A method, device, storage medium and equipment for predicting demand for spare modules

Abstract

The present invention proposes a method, device, storage medium and equipment for predicting demand for spare modules. The method comprises: obtaining replacement data records and usage data records of spare modules to be analyzed in a preset time period in a region to be analyzed; performing segmented statistics by life cycle segment according to the replacement data records and usage data records to obtain predicted module replacement rates for each life cycle segment corresponding to the spare modules to be analyzed; obtaining usage data corresponding to the spare modules to be analyzed in the region to be analyzed; calculating based on the predicted module replacement rates for each life cycle segment corresponding to all the spare modules to be analyzed and the usage data to obtain the corresponding demand quantity for the spare modules to be analyzed in the region to be analyzed. The present invention prevents excess or severe shortage of spare module inventory, meets the requirement of rapid replacement of spare modules to solve equipment failures, and reduces costs.

Description

Technical Field

[0001] The present invention relates to the technical field of inventory management, and in particular to a method, device, storage medium and equipment for forecasting demand for spare modules. Background Art

[0002] Self-service equipment, especially financial self-service terminals such as ATMs, are widely distributed geographically. Each self-service point installs 1-2 devices, and bank branches generally do not exceed 7-8 devices. There are many equipment suppliers, resulting in a wide variety of spare modules for the equipment. When a device fails, it needs to be repaired or even replaced with a spare module. In order to quickly restore the equipment to normal, multiple after-sales spare module libraries are generally established. If the number of spare modules in the after-sales spare module library is too large, the transportation, idleness, and storage of spare modules require certain costs, resulting in a waste of resources; if the number of spare modules is too small, they need to be transferred from other after-sales spare module libraries, delaying the replacement of spare modules, affecting the startup rate of the equipment, causing stagnation in customer service, reducing the satisfaction of bank and financial terminal users, and affecting the brand awareness of products and services. Therefore, it is particularly important to develop a spare module demand prediction method that prevents excess or severe shortage of spare module inventory and satisfies the need for rapid replacement of spare modules to solve equipment failures. Summary of the invention

[0003] Based on this, it is necessary to propose a method, device, storage medium and equipment for predicting demand for spare modules in order to address the above problems.

[0004] In a first aspect, the present invention provides a method for predicting demand for a standby module, the method comprising:

[0005] Obtain replacement data records and usage data records of the standby modules to be analyzed in the area to be analyzed within a preset time period;

[0006] Perform segmented statistics according to the replacement data records and the usage data records by life cycle segment to obtain a predicted module replacement rate for each life cycle segment corresponding to the standby module to be analyzed;

[0007] Obtain current usage data corresponding to the standby modules to be analyzed in the area to be analyzed;

[0008] The demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed is obtained by calculation based on the predicted module replacement rates of the various life cycle segments corresponding to all the spare modules to be analyzed and the current usage data.

[0009] In a second aspect, the present invention further proposes a standby module demand prediction device, the device comprising:

[0010] A spare module replacement rate calculation module is used to obtain replacement data records and usage data records of the spare modules to be analyzed in the area to be analyzed within a preset time period, and to perform segmented statistics according to the replacement data records and usage data records by life cycle segment to obtain a predicted module replacement rate for each life cycle segment corresponding to the spare modules to be analyzed;

[0011] The spare module demand data calculation module is used to obtain the current usage data corresponding to the spare modules to be analyzed in the area to be analyzed, and calculate according to the predicted module replacement rate of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0012] In a third aspect, the present invention further proposes a storage medium storing a computer instruction program, wherein when the computer instruction program is executed by a processor, the processor executes the steps of any one of the methods described in the first aspect.

[0013] In a fourth aspect, the present invention further proposes a computer device, comprising at least one memory and at least one processor, wherein the memory stores a computer instruction program, and when the computer instruction program is executed by the processor, the processor executes the steps of any one of the methods described in the first aspect.

[0014] In summary, the spare module demand prediction method of the present invention calculates the predicted module replacement rate of each life cycle segment through the replacement data record and the usage data record within the preset time period, and then calculates the corresponding demand quantity of the spare modules to be analyzed in the area to be analyzed according to the usage data corresponding to the current spare module to be analyzed and the predicted module replacement rate of each life cycle segment. Since the demand quantity is calculated based on the historical replacement data and the current usage data, stocking according to the demand quantity can prevent the spare module inventory from being excessive or seriously short, avoid waste and reduce costs; since the demand quantity is based on the historical replacement data record and the usage data record and the current usage data of the spare module to be analyzed in the area to be analyzed, the after-sales spare module library established according to the area to be analyzed meets the replacement demand of the corresponding area to be analyzed, avoiding the transfer of goods from other after-sales spare module libraries, thereby meeting the rapid replacement of spare modules to solve equipment failures. Therefore, the present invention achieves the prevention of excessive or serious shortage of spare module inventory, meets the rapid replacement of spare modules to solve equipment failures, and reduces costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] in:

[0017] Figure 1 A flowchart of a method for predicting demand for a standby module in one embodiment;

[0018] Figure 2 for Figure 1 A flowchart of the spare module demand forecasting method for calculating the forecast module replacement rate;

[0019] Figure 3 for Figure 1 A flowchart of determining the start time and end time of a life cycle segment of a spare module demand forecasting method;

[0020] Figure 4 for Figure 2 A flowchart of the spare module demand forecasting method for calculating the forecast module replacement rate;

[0021] Figure 5 for Figure 2 A flowchart of the spare module demand forecasting method for calculating the forecast module replacement rate;

[0022] Figure 6 for Figure 1 A flowchart of calculating the required quantity of the spare module demand forecasting method;

[0023] Figure 7 A flowchart of a method for predicting demand for a standby module in one embodiment;

[0024] Figure 8 It is a structural block diagram of a standby module demand prediction device in one embodiment;

[0025] Fig. 9 FIG. 4 is a structural block diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0026] 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.

[0027] like Figure 1 As shown, in one embodiment, a method for predicting demand for a standby module is proposed, the method comprising:

[0028] S102, obtaining replacement data records and usage data records of the standby modules to be analyzed in the area to be analyzed within a preset time period;

[0029] Specifically, each time, the replacement data record and the usage data record of a spare module to be analyzed in a region to be analyzed within a preset time period are obtained.

[0030] The area to be analyzed is to divide the product's coverage market into regions, and an after-sales spare module library is established in each of the areas to be analyzed for faster after-sales maintenance.

[0031] The spare module refers to a normally working component stored in an after-sales spare module library and used for after-sales equipment maintenance. For example, the spare module includes a movement, which is not specifically limited in this example.

[0032] The preset time period refers to the time period between the start date and the end time, for example, the start date is January 1 and the end date is March 31, and no specific limitation is given here.

[0033] Acquiring the replacement data record and the usage data record within the preset time period is to acquire the replacement data record and the usage data record from the start date to the end date, including the replacement data record and the usage data record of the start date and the replacement data record and the usage data record of the end date.

[0034] The replacement data record of the spare modules to be analyzed in the area to be analyzed within the preset time period refers to the record of the broken spare modules replaced from the equipment during after-sales maintenance of the spare modules to be analyzed in the area to be analyzed within the preset time period, including the spare module identification, the spare module name, the spare module activation time, the area to which the spare module belongs, and the replacement date.

[0035] The usage data record of the standby module to be analyzed in the area to be analyzed within the preset time period refers to the standby module of the equipment put into use by the customer in the area to be analyzed within the preset time period, including the standby module identification, standby module name, standby module activation time, and standby module belonging to the area to be analyzed. It can be understood that the equipment put into use by the customer refers to the equipment purchased by the customer that is in use or expected to be used, and this example is not specifically limited.

[0036] The activation time of the standby module is used as the starting point of the life cycle to calculate the usage time of the standby module.

[0037] It can be understood that, in another embodiment, the production time of the spare module is used as the starting point of the life cycle to calculate the usage time of the spare module.

[0038] The standby module identifier is used to uniquely identify a standby module, and the standby module identifier may be an identifier such as a standby module ID that can be used to uniquely identify the standby module.

[0039] S104, performing segmented statistics according to the replacement data records and the usage data records by life cycle segment to obtain a predicted module replacement rate for each life cycle segment corresponding to the standby module to be analyzed;

[0040] Specifically, the module replacement rate corresponding to each life cycle segment is obtained by segmenting and counting the replacement data records and the usage data records according to the life cycle segment, and then the predicted module replacement rate of each life cycle segment is calculated based on the module replacement rate corresponding to each life cycle segment.

[0041] The life cycle refers to the length of time from when the spare module is put into use to when it is scrapped. It can be understood that the life cycle of the present application refers to the average life cycle of the spare module, and no specific limitation is given in this example.

[0042] The life cycle segment divides the life cycle of the standby module into multiple segments. For example, the life cycle segment includes young, middle-aged, old, and residual heat. The young segment is 30% of the total life cycle time, the middle-aged segment is 40% of the total life cycle time, the old segment is 20% of the total life cycle time, and the residual heat segment is 10% of the total life cycle time. This example is not specifically limited.

[0043] S106, obtaining current usage data corresponding to the standby module to be analyzed in the area to be analyzed;

[0044] Specifically, the usage data corresponding to the standby module to be analyzed corresponding to the replacement data record and the usage data record in the area to be analyzed corresponding to the replacement data record and the usage data record is obtained. It can be understood that the usage data can be current usage data.

[0045] The currently used data refers to the data of the spare modules of the equipment put into use by the customer, including the spare module identification, spare module name, spare module activation time, and spare module belonging to the area to be analyzed. It can be understood that the equipment put into use by the customer refers to the equipment purchased by the customer that is being used or expected to be used, and this example is not specifically limited.

[0046] S108, calculating according to the predicted module replacement rates of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data, to obtain the required quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0047] Specifically, the predicted module replacement rates of each life cycle segment corresponding to all the spare modules to be analyzed and the usage data are calculated for each life cycle segment to obtain the demand quantity of the life cycle segment, and then the demand quantities of all the life cycle segments are added together to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0048] When the module replacement rate refers to the monthly average replacement rate, the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed refers to the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed in one month.

[0049] The spare module demand prediction method of this embodiment calculates the predicted module replacement rate of each life cycle segment through the replacement data record and the usage data record within the preset time period, and then calculates the corresponding demand quantity of the spare modules to be analyzed in the area to be analyzed based on the usage data corresponding to the current spare module to be analyzed and the predicted module replacement rate of each life cycle segment. Since the demand quantity is calculated based on the historical replacement data and the current usage data, stocking according to the demand quantity can prevent the spare module inventory from being excessive or seriously short, avoiding waste and reducing costs; since the demand quantity is based on the historical replacement data record and the usage data record and the current usage data of the spare modules to be analyzed in the area to be analyzed, the after-sales spare module library established according to the area to be analyzed meets the replacement demand of the corresponding area to be analyzed, avoiding the transfer of goods from other after-sales spare module libraries, thereby meeting the rapid replacement of spare modules to solve equipment failures.

[0050] In one embodiment, steps S102 to S108 are repeatedly performed to calculate the demand quantity of multiple spare modules to be analyzed in multiple areas to be analyzed. By adding the demand quantity of the same spare module to be analyzed in multiple areas to be analyzed, the total demand quantity of the same spare module to be analyzed can be calculated, which is conducive to centralized planning of procurement, and increasing the procurement volume is conducive to negotiations to reduce procurement costs. By adding the demand quantity of multiple spare modules to be analyzed, the total stocking cost can be obtained, which provides a data reference for overall planning to reduce the total stocking cost.

[0051] like Figure 2 As shown, in one embodiment, the segmented statistics are performed according to the replacement data records and the usage data records by life cycle segment to obtain the predicted module replacement rate of each life cycle segment corresponding to the spare module to be analyzed, specifically including:

[0052] S202, performing segmented statistical calculations at preset time intervals according to the replacement data records and the usage data records to obtain module replacement rates corresponding to segmented time points;

[0053] Specifically, the replacement data record and the usage data record are segmented and counted at preset time intervals to obtain the module replacement rate of each preset time interval, and the module replacement rate of the preset time interval is used as the module replacement rate corresponding to the start time point of the preset time interval, and the start time point of the preset time interval is the segment time point. It can be understood that in another embodiment, the module replacement rate of the preset time interval is used as the module replacement rate corresponding to the end time point of the preset time interval, and the end time point of the preset time interval is the segment time point, which is not specifically limited in this example.

[0054] The preset time interval includes multiple time intervals, which can be equal intervals or unequal intervals. For example, the equal intervals include: 0 month to 1 month, 1 month to 2 months, 2 months to 3 months, 3 months to 4 months, 4 months to 5 months, and the segmented time points include 0 month, 1 month, 2 months, 3 months, 4 months, and 5 months; the unequal intervals include 0 month to 3 months, 3 months to 5 months, 5 months to 7 months, 7 months to 8 months, and 8 months to 9 months, and the segmented time points include 0 month, 3 months, 5 months, 7 months, 8 months, and 9 months. This example is not specifically limited.

[0055] The module replacement rate refers to the average monthly replacement rate. The total number of module replacements of the spare modules to be analyzed in the area to be analyzed within the preset time period is divided by the average number of modules used in the spare modules to be analyzed in the area to be analyzed within the preset time period, and the calculated result is further divided by the length of the preset time period (in months). If the average monthly replacement rate is a decimal, it is rounded to an integer, and no specific limitation is given in this example.

[0056] Among them, the average number of modules used by the standby modules to be analyzed in the area to be analyzed within the preset time period refers to the actual number of modules used on the equipment in the area to be analyzed within the preset time period, and is calculated once as long as it is used within the preset time period. In another embodiment, the actual number of modules used on the equipment in the area to be analyzed at the beginning of the preset time period is added to the actual number of modules used on the equipment in the area to be analyzed at the end of the preset time period, and then divided by 2 to obtain the average number of modules used by the standby modules to be analyzed in the area to be analyzed within the preset time period, which is not specifically limited in this example.

[0057] S204, obtaining the number of preset life cycle segments;

[0058] The number of preset life cycle segments can be set as needed. For example, it can be set to 4 segments, namely young, middle-aged, old, and residual heat. It is understandable that it can also be set to 2, 3, 5, or 6 segments. This example is not specifically limited here.

[0059] S206, determining the start time and end time of each life cycle segment of the standby module to be analyzed according to the module replacement rate corresponding to the segmented time point and the number of the preset life cycle segments;

[0060] Among them, the module replacement rates corresponding to all the segmented time points are connected to generate a module replacement rate curve corresponding to the standby module to be analyzed, and the periodic time point of each life cycle segment is selected according to the change of the module replacement rate curve. The start time and end time of the standby module to be analyzed in each life cycle segment are determined according to the periodic time point corresponding to each life cycle segment.

[0061] In another embodiment, the life cycle of the backup module is divided into multiple segments, and the time length of the life cycle segment is evenly distributed according to the number of segments. For example, the total life cycle length of the A backup module is 2 years, which is divided into 4 segments. When the length of the life cycle segments is evenly distributed, the length of each life cycle segment is 6 months, the first segment D1 is 0 months ≤ D1 ≤ 6 months, the second segment D2 is 6 months < D2 ≤ 12 months, the third segment D3 is 12 months < D3 ≤ 18 months, and the fourth segment D4 is 18 months < D4 ≤ 24 months. No specific limitation is given in this example. It is understandable that different life cycle segments can be set with different time lengths according to the age of the backup module in the life cycle segment. For example, the total life cycle length of backup module A is 2 years, which is divided into 4 segments: young, middle-aged, old, and residual heat. The young D1 is 30% of the total life cycle time length, then 0 months ≤ D1 ≤ 7.2 months, the middle-aged D2 is 40% of the total life cycle time length, then 7.2 months ≤ D1 ≤ 16.8 months, the old D3 is 20% of the total life cycle time length, then 16.8 months ≤ D1 ≤ 21.6 months, and the residual heat D4 is 10% of the total life cycle time length, then 21.6 months ≤ D1 ≤ 24 months. This example is not specifically limited here.

[0062] S208 , calculating according to the module replacement rate corresponding to the segmented time point, the start time and the end time of each life cycle segment, to obtain the predicted module replacement rate of each life cycle segment corresponding to the standby module to be analyzed.

[0063] Specifically, the module replacement rate corresponding to the segmented time point and the start time and end time of each life cycle segment are calculated one by one to obtain the predicted module replacement rate of each life cycle segment.

[0064] This embodiment obtains predicted module replacement rates for each life cycle segment corresponding to the spare modules to be analyzed by performing segmented statistics according to the replacement data records and usage data records of the spare modules to be analyzed in the area to be analyzed according to the life cycle segments. The predicted module replacement rate is determined through historical replacement data records and usage data records so as to better meet the replacement needs of the corresponding area to be analyzed, avoid transferring goods from other after-sales spare module libraries, and thus meet the needs of quickly replacing spare modules to solve equipment failures.

[0065] like Figure 3 As shown, in one embodiment, the determining of the start time and end time of each life cycle segment of the standby module to be analyzed according to the module replacement rate corresponding to the segmented time point and the number of the preset life cycle segments specifically includes:

[0066] S302, connecting the module replacement rates corresponding to all the segmented time points to generate a module replacement rate curve corresponding to the standby module to be analyzed;

[0067] Specifically, the life cycle of the standby module is taken as the X-axis, the module replacement rate is taken as the Y-axis, and the module replacement rates corresponding to all the segmented time points are connected to generate a module replacement rate curve corresponding to the standby module to be analyzed.

[0068] S304, calculating the slope of the connecting line of the module replacement rates at adjacent segment time points according to the module replacement rate curve;

[0069] Specifically, the slope k of the connecting line of the module replacement rate at adjacent segment time points relative to the X-axis is calculated according to the module replacement rate curve. i , the calculation formula is as follows:

[0070]

[0071] Among them, k i is the slope of the i-th segment connecting line relative to the X-axis. The coordinate of the first segment time point of the i-th segment in the module replacement rate curve is (x i ,y i ), the coordinate of the second segment time point of the i-th segment in the module replacement rate curve is (x i+1 ,y i+1 ), x i and x i+1 is the time corresponding to the adjacent segment time points of the backup module, y i For x i The corresponding module replacement rate, y i+1 For x i+1 Corresponding module replacement rate.

[0072] For example, when the module replacement rate of spare module A is 0.3 at two adjacent segment time points M2 (6 months) and 0.5 at M3 (12 months), the calculation formula for the slope K2 of the line connecting M2 and M3 relative to the X-axis is:

[0073]

[0074] It is understandable that k i When it is an infinite decimal, it is rounded to two decimal places. This example is not specifically limited here.

[0075] S306, calculating the slope growth rate corresponding to the two adjacent connecting lines according to the slopes of the two adjacent connecting lines;

[0076] For example, the segmented time points of backup module A are M1, M2, M3, M4, M5, and M6, and the connecting lines include M1M2, M2M3, M3M4, M4M5, and M5M6. The growth rate of the slope k2 of M2M3 relative to the slope k1 of M1M2, the growth rate of the slope k3 of M3M4 relative to the slope k2 of M2M3, the growth rate of the slope k4 of M4M5 relative to the slope k3 of M3M4, and the growth rate of the slope k5 of M5M6 relative to the slope k4 of M4M5 are calculated.

[0077] S308, determining a periodic time point corresponding to each life cycle segment on the module replacement rate curve according to the number of the preset life cycle segments and the slope growth rate corresponding to the two adjacent connecting lines;

[0078] Specifically, a slope growth rate that is the same as the number of the preset life cycle segments is selected from the slope growth rates corresponding to the two adjacent connecting lines and the selection starts from the one with the largest slope growth rate, and the intersecting segment time point of the connecting line of the module replacement rate of the two adjacent segment time points corresponding to the slope growth rate is taken as the cycle time point.

[0079] For example, the growth rate of the slope k3 of M3M4 relative to the slope k2 of M2M3 is selected, and the connection point M3 of M3M4 and M2M3 is taken as the periodic time point.

[0080] S310: Determine the start time and end time of each life cycle segment of the standby module to be analyzed according to the periodic time point corresponding to each life cycle segment.

[0081] For example, the segmented time points of backup module A are M1, M2, M3, M4, M5, M6, M7, and M8, and the number of life cycle segments is 4, of which M1, M3, M5, M7, and M8 are periodic time points. The first segment of the life cycle segment is the start time M1 and the end time M3, the second segment of the life cycle segment is the start time M3 and the end time M5, the third segment of the life cycle segment is the start time M5 and the end time M7, and the fourth segment of the life cycle segment is the start time M7 and the end time M8. No specific limitation is given in this example.

[0082] like Figure 4 As shown, in one embodiment, the calculation is performed based on the module replacement rate corresponding to the segmented time point, the start time and the end time of each life cycle segment to obtain the predicted module replacement rate of each life cycle segment corresponding to the standby module to be analyzed, specifically including:

[0083] S402, according to the start time and end time of each life cycle segment, respectively obtaining the module replacement rate corresponding to the start time and the module replacement rate corresponding to the end time in the life cycle segment from the module replacement rates corresponding to the segment time points;

[0084] For example, the segmented time points of backup module A are M1, M2, M3, M4, M5, M6, M7, and M8, and the number of life cycle segments is 4. The first segment of the life cycle segment is the start time M1 and the end time M3. The module replacement rate corresponding to M1 is obtained from the module replacement rate corresponding to the segmented time points as the module replacement rate corresponding to the start time of the first segment of the life cycle segment, and the module replacement rate corresponding to M3 is obtained from the module replacement rate corresponding to the segmented time points as the module replacement rate corresponding to the end time of the first segment of the life cycle segment. No specific limitation is made in this example.

[0085] S404, calculating the average replacement rate of the corresponding life cycle segment according to the module replacement rate corresponding to the start time of the life cycle segment and the module replacement rate corresponding to the end time of the life cycle segment, and using the average replacement rate as the predicted module replacement rate of the life cycle segment.

[0086] For example, the segmented time points of spare module A are M1, M2, M3, M4, M5, M6, M7, and M8 respectively, and the number of life cycle segments is 4. The first segment of the life cycle segment is the start time M1 and the end time M3. The module replacement rate corresponding to M1 is added to the module replacement rate corresponding to M3 and then divided by 2 to obtain the average replacement rate of the first segment of the life cycle segment. The average replacement rate of the first segment of the life cycle segment is used as the predicted module replacement rate of the first segment of the life cycle segment, which is not specifically limited in this example.

[0087] like Figure 5As shown, in another embodiment, the module replacement rate corresponding to the segmented time point, the start time and the end time of each life cycle segment are calculated to obtain the predicted module replacement rate of each life cycle segment corresponding to the standby module to be analyzed, specifically including:

[0088] S502, connecting the module replacement rates corresponding to all the segmented time points to generate a module replacement rate curve corresponding to the standby module to be analyzed;

[0089] S504, calculating the area under the curve according to the module replacement rate curve, the start time and the end time of each life cycle segment, to obtain the area under the curve corresponding to the life cycle segment;

[0090] Specifically, the area under the module replacement rate curve is calculated by integrating and derivation according to the start time and end time of each life cycle segment.

[0091] The area P under the curve corresponding to the life cycle segment i The calculation formula is:

[0092] P i =∫f(t)dt

[0093] Among them, P i Calculate the area under the module replacement rate curve corresponding to the i-th segment of the life cycle segment, f(t) is the module replacement rate curve corresponding to the i-th segment of the life cycle segment, and dt is the length of the small arc segment of the module replacement rate curve corresponding to the i-th segment of the life cycle segment.

[0094] For example, the segmented time points of spare module A are M1, M2, M3, M4, M5, M6, M7, and M8 respectively. The number of life cycle segments is 4. The first segment of the life cycle segment has a start time of M1 and an end time of M3. M1 is 0 months, M2 is 2 months, and M3 is 4 months. The area P1 under the module replacement rate curve corresponding to the first segment of the life cycle segment is the area under the curves of M1M2 and M2M3, which is not specifically limited in this example.

[0095] S506: Calculate the duration of each life cycle segment according to the start time and end time of each life cycle segment;

[0096] Specifically, the end time of each life cycle segment is subtracted from the start time of each life cycle segment to obtain the duration of the life cycle segment.

[0097] S508. Obtain an average replacement rate for the life cycle segment by dividing the area under the curve corresponding to the life cycle segment by the duration of the life cycle segment, and use the average replacement rate as the predicted module replacement rate for the life cycle segment.

[0098] For example, the segmented time points of spare module A are M1, M2, M3, M4, M5, M6, M7, and M8, and the number of life cycle segments is 4. The first segment of the life cycle segment has a start time M1 and an end time M3. M1 is 0 months, M2 is 2 months, and M3 is 4 months. The area P1 under the module replacement rate curve corresponding to the first segment of the life cycle segment is the area under the curves of M1M2 and M2M3. P1 is divided by the life cycle length of the first segment of the life cycle segment, which is 4 months (M3 minus M1), to obtain the average replacement rate of the first segment of the life cycle segment. The average replacement rate of the first segment of the life cycle segment is used as the predicted module replacement rate of the first segment of the life cycle segment, and no specific limitation is given in this example.

[0099] like Figure 6 As shown, in one embodiment, the calculation based on the predicted module replacement rate of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data to obtain the required number of spare modules to be analyzed in the area to be analyzed specifically includes:

[0100] S602, calculating segmented statistics by life cycle segment based on the current usage data to obtain current usage data for each life cycle segment;

[0101] Specifically, the usage data is segmented and counted according to the life cycle segment to obtain the usage data of each life cycle segment, and the usage data of the life cycle segment is used as the usage data corresponding to the start time of the life cycle segment. It can be understood that in another embodiment, the usage data of the life cycle segment is used as the usage data corresponding to the end time of the life cycle segment, which is not specifically limited in this example.

[0102] S604: Perform weighted summation based on the current usage data of all the life cycle segments and the predicted module replacement rates of all the spare modules to be analyzed in each life cycle segment to obtain the required quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0103] Specifically, based on the usage data of all the life cycle segments and the predicted module replacement rates of each life cycle segment corresponding to all the spare modules to be analyzed, the weighted sum of the usage data of the life cycle segment and the predicted module replacement rates corresponding to the life cycle segment is calculated life cycle segment by life cycle segment to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0104] For example, the number of life cycle segments of spare module A is young, middle-aged, old, and residual heat, a total of 4 segments. The usage data for the young is S1, the usage data for the middle-aged is S2, the usage data for the old is S3, and the usage data for the residual heat is S4. The predicted module replacement rate for the young is G1, the predicted module replacement rate for the middle-aged is G2, the predicted module replacement rate for the old is G3, and the predicted module replacement rate for the residual heat is G4. The calculation formula for the demand quantity F corresponding to the spare modules A to be analyzed in the area to be analyzed is: F=S1×G1+S2×G2+S3×G3+S4×G4, which is not specifically limited in this example.

[0105] The corresponding demand quantity of the spare modules to be analyzed in the area to be analyzed is based on the historical replacement data records, usage data records and current usage data of the spare modules to be analyzed in the area to be analyzed, so that the after-sales spare module library established according to the area to be analyzed meets the corresponding replacement needs of the area to be analyzed, avoiding the transfer of goods from other after-sales spare module libraries, thereby meeting the needs of quickly replacing spare modules to solve equipment failures.

[0106] like Figure 7 As shown, in one embodiment, a method for predicting demand for a standby module is proposed, the method comprising:

[0107] S702, obtaining replacement data records and usage data records of the standby modules to be analyzed in the area to be analyzed within a preset time period;

[0108] S704, performing segmented statistics according to the replacement data records and the usage data records by life cycle segment to obtain a predicted module replacement rate for each life cycle segment corresponding to the standby module to be analyzed;

[0109] S706, obtaining current usage data corresponding to the standby module to be analyzed in the area to be analyzed;

[0110] S708, calculating according to the predicted module replacement rates of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data, to obtain the required quantity corresponding to the spare modules to be analyzed in the area to be analyzed;

[0111] S710, obtaining prices of all spare modules to be analyzed;

[0112] The price of the spare module to be analyzed refers to the stocking cost, which is the sum of the purchase cost, transportation cost, and storage cost of the spare module. It can be understood that in another embodiment, the price of the spare module to be analyzed refers to the sum of the purchase cost and transportation cost of the spare module, which is not specifically limited in this example.

[0113] S712, obtaining a minimum value from the prices of all the standby modules to be analyzed as a price factor;

[0114] For example, the price of spare module A is 200 yuan, the price of spare module B is 500 yuan, and the price of spare module C is 300 yuan. The minimum price of spare module A, 200 yuan, is taken from the prices of spare modules A, B, and C as the price factor.

[0115] S714, calculating a price optimization coefficient corresponding to the standby module to be analyzed according to the price factor and the price of the standby module to be analyzed;

[0116] Specifically, the price factor is divided by the price of the spare module to be analyzed to obtain a price optimization coefficient corresponding to the spare module to be analyzed.

[0117] For example, the price of spare module A is 200 yuan, the price of spare module B is 500 yuan, and the price of spare module C is 300 yuan. The minimum price of spare module A, 200 yuan, is taken as the price factor from the prices of spare modules A, B, and C. The price optimization coefficient corresponding to spare module A is 200 yuan divided by 200 yuan, the price optimization coefficient corresponding to spare module B is 200 yuan divided by 500 yuan, and the price optimization coefficient corresponding to spare module C is 200 yuan divided by 300 yuan.

[0118] When the price optimization coefficient is an infinite decimal, it is rounded to two decimal places, and no specific limitation is given in this example.

[0119] S716 , calculating according to the price optimization coefficient corresponding to the spare module to be analyzed and the demand quantity corresponding to the spare module to be analyzed in the area to be analyzed, to obtain the target demand quantity corresponding to the spare module to be analyzed in the area to be analyzed.

[0120] For example, the price optimization coefficient corresponding to the A spare module in the area to be analyzed is 200 yuan divided by 200 yuan, the price optimization coefficient corresponding to the B spare module in the area to be analyzed is 200 yuan divided by 500 yuan, and the price optimization coefficient corresponding to the C spare module in the area to be analyzed is 200 yuan divided by 300 yuan. Then, the target demand quantity of the A spare module in the area to be analyzed is the demand quantity corresponding to the A spare module in the area to be analyzed multiplied by the price optimization coefficient corresponding to the A spare module in the area to be analyzed, the target demand quantity of the B spare module in the area to be analyzed is the demand quantity corresponding to the B spare module in the area to be analyzed multiplied by the price optimization coefficient corresponding to the B spare module in the area to be analyzed, and the target demand quantity of the C spare module in the area to be analyzed is the demand quantity corresponding to the C spare module in the area to be analyzed multiplied by the price optimization coefficient corresponding to the C spare module in the area to be analyzed. No specific limitation is made in this example.

[0121] This embodiment calculates the price optimization coefficient through the price of the spare modules, so that low-priced ones are stocked according to the demand quantity. The higher the price, the lower the stocking rate, thereby effectively reducing the after-sales cost. Sufficient stocking quantity of low-priced ones can avoid serious shortages and improve customer satisfaction; high-priced ones reduce the stocking rate to avoid excessive surplus of high-priced spare modules, thereby avoiding waste and reducing after-sales costs.

[0122] like Figure 8 As shown, in one embodiment, a standby module demand prediction device is proposed, the device comprising:

[0123] The spare module replacement rate calculation module 802 is used to obtain replacement data records and usage data records of the spare modules to be analyzed in the area to be analyzed within a preset time period, and perform segmented statistics according to the replacement data records and usage data records by life cycle segment to obtain the predicted module replacement rate of each life cycle segment corresponding to the spare modules to be analyzed;

[0124] The spare module demand data calculation module 804 is used to obtain the current usage data corresponding to the spare modules to be analyzed in the area to be analyzed, and calculate according to the predicted module replacement rate of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0125] The spare module demand prediction device of this embodiment calculates the predicted module replacement rate of each life cycle segment through the replacement data record and the usage data record within the preset time period, and then calculates the corresponding demand quantity of the spare modules to be analyzed in the area to be analyzed based on the usage data corresponding to the current spare module to be analyzed and the predicted module replacement rate of each life cycle segment. Since the demand quantity is calculated based on the historical replacement data and the current usage data, stocking according to the demand quantity can prevent the spare module inventory from being excessive or seriously short, avoiding waste and reducing costs; since the demand quantity is based on the historical replacement data record and the usage data record and the current usage data of the spare modules to be analyzed in the area to be analyzed, the after-sales spare module library established according to the area to be analyzed meets the replacement demand of the corresponding area to be analyzed, avoiding the transfer of goods from other after-sales spare module libraries, thereby meeting the rapid replacement of spare modules to solve equipment failures.

[0126] In one embodiment, the device further includes: a price optimization coefficient calculation module 806, a target demand data calculation module 808;

[0127] The price optimization coefficient calculation module 806 is used to obtain the prices of all the standby modules to be analyzed, obtain the minimum value from the prices of all the standby modules to be analyzed as the price factor, and calculate the price optimization coefficient corresponding to the standby module to be analyzed according to the price factor and the prices of the standby modules to be analyzed;

[0128] The target demand data calculation module 808 is used to calculate the target demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed according to the price optimization coefficient corresponding to the spare modules to be analyzed and the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0129] Fig. 9 The figure shows the internal structure of a computer device in one embodiment. The computer device may be a server and a terminal device, wherein the server includes but is not limited to a high-performance computer and a high-performance computer cluster; the terminal device includes but is not limited to a mobile terminal device and a desktop terminal device, wherein the mobile terminal device includes but is not limited to a mobile phone, a tablet computer, a smart watch and a laptop computer; and the desktop terminal device includes but is not limited to a desktop computer and a car computer. Fig. 9 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement a method for predicting demand for a standby module. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement a method for predicting demand for a standby module. Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0130] In one embodiment, the method for predicting demand for a standby module provided by the present application can be implemented in the form of a computer program. The computer program can be used in Fig. 9 The computer device shown in the figure is run. The memory of the computer device can store various program templates of a spare module demand forecasting device. For example, a spare module replacement rate calculation module 802, a spare module demand data calculation module 804, a price optimization coefficient calculation module 806, and a target demand data calculation module 808.

[0131] In one embodiment, a storage medium is provided, storing a computer instruction program, wherein when the computer instruction program is executed by a processor, the processor implements the following method steps:

[0132] Obtain replacement data records and usage data records of the standby modules to be analyzed in the area to be analyzed within a preset time period;

[0133] Perform segmented statistics according to the replacement data records and the usage data records by life cycle segment to obtain a predicted module replacement rate for each life cycle segment corresponding to the standby module to be analyzed;

[0134] Obtain current usage data corresponding to the standby modules to be analyzed in the area to be analyzed;

[0135] The demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed is obtained by calculation based on the predicted module replacement rates of the various life cycle segments corresponding to all the spare modules to be analyzed and the current usage data.

[0136] The method steps for implementing this embodiment calculate the predicted module replacement rate of each life cycle segment through the replacement data records and usage data records within a preset time period, and then calculate the corresponding demand quantity of the spare modules to be analyzed in the area to be analyzed based on the usage data corresponding to the current spare module to be analyzed and the predicted module replacement rate of each life cycle segment. Since the demand quantity is calculated based on the historical replacement data and the current usage data, stocking according to the demand quantity can prevent the spare module inventory from being excessive or seriously short, avoiding waste and reducing costs; since the demand quantity is based on the historical replacement data records and usage data records and the current usage data of the spare modules to be analyzed in the area to be analyzed, the after-sales spare module library established according to the area to be analyzed meets the replacement needs of the corresponding area to be analyzed, avoiding the transfer of goods from other after-sales spare module libraries, thereby meeting the needs of quickly replacing spare modules to solve equipment failures.

[0137] In one embodiment, segmented statistics are performed according to life cycle segments based on the replacement data records and the usage data records to obtain predicted module replacement rates for each life cycle segment corresponding to the standby module to be analyzed, specifically including: segmented statistical calculations are performed according to preset time intervals based on the replacement data records and the usage data records to obtain the module replacement rates corresponding to the segmented time points; the number of preset life cycle segments is obtained; the start time and end time of each life cycle segment of the standby module to be analyzed are determined according to the module replacement rates corresponding to the segmented time points and the number of preset life cycle segments; calculations are performed according to the module replacement rates corresponding to the segmented time points and the start time and end time of each life cycle segment to obtain predicted module replacement rates for each life cycle segment corresponding to the standby module to be analyzed.

[0138] In one embodiment, the method of determining the start time and end time of each life cycle segment of the standby module to be analyzed based on the module replacement rate corresponding to the segmented time points and the number of preset life cycle segments specifically includes: connecting the module replacement rates corresponding to all the segmented time points to generate a module replacement rate curve corresponding to the standby module to be analyzed; calculating the slope of the connecting line of the module replacement rates of adjacent segmented time points based on the module replacement rate curve; obtaining the slope growth rate corresponding to two adjacent connecting lines based on the slopes of two adjacent connecting lines; determining the periodic time point corresponding to each life cycle segment on the module replacement rate curve based on the number of preset life cycle segments and the slope growth rate corresponding to the two adjacent connecting lines; and determining the start time and end time of each life cycle segment of the standby module to be analyzed based on the periodic time point corresponding to each life cycle segment.

[0139] In one embodiment, the module replacement rate corresponding to the segmented time points and the start time and end time of each life cycle segment are calculated to obtain the predicted module replacement rate of each life cycle segment corresponding to the backup module to be analyzed, specifically including: obtaining the module replacement rate corresponding to the start time and the module replacement rate corresponding to the end time of the life cycle segment from the module replacement rate corresponding to the segmented time points according to the start time and the end time of each life cycle segment; calculating the average replacement rate of the corresponding life cycle segment according to the module replacement rate corresponding to the start time of the life cycle segment and the module replacement rate corresponding to the end time of the life cycle segment, and using the average replacement rate as the predicted module replacement rate of the life cycle segment.

[0140] In one embodiment, the module replacement rate corresponding to the segmented time points and the start time and end time of each life cycle segment are calculated to obtain the predicted module replacement rate of each life cycle segment corresponding to the standby module to be analyzed, specifically including: connecting the module replacement rates corresponding to all the segmented time points to generate a module replacement rate curve corresponding to the standby module to be analyzed; calculating the area under the curve according to the module replacement rate curve and the start time and end time of each life cycle segment to obtain the area under the curve corresponding to the life cycle segment; calculating the duration of the life cycle segment according to the start time and end time of each life cycle segment; and obtaining the average replacement rate of the life cycle segment by dividing the area under the curve corresponding to the life cycle segment by the duration of the life cycle segment, and using the average replacement rate as the predicted module replacement rate of the life cycle segment.

[0141] In one embodiment, the calculation is performed based on the predicted module replacement rates of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed, which specifically includes: performing segmented statistics by life cycle segment according to the current usage data to obtain the current usage data of each life cycle segment; performing weighted summation based on the current usage data of all the life cycle segments and the predicted module replacement rates of each life cycle segment corresponding to all the spare modules to be analyzed to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0142] In one embodiment, after obtaining the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed by calculating according to the predicted module replacement rate of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data, it also includes: obtaining the prices of all the spare modules to be analyzed; obtaining the minimum value from the prices of all the spare modules to be analyzed as the price factor; calculating the price optimization coefficient corresponding to the spare modules to be analyzed according to the price factor and the prices of the spare modules to be analyzed; calculating according to the price optimization coefficient corresponding to the spare modules to be analyzed and the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed, to obtain the target demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0143] In one embodiment, a computer device is provided, comprising at least one memory and at least one processor, wherein the memory stores a computer instruction program, and when the computer instruction program is executed by the processor, the processor implements the following method steps:

[0144] Obtain replacement data records and usage data records of the standby modules to be analyzed in the area to be analyzed within a preset time period;

[0145] Perform segmented statistics according to the replacement data records and the usage data records by life cycle segment to obtain a predicted module replacement rate for each life cycle segment corresponding to the standby module to be analyzed;

[0146] Obtain current usage data corresponding to the standby modules to be analyzed in the area to be analyzed;

[0147] The demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed is obtained by calculation based on the predicted module replacement rates of the various life cycle segments corresponding to all the spare modules to be analyzed and the current usage data.

[0148] The method steps for implementing this embodiment calculate the predicted module replacement rate of each life cycle segment through the replacement data records and usage data records within a preset time period, and then calculate the corresponding demand quantity of the spare modules to be analyzed in the area to be analyzed based on the usage data corresponding to the current spare module to be analyzed and the predicted module replacement rate of each life cycle segment. Since the demand quantity is calculated based on the historical replacement data and the current usage data, stocking according to the demand quantity can prevent the spare module inventory from being excessive or seriously short, avoiding waste and reducing costs; since the demand quantity is based on the historical replacement data records and usage data records and the current usage data of the spare modules to be analyzed in the area to be analyzed, the after-sales spare module library established according to the area to be analyzed meets the replacement needs of the corresponding area to be analyzed, avoiding the transfer of goods from other after-sales spare module libraries, thereby meeting the needs of quickly replacing spare modules to solve equipment failures.

[0149] In one embodiment, segmented statistics are performed according to life cycle segments based on the replacement data records and the usage data records to obtain predicted module replacement rates for each life cycle segment corresponding to the standby module to be analyzed, specifically including: segmented statistical calculations are performed according to preset time intervals based on the replacement data records and the usage data records to obtain the module replacement rates corresponding to the segmented time points; the number of preset life cycle segments is obtained; the start time and end time of each life cycle segment of the standby module to be analyzed are determined according to the module replacement rates corresponding to the segmented time points and the number of preset life cycle segments; calculations are performed according to the module replacement rates corresponding to the segmented time points and the start time and end time of each life cycle segment to obtain predicted module replacement rates for each life cycle segment corresponding to the standby module to be analyzed.

[0150] In one embodiment, the method of determining the start time and end time of each life cycle segment of the standby module to be analyzed based on the module replacement rate corresponding to the segmented time points and the number of preset life cycle segments specifically includes: connecting the module replacement rates corresponding to all the segmented time points to generate a module replacement rate curve corresponding to the standby module to be analyzed; calculating the slope of the connecting line of the module replacement rates of adjacent segmented time points based on the module replacement rate curve; obtaining the slope growth rate corresponding to two adjacent connecting lines based on the slopes of two adjacent connecting lines; determining the periodic time point corresponding to each life cycle segment on the module replacement rate curve based on the number of preset life cycle segments and the slope growth rate corresponding to the two adjacent connecting lines; and determining the start time and end time of each life cycle segment of the standby module to be analyzed based on the periodic time point corresponding to each life cycle segment.

[0151] In one embodiment, the module replacement rate corresponding to the segmented time points and the start time and end time of each life cycle segment are calculated to obtain the predicted module replacement rate of each life cycle segment corresponding to the backup module to be analyzed, specifically including: obtaining the module replacement rate corresponding to the start time and the module replacement rate corresponding to the end time of the life cycle segment from the module replacement rate corresponding to the segmented time points according to the start time and the end time of each life cycle segment; calculating the average replacement rate of the corresponding life cycle segment according to the module replacement rate corresponding to the start time of the life cycle segment and the module replacement rate corresponding to the end time of the life cycle segment, and using the average replacement rate as the predicted module replacement rate of the life cycle segment.

[0152] In one embodiment, the module replacement rate corresponding to the segmented time points and the start time and end time of each life cycle segment are calculated to obtain the predicted module replacement rate of each life cycle segment corresponding to the standby module to be analyzed, specifically including: connecting the module replacement rates corresponding to all the segmented time points to generate a module replacement rate curve corresponding to the standby module to be analyzed; calculating the area under the curve according to the module replacement rate curve and the start time and end time of each life cycle segment to obtain the area under the curve corresponding to the life cycle segment; calculating the duration of the life cycle segment according to the start time and end time of each life cycle segment; and obtaining the average replacement rate of the life cycle segment by dividing the area under the curve corresponding to the life cycle segment by the duration of the life cycle segment, and using the average replacement rate as the predicted module replacement rate of the life cycle segment.

[0153] In one embodiment, the calculation is performed based on the predicted module replacement rates of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed, which specifically includes: performing segmented statistics by life cycle segment according to the current usage data to obtain the current usage data of each life cycle segment; performing weighted summation based on the current usage data of all the life cycle segments and the predicted module replacement rates of each life cycle segment corresponding to all the spare modules to be analyzed to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0154] In one embodiment, after obtaining the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed by calculating according to the predicted module replacement rate of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data, it also includes: obtaining the prices of all the spare modules to be analyzed; obtaining the minimum value from the prices of all the spare modules to be analyzed as the price factor; calculating the price optimization coefficient corresponding to the spare modules to be analyzed according to the price factor and the prices of the spare modules to be analyzed; calculating according to the price optimization coefficient corresponding to the spare modules to be analyzed and the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed, to obtain the target demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

[0155] It should be noted that the above-mentioned method for predicting demand for a spare module, device for predicting demand for a spare module, storage medium and computer equipment belong to a general inventive concept, and the contents in the embodiments of the method for predicting demand for a spare module, device for predicting demand for a spare module, storage medium and computer equipment can be applied to each other.

[0156] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0157] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0158] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting demand for a standby module, the method comprising: Obtain replacement data records and usage data records of the standby modules to be analyzed in the area to be analyzed within a preset time period; Perform segmented statistical calculations at preset time intervals according to the replacement data records and the usage data records to obtain the module replacement rate corresponding to the segmented time points; obtain the number of preset life cycle segments; determine the start time and end time of the standby module to be analyzed in each life cycle segment according to the module replacement rate corresponding to the segmented time points and the number of preset life cycle segments; calculate according to the module replacement rate corresponding to the segmented time points and the start time and end time of each life cycle segment to obtain the predicted module replacement rate of each life cycle segment corresponding to the standby module to be analyzed; Obtain current usage data corresponding to the standby modules to be analyzed in the area to be analyzed; According to the current usage data, segmented statistics are calculated by life cycle segment to obtain the current usage data of each life cycle segment; based on the current usage data of all the life cycle segments and the predicted module replacement rates of each life cycle segment corresponding to all the spare modules to be analyzed, weighted summation is performed to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

2. The method according to claim 1, characterized in that The step of determining the start time and end time of each life cycle segment of the standby module to be analyzed according to the module replacement rate corresponding to the segmented time point and the number of the preset life cycle segments specifically includes: Connect the module replacement rates corresponding to all the segmented time points to generate a module replacement rate curve corresponding to the standby module to be analyzed; Calculate the slope of the connecting line of the module replacement rates at adjacent segmented time points according to the module replacement rate curve; The slope growth rate corresponding to the two adjacent connecting lines is calculated according to the slopes of the two adjacent connecting lines; Determining the periodic time point corresponding to each life cycle segment on the module replacement rate curve according to the number of the preset life cycle segments and the slope growth rate corresponding to the two adjacent connecting lines; The start time and the end time of each life cycle segment of the standby module to be analyzed are determined according to the periodic time point corresponding to each life cycle segment.

3. The method according to claim 1, characterized in that The calculation is performed based on the module replacement rate corresponding to the segmented time point, the start time and the end time of each life cycle segment, to obtain the predicted module replacement rate of each life cycle segment corresponding to the standby module to be analyzed, specifically including: According to the start time and end time of each life cycle segment, respectively obtaining the module replacement rate corresponding to the start time and the module replacement rate corresponding to the end time in the life cycle segment from the module replacement rates corresponding to the segment time points; The average replacement rate of the corresponding life cycle segment is calculated based on the module replacement rate corresponding to the start time of the life cycle segment and the module replacement rate corresponding to the end time of the life cycle segment, and the average replacement rate is used as the predicted module replacement rate of the life cycle segment.

4. The method according to claim 2, characterized in that: The calculation is performed based on the module replacement rate corresponding to the segmented time point, the start time and the end time of each life cycle segment, to obtain the predicted module replacement rate of each life cycle segment corresponding to the standby module to be analyzed, specifically including: Connect the module replacement rates corresponding to all the segmented time points to generate a module replacement rate curve corresponding to the standby module to be analyzed; Calculating the area under the curve according to the module replacement rate curve and the start time and end time of each life cycle segment to obtain the area under the curve corresponding to the life cycle segment; The duration of each life cycle segment is calculated based on the start time and end time of each life cycle segment; The average replacement rate of the life cycle segment is obtained by dividing the area under the curve corresponding to the life cycle segment by the duration of the life cycle segment, and the average replacement rate is used as the predicted module replacement rate of the life cycle segment.

5. The method according to any one of claims 1 to 4, characterized in that: After obtaining the required number of spare modules to be analyzed in the area to be analyzed by calculating the predicted module replacement rate of each life cycle segment corresponding to all spare modules to be analyzed and the current usage data, the method further includes: Get the prices of all spare modules to be analyzed; Obtaining a minimum value from the prices of all the standby modules to be analyzed as a price factor; Calculate the price optimization coefficient corresponding to the standby module to be analyzed according to the price factor and the price of the standby module to be analyzed; A calculation is performed according to the price optimization coefficient corresponding to the spare module to be analyzed and the demand quantity corresponding to the spare module to be analyzed in the area to be analyzed, so as to obtain the target demand quantity corresponding to the spare module to be analyzed in the area to be analyzed.

6. A spare module demand prediction device, characterized in that: The device comprises: A spare module replacement rate calculation module is used to obtain replacement data records and usage data records of the spare modules to be analyzed in the area to be analyzed within a preset time period, and to perform segmented statistics according to the replacement data records and usage data records by life cycle segment to obtain a predicted module replacement rate for each life cycle segment corresponding to the spare modules to be analyzed; The spare module demand data calculation module is used to obtain the current usage data corresponding to the spare modules to be analyzed in the area to be analyzed, and calculate according to the predicted module replacement rate of each life cycle segment corresponding to all the spare modules to be analyzed and the current usage data to obtain the demand quantity corresponding to the spare modules to be analyzed in the area to be analyzed.

7. A storage medium storing a computer instruction program, characterized in that: When the computer instruction program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 5.

8. A computer device, characterized in that: The method comprises at least one memory and at least one processor, wherein the memory stores a computer instruction program, and when the computer instruction program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 5.

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