Service resource analysis method and device, electronic equipment and storage medium

Abstract

The invention provides a service resource analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: adjusting a first delivery amount change curve of a comparison website with the delivery amount approaching an experiment website as a target; the first express delivery quantity change curve is a curve of the express delivery quantity of the comparison network point along with time change; based on the adjusted first mailing amount change curve, determining a first mailing amount of the contrast network point; the first mailing amount comprises a mailing amount of the contrast network at a first time, and the first time is after the experimental network obtains service resources; and according to the first mailing amount and the mailing amount of the experimental website at the first time, determining the influence of the service resource on the mailing amount of the experimental website.

Description

Technical Field

[0001] This application relates to the field of data analysis, and in particular to a service resource analysis method, apparatus, electronic device, and storage medium. Background Art

[0002] With the continuous development of computer technology, more and more users choose to send and receive items by express delivery.

[0003] In order to increase the number of items sent and received at express delivery outlets, logistics companies usually launch some shipping discounts and shipping activities. However, considering the actual situations of different logistics outlets (such as the user groups and geographical locations of logistics outlets), the implementation of some shipping discounts and shipping activities at some logistics outlets may not achieve the expected results.

[0004] Therefore, it is necessary to analyze the impact of shipping discounts and shipping activities on logistics outlets in order to reasonably distribute shipping discounts and shipping activities. Summary of the Invention

[0005] Based on the above technical status quo, this application proposes a service resource analysis method, apparatus, electronic device, and storage medium.

[0006] This application provides a service resource analysis method on the one hand, including:

[0007] Taking the shipping volume close to that of the experimental outlet as the target, adjusting the first shipping volume change curve of the control outlet; the first shipping volume change curve is the curve of the shipping volume of the control outlet changing with time;

[0008] Based on the adjusted first shipping volume change curve, determining the first shipping volume of the control outlet; the first shipping volume includes the shipping volume of the control outlet at the first time, and the first time is after the experimental outlet obtains the service resource;

[0009] According to the first shipping volume and the shipping volume of the experimental outlet at the first time, determining the impact of the service resource on the shipping volume of the experimental outlet.

[0010] In an optional implementation manner of this application, the taking the shipping volume close to that of the experimental outlet as the target and adjusting the first shipping volume change curve of the control outlet includes:

[0011] Obtaining the first shipping volume change curve of the control outlet;

[0012] According to the shipping volumes of the experimental outlet at different times, increasing or decreasing the shipping volume of the first shipping volume change curve year-on-year to obtain the adjusted first shipping volume change curve.

[0013] In an alternative embodiment of the present application, obtaining an adjusted first shipment volume change curve by increasing or decreasing the shipment volume of the first shipment volume change curve year-on-year according to the shipment volumes of the experimental outlets at different times includes:

[0014] Inputting the shipment volume of the experimental outlet and the first shipment volume change curve into a pre-trained data adjustment model, so that the data adjustment model increases or decreases the shipment volume of the first shipment volume change curve year-on-year according to the shipment volumes of the experimental outlet at different times, and obtains an adjusted first shipment volume change curve.

[0015] In an alternative embodiment of the present application, determining the influence of the service resources on the shipment volume of the experimental outlet according to the first shipment volume and the shipment volume of the experimental outlet at the first time includes:

[0016] Determining the influence of the service resources on the shipment volume of the experimental outlet according to the difference between the first shipment volume and the shipment volume of the experimental outlet at the first time.

[0017] In an alternative embodiment of the present application, determining the influence of the service resources on the shipment volume of the experimental outlet according to the difference between the first shipment volume and the shipment volume includes:

[0018] Based on the first shipment volume change curve, determining a second shipment volume change curve of the control outlet after the experimental outlet obtains the service resources;

[0019] Obtaining a third shipment volume change curve of the experimental outlet after the experimental outlet obtains the service resources according to the first shipment volume;

[0020] Determining the influence of the service resources on the shipment volume of the experimental outlet according to the difference between the second shipment volume change curve and the third shipment volume change curve;

[0021] Wherein, the second shipment volume change curve is a curve of the shipment volume reflected by the first shipment volume change curve changing with time after the experimental outlet obtains the service resources; the third shipment volume change curve is a curve of the shipment volume of the experimental outlet changing with time after the experimental outlet obtains the service resources.

[0022] In an alternative embodiment of the present application, it further includes:

[0023] Determining a control outlet similar to the experimental outlet according to the shipment volume of the experimental outlet.

[0024] In an alternative embodiment of the present application, determining a control outlet similar to the experimental outlet according to the shipment volume of the experimental outlet includes:

[0025] Obtain the shipment volume of the experimental network points and the shipment volume of the candidate control network points;

[0026] Determine a control network point similar to the experimental network point from the candidate control network points according to the temporal similarity between the shipment volume of the experimental network point and the shipment volume of the candidate control network points.

[0027] On the other hand, the present application provides a service resource analysis device, including:

[0028] A first unit for adjusting the first shipment volume change curve of the control network point with the target of approaching the shipment volume of the experimental network point; the first shipment volume change curve is the curve of the shipment volume of the control network point changing with time;

[0029] A second unit for determining the first shipment volume of the control network point based on the adjusted first shipment volume change curve; the first shipment volume includes the shipment volume of the control network point at a first time, and the first time is after the experimental network point obtains the service resource;

[0030] A third unit for determining the influence of the service resource on the shipment volume of the experimental network point according to the first shipment volume and the shipment volume of the experimental network point at the first time.

[0031] On the other hand, the present application provides an electronic device, including:

[0032] A processor;

[0033] A memory for storing executable instructions of the processor;

[0034] The processor is used to execute the above-mentioned service resource analysis method by running the instructions in the memory.

[0035] On the other hand, the present application provides a computer-readable storage medium, and the storage medium stores a computer program, and when the computer program is run by a processor, the above-mentioned service resource analysis method is executed.

[0036] Compared with the prior art, the present application has the following advantages:

[0037] The present application provides a service resource analysis method, apparatus, electronic device and storage medium. The service resource analysis method includes: taking the shipment volume close to the experimental network point as the target, adjusting the first shipment volume change curve of the control network point; the first shipment volume change curve is the curve of the shipment volume of the control network point changing with time; based on the adjusted first shipment volume change curve, determining the first shipment volume of the control network point; the first shipment volume includes the shipment volume of the control network point at the first time, and the first time is after the experimental network point obtains the service resource; according to the first shipment volume and the shipment volume of the experimental network point at the first time, determining the influence of the service resource on the shipment volume of the experimental network point.

[0038] The service resource analysis method adjusts the first shipment volume change curve of the control network point based on the shipment volume of the experimental network point. In the case where the control network point does not obtain the service resource, the shipment volume of the control network point is obtained, realizing the setting of the control group of the experimental network point; then, according to the shipment volumes of the control group and the experimental group at the first time, the influence of the service resource on the shipment volume of the experimental network point is analyzed, where the first time is the time after the experimental network point obtains the service resource. This method can analyze the influence of the service resource on the shipment volume of the experimental network point to facilitate the reasonable distribution of service resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0040] Figure 1 It is a flowchart of the service resource analysis method provided by the embodiment of the present application;

[0041] Figure 2 It is a schematic diagram of data analysis provided by the embodiment of the present application;

[0042] Figure 3 It is a schematic structural diagram of the service resource analysis apparatus provided by the embodiment of the present application;

[0043] Figure 4 It is a schematic structural diagram of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] With the continuous development of computer technology, more and more users choose to send and receive items in the form of express delivery.

[0045] To increase the number of received and sent packages at express delivery outlets, logistics companies usually launch some shipping discounts and shipping activities. However, considering the actual situations of different logistics outlets (such as the user groups of logistics outlets and the geographical locations of logistics outlets), the implementation of some shipping discounts and shipping activities at some logistics outlets may not achieve the expected results.

[0046] Therefore, it is necessary to analyze the impact of shipping discounts and shipping activities on logistics outlets in order to reasonably distribute shipping discounts and shipping activities.

[0047] In the prior art, in order to analyze the impact of shipping discounts and shipping activities on logistics outlets, the impact of shipping discounts and shipping activities on the revenue of logistics outlets is usually determined based on year-on-year and month-on-month comparisons.

[0048] Among them, year-on-year comparison means comparing the data at the current time point with that of the previous year at the same time point. For example, after implementing a shipping discount at a certain logistics outlet in the third quarter of this year, compare the shipping volume of the third quarter when the shipping discount was implemented at the logistics outlet this year with the shipping volume in the third quarter of last year when the shipping discount was not implemented, so as to obtain the impact of the shipping discount on the shipping volume of this logistics outlet.

[0049] Month-on-month comparison means comparing the data at the current time point with the data at the previous time point. For example, after implementing a shipping discount at a certain logistics outlet in the third quarter of this year, compare the shipping volume of the third quarter after the implementation of the discount at the logistics outlet this year with the shipping volume in the first quarter of this year when the shipping discount was not implemented, so as to obtain the impact of the shipping discount on the shipping volume of this logistics outlet.

[0050] However, it can be understood that the factors affecting the shipping volume of logistics outlets not only include shipping discounts and shipping activities, but also changes in the general environment (such as factors like the epidemic and holidays) will also affect the shipping volume of logistics outlets. However, the year-on-year and month-on-month comparison methods in the prior art cannot eliminate this impact and cannot accurately analyze the impact of shipping discounts and shipping activities on logistics outlets.

[0051] To solve the technical problems existing in the prior art, the present application provides a service resource analysis method, device, electronic device and storage medium. The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0052] Exemplary method

[0053] An embodiment of the present application first provides a service resource analysis method. The core lies in adjusting the first shipment volume change curve of a control network point based on the shipment volume of an experimental network point. In the case where the control network point does not obtain service resources, the shipment volume of the control network point is obtained, realizing the setting of the control group of the experimental network point, and then eliminating the part of the shipment volume affected by the environment in year-on-year and month-on-month comparisons; then, according to the shipment volumes of the control group and the experimental group at a first time, the impact of service resources on the shipment volume of the experimental network point is analyzed, where the first time is the time after the experimental network point obtains the service resources.

[0054] In an alternative embodiment of the present application, the implementation entity of the service resource analysis method can be various types of user terminals such as laptop computers, tablet computers, desktop computers, mobile devices (e.g., mobile phones, portable music players, personal digital assistants, dedicated messaging devices, game consoles), or a combination of any two or more of these data processing devices, or a server.

[0055] Please refer to Figure 1 , Figure 1 which is the flowchart of the service resource analysis method provided by the embodiment of the present application.

[0056] As Figure 1 shown, the service resource analysis method includes the following steps S101 to S103:

[0057] Step S101: Adjust the first shipment volume change curve of the control network point with the goal of approaching the shipment volume of the experimental network point; the first shipment volume change curve is the curve of the shipment volume of the control network point changing with time.

[0058] The experimental network point and the control network point can be understood as two different logistics network points. The purpose of the embodiment of the present application is to analyze the impact of shipping discounts and / or shipping activities on the shipment volume of the experimental network point. The control network point serves as a control group and is compared with the shipment volume of the experimental network point that has implemented shipping discounts and / or shipping activities to facilitate determining the impact of shipping discounts and / or shipping activities on the shipment volume of the experimental network point.

[0059] In an alternative embodiment of the present application, in order to make the shipment volumes of the control network point and the experimental network point basically the same at different times, so as to control a single variable (presence or absence of shipping discounts and / or shipping activities) and compare the shipment volume of the control network point with that of the experimental network point, the control network point can be determined through the following steps S1 and S2:

[0060] Step S1: Obtain the shipment volume of the experimental network point and the shipment volume of the candidate control network point;

[0061] Step S2: Determine a control outlet similar to the experimental outlet from the candidate control outlets according to the temporal similarity between the shipment volume of the experimental outlet and that of the candidate control outlets.

[0062] Among them, the shipment volume of the experimental outlet and that of the candidate control outlets can be the daily shipment volumes of the experimental outlet and each candidate control outlet in a certain stage. For example, the certain stage can be one year, or at least one quarter, or at least one month. Assuming that x is the shipment volume of the experimental outlet on a certain day and y is the shipment volume of the control outlet on a certain day, then the shipment volume of the experimental outlet in step S1 above can be represented by time series data as {x1, x2, x3... x n}, and the shipment volume of the candidate control outlets can be represented by time series data as {y1, y2, y3... y n}.

[0063] Furthermore, the temporal similarity between the shipment volume of the experimental outlet and that of the candidate control outlets can be calculated based on the Euclidean distance or cosine distance between the above time series data, and the candidate control outlet with the highest temporal similarity is selected as the final control outlet.

[0064] In an alternative embodiment of the present application, in order to simplify the selection of candidate control outlets, a logistics outlet whose first and last shipment volumes in the above time series data are the same as those of the experimental outlet can be selected as the candidate control outlet. For example, a logistics outlet where y1 = x1 and y n = x n is selected as the candidate control outlet.

[0065] Furthermore, aiming at approaching the shipment volume of the experimental outlet, adjusting the first shipment volume change curve of the control outlet includes the following steps S3 and S4:

[0066] Step S3: Obtain the first shipment volume change curve of the control outlet;

[0067] Step S4: Increase or decrease the first shipment volume change curve proportionally according to the shipment volume of the experimental outlet at different times to obtain the adjusted first shipment volume change curve.

[0068] Among them, the first shipment volume change curve of the control outlet is the shipment volume change curve obtained according to the shipment volumes of the control outlet at different times within a certain time range.

[0069] It should be noted that the control outlet, as a control group, is used to compare with the shipment volume of the experimental outlet that has implemented shipment discounts and / or shipment activities, so as to determine the impact of shipment discounts and / or shipment activities on the shipment volume of the experimental outlet.

[0070] Therefore, in order to ensure that the only difference between the experimental network points and the control network points during the comparison process lies in the presence or absence of the consignment discount and / or consignment activities, the time range for obtaining the consignment volume of the control network points should be the same as that for obtaining the consignment volume of the experimental network points.

[0071] However, it should be noted that in order to ensure that the adjustment process of the first consignment volume change curve is not affected by the consignment discount and / or consignment activities, the consignment volume of the experimental network points at different times does not include the consignment volume between the implementation of the consignment discount and / or consignment activities by the experimental network points.

[0072] Furthermore, since the temporal similarity between the experimental network points and the control network points is ensured during the determination of the control network points. Therefore, the change amplitude of the consignment volume of the experimental network points at different times should be basically similar to the first consignment volume change curve. On this basis, the purpose of increasing or decreasing the first consignment volume change curve year-on-year is to reduce the difference in network scale between the experimental network points and the control network points.

[0073] For example, assume that the experimental network point is a logistics network point in the first city, and the control network point is a logistics network point in the second city. However, due to the influence of the population sizes of the two cities (the population of the first city is larger than that of the second city), the network scale of the experimental network point will be larger than that of the control network point. In this case, although the consignment volume change curve of the experimental network point is similar to that of the control network point, the consignment volumes of the two network points differ by several times. Therefore, it is necessary to further adjust the consignment volume of the control network point.

[0074] In an alternative embodiment of the present application, the first consignment volume change curve can be adjusted by means of machine learning.

[0075] Specifically, increasing or decreasing the consignment volume of the first consignment volume change curve year-on-year according to the consignment volume of the experimental network points at different times to obtain an adjusted first consignment volume change curve includes:

[0076] Inputting the consignment volume of the experimental network points and the first consignment volume change curve into a pre-trained data adjustment model, so that the data adjustment model increases or decreases the consignment volume of the first consignment volume change curve year-on-year according to the consignment volume of the experimental network points at different times to obtain an adjusted first consignment volume change curve.

[0077] Among them, the data adjustment model can be understood as a neural network. In the specific application process, this application uses machine learning (ML) to train and obtain the personalized transcription model. Machine learning (a multi-disciplinary field that involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory) is dedicated to studying how to obtain new knowledge or skills through training samples, reorganize the existing knowledge structure, and continuously improve its own performance. Machine learning usually includes techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, and inductive learning, and belongs to a branch of artificial intelligence (AI) technology.

[0078] In an alternative embodiment of this application, the data adjustment model can be trained in the following way:

[0079] Obtain the sample shipment volumes of the sample experimental outlets at different times, the fourth shipment volume change curve of the sample control outlets, and the sample shipment volume change curve, where the sample shipment change curve is the adjusted fourth shipment change curve;

[0080] Input the sample shipment volume and the fourth shipment volume change curve into a pre-constructed data adjustment model, so that the data adjustment model increases or decreases the shipment volume of the fourth shipment volume change curve year-on-year according to the shipment volumes of the sample experimental outlets at different times, and obtains the adjusted fourth shipment volume change curve;

[0081] Train the data adjustment model according to the difference between the sample shipment volume change curve and the fourth shipment volume change curve.

[0082] In the actual application process, training the data adjustment model according to the difference between the sample shipment volume change curve and the fourth shipment volume change curve may be to construct a corresponding loss function according to the difference between the sample shipment volume change curve and the fourth shipment volume change curve, and then complete the training of the data adjustment model based on this loss function.

[0083] In the actual application process, the loss function can be a mean square error loss function, a cross-entropy loss function, etc. This application does not limit this.

[0084] In another alternative embodiment of this application, in order to adjust the first shipment volume change curve, it is also possible to determine the shipment volume adjustment ratio for adjusting the first shipment volume change curve by calculating the difference between the daily shipment volume of the experimental outlet and the daily shipment volume of the control outlet, and then increase or decrease the first shipment volume change curve year-on-year according to the shipment volume adjustment ratio.

[0085] By setting the control network points of the experimental network points in this application, and combining the shipment volumes of the experimental network points and the control network points, the impact of shipment activities and / or shipment discounts on the experimental network points is analyzed, eliminating the influence of environmental factors on the shipment volume in year-on-year or month-on-month comparisons in the prior art. At the same time, the above step S101 aims at the shipment volume close to that of the experimental network points and adjusts the first shipment volume change curve of the control network points, which is conducive to eliminating the differences between the experimental network points and the control network points themselves and improving the accuracy of subsequent analysis of the impact of shipment activities and / or shipment discounts on the experimental network points.

[0086] Step S102: Based on the adjusted first shipment volume change curve, determine the first shipment volume of the control network points; the first shipment volume includes the shipment volume of the control network points at the first time, and the first time is after the experimental network points obtain service resources.

[0087] The service resources can be understood as the shipment discounts and / or shipment activities. The first shipment volume is the shipment volume of the control network points at the first time when the shipment discounts and / or shipment activities are not implemented at the control network points.

[0088] The first time can be understood as a time range. For example, when the service resources correspond to a long-term shipment discount and / or shipment activity, the time range corresponding to the first time can be from the start time to the end time of the shipment discount and / or shipment activity; when the service resources correspond to a shipment discount and / or shipment activity valid on a certain day, the first time can correspond to the date of the shipment discount and / or shipment activity.

[0089] Step S103: According to the first shipment volume and the shipment volume of the experimental network points at the first time, determine the impact of the service resources on the shipment volume of the experimental network points.

[0090] After obtaining the first shipment volume and the shipment volume of the experimental network points at the first time, the impact of the service resources on the shipment volume of the experimental network points can be determined according to the difference between the first shipment volume and the shipment volume of the experimental network points at the first time.

[0091] Specifically, determining the impact of the service resources on the shipment volume of the experimental network points according to the difference between the first shipment volume and the shipment volume of the experimental network points at the first time includes the following steps S5 to S7:

[0092] Step S5: Based on the first shipment volume change curve, determine the second shipment volume change curve of the control network points after the experimental network points obtain service resources;

[0093] Step S6: Obtain the third shipment volume change curve after the experimental network obtains service resources according to the first shipment volume.

[0094] Step S7: Determine the impact of the service resources on the shipment volume of the experimental network according to the difference between the second shipment volume change curve and the third shipment volume change curve.

[0095] Wherein, the second shipment volume change curve is the curve of the shipment volume changing with time reflected by the first shipment volume change curve after the experimental network obtains service resources; the third shipment volume change curve is the curve of the shipment volume of the experimental network changing with time after the experimental network obtains service resources.

[0096] That is, within the time range after the experimental network obtains service resources, determine the difference in shipment volume between the control network and the experimental network as the impact of the service resources on the shipment volume of the experimental network.

[0097] In the embodiment of the present application, the impact of the service resources on the shipment volume of the experimental network can be expressed by the following formula:

[0098] ACE(Z→Y) = E(Y|do(Z)=1) ― E(X|do(Z)=0)

[0099] Wherein, ACE(Z→Y) represents the impact of the service resources on the shipment volume of the experimental network, E(Y|do(Z)=1) represents the shipment volume of the experimental network at the first time; E(X|do(Z)=0) represents the shipment volume of the control network at the first time; Z represents the service resources.

[0100] To facilitate the understanding of the above Steps S5 to S7, the following is a detailed introduction in combination with Figure 2 it.

[0101] Please refer to Figure 2 , Figure 2 which is a schematic diagram of data analysis provided by the embodiment of the present application.

[0102] As Figure 2 shown, Figure 2 it includes three parts: a, b, and c.

[0103] Part a includes the first shipment volume change curve, the points representing the shipment volume of the experimental group at different times, and the time when the experimental network obtains service resources represented by a vertical line.

[0104] In a, after the experimental network points obtain service resources, the points representing the shipment volumes of the experimental group at different times form a third shipment volume change curve; in the first shipment volume change curve, the curve after the time when the experimental network points obtain service resources forms a second shipment volume change curve. Among them, the control network points do not obtain the service resources.

[0105] The curve in b is used to represent the difference between the shipment volumes of the experimental network points and the control network points at different times, including a first difference curve and a second difference curve. Among them, the first difference curve is used to represent the difference in shipment volumes between the experimental network points and the control network points at different time points before the experimental network points obtain service resources; the second difference curve is used to represent the difference in shipment volumes between the experimental network points and the control network points at different time points after the experimental network points obtain service resources.

[0106] In the embodiment of the present application, the second difference curve is obtained based on the difference between the second shipment volume change curve and the third shipment volume change curve.

[0107] The curve in c is obtained by integrating the second difference curve, and the area of the second difference curve is used to represent the influence of the service resources on the shipment volume of the experimental network points.

[0108] In summary, the service resource analysis method provided by the present application adjusts the first shipment volume change curve of the control network points based on the shipment volume of the experimental network points, and obtains the shipment volume of the control network points in the case where the control network points do not obtain service resources, realizing the setting of the control group of the experimental network points; then, according to the shipment volumes of the control group and the experimental group at the first time, the influence of the service resources on the shipment volume of the experimental network points is analyzed, where the first time is the time after the experimental network points obtain the service resources. This method can analyze the influence of service resources on the shipment volume of experimental network points to facilitate the reasonable distribution of service resources.

[0109] Exemplary device

[0110] The present application also provides a service resource analysis device. Please refer to Figure 3 , Figure 3 which is the structural schematic diagram of the service resource analysis device provided by the embodiment of the present application.

[0111] As Figure 3 shown, the service resource analysis device includes:

[0112] A first unit 301, configured to adjust the first shipment volume change curve of the control network points with the shipment volume of the experimental network points as the target; the first shipment volume change curve is the curve of the shipment volume of the control network points changing with time;

[0113] The second unit 302 is configured to determine the first shipment volume of the control network point based on the adjusted first shipment volume change curve; the first shipment volume includes the shipment volume of the control network point at the first time, and the first time is after the experimental network point obtains service resources;

[0114] The third unit 303 is configured to determine the influence of the service resources on the shipment volume of the experimental network point according to the first shipment volume and the shipment volume of the experimental network point at the first time.

[0115] In an alternative embodiment of the present application, the adjusting the first shipment volume change curve of the control network point with the target of approaching the shipment volume of the experimental network point includes:

[0116] Obtaining the first shipment volume change curve of the control network point;

[0117] According to the shipment volume of the experimental network point at different times, increasing or decreasing the shipment volume of the first shipment volume change curve year-on-year to obtain an adjusted first shipment volume change curve.

[0118] In an alternative embodiment of the present application, the step of increasing or decreasing the shipment volume of the first shipment volume change curve year-on-year according to the shipment volume of the experimental network point at different times to obtain an adjusted first shipment volume change curve includes:

[0119] Inputting the shipment volume of the experimental network point and the first shipment volume change curve into a pre-trained data adjustment model, so that the data adjustment model increases or decreases the shipment volume of the first shipment volume change curve year-on-year according to the shipment volume of the experimental network point at different times to obtain an adjusted first shipment volume change curve.

[0120] In an alternative embodiment of the present application, the step of determining the influence of the service resources on the shipment volume of the experimental network point according to the first shipment volume and the shipment volume of the experimental network point at the first time includes:

[0121] Determining the influence of the service resources on the shipment volume of the experimental network point according to the difference between the first shipment volume and the shipment volume of the experimental network point at the first time.

[0122] In an alternative embodiment of the present application, the step of determining the influence of the service resources on the shipment volume of the experimental network point according to the difference between the first shipment volume and the shipment volume includes:

[0123] Based on the first shipment volume change curve, determining a second shipment volume change curve of the control network point after the experimental network point obtains service resources;

[0124] Obtain the third change curve of the shipment volume after the experimental network point obtains service resources according to the first shipment volume;

[0125] Determine the influence of the service resources on the shipment volume of the experimental network point according to the difference between the second change curve of the shipment volume and the third change curve of the shipment volume;

[0126] Wherein, the second change curve of the shipment volume is the curve of the shipment volume changing with time reflected by the first change curve of the shipment volume after the experimental network point obtains service resources; the third change curve of the shipment volume is the curve of the shipment volume of the experimental network point changing with time after the experimental network point obtains service resources.

[0127] In an alternative embodiment of the present application, the service resource analysis device is further configured to determine a control network point similar to the experimental network point according to the shipment volume of the experimental network point.

[0128] In an alternative embodiment of the present application, the determining a control network point similar to the experimental network point according to the shipment volume of the experimental network point includes:

[0129] Obtain the shipment volume of the experimental network point and the shipment volume of the candidate control network point;

[0130] Determine a control network point similar to the experimental network point from the candidate control network points according to the temporal similarity between the shipment volume of the experimental network point and the shipment volume of the candidate control network point.

[0131] The service resource analysis device provided in this embodiment belongs to the same inventive concept as the service resource analysis method provided in the above embodiments of the present application, and can execute the service resource analysis method provided in any of the above embodiments of the present application, and has corresponding functional modules and beneficial effects for executing the service resource analysis method. For technical details not described in detail in this embodiment, reference may be made to the specific processing content of the service resource analysis method provided in the above embodiments of the present application, which will not be elaborated here.

[0132] The functions implemented by the above analysis unit 501 and determination unit 502 can be realized by the same or different processors respectively, and the embodiments of the present application do not make limitations.

[0133] It should be understood that the units in the above device can be implemented in the form of a processor calling software. For example, the device includes a processor, which is connected to a memory. Instructions are stored in the memory, and the processor calls the instructions stored in the memory to implement any of the above methods or the functions of each unit of the device. The processor can be a general-purpose processor, such as a CPU or a microprocessor, etc., and the memory can be a memory inside the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of hardware circuits. By designing the hardware circuits, the functions of some or all of the units can be realized. The hardware circuits can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and by designing the logical relationships of the components in the circuit, the functions of some or all of the above units are realized. Again, for example, in another implementation, the hardware circuit can be implemented by a PLD. Taking an FPGA as an example, it can include a large number of logic gate circuits, and the connection relationships between the logic gate circuits are configured through a configuration file, so as to realize the functions of some or all of the above units. All units of the above device can be all implemented in the form of a processor calling software, or all implemented in the form of hardware circuits, or some implemented in the form of a processor calling software, and the remaining part implemented in the form of hardware circuits.

[0134] In the embodiments of the present application, a processor is a circuit with the ability to process signals. In one implementation, the processor can be a circuit with the ability to read and execute instructions, such as a CPU, a microprocessor, a GPU, or a DSP, etc. In another implementation, the processor can realize certain functions through the logical relationships of hardware circuits, and the logical relationships of the hardware circuits are fixed or can be reconstructed. For example, the processor is a hardware circuit implemented by an ASIC or a PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to realize the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as a kind of ASIC, such as an NPU, a TPU, a DPU, etc.

[0135] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above methods, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0136] In addition, each unit in the above device can be integrated in whole or in part, or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a System on Chip (SOC). The SOC can include at least one processor for implementing any of the above methods or the functions of each unit of the device. The types of the at least one processor can be different, such as including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0137] Exemplary electronic device

[0138] Another embodiment of the present application further provides an electronic device. Please refer to Figure 4 , Figure 4 , which is a schematic structural diagram of the electronic device provided by the embodiment of the present application.

[0139] As Figure 4 shown, the electronic device includes:

[0140] a memory 200 and a processor 210;

[0141] Among them, the memory 200 is connected to the processor 210 and is used for storing programs;

[0142] The processor 210 is configured to implement the service resource analysis method disclosed in any of the above embodiments by running the programs stored in the memory 200.

[0143] Specifically, the above electronic device may further include: a bus, a communication interface 220, an input device 230, and an output device 240.

[0144] The processor 210, the memory 200, the communication interface 220, the input device 230, and the output device 240 are interconnected through the bus. Among them:

[0145] The bus may include a path for transmitting information between various components of the computer system.

[0146] The processor 210 may be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or may be an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention solution. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0147] The processor 210 may include a main processor, and may further include a baseband chip, a modem, etc.

[0148] The program for implementing the technical solution of the present invention is stored in the memory 200, and the operating system and other key services can also be stored. Specifically, the program may include program codes, and the program codes include computer operation instructions. More specifically, the memory 200 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk memory, a flash memory, etc.

[0149] The input device 230 may include devices for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor, etc.

[0150] The output device 240 may include devices for allowing information to be output to a user, such as a display screen, a printer, a speaker, etc.

[0151] The communication interface 220 may include devices of any transceiver type for communicating with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0152] The processor 210 executes the program stored in the memory 200 and calls other devices, and can be used to implement each step of any one of the service resource analysis methods provided in the above embodiments of the present application.

[0153] Exemplary computer program product and storage medium

[0154] In addition to the above methods and devices, the embodiments of the present application may also be a computer program product, which includes computer program instructions. When the computer program instructions are run by a processor, the processor is caused to execute the steps in the service resource analysis method according to various embodiments of the present application described in the "Exemplary Method" section of the present specification.

[0155] The computer program product can be written in any combination of one or more programming languages for executing the program codes for operating the embodiments of the present application. The programming languages include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as the "C" language or similar programming languages. The program codes can be executed entirely on a user computing device, partially on a user device, executed as an independent software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0156] In addition, an embodiment of the present application may also be a storage medium storing a computer program, which is executed by a processor to perform the steps in the service resource analysis method according to various embodiments of the present application described in the "Exemplary Method" section above of this specification. Specifically, the following steps may be implemented:

[0157] Step S101: Adjust the first shipment volume change curve of the control network point with the shipment volume of the experimental network point as the target; the first shipment volume change curve is the curve of the shipment volume of the control network point changing with time;

[0158] Step S102: Determine the first shipment volume of the control network point based on the adjusted first shipment volume change curve; the first shipment volume includes the shipment volume of the control network point at a first time, and the first time is after the experimental network point obtains the service resource;

[0159] Step S103: Determine the influence of the service resource on the shipment volume of the experimental network point according to the first shipment volume and the shipment volume of the experimental network point at the first time.

[0160] For the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps may be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0161] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0162] The steps in the methods of the embodiments of the present application can be adjusted, combined, and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0163] The modules and sub-modules in the devices and terminals in the embodiments of the present application can be combined, divided, and deleted according to actual needs.

[0164] In several embodiments provided by the present application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the terminal embodiments described above are merely illustrative. For example, the division of modules or sub-modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple sub-modules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be an indirect coupling or communication connection through some interfaces, devices, or modules, and can be in electrical, mechanical, or other forms.

[0165] The modules or sub-modules described as separate components may or may not be physically separated. The components as modules or sub-modules may or may not be physical modules or sub-modules, that is, they can be located in one place, or can be distributed to multiple network modules or sub-modules. Some or all of the modules or sub-modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, each functional module or sub-module in various embodiments of the present application can be integrated in a processing module, or each module or sub-module can exist physically alone, or two or more modules or sub-modules can be integrated in one module. The above-mentioned integrated modules or sub-modules can be implemented in the form of hardware, or in the form of software functional modules or sub-modules.

[0167] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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.

[0168] The steps of the methods or algorithms described in combination with the embodiments disclosed in this article can be directly implemented by hardware, software units executed by a processor, or a combination of the two. The software units can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0169] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0170] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A service resource analysis method, characterized in that Including: Taking the shipment volume of the experimental network point as the target, adjusting the first shipment volume change curve of the control network point; the first shipment volume change curve is the curve of the shipment volume of the control network point changing with time; Based on the adjusted first shipment volume change curve, determining the first shipment volume of the control network point; the first shipment volume includes the shipment volume of the control network point at the first time, and the first time is after the experimental network point obtains service resources; According to the first shipment volume and the shipment volume of the experimental network point at the first time, determining the influence of the service resources on the shipment volume of the experimental network point.

2. The method according to claim 1, characterized in that, The step of taking the shipment volume of the experimental network point as the target and adjusting the first shipment volume change curve of the control network point includes: Obtaining the first shipment volume change curve of the control network point; According to the shipment volumes of the experimental network point at different times, increasing or decreasing the shipment volume of the first shipment volume change curve year-on-year to obtain the adjusted first shipment volume change curve.

3. The method according to claim 2, wherein The step of according to the shipment volumes of the experimental network point at different times, increasing or decreasing the shipment volume of the first shipment volume change curve year-on-year to obtain the adjusted first shipment volume change curve includes: Inputting the shipment volume of the experimental network point and the first shipment volume change curve into a pre-trained data adjustment model, so that the data adjustment model increases or decreases the shipment volume of the first shipment volume change curve year-on-year according to the shipment volumes of the experimental network point at different times to obtain the adjusted first shipment volume change curve.

4. The method according to claim 1, wherein The step of according to the first shipment volume and the shipment volume of the experimental network point at the first time, determining the influence of the service resources on the shipment volume of the experimental network point includes: Determining the influence of the service resources on the shipment volume of the experimental network point according to the difference between the first shipment volume and the shipment volume of the experimental network point at the first time.

5. The method according to claim 4, characterized in that, The step of determining the influence of the service resources on the shipment volume of the experimental network point according to the difference between the first shipment volume and the shipment volume includes: Based on the first shipment volume change curve, determining the second shipment volume change curve of the control network point after the experimental network point obtains service resources; According to the first shipment volume, obtaining the third shipment volume change curve after the experimental network point obtains service resources; According to the difference between the second shipment volume change curve and the third shipment volume change curve, determining the influence of the service resources on the shipment volume of the experimental network point; Wherein, the second shipment volume change curve is the curve of the shipment volume reflected by the first shipment volume change curve changing with time after the experimental network point obtains service resources; the third shipment volume change curve is the curve of the shipment volume of the experimental network point changing with time after the experimental network point obtains service resources.

6. The method according to claim 1, wherein It also includes: According to the shipment volume of the experimental network point, determining a control network point similar to the experimental network point.

7. The method according to claim 6, wherein The step of according to the shipment volume of the experimental network point, determining a control network point similar to the experimental network point includes: Obtaining the shipment volume of the experimental network point and the shipment volume of the candidate control network point; Determine a control network point similar to the experimental network point from the candidate control network points according to the temporal similarity between the shipment volume of the experimental network point and the shipment volume of the candidate control network points.

8. A service resource analysis device, characterized in that Including: A first unit, configured to adjust a first shipment volume change curve of a control network point with the goal of approaching the shipment volume of the experimental network point; the first shipment volume change curve is a curve of the shipment volume of the control network point changing with time; A second unit, configured to determine the first shipment volume of the control network point based on the adjusted first shipment volume change curve; the first shipment volume includes the shipment volume of the control network point at a first time, and the first time is after the experimental network point obtains service resources; A third unit, configured to determine the influence of the service resources on the shipment volume of the experimental network point according to the first shipment volume and the shipment volume of the experimental network point at the first time.

9. An electronic device, characterized in that, Including: A processor; A memory for storing instructions executable by the processor; The processor is configured to execute the service resource analysis method according to any one of claims 1 to 7 by running the instructions in the memory.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is run by the processor, it executes the service resource analysis method according to any one of claims 1 to 7.

Images