of the deviated teaching equipment; if not, proceed to the next step;

[0049] Determine the anomaly probability of different types of teaching equipment according to the deviation amount between the selling price of different types of teaching equipment in the order and the preset price range, and the sales quantity of different types of teaching equipment, and determine the deviated teaching equipment through the anomaly probability. Judge whether the quantity of the deviated teaching equipment meets the requirements. If so, proceed to the next step; if not, determine the data anomaly probability of the order through the quantity of the deviated teaching equipment;

[0050] Determine the data anomaly probability of the order through the quantity of the deviated teaching equipment, the anomaly probability of different deviated teaching equipment, the anomaly probability of different types of teaching equipment, and the quantity of types of teaching equipment in the order.

[0051] In another embodiment, the method for determining the data anomaly probability of the order in the above step S1 is as follows:

[0052] When it is determined that there is no teaching equipment with deviation based on the deviation amount between the selling unit prices of different types of teaching equipment in the order and the preset price range, the data anomaly probability of the order is determined based on the preset anomaly probability;

[0053] When it is determined that there is teaching equipment with deviation based on the deviation amount between the selling unit prices of different types of teaching equipment in the order and the preset price range, when the maximum value of the deviation amount of the teaching equipment with deviation does not meet the requirements, the data anomaly probability of the order is determined through the maximum value of the deviation amount of the teaching equipment with deviation;

[0054] When the maximum value of the deviation amount of the teaching equipment with deviation meets the requirements, the anomaly probabilities of different types of teaching equipment are determined based on the deviation amount between the selling unit prices of different types of teaching equipment in the order and the preset price range and the selling quantities of different types of teaching equipment. When there is teaching equipment with an anomaly probability that does not meet the requirements, the data anomaly probability of the order is determined through the maximum value of the anomaly probability of the teaching equipment with an anomaly probability that does not meet the requirements;

[0055] When there is no teaching equipment with an anomaly probability that does not meet the requirements, the anomaly teaching equipment is determined through the anomaly probability, and it is judged whether the quantity of the anomaly teaching equipment meets the requirements. If so, the next step is entered. If not, the data anomaly probability of the order is determined through the quantity of the anomaly teaching equipment;

[0056] The data anomaly probability of the order is determined through the quantity of the anomaly teaching equipment, the anomaly probabilities of different anomaly teaching equipment, the anomaly probabilities of different types of teaching equipment, and the quantity of the types of teaching equipment in the order.

[0057] Specifically, it is determined that data verification is required based on the quantity of un-verified sales files and the data anomaly probability of the corresponding order, which specifically includes:

[0058] Taking the order corresponding to the un-verified sales file as the file matching order, the comprehensive anomaly probability is determined based on the quantities of different un-verified sales files and the data anomaly probability of the file matching order, and it is determined whether data verification is required based on the comprehensive anomaly probability.

[0059] It can be understood that determining whether data verification is required based on the comprehensive anomaly probability specifically includes:

[0060] When the comprehensive anomaly probability does not meet the requirements, it is determined that data verification is required.

[0061] Specifically, it is determined that data verification is required based on the quantity of un-verified sales files and the data anomaly probability of the corresponding order, which specifically includes:

[0062] When the number of the un-verified sales files is greater than the preset file number, it is determined that data verification is required;

[0063] When the number of the un-verified sales files is not greater than the preset file number, and it is determined that there are no abnormal sales files according to the data abnormal probability of the files matching the orders of the un-verified sales files, it is determined that data verification is not required;

[0064] When it is determined that there are abnormal sales files according to the data abnormal probability of the files matching the orders of the un-verified sales files, and there are abnormal sales files with a data abnormal probability greater than the preset probability threshold, it is determined that data verification is required;

[0065] When there are no abnormal sales files with a data abnormal probability greater than the preset probability threshold, obtain the number of abnormal sales files, and when the number of abnormal sales files does not meet the requirements, it is determined that data verification is required;

[0066] When the number of abnormal sales files meets the requirements, determine the comprehensive abnormal probability through the number of abnormal sales files, the data abnormal probability of the files matching the orders of the abnormal sales files, the number of un-verified sales files, and the average value of the data abnormal probability of the files matching the orders, and determine whether data verification is required based on the comprehensive abnormal probability.

[0067] In another embodiment, determining that data verification is required according to the number of un-verified sales files and the data abnormal probability of the corresponding orders specifically includes:

[0068] S21 Judge whether there are sales files with a data abnormal probability greater than the preset probability threshold in the files matching the orders of the un-verified sales files. If so, it is determined that data verification is required. If not, proceed to the next step;

[0069] S22 Obtain the number of the un-verified sales files, and judge whether the number of the un-verified sales files is greater than the second preset file number. If so, proceed to the next step. If not, proceed to step S25;

[0070] S23 Determine the abnormal sales files in the sales files according to the data abnormal probability of the files matching the orders of the sales files, and judge whether the number of the abnormal sales files meets the requirements. If so, proceed to the next step. If not, it is determined that data verification is required;

[0071] S24 determines the comprehensive data anomaly probability of the abnormal sales file by the number of abnormal sales files and the data anomaly probability of the file-matched orders of the abnormal sales files, determines whether the comprehensive data anomaly probability of the abnormal sales file meets the requirements. If so, proceed to the next step. If not, determine that data verification is required;

[0072] S25 obtains the average value of the number of the unverified sales files and the data anomaly probability of the file-matched orders, and determines the comprehensive anomaly probability in combination with the comprehensive data anomaly probability of the abnormal sales file, and determines whether data verification is required based on the comprehensive anomaly probability.

[0073] Further, the business data of the other business systems includes the financial change data of the financial system and the inventory change data of the warehouse management system.

[0074] In one possible embodiment, the method for determining the sales popularity of the teaching equipment in the above steps is:

[0075] Based on the sales data, determine the number and total sales volume of the sales orders of the teaching equipment within the preset time, and determine the basic sales popularity of the teaching equipment through the number and total sales volume of the sales orders of the teaching equipment within the preset time;

[0076] Determine the average sales volume per single order of the teaching equipment through the number and total sales volume of the sales orders of the teaching equipment within the preset time, and determine large orders based on the average sales volume and the number of different sales orders. Determine the corrected sales popularity of the teaching equipment based on the number of large orders and the sales volume of different large orders;

[0077] Determine the sales popularity of the teaching equipment through the corrected sales popularity and the basic sales popularity.

[0078] In another possible embodiment, the method for determining the sales popularity of the teaching equipment in the above steps is:

[0079] S31 Based on the sales data, determine the number and total sales volume of the sales orders of the teaching equipment within the preset time, and determine the basic sales popularity of the teaching equipment through the number and total sales volume of the sales orders of the teaching equipment within the preset time;

[0080] S32 Determine the average sales volume per single order of the teaching equipment through the number and total sales volume of the sales orders of the teaching equipment within the preset time, and determine whether there are large orders based on the average sales volume and the number of different sales orders. If so, proceed to step S34. If not, proceed to the next step;

[0081] S33 determines whether there are small orders based on the average sales volume and the number of different sales orders. If so, it proceeds to step S34; if not, it takes the basic sales popularity as the sales popularity of the teaching equipment.

[0082] S34 determines whether the proportion of the sum of the sales volume of the large orders and the sales volume of the small orders in the total sales volume of the teaching equipment within a preset time is less than a preset sales volume proportion. If so, it takes the basic sales popularity as the sales popularity of the teaching equipment; if not, it proceeds to the next step.

[0083] S35 determines the corrected sales popularity of the large orders by using the number of the large orders and the sales volume of different large orders, determines the corrected sales popularity of the small orders by using the number of the small orders and the sales volume of different small orders, and determines the sales popularity of the teaching equipment through the corrected sales popularity of the large orders, the corrected sales popularity of the small orders, and the basic sales popularity.

[0084] It can be understood that the method for determining the damage risk of the teaching equipment is as follows:

[0085] Based on the transportation damage data, determine the number of orders with damage of the teaching equipment within a preset time, and take it as the damaged orders. Determine the order abnormal probability of the transportation damage data through the number of the damaged orders, the transportation mileage of different damaged orders, and the total number of orders of the teaching equipment within a preset time.

[0086] Obtain the damage quantity and damage ratio of the teaching equipment in different damaged orders, and determine the damage abnormal evaluation quantity of the teaching equipment in combination with the total damage quantity of the teaching equipment within a preset time.

[0087] Determine the damage risk of the teaching equipment through the order abnormal probability of the transportation damage data and the damage abnormal evaluation quantity of the teaching equipment.

[0088] Furthermore, determine the minimum inventory of different types of teaching equipment through the sales popularity and the damage risk, specifically including:

[0089] Determine the recommended inventory of the teaching equipment through the sales popularity, and determine the minimum inventory of the teaching equipment in combination with the inventory compensation value constructed based on the damage risk. Embodiment 2

[0090] On the other hand, as Figure 3 shown, the present invention provides an archive management system, adopting an archive management method, which is characterized in that it specifically includes:

[0091] File formation module; verification and evaluation module; popularity evaluation module; production management module;

[0092] The file formation module is responsible for obtaining the original documents of different orders of teaching equipment, determining the sales data of different types of teaching equipment for different orders according to the recognition results of the original documents, and automatically generating sales files corresponding to different orders;

[0093] The verification and evaluation module is responsible for determining the sales unit prices of different types of teaching equipment for different orders according to the sales data, combining the preset price range to determine the data anomaly probability of different orders, and determining whether data verification is required according to the number of un-verified sales files and the data anomaly probability of the corresponding orders;

[0094] The popularity evaluation module is responsible for verifying the data of un-verified sales files based on the business data of other business systems within the enterprise, and when there are no problems with the data of the sales files, determining the sales popularity of different types of teaching equipment through the sales data of different types of teaching equipment in the sales files;

[0095] The production management module is responsible for determining the transportation damage data of different types of teaching equipment for different orders through the recognition results of the original documents of different orders, determining the damage risk of different types of teaching equipment according to the transportation damage data, determining the minimum inventory of different types of teaching equipment through the sales popularity and the damage risk, and determining whether production management is required in combination with the inventory data of different types of teaching equipment.

[0096] Based on the above embodiments, the present application has the following beneficial effects:

[0097] 1. Determining whether data verification is required according to the number of un-verified sales files and the data anomaly probability of the corresponding orders not only takes into account the number of un-verified sales files, but also realizes the evaluation of the data anomaly probability of un-verified sales files through the verification of the data anomaly probability of orders, thus avoiding the technical problems of large data processing pressure or low data processing efficiency caused by frequent data verification while ensuring data accuracy.

[0098] 2. Determining the minimum inventory of different types of teaching equipment through the sales popularity and the damage risk realizes the accurate evaluation of the inventory requirements of teaching equipment from two perspectives of sales demand and damage situation, avoiding the technical problem that the inventory cannot meet the requirements and resulting in the inability to process orders in a timely manner, and realizing the efficient utilization of the production line.

[0099] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the description of the method embodiments.

[0100] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The above is only one or more embodiments of this specification and is not used to limit this specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A file management method, characterized in that: Specifically include: Acquire original files of different orders for teaching equipment, determine sales data of different types of teaching equipment for different orders according to the recognition results of the original files, and automatically generate sales files corresponding to different orders; Determine the sales unit prices of different types of teaching equipment for different orders based on the sales data, and determine the data anomaly probability of different orders in combination with the preset price range. When it is determined that data verification is required based on the number of unverified sales files and the data anomaly probability of the corresponding orders, proceed to the next step; Based on the business data of other business systems within the enterprise, the unverified sales files are verified, and when there is no problem with the data of the sales files, the sales popularity of different types of teaching equipment is determined through the sales data of different types of teaching equipment in the sales files; Determine the transportation damage data of different types of teaching equipment for different orders through the recognition results of the original documents of different orders, determine the damage risk of different types of teaching equipment based on the transportation damage data, determine the minimum inventory quantity of different types of teaching equipment based on sales popularity and damage risk, and determine whether production management is needed based on the inventory data of different types of teaching equipment; The method for determining the data anomaly probability of the order is: Determine the abnormal probability of different types of teaching equipment according to the deviation between the sales unit price of different types of teaching equipment in the order and the preset price range and the sales quantity of different types of teaching equipment; Determining a data anomaly probability of the order based on anomaly probabilities of different types of teaching devices and the number of types of teaching devices in the order; The need for data verification is determined based on the number of unverified sales files and the probability of data anomalies of the corresponding orders, including: The orders corresponding to the unverified sales files are used as file matching orders, and a comprehensive abnormal probability is determined according to the number of different unverified sales files and the data abnormal probability of the file matching orders, and whether data verification is required is determined based on the comprehensive abnormal probability; The method for determining the sales popularity of the teaching equipment is: S31 determines the number of sales orders and the total sales volume of the teaching device within a preset time based on the sales data, and determines the basic sales popularity of the teaching device through the number of sales orders and the total sales volume of the teaching device within the preset time; S32 determines the average sales volume of a single order of the teaching device by the number of sales orders and the total sales volume of the teaching device within a preset time, and determines whether there is a large order based on the average sales volume and the number of different sales orders. If yes, proceed to step S34; if not, proceed to the next step; S33 determines whether there is a small order based on the average sales volume and the number of different sales orders. If yes, proceeds to step S34. If no, takes the basic sales popularity as the sales popularity of the teaching device. S34 determines whether the sum of the sales volume of the large order and the sales volume of the small order accounts for less than the preset sales volume ratio of the total sales volume of the teaching device within the preset time. If so, the basic sales popularity is used as the sales popularity of the teaching device. If not, proceed to the next step. S35 determines the revised sales heat of large orders by using the number of large orders and the sales volume of different large orders, determines the revised sales heat of small orders by using the number of small orders and the sales volume of different small orders, and determines the sales heat of the teaching equipment by the revised sales heat of large orders, the revised sales heat of small orders and the basic sales heat.

2. A file management method according to claim 1, characterized in that: The original documents include order documents, shipping documents and sales contract documents.

3. A file management method as claimed in claim 1, characterized in that: The sales data of the teaching equipment includes the seller, sales volume and sales unit price of the teaching equipment.

4. A file management method as claimed in claim 1, characterized in that: Automatically generate sales files corresponding to different orders, including: A sales archive file is automatically generated based on the sales data of different types of teaching equipment of the order, and sales archives corresponding to different orders are automatically generated through original files and sales archive files.

5. A file management method as claimed in claim 1, characterized in that: The deviation amount between the sales unit price of the teaching equipment and the preset price range is determined according to the endpoint value of the preset price range close to the sales unit price, specifically according to the deviation amount between the endpoint value and the sales unit price.

6. A file management method as claimed in claim 1, characterized in that: Determining whether data verification is required based on the comprehensive abnormal probability specifically includes: When the comprehensive abnormal probability does not meet the requirements, it is determined that data verification is required.

7. A file management method as claimed in claim 1, characterized in that: The business data of other business systems include financial change data of the financial system and inventory change data of the warehouse management system.

8. A file management system, using a file management method according to any one of claims 1 to 7, characterized in that: Specifically include: File formation module; verification and evaluation module; heat evaluation module; Production management module; The file formation module is responsible for obtaining original files of different orders of teaching equipment, determining sales data of different types of teaching equipment of different orders according to the recognition results of the original files, and automatically generating sales files corresponding to different orders; The verification and evaluation module is responsible for determining the sales unit prices of different types of teaching equipment for different orders based on the sales data, and determining the data anomaly probability of different orders in combination with the preset price range, and determining whether data verification is required based on the number of unverified sales files and the data anomaly probability of the corresponding orders; The popularity evaluation module is responsible for verifying the data of unverified sales files based on the business data of other business systems within the enterprise, and when there is no problem with the data of the sales files, determining the sales popularity of different types of teaching equipment through the sales data of different types of teaching equipment in the sales files; The production management module is responsible for determining the transportation damage data of different types of teaching equipment for different orders through the recognition results of the original files of different orders, determining the damage risk of different types of teaching equipment based on the transportation damage data, determining the minimum inventory quantity of different types of teaching equipment through sales popularity and damage risk, and determining whether production management is required based on the inventory data of different types of teaching equipment.

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