Inventory management scheduling optimization system and method based on data prediction

Abstract

The invention discloses an inventory management scheduling optimization system and method based on data prediction, relates to the technical field of inventory management, is used for improving the problems of insufficient or excessive equipment inventory and the like caused by equipment iteration speed change, and comprises the steps of identifying equipment models, determining equipment information, retrieving equipment according to the equipment information, and optimizing the inventory management scheduling. The method comprises the following steps: acquiring historical data of equipment of the same type, delimiting equipment categories by utilizing recursive segmentation according to the historical data, setting analysis time, acquiring an equipment transfer quantity in the analysis time, integrating the equipment transfer quantity and the equipment categories to set sensitive time grades of the equipment, and formulating an inventory calling mode according to the sensitive time grades of the equipment. According to an inventory calling mode, a change prediction coefficient of equipment calling is calculated, meanwhile, information published by new equipment of the same type is monitored, and the inventory calling mode is changed by integrating the information published by the new equipment of the same type and the change prediction coefficient and utilizing a Robust Scaling algorithm and a decision function, so that the problem of insufficient inventory or excessive inventory caused by the iteration rate of different equipment is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of inventory management, and more specifically, to an inventory management scheduling optimization system and method based on data prediction. Background Art

[0002] Inventory management technology is an important tool to ensure optimized inventory levels, reduce costs and improve service levels. Inventory management technology is applied in electronic equipment warehouses to improve the management efficiency of electronic equipment and provide assistance in formulating electronic equipment transfer plans.

[0003] The prior art has the following deficiencies: In the past, when electronic equipment was called up from inventory, a storage threshold was set in advance. Only when the storage quantity in the store was lower than the storage threshold would it be called up from the inventory in the warehouse. However, a large number of items called up would increase the calling time. As technological products, electronic equipment is updated and iterated quickly, and different products have different update and iteration speeds. Under a fixed storage threshold, different product iteration speeds can lead to multiple problems such as overstocking and understocking. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an inventory management scheduling optimization system and method based on data prediction. By analyzing the categories of electronic devices and analyzing the sensitive time of the marked devices, a sensitive time verification mechanism is set to select different calling modes to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The inventory management scheduling optimization method based on data prediction includes the following steps: Step S1: Identify the device model to determine the device information, retrieve the historical data of the same type of device according to the device information, and define the device category according to the historical data; Step S2: Set the analysis time, obtain the equipment transfer quantity, and set the sensitive time level based on the equipment transfer quantity and equipment category; Step S3: Select the inventory retrieval mode according to the sensitive time level. When the sensitive time of the equipment is low, collect the equipment name entry ratio and the warehouse retrieval quantity to calculate the change prediction coefficient; Step S4: monitor the release information of new equipment of the same type, and change the inventory retrieval mode based on the release information of new equipment of the same type and the change prediction coefficient.

[0006] In a preferred embodiment, in step S1, the information tag of the electronic device is identified by the RFID system, the device name and production place are obtained, the devices with the same name prefix are classified as the same series of devices, the device name prefix is ​​searched on the website of the corresponding production place, the number of devices in the same series and the release time interval of devices in the same series are obtained, and the release time intervals of devices in the same series are summed and averaged to obtain the series update value of the corresponding electronic device; The RFID system is a wireless communication technology that identifies electronic devices through radio frequency signals. The website where the electronic device is produced is used to provide information related to the manufacture of the electronic device, including the device name prefix.

[0007] In a preferred embodiment, in step S1, the series update values ​​of different series of devices of the same type are obtained through the website of the electronic device manufacturer, and the device classification threshold is set by recursive segmentation according to the series update values ​​of different series of devices. The specific steps are as follows: Arrange the series update values ​​of different series of equipment from small to large, set the initial segmentation score and the upper limit segmentation score, use the initial segmentation score to segment the sequence, divide the sequence into two parts, calculate the mean of the two parts, compare the two parts of the sequence, screen out the sequence with a large mean, reduce the denominator of the segmentation score by a preset reduction coefficient to increase the value of the segmentation score, use the segmentation score to perform the same segmentation on the remaining sequences, screen out the sequence with a small mean, repeat the operation until the segmentation score reaches the upper limit segmentation score, and take the average value of the values ​​in the remaining sequences as the device classification threshold; Devices of different series have different prefixes in their device names.

[0008] In a preferred embodiment, in step S1, the series update value of the corresponding electronic device is compared with the calculated device classification threshold to classify the electronic device. When the series update value of the electronic device exceeds the device classification threshold, the electronic device is classified as a low-speed iteration device; when the series update value of the electronic device is lower than the device classification threshold, the electronic device is classified as a high-speed iteration device.

[0009] In a preferred embodiment, in step S2, a period of time is selected as the analysis time to mark the electronic device, the ratio of the equipment transfer quantity of the marked device to the total inventory is used as the transfer ratio, the equipment transfer ratio outside the electronic device series where the marked device is located is calculated, and the average value is taken as the transfer threshold; When the marking device is a high-speed iteration device, the iteration coefficient of the marking device is set to 1; when the marking device is a low-speed iteration device, the iteration coefficient of the marking device is set to 0; Set the sensitive time level rules according to the mobilization ratio, mobilization threshold and iteration coefficient of the marked device. The specific steps are as follows: Rule 1: The iteration coefficient of the marking device and the mobilization ratio are summed to obtain the sensitivity judgment index, and the mobilization threshold and 1 are summed to obtain the sensitivity judgment threshold; Rule 2: When the sensitivity judgment index of the marking device exceeds the sensitivity judgment threshold, the sensitive time level of the marking device is judged to be high; when the sensitivity judgment index of the marking device is lower than the sensitivity judgment threshold, the sensitive time level of the marking device is judged to be low.

[0010] In a preferred embodiment, in step S3, the warehouse retrieval speed of the marking device is detected and the timeliness coefficient of the marking device is calculated, and the change prediction coefficient of the device is determined by combining the warehouse retrieval speed of the marking device and the timeliness coefficient; Collect the search ratio of the name terms of the marked device, take the ratio of the name terms of the marked device to the total number of search terms as the actual timeliness value of the marked device, take the ratio of the category to which the marked device belongs to the total number of categories of all electronic equipment at the production site as the predicted timeliness value of the marked device, and take the ratio of the actual timeliness value of the marked device to the predicted timeliness value as the timeliness coefficient of the marked device; The warehouse retrieval quantity of all electronic equipment at the production site is recorded and the average value is calculated. The ratio of the warehouse retrieval quantity of the marking equipment to the calculated average value is used as the warehouse retrieval speed of the marking equipment.

[0011] In a preferred embodiment, in step S3, the warehouse retrieval speed of all electronic devices at the production site is calculated, and the change prediction coefficient is calculated using the Robust Scaling algorithm. The specific steps are as follows: The warehouse retrieval speed of each electronic device is combined into a speed regulation data set, and the warehouse regulation rate of the marked device is calculated using the Robust Scaling formula: , where y is the warehouse adjustment rate of the marking device, and x is the warehouse retrieval speed of the marking device. is the average value in the speed regulation data set, It is the difference between the 1 / 4 quantile and the 3 / 4 quantile when the speed regulation data set is arranged from small to large; the change prediction coefficient is obtained by summing the warehouse regulation rate and the time efficiency coefficient of the marked equipment.

[0012] In a preferred embodiment, in step S4, the release information of new devices of the same type as the marked device is monitored, the release date is recorded, the monitoring date is subtracted from the release date to obtain the iteration time difference, the monitoring date is subtracted from the release date of the marked device to obtain the scheduling time difference, the ratio of the scheduling time difference to the iteration time difference is used as the iteration coefficient, and the decision function is used to change the inventory retrieval rule by combining the iteration coefficient and the change prediction coefficient. The specific steps are as follows: The iteration coefficient and change prediction coefficient calculated by the marking device are used to calculate the decision value through the decision function, and the formula is: , where x is the change prediction coefficient of the marking device, is the decision value of the marking device, is the iteration coefficient of the marking device, T and b are the iteration coefficient modulation and error respectively; When the calculated decision value of the marking device exceeds the preset decision threshold, it is determined that the marking device needs to be in the standard call mode, otherwise, it is determined that the marking device is in the simple call mode; The simple retrieval mode is to regularly use the preset retrieval volume to retrieve the device; The standard retrieval mode uses the preset retrieval quantity to retrieve equipment by multiplying the warehouse retrieval speed by the preset retrieval quantity as the adjusted retrieval quantity for the marked equipment.

[0013] The inventory management scheduling optimization system based on data prediction is used to implement the above-mentioned inventory management scheduling optimization method based on data prediction, including a data acquisition module, a sensitive determination module, an information monitoring module and a retrieval processing module; The data collection module is used to determine the equipment information by equipment model, collect historical data of the same type of equipment, and transfer the equipment transfer quantity to the sensitive judgment module; The sensitive determination module is used to set the sensitive time level for the marking device and calculate the change prediction coefficient to be transmitted to the retrieval processing module; The information monitoring module is used to detect the release information of new devices of the same type, and calculate the iteration coefficient of the marked device and pass it to the call processing module; The retrieval processing module is used to select simple retrieval mode processing or standard retrieval mode processing for the marking device.

[0014] Technical effects and advantages of the inventory management scheduling optimization system and method based on data prediction of the present invention: The present invention determines device information by identifying device models, searches for devices according to the device information, obtains historical data of devices of the same type, categorizes devices according to historical data, and processes devices separately by categorizing equipment categories, thereby narrowing the selection direction of retrieval modes, reducing equipment inventory failure problems, setting analysis time, obtaining equipment transfer quantities within the analysis time, setting equipment sensitive time levels based on the equipment transfer quantities and equipment categories, formulating inventory retrieval modes based on the equipment sensitive time levels, calculating change prediction coefficients of equipment transfers based on the inventory retrieval modes, and simultaneously monitoring release information of new equipment of the same type, changing the inventory retrieval modes based on the release information of new equipment of the same type and the change prediction coefficients, thereby reducing the problems of insufficient or excessive inventory caused by the iteration rates of different devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the inventory management scheduling optimization method based on data prediction of the present invention, Figure 2 This is a flow chart of the inventory management scheduling optimization system based on data forecasting of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The present invention determines device information by identifying the device model, searches for the device according to the device information, obtains historical data of the same type of equipment, defines the equipment category according to the historical data, sets analysis time, obtains the equipment transfer quantity within the analysis time, sets the equipment's sensitive time level based on the equipment transfer quantity and the equipment category, formulates an inventory retrieval mode based on the equipment's sensitive time level, calculates the change prediction coefficient of equipment transfer based on the inventory retrieval mode, monitors the release information of new equipment of the same type at the same time, changes the inventory retrieval mode based on the release information of new equipment of the same type and the change prediction coefficient, thereby reducing the problem of insufficient or excessive inventory caused by the iteration rate of different devices.

[0018] Example 1, an inventory management scheduling optimization method based on data prediction, such as Figure 1 As shown, the following steps are included: Step S1: Identify the device model to determine the device information, retrieve the historical data of the same type of device according to the device information, and define the device category according to the historical data; Step S2: Set the analysis time, obtain the equipment transfer quantity, and set the sensitive time level based on the equipment transfer quantity and equipment category; Step S3: Formulate an inventory retrieval mode according to the sensitive time level, and calculate the change prediction coefficient according to the inventory retrieval mode; Step S4: monitor the release information of new equipment of the same type, and change the inventory retrieval mode based on the release information of new equipment of the same type and the change prediction coefficient.

[0019] The specific implementation is as follows: In step S1, an information tag is generated when the electronic device is produced. The information tag contains basic information of the electronic device, including the device name, production location, and warranty period. When the electronic device enters the warehouse, the electronic device is identified through the RFID system to determine the type of electronic device, brand, and device series of the production company of the electronic device. The type of electronic device can use a pre-set device name tag. For example, the types of electronic devices include laptops, smart furniture, etc.

[0020] The device series refers to the same series of devices of the corresponding manufacturing company for the identified electronic equipment. Usually, the manufacturing company will keep the name prefixes of the same series of devices consistent, and add a digital serial number to the name suffix to identify different generations of products in the same series. Therefore, devices with the same name prefix can be classified as devices of the same series.

[0021] The historical data is the release time interval of the same series of equipment. The information tag of the electronic equipment is identified through the RFID system to obtain the basic information of the electronic equipment, confirm the device name and production place of the electronic equipment, visit the website of the production place of the electronic equipment to search for the device name prefix, obtain the number of devices in the same series and the release time interval of the same series of devices, sum and average the release time intervals of the same series of devices to obtain the series update value of the corresponding electronic equipment.

[0022] Use the same method to calculate the series update values ​​of different series of equipment of the same type in the same production site, and use recursive segmentation to set the equipment classification threshold according to the series update values ​​of different series of equipment. The specific steps are as follows: Arrange the series update values ​​of different series of equipment from small to large, set the initial segmentation score and the upper limit segmentation score, use the initial segmentation score to segment the sequence, discard the sequence with a small mean and a small number of values, reduce the denominator of the segmentation score by a preset reduction coefficient to increase the value of the segmentation score, use the segmentation score to perform the same segmentation on the remaining sequences, discard the sequence with a large mean and a small number of values, repeat the operation until the segmentation score reaches the upper limit segmentation score, and take the average value of the values ​​in the remaining sequences as the device classification threshold.

[0023] For example, there are 8 series of the same type of equipment in the same production site. The series update values ​​of the 8 series of equipment are calculated and arranged from small to large. The initial split score is set to 1 / 4, the upper limit split score is set to 1 / 2, and the series update value sequence is split into two parts according to 1 / 4. The mean of the two parts is calculated, and the sequence with a smaller mean and a smaller number of values ​​is discarded. The increase coefficient is 1, and the series update values ​​of the remaining 6 series of equipment are sorted from small to large. The split score becomes 1 / 3, and the series update value sequence is split according to 1 / 3. The sequence with a larger mean and a smaller number of values ​​is discarded. Repeat the operation until the split score reaches the upper limit split score, and calculate the average value of the remaining values ​​as the equipment classification threshold.

[0024] The series update value of the corresponding electronic device is compared with the calculated device classification threshold to classify the electronic device. When the series update value of the electronic device exceeds the device classification threshold, the electronic device is classified as a low-speed iteration device; when the series update value of the electronic device is lower than the device classification threshold, the electronic device is classified as a high-speed iteration device.

[0025] It should be noted that the RFID system is a wireless communication technology used to identify and track objects through radio frequency signals. It can be used to identify electronic devices. The website of the electronic device production site is used to provide information and services related to the manufacture of electronic products. It can obtain the types and types of electronic devices produced at the corresponding production site and the release time of different types or types of electronic devices. The series update value of the electronic device reflects the iteration rate of the corresponding electronic device. The higher the iteration rate, the shorter the sensitive time of inventory placement. The recursive segmentation method is used to set the device classification threshold. The upper limit segmentation threshold can be adjusted and the coefficient can be reduced to adjust the requirements or accuracy of the device classification, providing a data basis for the subsequent setting of sensitive time levels.

[0026] In step S2, a period of time is selected as the analysis time, and the equipment transfer quantity of electronic equipment is collected during the analysis time. The electronic equipment is marked, and the sales ratio is calculated based on the equipment transfer quantity and the total inventory. The ratio of the equipment transfer quantity to the total inventory is used as the mobilization ratio. The mobilization ratio of electronic equipment other than the type of electronic equipment produced at the marked electronic equipment production place is calculated using the same method, and the average value is taken as the mobilization threshold.

[0027] When the marking device is a high-speed iteration device, the iteration coefficient of the marking device is set to 1; when the marking device is a low-speed iteration device, the iteration coefficient of the marking device is set to 0.

[0028] Set the sensitive time level rules according to the mobilization ratio, mobilization threshold and iteration coefficient of the marked device. The specific steps are as follows: Rule 1: The iteration coefficient of the marking device and the mobilization ratio are summed to obtain the sensitivity judgment index, and the mobilization threshold and 1 are summed to obtain the sensitivity judgment threshold; Rule 2: When the sensitivity judgment index of the marking device exceeds the sensitivity judgment threshold, the sensitive time level of the marking device is judged to be high; when the sensitivity judgment index of the marking device is lower than the sensitivity judgment threshold, the sensitive time level of the marking device is judged to be low.

[0029] It should be noted that sensitive time refers to the high-frequency time when electronic equipment is scheduled in the warehouse. The sensitive time is determined by the iteration rate and mobilization ratio of the electronic equipment. When the electronic equipment of the same category is iterated, the scheduling rate in the warehouse will decrease. If the sensitive time is long, the scheduling rate of the electronic equipment will be at a high level for a long time, and if the sensitive time is short, the scheduling rate of the electronic equipment will be at a low level for a long time.

[0030] In step S3, when the sensitive time of the marking device is high, a simple call mode is performed; when the sensitive time of the marking device is low, a sensitive time check mechanism is entered to check the sensitive time of the marking device and determine whether to perform a standard call mode.

[0031] When the sensitive time level of the marking equipment is high, that is, the high-frequency time of warehouse scheduling is sufficient, the fault tolerance of inventory marking equipment reservation and call-out is higher, and in the simple retrieval mode, goods are transferred out and the warehouse is replenished through preset time intervals.

[0032] When the sensitive time level of the marking equipment is low, that is, the high-frequency time of warehouse scheduling is insufficient, and the fault tolerance of the reserved and mobilized marking equipment in the inventory is low, it is necessary to detect and adjust the mobilization behavior in real time to balance the supply and demand of the marking equipment.

[0033] Before the marking device is processed in the standard retrieval mode, the warehouse retrieval speed of the marking device is detected and the time efficiency coefficient of the marking device is calculated. The preset quantity of goods transfer and warehouse replenishment is adjusted based on the warehouse retrieval speed and time efficiency coefficient of the marking device.

[0034] When calculating the effectiveness coefficient of the marking device, the search ratio of the name terms of the marking device in the website of the electronic equipment production place is recorded, and the ratio of the name terms of the marking device to the total number of search terms recorded in the website database is used as the actual effectiveness value of the marking device. The ratio of the category type to which the marking device belongs to the total number of categories of all electronic equipment in the website database is used as the predicted effectiveness value of the marking device. The ratio of the actual effectiveness value of the marking device to the predicted effectiveness value is used as the effectiveness coefficient of the marking device.

[0035] When the effectiveness coefficient of the marking device exceeds 1, that is, the actual effectiveness value of the marking device is greater than the predicted effectiveness value, it is judged that the marking device is still within the effectiveness period, and the retrieval is still at a high frequency level; when the effectiveness coefficient of the marking device is lower than 1, that is, the actual effectiveness value of the marking device is lower than the predicted effectiveness value, it is judged that the marking device is outside the effectiveness period, and the retrieval level decreases.

[0036] Select a time interval to record the warehouse retrieval quantity of the marking equipment, record the warehouse retrieval quantity of all electronic devices in the website database within the same time interval to calculate the average value, take the ratio of the warehouse retrieval quantity of the marking equipment to the calculated average value as the warehouse retrieval speed of the marking equipment, and take the product of the warehouse retrieval speed and the preset initial goods transfer and warehouse replenishment quantity as the adjusted goods transfer and warehouse replenishment quantity of the marking equipment.

[0037] The same method is used to calculate the warehouse retrieval speed of all electronic devices, and the Robust Scaling algorithm is used to calculate the change prediction coefficient. The specific steps are as follows: The warehouse retrieval speed of each electronic device is combined into a speed regulation data set, and the warehouse regulation rate of the marked device is calculated using the Robust Scaling formula: , where y is the warehouse adjustment rate of the marking device, and x is the warehouse retrieval speed of the marking device. is the average value in the speed regulation data set, It is the difference between the 1 / 4 quantile and the 3 / 4 quantile when the speed regulation data set is arranged from small to large. The change prediction coefficient is obtained by summing the warehouse regulation rate and the time efficiency coefficient of the marked equipment.

[0038] It should be noted that the Robust Scaling algorithm is a data standardization algorithm. In this example, by compressing the warehouse retrieval speed of the marking equipment and calculating the change prediction coefficient, it is easier to control the subsequent changes in the inventory retrieval mode.

[0039] In step S4, the release information of new devices of the same type as the marked device is monitored, the release date is recorded, the monitoring date is subtracted from the release date to obtain the iteration time difference, the monitoring date is subtracted from the release date of the marked device to obtain the scheduling time difference, the ratio of the scheduling time difference to the iteration time difference is used as the iteration coefficient, the comprehensive iteration coefficient and the change prediction coefficient are used to modulate the sensitive time level using the decision function, and the inventory retrieval rules are changed. The specific steps are as follows: The iteration coefficient and change prediction coefficient calculated by the marking device are used to calculate the decision value through the decision function, and the formula is: , where x is the change prediction coefficient of the marking device, is the decision value of the marking device, is the iteration coefficient of the marking device, T and b are the iteration coefficient modulation amount and error amount respectively. The larger the iteration coefficient or the larger the change prediction coefficient, the larger the decision value of the marking device, and the more necessary the standard call mode is.

[0040] By calculating the decision value of the marking device, when the calculated decision value of the marking device exceeds the preset decision threshold, it is judged that the marking device needs to perform the standard call mode, otherwise, it is judged that the marking device performs the simple call mode.

[0041] It should be noted that the iteration coefficient modulation amount, the error amount and the decision threshold can be set through experiments or following the opinions of professionals in the field, and will not be analyzed here.

[0042] Inventory management scheduling optimization system based on data prediction, such as Figure 2 As shown, a method for realizing inventory management scheduling optimization based on data prediction includes a data acquisition module, a sensitive determination module, an information monitoring module and a retrieval processing module; The data collection module is used to determine the equipment information by equipment model, collect historical data of the same type of equipment, and transfer the equipment transfer quantity to the sensitive judgment module; The sensitive determination module is used to set the sensitive time level for the marking device and calculate the change prediction coefficient to be transmitted to the retrieval processing module; The information monitoring module is used to detect the release information of new devices of the same type, and calculate the iteration coefficient of the marked device and pass it to the call processing module; The retrieval processing module is used to select simple retrieval mode processing or standard retrieval mode processing for the marking device.

[0043] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0044] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0045] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0046] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0047] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The inventory management scheduling optimization method based on data prediction is characterized by: The following steps are included: Step S1: Identify the device model to determine the device information, retrieve the historical data of the same type of device according to the device information, and define the device category according to the historical data; Step S2: Set the analysis time, obtain the equipment transfer quantity, and set the sensitive time level based on the equipment transfer quantity and equipment category; Step S3: Select the inventory retrieval mode according to the sensitive time level. When the sensitive time of the equipment is low, collect the equipment name entry ratio and the warehouse retrieval quantity to calculate the change prediction coefficient; Step S4: monitor the release information of new equipment of the same type, and change the inventory retrieval mode based on the release information of new equipment of the same type and the change prediction coefficient.

2. The inventory management scheduling optimization method based on data prediction according to claim 1 is characterized by: In step S1, the information tag of the electronic device is identified by the RFID system to obtain the device name and production place, and the devices with the same name prefix are classified as the same series of devices. The device name prefix is ​​searched on the website of the corresponding production place to obtain the number of devices in the same series and the release time interval of the devices in the same series. The release time intervals of the devices in the same series are summed and averaged to obtain the series update value of the corresponding electronic device; The RFID system is a wireless communication technology that identifies electronic devices through radio frequency signals. The website where the electronic device is produced is used to provide information related to the manufacture of the electronic device, including the device name prefix.

3. The inventory management scheduling optimization method based on data prediction according to claim 2 is characterized by: In step S1, the series update values ​​of different series of devices of the same type are obtained through the website of the electronic device manufacturer, and the device classification threshold is set by recursive segmentation according to the series update values ​​of different series of devices. The specific steps are as follows: Arrange the series update values ​​of different series of equipment from small to large, set the initial segmentation score and the upper limit segmentation score, use the initial segmentation score to segment the sequence, divide the sequence into two parts, calculate the mean of the two parts, compare the two parts of the sequence, screen out the sequence with a large mean, reduce the denominator of the segmentation score by a preset reduction coefficient to increase the value of the segmentation score, use the segmentation score to perform the same segmentation on the remaining sequences, screen out the sequence with a small mean, repeat the operation until the segmentation score reaches the upper limit segmentation score, and take the average value of the values ​​in the remaining sequences as the device classification threshold; Devices of different series have different prefixes in their device names.

4. The inventory management scheduling optimization method based on data prediction according to claim 2 is characterized by: In step S1, the series update value of the corresponding electronic device is compared with the calculated device classification threshold to classify the electronic device. When the series update value of the electronic device exceeds the device classification threshold, the electronic device is classified as a low-speed iteration device; when the series update value of the electronic device is lower than the device classification threshold, the electronic device is classified as a high-speed iteration device.

5. The inventory management scheduling optimization method based on data prediction according to claim 4 is characterized by: In step S2, a period of time is selected as the analysis time to mark the electronic devices, the ratio of the equipment transfer quantity of the marked device to the total inventory is used as the transfer ratio, the equipment transfer ratio outside the electronic equipment series where the marked device belongs is calculated, and the average value is taken as the transfer threshold; When the marking device is a high-speed iteration device, the iteration coefficient of the marking device is set to 1; when the marking device is a low-speed iteration device, the iteration coefficient of the marking device is set to 0; Set the sensitive time level rules according to the mobilization ratio, mobilization threshold and iteration coefficient of the marked device. The specific steps are as follows: Rule 1: The iteration coefficient of the marking device and the mobilization ratio are summed to obtain the sensitivity judgment index, and the mobilization threshold and 1 are summed to obtain the sensitivity judgment threshold; Rule 2: When the sensitivity judgment index of the marking device exceeds the sensitivity judgment threshold, the sensitive time level of the marking device is judged to be high; when the sensitivity judgment index of the marking device is lower than the sensitivity judgment threshold, the sensitive time level of the marking device is judged to be low.

6. The inventory management scheduling optimization method based on data prediction according to claim 5 is characterized by: In step S3, the warehouse retrieval speed of the marking equipment is detected and the timeliness coefficient of the marking equipment is calculated, and the change prediction coefficient of the equipment is determined by combining the warehouse retrieval speed of the marking equipment and the timeliness coefficient; Collect the search ratio of the name terms of the marked device, take the ratio of the name terms of the marked device to the total number of search terms as the actual timeliness value of the marked device, take the ratio of the category to which the marked device belongs to the total number of categories of all electronic equipment at the production site as the predicted timeliness value of the marked device, and take the ratio of the actual timeliness value of the marked device to the predicted timeliness value as the timeliness coefficient of the marked device; The warehouse retrieval quantity of all electronic equipment at the production site is recorded and the average value is calculated. The ratio of the warehouse retrieval quantity of the marking equipment to the calculated average value is used as the warehouse retrieval speed of the marking equipment.

7. The inventory management scheduling optimization method based on data prediction according to claim 6 is characterized by: In step S3, the warehouse retrieval speed of all electronic devices at the production site is calculated, and the change prediction coefficient is calculated using the Robust Scaling algorithm. The specific steps are as follows: The warehouse retrieval speed of each electronic device is combined into a speed regulation data set, and the warehouse regulation rate of the marked device is calculated using the Robust Scaling formula: , where y is the warehouse adjustment rate of the marking device, and x is the warehouse retrieval speed of the marking device. is the average value in the speed regulation data set, It is the difference between the 1 / 4 quantile and the 3 / 4 quantile when the speed regulation data set is arranged from small to large; the change prediction coefficient is obtained by summing the warehouse regulation rate and the time efficiency coefficient of the marked equipment.

8. The inventory management scheduling optimization method based on data prediction according to claim 7 is characterized by: In step S4, the release information of new devices of the same type as the marked device is monitored, the release date is recorded, the monitoring date is subtracted from the release date to obtain the iteration time difference, the monitoring date is subtracted from the release date of the marked device to obtain the scheduling time difference, the ratio of the scheduling time difference to the iteration time difference is used as the iteration coefficient, and the decision function is used to change the inventory retrieval rule based on the iteration coefficient and the change prediction coefficient. The specific steps are as follows: The iteration coefficient and change prediction coefficient calculated by the marking device are used to calculate the decision value through the decision function, and the formula is: , where x is the change prediction coefficient of the marking device, is the decision value of the marking device, is the iteration coefficient of the marking device, T and b are the iteration coefficient modulation and error respectively; When the calculated decision value of the marking device exceeds the preset decision threshold, it is determined that the marking device needs to be in the standard call mode, otherwise, it is determined that the marking device is in the simple call mode; The simple retrieval mode is to regularly use the preset retrieval volume to retrieve the device; The standard retrieval mode uses the preset retrieval quantity to retrieve equipment by multiplying the warehouse retrieval speed by the preset retrieval quantity as the adjusted retrieval quantity for the marked equipment.

9. An inventory management scheduling optimization system based on data prediction, based on the inventory management scheduling optimization method based on data prediction according to any one of claims 1 to 8, characterized in that: It includes data collection module, sensitivity determination module, information monitoring module and retrieval processing module; The data collection module is used to determine the equipment information by equipment model, collect historical data of the same type of equipment, and transfer the equipment transfer quantity to the sensitive judgment module; The sensitive determination module is used to set the sensitive time level for the marking device and calculate the change prediction coefficient to be transmitted to the retrieval processing module; The information monitoring module is used to detect the release information of new devices of the same type, and calculate the iteration coefficient of the marked device and pass it to the call processing module; The retrieval processing module is used to select simple retrieval mode processing or standard retrieval mode processing for the marking device.

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