Determination method and device for equipment optimization, equipment and storage medium
By determining the resource cost of the equipment during maintenance and use, calculating the predicted cost of the equipment, and comparing it with the benefits, the problem of inaccurate equipment cost calculation in the prior art is solved, and more accurate equipment optimization judgment is achieved.
Patent Information
- Application Number
- CN202411658660.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2025-02-25
AI Technical Summary
In the prior art, equipment cost calculation mainly relies on depreciation of fixed assets, ignoring the cost of equipment investment and costs during use, resulting in inaccurate cost calculation and inaccurate determination of whether the equipment needs optimization.
By determining the first resource consumed during maintenance and the second resource consumed during use, combining the cost of these resources, the predicted cost of the device is calculated, and determining whether optimization is required based on the predicted cost and the benefit of the device.
It realizes a more accurate judgment on whether the equipment needs optimization, and combines the costs during maintenance and use, providing a more comprehensive equipment cost analysis, avoiding equipment replacement errors caused by inaccurate cost calculations.
Smart Images
Figure CN119512756A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of communication technology, and in particular to a method, apparatus, device and storage medium for determining device optimization. Background Art
[0002] Equipment cost calculation plays a vital role in enterprise management. It is directly related to the enterprise's financial planning and cost control. It can also be used to analyze whether the equipment needs to be optimized based on the cost calculated by the equipment. At present, there are various ways to calculate equipment costs, but the most common and widely accepted one is to calculate according to the depreciation of fixed assets. Depreciation of fixed assets refers to the original cost of fixed assets being gradually included in the cost or expense according to a certain method during its service life.
[0003] However, the fixed asset depreciation method ignores the equipment investment cost and the cost during the use of the equipment, which makes the cost calculation inaccurate and makes it impossible to accurately judge whether the equipment needs to be optimized. Summary of the invention
[0004] The present application provides a method, apparatus, device and storage medium for determining device optimization, which can accurately determine whether a device needs to be optimized.
[0005] In order to achieve the above objectives, this application adopts the following technical solutions:
[0006] In a first aspect, the present application provides a method for determining equipment optimization, the method comprising: determining a first resource consumed by the equipment during maintenance and a second resource consumed during use; and determining whether the equipment needs to be optimized based on the first resource and the second resource.
[0007] According to the above technical means, the first resource consumed by the device during the maintenance process and the second resource consumed during the use process can be determined, and based on the first resource and the second resource, it is determined whether the device needs to be optimized. Therefore, it is possible to more accurately determine whether the device needs to be optimized by combining the resources consumed by the device during the maintenance process and the resources consumed by the device during the use process.
[0008] In a possible implementation, the above-mentioned "determining whether the equipment needs to be optimized based on the first resource and the second resource" includes: determining the cost corresponding to the first resource and the cost corresponding to the second resource; the cost includes labor cost and / or financial cost; based on the cost corresponding to the first resource and the cost corresponding to the second resource, determining the predicted cost of the equipment; determining whether the equipment needs to be optimized based on the predicted cost of the equipment.
[0009] According to the above technical means, the cost corresponding to the first resource and the cost corresponding to the second resource can be determined, and based on the cost corresponding to the first resource and the cost corresponding to the second resource, the predicted cost of the equipment can be determined, and based on the predicted cost of the equipment, it can be determined whether the equipment needs to be optimized. Therefore, the predicted cost of the equipment can be accurately determined based on the cost of the equipment during use and the cost of the equipment during maintenance, and based on the predicted cost, it can be accurately determined whether the equipment needs to be optimized.
[0010] In a possible implementation, the above-mentioned “determining whether the device needs to be optimized based on the predicted cost of the device” includes: obtaining the revenue generated by the device; and determining whether the device needs to be optimized based on the predicted cost of the device and the revenue of the device.
[0011] According to the above technical means, the revenue generated by the equipment can be obtained, and whether the equipment needs to be optimized can be determined based on the predicted cost of the equipment and the revenue of the equipment. Therefore, it can be accurately determined whether the equipment needs to be optimized by combining the predicted cost of the equipment and the revenue of the equipment.
[0012] In a possible implementation, the above-mentioned “determining whether the device needs to be optimized based on the predicted cost of the device and the revenue of the device” includes: when the predicted cost of the device is less than the revenue of the device, determining that the device needs to be optimized; when the predicted cost of the device is greater than or equal to the revenue of the device, determining that the device does not need to be optimized.
[0013] According to the above technical means, when the predicted cost of the equipment is less than the benefit of the equipment, it can be accurately determined that the equipment needs to be optimized, and when the predicted cost of the equipment is greater than or equal to the benefit of the equipment, it can be accurately determined that the equipment does not need to be optimized.
[0014] In a possible implementation manner, the cost corresponding to the second resource includes an additional cost of the equipment, and the additional cost of the equipment is determined based on the purchase cost of the equipment and the effective use time of the equipment.
[0015] According to the above technical means, the additional cost in the cost corresponding to the second resource of the device can be accurately determined based on the purchase cost of the device and the effective use time of the device.
[0016] In a possible implementation manner, the additional cost of the above equipment satisfies a first preset formula, which is:
[0017]
[0018] Among them, A represents the additional cost of the equipment, P represents the purchase cost of the equipment, i represents the interest rate of the additional cost of the equipment, and n represents the effective use time of the equipment.
[0019] According to the above technical means, the additional cost of the equipment can be determined more conveniently and accurately based on the first preset formula.
[0020] In a possible implementation, the method further includes: when the predicted rate of return of the equipment is greater than a preset threshold, determining that the equipment needs to be replaced; the predicted rate of return of the equipment is the ratio of the difference between the income of the equipment and the predicted cost of the equipment to the predicted cost of the equipment; when the predicted rate of return of the equipment is less than or equal to the preset threshold, determining that the equipment does not need to be replaced.
[0021] According to the above technical means, when the predicted rate of return of the equipment is greater than the preset threshold, it can be accurately determined that the equipment needs to be replaced, and when the predicted rate of return of the equipment is less than or equal to the preset threshold, it can be accurately determined that the equipment does not need to be replaced. In this way, it is possible to accurately determine whether the equipment needs to be replaced, avoiding financial losses caused by unnecessary equipment replacement.
[0022] In a second aspect, the present application provides a device for determining equipment optimization, the device comprising: a determination unit for determining a first resource consumed by the equipment during maintenance and a second resource consumed during use; the determination unit is also used to determine whether the equipment needs to be optimized based on the first resource and the second resource.
[0023] In a possible implementation, the determination unit is specifically used to determine the cost corresponding to the first resource and the cost corresponding to the second resource; the cost includes labor cost and / or financial cost; based on the cost corresponding to the first resource and the cost corresponding to the second resource, determine the predicted cost of the equipment; based on the predicted cost of the equipment, determine whether the equipment needs to be optimized.
[0024] In a possible implementation manner, the determination unit is further configured to obtain the revenue generated by the device; and determine whether the device needs to be optimized according to the predicted cost of the device and the revenue of the device.
[0025] In a possible implementation, the determination unit is further configured to determine that the device needs to be optimized when the predicted cost of the device is less than the revenue of the device; and to determine that the device does not need to be optimized when the predicted cost of the device is greater than or equal to the revenue of the device.
[0026] In a possible implementation, the determination unit is further configured to determine an additional cost of the device, where the cost corresponding to the second resource includes the additional cost of the device, and the additional cost of the device is determined based on a purchase cost of the device and an effective use time of the device.
[0027] In a possible implementation manner, the determination unit is further configured to determine an additional cost of the device, and the additional cost of the device satisfies a first preset formula, which is:
[0028]
[0029] Among them, A represents the additional cost of the equipment, P represents the purchase cost of the equipment, i represents the interest rate of the additional cost of the equipment, and n represents the effective use time of the equipment.
[0030] In a possible implementation, the determination unit is further specifically used to determine that the equipment needs to be replaced when the predicted rate of return of the equipment is greater than a preset threshold; the predicted rate of return of the equipment is the ratio of the difference between the income of the equipment and the predicted cost of the equipment to the predicted cost of the equipment; when the predicted rate of return of the equipment is less than or equal to the preset threshold, it is determined that the equipment does not need to be replaced.
[0031] In a third aspect, the present application provides an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the above-mentioned first aspect and any possible implementation manner thereof.
[0032] In a fourth aspect, the present application provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the method in the above-mentioned first aspect and any possible implementation method thereof.
[0033] In a fifth aspect, the present application provides a computer program product comprising instructions, which, when executed on a communication device, enables the communication device to execute the method in the above-mentioned first aspect and any possible implementation manner thereof. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] Figure 1 A schematic diagram of a framework of a device optimization system provided in an embodiment of the present application;
[0035] Figure 2 A schematic diagram of a flow chart of a method for determining device optimization provided in an embodiment of the present application;
[0036] Figure 3 A flowchart of a method for determining an additional cost of a server and a predicted cost provided in an embodiment of the present application;
[0037] Figure 4 A schematic diagram of the structure of a device for determining device optimization provided in an embodiment of the present application;
[0038] Figure 5 A block diagram of an electronic device of a device optimization determination apparatus provided in an embodiment of the present application. DETAILED DESCRIPTION
[0039] The following is a detailed description of a method, apparatus, device and storage medium for determining device optimization provided by an embodiment of the present application in conjunction with the accompanying drawings.
[0040] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.
[0041] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.
[0042] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.
[0043] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.
[0044] Cost estimation is a key part of enterprise management. It involves estimating and calculating the cost of equipment to ensure that the enterprise can formulate reasonable pricing strategies, control costs and achieve profitability. At present, cost estimation is mainly calculated through the depreciation of fixed assets. Depreciation of fixed assets is a method of averaging the cost of fixed assets over different time periods within their useful life.
[0045] However, the fixed asset depreciation method ignores the equipment investment cost and the cost during the use of the equipment, resulting in an inability to accurately determine whether the equipment needs to be optimized.
[0046] In order to solve the above technical problems, the present application provides a method for determining device optimization. The method includes: determining a first resource consumed by the device during maintenance and a second resource consumed during use; and determining whether the device needs to be optimized based on the first resource and the second resource. Therefore, it is possible to more accurately determine whether the device needs to be optimized by combining the resources consumed by the device during maintenance and the resources consumed by the device during use.
[0047] The execution subject in the embodiment of the present application may be a device control platform, and the embodiment is described in detail below.
[0048] For ease of understanding, the method for determining equipment optimization provided in this application is specifically introduced below with reference to the accompanying drawings.
[0049] Figure 1 A schematic diagram of a device optimization system provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the framework diagram includes a device control platform 101 and multiple devices 102.
[0050] It should be noted that the device control platform 101 in the present application may be connected to multiple devices 102. The device control platform 101 may manage multiple devices 102 according to the identifier corresponding to each device.
[0051] The device control platform 101 is used to receive resource information reported by multiple devices 102, and determine the predicted cost of each device according to the resource information of each device in the multiple devices. The resource information may include the purchase cost of the equipment, the maintenance cost of the equipment, the labor cost of the equipment, the site cost of the equipment, and the energy consumption cost of the equipment, etc., which is not limited in the embodiments of the present application.
[0052] It should be noted that the device control platform 101 in the present application includes multiple databases. For example, the device control platform 101 may include a financial database, a data center management system database, and an energy management system database. Among them, the financial database can be used to store the purchase cost and maintenance cost of the device. The data center management system database can be used to store the site cost of the device. The energy management system database can be used to store the energy consumption cost of the device.
[0053] The device 102 is used to report resource information to the device control platform 101. In the embodiment of the present application, the device 102 may include communication devices such as a server, a switch, and a router. For the convenience of description, the embodiment of the present application is described by taking a server as an example.
[0054] Figure 2 A flow chart of a method for determining device optimization provided in an embodiment of the present application, the method comprising the following S201-S202.
[0055] The following device in the embodiment of the present application is described in detail by taking a server as an example, wherein the server may be one or more of a cloud server, a resource server, and an application server.
[0056] S201: Determine a first resource consumed by a server during maintenance and a second resource consumed by a server during use.
[0057] The first resource may include the cost consumed by the server during the maintenance process. For the convenience of description, the maintenance cost is taken as an example for detailed description. The second resource may include the cost consumed by the server during use. The cost may include labor cost and / or capital cost.
[0058] For example, the cost consumed by the server during the maintenance process refers to the labor cost, material cost, transportation cost and other costs additionally incurred when the server fails during operation. The cost consumed by the server during use refers to the purchase cost of the server, the site cost incurred by arranging the server rental site, the energy consumption cost and additional costs incurred during the use of the server, etc., which are not limited in the embodiments of the present application.
[0059] In one possible implementation, the server database stores the cost consumed by the server during maintenance and the cost consumed by the server during use. In response to the user clicking the option to evaluate the device cost, the device control platform sends instruction information to the server. For example, the instruction information may be to report the cost consumed by the server during maintenance and the cost consumed by the server during use.
[0060] After receiving the instruction information sent by the device control platform, the server sends the cost consumed by the server during maintenance and the cost consumed by the server during use to the device control platform. Correspondingly, the device control platform receives the cost consumed by the server during maintenance and the cost consumed by the server during use.
[0061] For example, in the embodiment of the present application, the storage form of the first resource (including the cost consumed during the maintenance process) and the second resource (including the cost consumed during the use process) of the server stored in the device control platform is not limited, and can be stored in the form of text or in the form of a table. For example, Table 1 shows a storage method in the form of a table.
[0062] Table 1 shows the first resource and the second resource stored in the device control platform.
[0063] Table 1
[0064]
[0065] S202: Determine whether the server needs to be optimized based on the first resource and the second resource.
[0066] In one possible implementation, the device control platform may determine the predicted cost of the server based on the cost corresponding to the first resource and the cost corresponding to the second resource determined in step S201. The device control platform determines whether the server needs to be optimized based on the predicted cost of the server.
[0067] In some embodiments, the predicted cost of the server is the sum of the maintenance cost, site cost, energy cost, and additional cost of the server.
[0068] For example, taking Table 1 above as an example, the predicted cost of server A is the sum of the maintenance cost of server A, the site cost of server A, the energy consumption cost of server A and the additional cost of server A, that is, the predicted cost of server A is 1000+7000+10200+39820=58020 yuan.
[0069] In some other embodiments, the device control platform may also input the maintenance cost, site cost, energy cost, and additional cost of the server into a pre-trained cost estimation model for the server, and the cost estimation model for the server outputs the predicted cost of the server.
[0070] The training process of the cost estimation model may include data collection, data preprocessing, feature selection, and model training. Data collection can collect a large amount of historical data on server costs, including maintenance costs, site costs, energy costs, and additional costs. Data preprocessing means cleaning and organizing the collected data to eliminate outliers and duplicate data and improve data quality. Feature selection means extracting features that have an important impact on server costs from the original data, such as the server's hardware configuration, effective usage time, power consumption, etc. Model training means selecting a matching machine learning algorithm (such as linear regression, decision tree, random forest, etc.) and using historical data to train the model to determine the mapping relationship between features and costs.
[0071] In an embodiment of the present application, the device control platform can also obtain the revenue generated by the server from the financial database, and determine whether the server needs to be optimized based on the predicted cost of the server and the revenue of the server.
[0072] In the embodiment of the present application, whether the server needs to be optimized includes the following two situations:
[0073] Case 1: When the predicted cost of the server is less than the revenue of the server, it is determined that the server needs to be optimized.
[0074] In the embodiment of the present application, the device control platform can determine the predicted rate of return of the server based on the predicted cost of the server and the revenue of the server. The predicted rate of return of the server is the ratio of the difference between the revenue of the server and the predicted cost of the server to the predicted cost of the server. That is, the predicted rate of return of the server = (revenue of the server - predicted cost of the server) / predicted cost of the server.
[0075] When the predicted rate of return of the server is greater than the preset threshold, the device control platform displays optimization information, which can be used to determine when the server needs to be replaced.
[0076] When the predicted rate of return of the server is less than or equal to the preset threshold, the device control platform displays a prompt message, which may be that the effective working time of the server is n years and it can continue to work for m years.
[0077] Case 2: When the predicted cost of the server is greater than or equal to the revenue of the server, it is determined that the server does not need to be optimized.
[0078] based on Figure 2 According to the technical solution shown in the embodiment of the present application, the maintenance cost consumed by the server during the maintenance process and the energy consumption cost, site cost, and additional cost during the use process can be determined, and based on the maintenance cost consumed by the server during the maintenance process and the energy consumption cost, site cost, and additional cost during the use process, it is determined whether the server needs to be optimized. Therefore, it is possible to more accurately determine whether the server needs to be optimized by combining the maintenance cost consumed by the server during the maintenance process and the energy consumption cost, site cost, and additional cost consumed by the server during the use process.
[0079] The detailed steps of the method for determining the additional cost of the server in step S201 and the predicted cost in step S202 are as follows. Figure 3 A flowchart of a method for determining an additional cost and a predicted cost of a server provided in an embodiment of the present application, the method includes the following S301-S303.
[0080] S301. Obtain the purchase cost and effective usage time of the server.
[0081] In one possible implementation, the server's financial database stores the server's purchase cost and the server's effective use time. In response to the user clicking the option to evaluate the device cost, the device control platform sends a command message to the server. After receiving the command message sent by the device control platform, the server sends the server's purchase cost and effective use time to the device control platform. Accordingly, the device control platform receives the server's purchase cost and effective use time.
[0082] S302: Determine the additional cost of the server according to the purchase cost and effective use time of the server.
[0083] Among them, additional costs can also be called capital costs or opportunity costs, which refer to the benefits generated by using capital for this investment and giving up other investment opportunities.
[0084] In a possible implementation, the device control platform may determine the interest rate of the additional cost of the server based on the purchase cost of the server and the effective use time of the server. The interest rate of the additional cost of the server may also be referred to as the capital cost rate.
[0085] The effective use time of the server can be determined in combination with the hardware status of the server. For example, the service life of the server can be determined based on the usage of the server's central processing unit (CPU), memory, disk, etc.
[0086] In the embodiment of the present application, the service life of the server hard disk can first be obtained through the hard disk label, the manufacturer's official website or professional hard disk detection tools (for example, CrystalDiskInfo) to obtain the hard disk's production date, usage time and other basic information, and use this information to preliminarily evaluate the disk's service life. Then, the evaluation result is corrected according to the physical environment of the server, such as temperature, humidity, etc.
[0087] For example, assuming that the service life of the disk of the server is 5 years, the effective service life of the server is also 5 years.
[0088] Furthermore, the device control platform determines the additional cost of the server according to the purchase cost of the server, the effective use time of the server, and the interest rate of the additional cost of the server.
[0089] The interest rate of the additional cost of the server can be determined based on the cost estimation model. The device control platform can input the purchase cost and effective use time of the server into the cost estimation model, and the cost estimation model determines the interest rate of the additional cost of the server through big data collection technology.
[0090] In the embodiments of the present application, the big data collection technology refers to obtaining a large amount of data related to server costs from multiple sources (including but not limited to server providers, market research reports, historical cost data, etc.). The cost estimation model uses advanced machine learning algorithms, such as regression analysis, neural networks, or decision trees, to learn the complex relationship between features and the server additional cost rate. The model is trained with training set data so that it can accurately predict the additional cost rate under different feature combinations.
[0091] For example, the cost estimation model uses big data collection technology to obtain that when a bank's purchase cost is RMB 350,000 and the effective use period is 6 years, the bank's deposit interest rate is determined to be 13%, that is, the interest rate of the additional cost is 13%.
[0092] After determining the interest rate of the additional cost of the server, the device control platform determines the additional cost of the server based on the purchase cost of the server, the effective use time of the server, and the interest rate of the additional cost of the server. Among them, the additional cost of the server satisfies Formula 1:
[0093]
[0094] Among them, A represents the additional cost of the server, P represents the purchase cost of the server, i represents the interest rate of the additional cost of the server, and n represents the effective use time of the server.
[0095] For example, assuming that the purchase cost of the server is RMB 1,394,990, the effective service life is 4 years, and the interest rate of the additional cost is 16.4%, then the additional cost per month of the server is
[0096] S303: Determine the predicted cost of the server according to the additional cost of the server.
[0097] In one possible implementation, the predicted cost of the server determined by the device control platform is the sum of the site cost, energy cost, additional cost, and maintenance cost of the server.
[0098] Among them, the site cost, energy consumption cost and maintenance cost of the server are reported by the device control platform after receiving the reporting instruction from the device control platform from the server. The specific steps can refer to step S201 and are not described in detail here.
[0099] For example, assuming that the sum of the server site cost and maintenance cost is 7,000 yuan / month, and the server energy cost is 10,200 yuan / month, then the server usage cost is 17,200 yuan / month. Assuming that the additional cost of the server is 39,820 yuan per month, the predicted cost of the server is 17,200+39,820=57,020.8 yuan.
[0100] based on Figure 3 The technical solution shown in the embodiment of the present application can accurately determine the additional cost of the server based on the purchase cost and effective use time of the server, and accurately determine the predicted cost of the server based on the additional cost of the server.
[0101] In some embodiments, the device control platform may also determine the depreciation cost of the server within a preset time period. The depreciation cost represents the average purchase cost of the server within a preset time period.
[0102] For example, assuming that the purchase cost of the server is M and the effective service life is n, the monthly depreciation cost of the server is M / (n*12).
[0103] The device control platform determines the cost error rate of the server calculated by the depreciation cost according to the numerical relationship between the depreciation cost of the server in the preset time period and the predicted cost of the server in the preset time period.
[0104] The cost error rate of the depreciation cost calculation is the ratio of the error value to the depreciation cost of the server in the preset time period, and the error value is the difference between the predicted cost of the server in the preset time period and the depreciation cost of the server in the preset time period.
[0105] The device control platform determines that the cost error calculated by the depreciation cost is large when the cost error rate is greater than the preset threshold value. When the cost error rate is less than or equal to the preset threshold value, the device control platform determines that the cost error calculated by the depreciation cost is small.
[0106] For example, assuming that the purchase cost of the server is 350,000 yuan and the effective service life is 4 years, the monthly depreciation cost of the server is 7,292 yuan. When the interest rate of the additional cost is 7.43%, the monthly depreciation cost of the server is 8,451 yuan. The cost error rate is (8,451-7,292) / 7,292=16%.
[0107] Based on the technical solution shown above, in the embodiment of the present application, the device control platform can accurately determine the cost error rate of the server calculated by the depreciation cost according to the depreciation cost and predicted cost of the server.
[0108] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, the device optimization determination device or electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. 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 exceed the scope of the present application.
[0109] The embodiment of the present application can divide the functional modules of the device or electronic device for determining device optimization according to the above method. For example, the device or electronic device for determining device optimization may include various functional modules corresponding to the various functional divisions, or two or more functions may be integrated into one processing module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.
[0110] Figure 4 A schematic diagram of a device for determining device optimization provided in an embodiment of the present application, the device comprising:
[0111] The determining unit 401 is used to determine a first resource consumed during the maintenance process and a second resource consumed during the use of the device.
[0112] The determination unit 401 is further configured to determine whether the device needs to be optimized based on the first resource and the second resource.
[0113] In a possible implementation, the determination unit 401 is specifically used to determine the cost corresponding to the first resource and the cost corresponding to the second resource; the cost includes labor cost and / or financial cost; based on the cost corresponding to the first resource and the cost corresponding to the second resource, determine the predicted cost of the equipment; based on the predicted cost of the equipment, determine whether the equipment needs to be optimized.
[0114] In a possible implementation, the determination unit 401 is further configured to obtain the revenue generated by the device; and determine whether the device needs to be optimized according to the predicted cost of the device and the revenue of the device.
[0115] In a possible implementation, the determination unit 401 is further specifically configured to determine that the device needs to be optimized when the predicted cost of the device is less than the revenue of the device; and to determine that the device does not need to be optimized when the predicted cost of the device is greater than or equal to the revenue of the device.
[0116] In a possible implementation, the determination unit 401 is further specifically used to determine the additional cost of the equipment, where the cost corresponding to the second resource includes the additional cost of the equipment, and the additional cost of the equipment is determined based on the purchase cost of the equipment and the effective use time of the equipment.
[0117] In a possible implementation, the determining unit 401 is further configured to determine an additional cost of the device, where the additional cost of the device satisfies a first preset formula, which is:
[0118]
[0119] Among them, A represents the additional cost of the equipment, P represents the purchase cost of the equipment, i represents the interest rate of the additional cost of the equipment, and n represents the effective use time of the equipment.
[0120] In a possible implementation, the determination unit 401 is further specifically used to determine that the equipment needs to be replaced when the predicted rate of return of the equipment is greater than a preset threshold; the predicted rate of return of the equipment is the ratio of the difference between the income of the equipment and the predicted cost of the equipment to the predicted cost of the equipment; when the predicted rate of return of the equipment is less than or equal to the preset threshold, determine that the equipment does not need to be replaced.
[0121] Figure 5 A block diagram of an electronic device of a device for determining device optimization involved in the above-mentioned embodiment is shown. The device for determining device optimization includes: a processor 501 and a communication interface 502. The processor 501 is used to determine the predicted cost of the device according to the purchase cost and effective use time of the device, and the communication interface 502 is used for communication between the device control platform and the device. The recommendation system may also include a memory 503 and a bus 504, and the memory 503 is used to store the purchase cost, effective use time and predicted cost of the device.
[0122] Among them, the memory 503 can be a memory in a determination device for device optimization, etc., and the memory can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid-state drive; the memory can also include a combination of the above types of memory.
[0123] The processor 501 may be a processor that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the present application. The processor may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The processor may be a processor that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.
[0124] The bus 504 may be an Extended Industry Standard Architecture (EISA) bus, etc. The bus 504 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.
[0125] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.
[0126] An embodiment of the present application provides a computer program product comprising instructions. When the computer program product is run on a computer, the computer is enabled to execute the determination method for device optimization in the above method embodiment.
[0127] An embodiment of the present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a computer, the computer executes the determination method of device optimization in the method flow shown in the above method embodiment.
[0128] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read products from the storage medium and can write products to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In the embodiments of the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device.
[0129] An embodiment of the present invention provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the device optimization determination method of the embodiment of the present application.
[0130] Since the device optimization determination apparatus, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above method, the technical effects that can be obtained can also refer to the above method embodiments, and the embodiments of the present invention will not be repeated here.
[0131] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0133] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0134] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application 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.
Claims
1. A method for determining equipment optimization, characterized in that: The method comprises: Determine a first resource consumed during maintenance of the equipment and a second resource consumed during use of the equipment; Based on the first resource and the second resource, it is determined whether the device needs to be optimized.
2. The method according to claim 1, characterized in that The determining whether the device needs to be optimized based on the first resource and the second resource includes: Determine a cost corresponding to the first resource and a cost corresponding to the second resource; the cost includes a human cost and / or a financial cost; Determining a predicted cost of the device based on a cost corresponding to the first resource and a cost corresponding to the second resource; According to the predicted cost of the equipment, it is determined whether the equipment needs to be optimized.
3. The method according to claim 2, characterized in that The determining whether the device needs to be optimized according to the predicted cost of the device includes: obtaining the revenue generated by the equipment; Determine whether the equipment needs to be optimized according to the predicted cost of the equipment and the revenue of the equipment.
4. The method according to claim 3, characterized in that The determining whether the device needs to be optimized according to the predicted cost of the device and the revenue of the device includes: In the case where the predicted cost of the equipment is less than the benefit of the equipment, determining that the equipment needs to be optimized; When the predicted cost of the device is greater than or equal to the revenue of the device, it is determined that the device does not need to be optimized.
5. The method according to claim 2, characterized in that: The cost corresponding to the second resource includes an additional cost of the device, where the additional cost of the device is determined based on a purchase cost of the device and an effective use time of the device.
6. The method according to claim 5, characterized in that The additional cost of the equipment satisfies a first preset formula, which is: Wherein, A represents the additional cost of the equipment, P represents the purchase cost of the equipment, i represents the interest rate of the additional cost of the equipment, and n represents the effective use time of the equipment.
7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: When the predicted rate of return of the equipment is greater than a preset threshold, it is determined that the equipment needs to be replaced; the predicted rate of return of the equipment is the ratio of the difference between the income of the equipment and the predicted cost of the equipment to the predicted cost of the equipment; When the predicted rate of return of the equipment is less than or equal to the preset threshold, it is determined that the equipment does not need to be replaced.
8. A device for determining equipment optimization, characterized in that: The device comprises: A determination unit, configured to determine a first resource consumed during maintenance of the device and a second resource consumed during use of the device; The determining unit is further configured to determine whether the device needs to be optimized based on the first resource and the second resource.
9. An electronic device, characterized in that: The electronic device comprises: processor; a memory configured to store instructions executable by the processor; The processor is configured to execute the instructions to implement the device optimization determination method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that: Instructions are stored on the computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by an electronic device, the electronic device is enabled to execute the device optimization determination method as described in any one of claims 1 to 7.
11. A computer program product, characterized in that The computer program product includes computer instructions; when part or all of the computer instructions are run on a computer, the method according to any one of claims 1 to 7 is executed.