Quantization parameter evaluation method and device, electronic equipment and storage medium

Abstract

The invention discloses a quantization parameter evaluation method and device, electronic equipment and a storage medium. The specific implementation scheme comprises the following steps: determining service support domain data; identifying the service support domain data to obtain at least one target scene of the to-be-predicted object; and for each target scene, according to the network quality data in the target scene, a quantization parameter evaluation result is determined, and the quantization parameter is used for quantifying the condition that the business service is continuously used. By analyzing the business support domain data related to the business service, the target scene where the object to be predicted is located is obtained, the analysis of the object to be predicted in different target scenes is realized, and the quantitative parameter evaluation result is determined according to the network quality data in each target scene. The influence of the network quality on the continuous use of the business service by the to-be-predicted user in different target scenes is focused, dynamic quantitative parameter evaluation is realized, and the data analysis cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method, apparatus, electronic device, and storage medium for quantitative parameter evaluation. Background Technology

[0002] Assessing whether users will continue to use telecommunications services is typically done by collecting user feedback through questionnaires or interviews. Telecommunications services refer to a general term for services such as voice calls, image transmission, and electronic data applications that utilize communication network resources such as electromagnetic or optoelectronic systems. Collecting user feedback through questionnaires or interviews relies on users' subjective feelings, which may lead to incomplete assessments or inconsistencies with actual usage.

[0003] Whether a user will continue to use telecommunications services is influenced by a variety of factors, including the user's environment and external conditions, which change rapidly over time. In different scenarios, questionnaires or interviews result in poor dynamism in the assessment. Furthermore, questionnaires or interviews are costly, typically involving high market research and data analysis costs when conducting a comprehensive analysis of users' continued telecommunications service usage intentions. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for quantitative parameter evaluation to enable users to evaluate the quantitative parameters of business services in different scenarios, thereby improving the dynamism of quantitative parameter evaluation and reducing costs.

[0005] According to one aspect of the present invention, a method for evaluating quantitative parameters is provided, comprising:

[0006] Determine the business support domain data, which is related to the business service corresponding to the object to be predicted;

[0007] The business support domain data is identified to obtain at least one target scenario of the object to be predicted, and the target scenario includes the scenario in which the object to be predicted uses the business service.

[0008] For each target scenario, a quantitative parameter evaluation result is determined based on the network quality data in the target scenario. The quantitative parameter is used to quantify the continuous use of the service. The quantitative parameter evaluation result indicates whether the object to be predicted will continue to use the service in the target scenario.

[0009] According to another aspect of the present invention, a quantitative parameter evaluation apparatus is provided, comprising:

[0010] The first determining module is used to determine business support domain data, which is related to the business service corresponding to the object to be predicted;

[0011] The identification module is used to identify the business support domain data to obtain at least one target scenario of the object to be predicted, the target scenario including the scenario when the object to be predicted uses the business service;

[0012] The second determining module is used to determine a quantitative parameter evaluation result for each target scenario based on network quality data in the target scenario. The quantitative parameter is used to quantify the continuous use of the service. The quantitative parameter evaluation result indicates whether the object to be predicted will continue to use the service in the target scenario.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the quantitative parameter evaluation method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the quantization parameter evaluation method according to any embodiment of the present invention.

[0018] The technical solution of this invention involves determining business support domain data; identifying the business support domain data to obtain at least one target scenario for the object to be predicted; and for each target scenario, determining a quantitative parameter evaluation result based on network quality data within that target scenario. The quantitative parameter is used to quantify the continued use of the business service. By analyzing business support domain data related to the business service, the target scenario of the object to be predicted is obtained, enabling analysis of the object to be predicted under different target scenarios. In each target scenario, a quantitative parameter evaluation result is determined based on network quality data. This approach focuses on the impact of network quality on the continued use of the business service by the predicted user under different target scenarios, achieving dynamic quantitative parameter evaluation and reducing data analysis costs.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a quantitative parameter evaluation method provided in Embodiment 1 of the present invention;

[0022] Figure 2 This is a flowchart of a target scene determination method provided in Embodiment 2 of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of a quantitative parameter evaluation device provided in Embodiment 3 of the present invention;

[0024] Figure 4 This is a block diagram of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] It is understood that before using the technical methods disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0028] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to autonomously choose whether to provide personal information to the software or hardware, such as the electronic device, application program, server, or storage medium performing the operations of this invention, based on the prompt message.

[0029] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0030] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0031] The acquisition, storage, use, and processing of data in the technical solution of this invention all comply with the relevant provisions of laws and regulations.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a quantitative parameter evaluation method provided in Embodiment 1 of the present invention. This embodiment can be applied to the situation where the business service is continuously used. The method can be executed by a quantitative parameter evaluation device, which can be implemented in hardware and / or software. The quantitative parameter evaluation device can be configured in an electronic device, which can be a PC or a server, etc.

[0034] Whether a user continues to use a telecommunications service is influenced by a variety of factors, typically directly related to user satisfaction with the service, the uniqueness and cost-effectiveness of the service, and user experience.

[0035] like Figure 1 As shown, the method includes:

[0036] S110. Determine the business support domain data.

[0037] The business support domain data is related to the business services corresponding to the object to be predicted.

[0038] In this embodiment, service support domain data can be understood as data composed of information generated during the process of the object to be predicted using service. Service support domain data may include the location data and data traffic usage of the object to be predicted. The object to be predicted can be understood as an object that can use service, and the object to be predicted can use service in multiple different scenarios. Service can be understood as a service that utilizes wired or wireless communication network resources to provide services such as voice calls, image transmission, and data applications; the service can be a service used by the object to be predicted.

[0039] Specifically, when the target object uses business services, data related to those services is collected. This data undergoes preprocessing such as data cleaning, and is then divided into time windows to obtain the business support domain data.

[0040] S120. Identify the business support domain data to obtain at least one target scenario of the object to be predicted.

[0041] The target scenario includes the scenario in which the object to be predicted uses the business service.

[0042] In this embodiment, the target scenario can be understood as the scenario in which the object to be predicted uses the business service. The target scenario can be a clustered location among the locations where the object to be predicted uses the business service.

[0043] Specifically, data related to the location of the object to be predicted can be extracted from the business support domain data, and this location-related data can be clustered to obtain the clustered locations within the object's location. For each clustered location, data related to the base station providing the service can be used to determine whether each clustered location is the target scenario for the object to be predicted. The base station providing the service to the object to be predicted includes at least one base station, and when the object to be predicted uses the service, the base station can switch based on the object's location.

[0044] For example, clustering algorithms can be used to cluster data related to the location of the object to be predicted in the business support domain data. Clustering algorithms such as density-based spatial clustering of applications with noise (DBSCAN) can be used.

[0045] S130. For each target scenario, determine the quantitative parameter evaluation result based on the network quality data in the target scenario.

[0046] The quantification parameter is used to quantify the continued use of the business service, and the evaluation result of the quantification parameter indicates whether the object to be predicted will continue to use the business service in the target scenario.

[0047] In this embodiment, network quality data can be understood as a quantitative indicator of the network quality of the target object when it uses the service. Network quality data may include signal strength, transmission speed, and latency of the device used by the target object in the target scenario. The quantitative parameter evaluation result indicates whether the target object will continue to use the service in the target scenario. The quantitative parameter can be understood as a parameter used to quantify the continued use of the service by the target object.

[0048] Specifically, for each target scenario, when the object to be predicted uses business services in that scenario, the network quality data in that scenario is determined. This network quality data can be analyzed using a multiple linear regression model to obtain quantitative parameter evaluation results for the object to be predicted in that target scenario.

[0049] For example, the evaluation results of these quantitative parameters can help entities providing business services identify the risk of churn among potential customers in advance. When the evaluation results indicate that a potential customer will not continue to use the business service in the target scenario, it means that the business service does not provide satisfactory service to the potential customer. Measures can then be developed to improve the business service, thereby increasing the potential customer's willingness to use it.

[0050] The technical solution of this invention involves determining business support domain data; identifying the business support domain data to obtain at least one target scenario for the object to be predicted; and for each target scenario, determining a quantitative parameter evaluation result based on network quality data within that target scenario. The quantitative parameter is used to quantify the continued use of the business service. By analyzing business support domain data related to the business service, the target scenario of the object to be predicted is obtained, enabling analysis of the object to be predicted under different target scenarios. In each target scenario, a quantitative parameter evaluation result is determined based on network quality data. This approach focuses on the impact of network quality on the continued use of the business service by the predicted user under different target scenarios, achieving dynamic quantitative parameter evaluation and reducing data analysis costs.

[0051] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0052] In one embodiment, determining the business support domain data includes:

[0053] Determine object-related data, which includes data on the object to be predicted when it uses the business service;

[0054] Determine at least one time window corresponding to the object to be predicted;

[0055] For each time window, extract the target object-related data corresponding to the time window from the object-related data;

[0056] The target object-related data corresponding to each of the aforementioned time windows are determined as business support domain data.

[0057] In this embodiment, object-related data can be understood as data collected during the process of the object to be predicted using business services. Object-related data can include data corresponding to various structured and unstructured information. A time window can be understood as a continuous interval in time, which can be divided according to the activity of the object to be predicted. For example, morning, noon, and evening are each a time window. Target object-related data can be understood as object-related data within a single time window. Target object-related data can be data of the object to be predicted using business services within a single time window.

[0058] Specifically, when the object to be predicted uses business services, relevant data is collected, and at least one time window is defined for each object. The collected data is then cleaned to obtain object-related data. Data cleaning may include removing erroneous data records, such as those with abnormal location coordinates or timestamps. For each time window, target object-related data for that time window is extracted from the object-related data. Finally, the target object-related data for each time window can be sequentially arranged to obtain the business support domain data.

[0059] In one embodiment, determining the quantitative parameter evaluation result based on network quality data in the target scenario includes:

[0060] Determine the scene type identifier corresponding to the target scene;

[0061] Based on the network quality data, the scenario type identifier, and the multiple linear regression model, the data usage is obtained, which includes the data traffic consumed by the object to be predicted when using the business service in the target scenario.

[0062] When the data usage exceeds the data usage threshold, the quantitative parameter evaluation result indicates that the object to be predicted will continue to use the business service in the target scenario;

[0063] Otherwise, the quantitative parameter evaluation result indicates that the object to be predicted will not continue to use the business service in the target scenario.

[0064] In this embodiment, the scene type identifier can be understood as an identifier indicating the type to which the target scene belongs. The multiple linear regression model can be understood as a statistical analysis method that can be used to process multiple variables contained in network quality data and evaluate the degree of influence of each variable on data usage. Data usage can be understood as the total amount of data traffic consumed by the object to be predicted when using business services in the target scene. Data usage can be used to determine the tendency of the object to be predicted to continuously use business services in the target scene.

[0065] Specifically, based on the type of the target scenario, a scenario type identifier is determined. This identifier is used to determine the coefficients of each variable in the multiple linear regression model. Therefore, the coefficients of the multiple linear regression model will differ for different target scenarios. After determining the coefficients of the multiple linear regression model using the scenario type identifier, network quality data is input into the model to output the data usage. When the data usage exceeds the data usage threshold, it indicates that the target object consumes a large amount of data traffic when using business services in that target scenario. Therefore, the quantitative parameter evaluation result indicates that the target object will continue to use business services in that target scenario. Otherwise, it indicates that the target object consumes a small amount of data traffic when using business services in that target scenario, and the quantitative parameter evaluation result indicates that the target object will not continue to use business services in that target scenario.

[0066] Optionally, the process of obtaining data usage based on the network quality data, the scene type identifier, and the multiple linear regression model includes:

[0067] The network quality data includes network signal strength, data transmission speed, and network latency. The network signal strength includes the signal strength in the target scene, the data transmission speed includes the transmission speed of the device used by the object to be predicted, and the network latency includes the signal delay time in the target scene.

[0068] Based on the scenario type identifier, the independent variable coefficients corresponding to the multiple linear regression model are determined, and the independent variable coefficients include the independent variable coefficients corresponding to the network signal strength, the data transmission speed, and the network latency;

[0069] The data usage is obtained by weighted summation of the network quality data and the independent variable coefficients.

[0070] In this embodiment, network signal strength can be understood as the signal strength in the target scene. Network signal strength can be a continuous variable, and can be the signal strength corresponding to data traffic in the target scene. Data transmission speed can be understood as the transmission speed of the device used by the object to be predicted, and can be the average transmission speed of the device used by the object to be predicted. Network latency can be understood as the delay time of the signal in the target scene, and can be the average delay time of network response in the target scene. The independent variable coefficients can be used to quantify the degree of influence of network signal strength, data transmission speed, and network latency on data usage.

[0071] For example, the multiple linear regression model used can be expressed as: Y = β0 + β1X1 + β2X2 + β3X3 + ... + βnXn + ϵ, where Y is the dependent variable, i.e., data usage; β0 is the intercept term; β1, β2, ..., βn are the coefficients of the independent variables; X1, X2, ..., Xn are the independent variables, i.e., network signal strength, data transmission speed, and network latency; and ϵ is the error term. For example, if the independent variables are: network signal strength -100 dBm, data transmission speed 5 Mbps, network latency 100 ms, and scenario type (C), then the dependent variable is calculated to be: data usage 2GB.

[0072] Example 2

[0073] Figure 2 This is a flowchart of a target scene determination method according to Embodiment 2 of the present invention. This embodiment focuses on the target scene determination method described in the above embodiment. Figure 2 As shown, the method includes:

[0074] S210. Determine the business support domain data.

[0075] S220. Identify the business support domain data to obtain at least one commonly used area location corresponding to the object to be predicted.

[0076] The commonly used regional locations include locations with clustering patterns among the locations of the object to be predicted.

[0077] In this embodiment, the commonly used regional location can be understood as the clustered location where the object to be predicted is located during the process of the object using the business service.

[0078] Specifically, data related to the location of the object to be predicted is extracted from the business support domain data, and clustering algorithms are used to cluster this location-related data. The clustering results represent the locations with clustered patterns among the locations of the object to be predicted, and these locations are designated as frequently visited areas. Frequently visited areas include those frequently accessed by the object to be predicted, such as its workplace or residence.

[0079] Optionally, the step of identifying the business support domain data to obtain at least one commonly used area location corresponding to the object to be predicted includes:

[0080] Extract object location data from the business support domain data, wherein the object location data indicates the location of the object to be predicted when using the business service;

[0081] Cluster the object location data to obtain at least one commonly used region location corresponding to the object to be predicted.

[0082] In this embodiment, object location data can be understood as data related to the location of the object to be predicted in the business support domain data, and the object to be predicted uses business services at the location indicated by the object location data.

[0083] For example, a clustering algorithm (such as DBSCAN) is used to cluster the object location data related to the location of the object to be predicted in the business support domain data to obtain at least one commonly used area location of the object to be predicted.

[0084] S230. Extract the switching record from the business support domain data.

[0085] The handover record indicates the number of times the serving base station is switched when the object to be predicted uses the service, and the serving base station includes the base station providing the service.

[0086] In this embodiment, the handover record can be understood as the number of times the serving base station is switched when the object to be predicted uses the service. The handover record can indicate whether the object to be predicted is engaging in frequent activities.

[0087] Specifically, during the process of the object to be predicted using the service, there is at least one serving base station providing the service to the object to be predicted. Therefore, if there are activity records when the object to be predicted uses the service, such as when it moves its location, the serving base station providing the service will switch. At this time, the number of switching of the serving base station providing the service to the base station to be predicted can be recorded as a switching record in the service support domain data.

[0088] S240. For each frequently used area location, determine whether the frequently used area location is the target scene of the object to be predicted based on the switching record.

[0089] Specifically, for each frequently used location, the switching records within that location can be extracted. These records indicate the activity of the object to be predicted within that location. Based on these activity records, it can be determined whether that location is the target scene for the object to be predicted.

[0090] Optionally, determining whether the frequently used area location is the target scene of the object to be predicted based on the switching record includes:

[0091] The first switching record and the second switching record included in the switching record are determined, wherein the time window corresponding to the first switching record is a working day, and the time window corresponding to the second switching record is a non-working day;

[0092] When the first handover record indicates that the number of handovers of the serving base station is less than a first set threshold, the commonly used area location is determined as the first target scenario of the object to be predicted, and the first target scenario indicates that the object to be predicted has a continuous activity record on the working day;

[0093] When the first handover record indicates that the number of handovers of the serving base station is greater than the second set threshold, the commonly used area location is determined as the second target scenario of the object to be predicted. The second target scenario indicates that the object to be predicted does not have a continuous activity record on the working day.

[0094] When the second handover record indicates that the number of handovers of the serving base station is less than the first set threshold, the commonly used area location is determined as the third target scenario of the object to be predicted, and the third target scenario indicates that the object to be predicted has a continuous activity record on the non-working day;

[0095] Wherein, the value of the first set threshold is less than the value of the second set threshold.

[0096] In this embodiment, the first handover record can be understood as a handover record with a time window of working days. The second handover record can be understood as a handover record with a time window of non-working days. The first set threshold and the second set threshold can be understood as thresholds used to determine the number of handovers of the serving base station, and the value of the first set threshold is less than the value of the second set threshold. The first target scenario indicates that the object to be predicted has continuous activity records on working days, and the first target scenario is a target scenario on working days, such as a work scenario. The second target scenario indicates that the object to be predicted does not have continuous activity records on working days, and the second target scenario is also a target scenario on working days, such as a commuting scenario. The third target scenario indicates that the object to be predicted has continuous activity records on non-working days, and the third target scenario is a target scenario on non-working days, such as a residential scenario.

[0097] Specifically, for each frequently used area location, a first handover record for weekdays and a second handover record for non-weekdays are determined. When the first handover record indicates that the number of handovers by the serving base station is less than a first set threshold, it indicates that the number of handovers by the serving base station is low, and the target object has continuous activity records on weekdays, thus the frequently used area location is determined as the first target scenario for the target object. When the first handover record indicates that the number of handovers by the serving base station is greater than a second set threshold, it indicates that the number of handovers by the serving base station is high, and the target object has no continuous activity records on weekdays, thus the frequently used area location is determined as the second target scenario for the target object. When the second handover record indicates that the number of handovers by the serving base station is less than the first set threshold, it indicates that the number of handovers by the serving base station is low, and the target object has continuous activity records on non-weekdays, thus the frequently used area location is determined as the third target scenario for the target object.

[0098] S250. For each target scenario, determine the quantitative parameter evaluation results based on the network quality data in the target scenario.

[0099] The technical solution of this invention involves identifying the business support domain data to obtain at least one commonly used area location corresponding to the object to be predicted; extracting switching records from the business support domain data; and determining, based on the switching records, whether the commonly used area location is the target scenario of the object to be predicted. By identifying the business support domain data related to business services, the commonly used area location of the object to be predicted is obtained, enabling scenario-based analysis of the object to be predicted. Based on the switching records in the business support domain data, it is determined whether the commonly used area location is the target scenario of the object to be predicted, thus achieving analysis of the object to be predicted under different target scenarios.

[0100] Example 3

[0101] Figure 3 This is a schematic diagram of a quantitative parameter evaluation device provided according to Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0102] The first determining module 310 is used to determine business support domain data, which is related to the business service corresponding to the object to be predicted;

[0103] The identification module 320 is used to identify the business support domain data to obtain at least one target scenario of the object to be predicted, the target scenario including the scenario when the object to be predicted uses the business service;

[0104] The second determining module 330 is used to determine a quantitative parameter evaluation result for each target scenario based on the network quality data in the target scenario. The quantitative parameter is used to quantify the continuous use of the service. The quantitative parameter evaluation result indicates whether the object to be predicted will continue to use the service in the target scenario.

[0105] The quantitative parameter evaluation device provided in this embodiment of the invention determines service support domain data through a first determining module; identifies the service support domain data through an identification module to obtain at least one target scenario for the object to be predicted; and determines a quantitative parameter evaluation result for each target scenario based on network quality data in the target scenario, wherein the quantitative parameters are used to quantify the continuous use of the service. Through the cooperation between the modules, the service support domain data related to the service is analyzed to obtain the target scenario where the object to be predicted is located. This enables the analysis of the object to be predicted under different target scenarios, and the determination of the quantitative parameter evaluation result based on network quality data in each target scenario. It focuses on the impact of network quality on the continuous use of the service by the predicted user under different target scenarios, achieving dynamic quantitative parameter evaluation and reducing data analysis costs.

[0106] In one embodiment, the first determining module 310 is specifically used for:

[0107] Determine object-related data, which includes data on the object to be predicted when it uses the business service;

[0108] Determine at least one time window corresponding to the object to be predicted;

[0109] For each time window, extract the target object-related data corresponding to the time window from the object-related data;

[0110] The target object-related data corresponding to each of the aforementioned time windows are determined as business support domain data.

[0111] In one embodiment, the identification module 320 includes:

[0112] The identification unit is used to identify the business support domain data and obtain at least one commonly used area location corresponding to the object to be predicted. The commonly used area location includes locations with clustering among the locations of the object to be predicted.

[0113] An extraction unit is configured to extract handover records from the service support domain data. The handover records indicate the number of times the serving base station is switched when the object to be predicted uses the service. The serving base station includes the base station that provides the service.

[0114] The first determining unit is used to determine, based on the switching record, whether the location of the commonly used area is the target scene of the object to be predicted for each commonly used area location.

[0115] In one embodiment, the first determining unit is specifically used for:

[0116] The first switching record and the second switching record included in the switching record are determined, wherein the time window corresponding to the first switching record is a working day, and the time window corresponding to the second switching record is a non-working day;

[0117] When the first handover record indicates that the number of handovers of the serving base station is less than a first set threshold, the commonly used area location is determined as the first target scenario of the object to be predicted, and the first target scenario indicates that the object to be predicted has a continuous activity record on the working day;

[0118] When the first handover record indicates that the number of handovers of the serving base station is greater than the second set threshold, the commonly used area location is determined as the second target scenario of the object to be predicted. The second target scenario indicates that the object to be predicted does not have a continuous activity record on the working day.

[0119] When the second handover record indicates that the number of handovers of the serving base station is less than the first set threshold, the commonly used area location is determined as the third target scenario of the object to be predicted, and the third target scenario indicates that the object to be predicted has a continuous activity record on the non-working day;

[0120] Wherein, the value of the first set threshold is less than the value of the second set threshold.

[0121] In one embodiment, the identification unit is specifically used for:

[0122] Extract object location data from the business support domain data, wherein the object location data indicates the location of the object to be predicted when using the business service;

[0123] Cluster the object location data to obtain at least one commonly used region location corresponding to the object to be predicted.

[0124] In one embodiment, the second determining module 330 includes:

[0125] The second determining unit is used to determine the scene type identifier corresponding to the target scene;

[0126] The regression unit is used to obtain the data usage based on the network quality data, the scene type identifier, and the multiple linear regression model. The data usage includes the data traffic consumed by the object to be predicted when using the business service in the target scene.

[0127] The first indication unit is used to indicate, when the data usage exceeds the data usage threshold, that the quantitative parameter evaluation result will indicate that the object to be predicted will continue to use the business service in the target scenario.

[0128] The second indication unit is used to indicate, otherwise, that the quantitative parameter evaluation result indicates that the object to be predicted will not continue to use the business service in the target scenario.

[0129] In one embodiment, the regression unit is specifically used for:

[0130] The network quality data includes network signal strength, data transmission speed, and network latency. The network signal strength includes the signal strength in the target scene, the data transmission speed includes the transmission speed of the device used by the object to be predicted, and the network latency includes the signal delay time in the target scene.

[0131] Based on the scenario type identifier, the independent variable coefficients corresponding to the multiple linear regression model are determined, and the independent variable coefficients include the independent variable coefficients corresponding to the network signal strength, the data transmission speed, and the network latency;

[0132] The data usage is obtained by weighted summation of the network quality data and the independent variable coefficients.

[0133] The quantitative parameter evaluation device provided in this embodiment of the invention can execute the quantitative parameter evaluation method provided in any embodiment of the invention. Through the cooperation and collaborative work between the modules, the quantitative parameter evaluation is completed, and it has the corresponding functional modules and beneficial effects of the execution method.

[0134] Example 4

[0135] According to embodiments of the present invention, the present invention also provides an electronic device and a computer-readable storage medium.

[0136] Figure 4This is a block diagram of an electronic device according to Embodiment 4 of the present invention, which implements the quantitative parameter evaluation method of the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0137] like Figure 4 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0138] Multiple components in the electronic device are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, optical disk, etc.; and a communication unit 419, such as a network card, modem, wireless transceiver, etc. The communication unit 419 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0139] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as quantization parameter evaluation methods.

[0140] In some embodiments, the quantization parameter evaluation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the quantization parameter evaluation method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured to perform the quantization parameter evaluation method by any other suitable means (e.g., by means of firmware).

[0141] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0144] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0145] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0146] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0147] The technical solution of this invention provides a quantitative parameter evaluation method, apparatus, electronic device, and storage medium. It involves determining service support domain data; identifying the service support domain data to obtain at least one target scenario for the object to be predicted; and for each target scenario, determining a quantitative parameter evaluation result based on network quality data within that target scenario. The quantitative parameters are used to quantify the continuous use of the service. By analyzing service support domain data related to the service, the target scenario of the object to be predicted is obtained, enabling analysis of the object to be predicted under different target scenarios. The quantitative parameter evaluation result is determined based on network quality data in each target scenario, focusing on the impact of network quality on the continuous use of the service by the predicted user under different target scenarios. This achieves dynamic quantitative parameter evaluation and reduces data analysis costs.

[0148] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0149] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method of quantization parameter assessment, characterized by, The method comprises: determining service support domain data, the service support domain data being related to a service to which a to-be-predicted object corresponds; identifying the service support domain data to obtain at least one target scenario of the to-be-predicted object, the target scenario including a scenario in which the to-be-predicted object uses the service; for each target scenario, determining a quantitative parameter evaluation result according to network quality data in the target scenario, the quantitative parameter being used to quantify a case in which the service is continuously used, and the quantitative parameter evaluation result indicating whether the to-be-predicted object will continuously use the service in the target scenario.

2. The method of claim 1, wherein, The determining of the service support domain data comprises: determining object-related data, the object-related data including data in which the to-be-predicted object uses the service; determining at least one time window corresponding to the to-be-predicted object; for each time window, extracting target object-related data corresponding to the time window from the object-related data; determining the target object-related data corresponding to each time window as service support domain data.

3. The method of claim 1, wherein, The identifying of the service support domain data to obtain at least one target scenario of the to-be-predicted object comprises: identifying the service support domain data to obtain at least one frequently-used area position corresponding to the to-be-predicted object, the frequently-used area position including a position in which the to-be-predicted object is aggregated; extracting a switching record from the service support domain data, the switching record indicating a number of times of switching of a service base station when the to-be-predicted object uses the service, the service base station including a base station providing the service; for each frequently-used area position, determining whether the frequently-used area position is a target scenario of the to-be-predicted object based on the switching record.

4. The method of claim 3, wherein, The determining of whether the frequently-used area position is the target scenario of the to-be-predicted object based on the switching record comprises: determining a first switching record and a second switching record included in the switching record, a time window corresponding to the first switching record being a weekday, and a time window corresponding to the second switching record being a non-weekday; when the first switching record indicates that the number of times of switching of the service base station is less than a first set threshold, determining that the frequently-used area position is a first target scenario of the to-be-predicted object, the first target scenario indicating that the to-be-predicted object has a continuous activity record on the weekday; when the first switching record indicates that the number of times of switching of the service base station is greater than a second set threshold, determining that the frequently-used area position is a second target scenario of the to-be-predicted object, the second target scenario indicating that the to-be-predicted object does not have a continuous activity record on the weekday; when the second switching record indicates that the number of times of switching of the service base station is less than the first set threshold, determining that the frequently-used area position is a third target scenario of the to-be-predicted object, the third target scenario indicating that the to-be-predicted object has a continuous activity record on the non-weekday; wherein a value of the first set threshold is less than a value of the second set threshold.

5. The method of claim 3, wherein, The identifying the business support domain data obtains at least one frequently-used regional position corresponding to the to-be-predicted object, and the method comprises the following steps: extracting object position data from the business support domain data, wherein the object position data indicates a position of the to-be-predicted object when using the business service; clustering the object position data to obtain at least one frequently-used regional position corresponding to the to-be-predicted object.

6. The method of claim 1, wherein, The method comprises the following steps: determining a scene type identifier corresponding to the target scene; based on the network quality data, the scene type identifier, and a multiple linear regression model, obtaining a data usage, wherein the data usage comprises a data flow consumed by the to-be-predicted object when using the business service in the target scene; when the data usage is greater than a data usage threshold, the quantification parameter evaluation result indicates that the to-be-predicted object will continuously use the business service in the target scene; otherwise, the quantification parameter evaluation result indicates that the to-be-predicted object will not continuously use the business service in the target scene.

7. The method of claim 6, wherein, The method comprises the following steps: determining network signal strength, data transmission speed, and network delay contained in the network quality data, wherein the network signal strength comprises the strength of the signal in the target scene, the data transmission speed comprises the transmission speed of the device used by the to-be-predicted object, and the network delay comprises the delay time of the signal in the target scene; determining an independent variable coefficient corresponding to the multiple linear regression model according to the scene type identifier, wherein the independent variable coefficient comprises an independent variable coefficient corresponding to the network signal strength, the data transmission speed, and the network delay; performing weighted summation on the network quality data and the independent variable coefficient to obtain the data usage.

8. A quantization parameter evaluation apparatus characterized by comprising: The method comprises the following steps: a first determining module is configured to determine business support domain data, wherein the business support domain data is related to a business service corresponding to a to-be-predicted object; an identifying module is configured to identify the business support domain data to obtain at least one target scene of the to-be-predicted object, wherein the target scene comprises a scene when the to-be-predicted object uses the business service; a second determining module is configured to, for each target scene, determine a quantification parameter evaluation result according to network quality data in the target scene, wherein the quantification parameter is used to quantify a continuous use situation of the business service, and the quantification parameter evaluation result indicates whether the to-be-predicted object will continuously use the business service in the target scene.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the quantification parameter evaluation method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the processor to implement the quantization parameter evaluation method in any one of claims 1-7 when executed.

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