Method, device, equipment, storage medium and program product for QCI calculation

Abstract

The application discloses a QCI calculation method, device, equipment, storage medium and program product. Specifically, the application comprises the following steps: obtaining to-be-detected user information, wherein the to-be-detected user information comprises information of different subjects in different service scenarios; determining whether the to-be-detected user information is consistent with user information stored in a historical record; in the case that the to-be-detected user information is inconsistent with the user information stored in the historical record, determining a target rule corresponding to the to-be-detected user information in a preset rule according to the information of the subject and the service scenario in the to-be-detected user information, wherein the preset rule comprises QCI calculation rules of different change subjects in different service scenarios; and calculating a QCI value by using the target rule. Thus, the accuracy of the QCI value calculation result can be improved.

Description

Technical Field

[0001] This application belongs to the field of communication technology, and in particular relates to a method, apparatus, device, storage medium and program product for QCI calculation. Background Technology

[0002] To better allocate network resources and provide differentiated services to different users, the Quality of Service (QoS) technology can be used in Long Term Evolution (LTE) networks to provide differentiated services to different users.

[0003] Among these, the QoS Class Identifier (QCI) in QoS technology can meet more differentiated service requirements. Currently, the QCI value is related to user location information. When the user's location information changes, the QCI value needs to be adjusted accordingly in order to provide differentiated services based on the changed QCI value.

[0004] However, when user profiles change, the corresponding services should also be adjusted accordingly. For example, if a user's level in their profile increases, it's necessary to provide better services and allocate more network resources to that user, requiring a recalculation of the QCI value. Therefore, the QCI value calculated using user location information is inaccurate, making it impossible to accurately provide differentiated services to different users. Summary of the Invention

[0005] This application provides a method, apparatus, device, storage medium, and program product for calculating QCI, so as to improve the accuracy of the calculated QCI value.

[0006] In a first aspect, embodiments of this application provide a method for calculating a Quality of Service Class Identifier (QCI), including:

[0007] Obtain the user information to be detected, which includes information from different entities in different business scenarios;

[0008] Determine whether the user information to be detected is consistent with the user information stored in the historical records;

[0009] In the case where the user information to be detected is inconsistent with the user information stored in the historical records, a target rule corresponding to the user information to be detected is determined in a preset rule according to the subject and business scenario information in the user information to be detected. The preset rule includes QCI calculation rules for different change subjects in different business scenarios.

[0010] The QCI value is calculated using the target rule.

[0011] In one possible implementation, determining whether the user information to be detected is consistent with the user information stored in the historical records includes:

[0012] The feature value string corresponding to the user information to be detected is generated according to the preset correspondence relationship. The preset correspondence relationship includes the feature values ​​corresponding to different change subjects in different business scenarios. The feature value string represents the change status of the change subject included in the user information to be detected in the corresponding business scenario.

[0013] Determine whether the sum of the feature values ​​included in the feature value string is greater than a preset value;

[0014] If the sum of the feature values ​​included in the feature value string is greater than a preset value, it is determined that the user information to be detected is inconsistent with the user information stored in the historical records;

[0015] If the sum of the feature values ​​included in the feature value string is less than or equal to a preset value, it is determined that the user information to be detected is consistent with the user information stored in the historical record.

[0016] In one possible implementation, the detected user information includes at least one subject; the step of generating the feature value string corresponding to the user information to be detected according to a preset correspondence includes:

[0017] Search for the subject included in the user information to be detected in the preset correspondence, and take the found subject as the target change subject;

[0018] Search the preset correspondence for the business scenario corresponding to the target change subject;

[0019] Based on the characteristic values ​​of the target change subject in each business scenario, a characteristic value string corresponding to the target change subject is generated.

[0020] In one possible implementation, determining the target rule corresponding to the user information to be detected from the preset rules includes:

[0021] The system searches for the entities included in the user information to be detected within the preset rules.

[0022] When the preset rules include the subjects in the user information to be detected, for each subject found, according to the business scenario in which the subject in the user information to be detected changes, the target business scenario is searched in the preset rules;

[0023] Determine the QCI calculation rules corresponding to the target business scenario to obtain the QCI calculation rule set corresponding to the user information to be detected;

[0024] From the set of QCI calculation rules, a target QCI calculation rule is randomly selected and used as the target rule.

[0025] In one possible implementation, each business scenario corresponds to a preset priority, and determining the target rule corresponding to the user information to be detected from the preset rules includes:

[0026] The system searches for the entities included in the user information to be detected within the preset rules.

[0027] When the preset rules include the subjects in the user information to be detected, for each subject found, according to the business scenario in which the subject in the user information to be detected changes, the target business scenario is searched in the preset rules;

[0028] The highest preset priority corresponding to each QCI calculation rule in the QCI calculation rule set is determined according to the preset priority corresponding to the business scenario, and the QCI calculation rule corresponding to the highest preset priority is taken as the target rule.

[0029] In one possible implementation, obtaining the user information to be detected includes:

[0030] Real-time acquisition of user information;

[0031] The acquired user information is processed and transformed, and the transformed user information is used as the user information to be tested.

[0032] Thirdly, embodiments of this application provide an apparatus for calculating a Quality of Service Class Identifier (QCI), comprising:

[0033] The acquisition module is used to acquire user information to be detected, which includes information of different subjects in different business scenarios;

[0034] The judgment module is used to determine whether the user information to be detected is consistent with the user information stored in the historical records;

[0035] The determination module is used to determine, in the case where the user information to be detected and the user information stored in the historical records are inconsistent, a target rule corresponding to the user information to be detected is determined in a preset rule according to the subject and business scenario information in the user information to be detected. The preset rule includes QCI calculation rules for different change subjects in different business scenarios.

[0036] The calculation module is used to calculate the QCI value using the target rule.

[0037] Thirdly, embodiments of this application provide an electronic device, the device including: a processor and a memory storing computer program instructions;

[0038] The method by which a processor performs QCI calculations as described in the first aspect when executing computer program instructions.

[0039] Fourthly, embodiments of this application provide a computer storage medium on which computer program instructions are stored, which, when executed by a processor, implement the QCI calculation method as described in the first aspect.

[0040] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the QCI calculation method as described in the first aspect.

[0041] The QCI calculation method, apparatus, device, storage medium, and program product provided in this application, after obtaining the user information to be detected, determines whether the user information to be detected is consistent with the user information stored in the historical records. If the user information to be detected is inconsistent with the user information stored in the historical records, it indicates that the user information to be detected has changed, and therefore the QCI value corresponding to the user information to be detected needs to be recalculated. According to the subject and business scenario in the user information to be detected, a target rule is matched in a preset rule, thereby updating the QCI value using the target rule. In this way, when the user information to be detected changes, the corresponding target rule can be matched in a timely manner using the user information to be detected, and the QCI value can be updated based on the obtained user data, improving the accuracy of QCI value calculation and thus enabling better differentiated services to different users. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating the QCI calculation method provided in the embodiments of this application;

[0044] Figure 2 This is a schematic diagram of the structure of a user-differentiated network service computing device provided in an embodiment of this application;

[0045] Figure 3This is an exemplary schematic diagram of a QCI calculation method provided in an embodiment of this application;

[0046] Figure 4 This is a schematic diagram of the structure of a QCI calculation device provided in an embodiment of this application;

[0047] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0048] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0049] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0050] To address the problems of existing technologies, embodiments of this application provide a method, apparatus, device, storage medium, and program product for QCI calculation. The following are combined with... Figure 1 This application introduces a QCI calculation method provided in its embodiments. This method is applied to a server, such as... Figure 1 As shown, the method includes:

[0051] S101. Obtain the information of the user to be tested.

[0052] The user information to be detected includes information about at least one entity in different business scenarios. An entity is used to represent different users; for example, the entity could be user A's account information.

[0053] In one example, the business scenarios in the user information to be detected can be user status, user star rating, user brand, user subscription, user terminal attributes, key customer attributes (such as sensitive complaint users, red list users, and high-value users), location information, user billing information, or user detailed bill data, etc.

[0054] It should be noted that the server can obtain the user information to be tested from various source systems. A source system refers to a system connected to the server that stores relevant data about each user. For example, if source system A is connected to the server, and user 1 registers their user information in source system A, the server can obtain user 1's user information from source system A.

[0055] S102. Determine whether the user information to be detected is consistent with the user information stored in the historical records.

[0056] Understandably, the server can store the user information it obtains locally each time. When new user information to be detected is obtained, it can check the historical records to determine whether the user information to be detected is consistent with the user information in the historical records.

[0057] Specifically, it can be determined whether the user is the same person by using account information or customer information. If the user information to be detected is the same user as the user in the history record, the user information to be detected and the user information stored in the history record are compared.

[0058] In one example, the server determines that the user corresponding to the user information to be detected is user A by reading account information, such as the user's mobile phone number. If the server's history records user information for user A, it then checks whether the user information to be detected matches the user information for user A stored in the history records.

[0059] S103. In the case where the user information to be detected is inconsistent with the user information stored in the historical records, determine the target rule corresponding to the user information to be detected in the preset rules according to the subject and business scenario information in the user information to be detected.

[0060] The preset rules include QCI calculation rules for different change subjects in different business scenarios.

[0061] S104. Calculate the QCI value using the target rule.

[0062] The target rule is the QCI calculation rule mentioned above.

[0063] Using the above method, after obtaining the user information to be detected, it is determined whether the user information to be detected is consistent with the user information stored in the historical records. If the user information to be detected is inconsistent with the user information stored in the historical records, it indicates that the user information to be detected has changed, and therefore the QCI value corresponding to the user information to be detected needs to be recalculated. The target rule is matched against preset rules according to the subject and business scenario in the user information to be detected, so that the QCI value can be updated using the target rule. In this way, when the user information to be detected changes, the corresponding target rule can be matched in a timely manner using the user information to be detected, and the QCI value can be updated based on the obtained user data, improving the accuracy of the QCI value calculation and thus enabling better differentiated services to be provided to different users.

[0064] It should be noted that, regarding S101 above, obtaining the user information to be detected, the server can obtain user data from the source system in real time, which can be implemented as follows:

[0065] Real-time acquisition of user information. The acquired user information undergoes data transformation and processing, and the transformed user information is used as the user information to be detected.

[0066] The server can obtain user information in real time through message middleware.

[0067] Specifically, the server connects to the source system through a message middleware. When user information changes in the source system, the source system can send the changed user information to the message middleware, and then the server can retrieve the changed user information from the message middleware.

[0068] It's important to note that if the data format in the source system differs from the data format in the server, the server needs to convert the acquired user information to ensure that the converted data format matches the data format processed on the server. This allows the server to process the user data to be tested correctly. Furthermore, when user information changes in the source system, the server can obtain the updated information in real time through message brokers, enabling it to promptly calculate the corresponding QCI value.

[0069] In some embodiments of this application, the step of determining whether the user information to be detected is consistent with the user information stored in the historical records in step S102 can be specifically implemented as follows:

[0070] Step A: Generate the feature value string corresponding to the user information to be detected according to the preset correspondence.

[0071] The preset correspondence is set in advance according to actual needs. The preset correspondence includes the feature values ​​corresponding to different change subjects in different business scenarios. The feature value string represents the change status of the change subject included in the user information to be detected in the corresponding business scenario.

[0072] Specifically, the feature value string corresponding to the user information to be detected can be generated as follows:

[0073] First, the system searches for entities included in the user information to be detected within a pre-defined mapping relationship, and designates these entities as the target change entities. Then, it searches the pre-defined mapping relationship for the business scenarios corresponding to the target change entities. Finally, based on the feature values ​​of the target change entities in each business scenario, it generates a feature value string corresponding to the target change entities.

[0074] The following section, in conjunction with Table 1, describes how to generate feature value strings. Table 1 illustrates three exemplary business scenarios:

[0075] Table 1

[0076]

[0077]

[0078] The server reads information that can characterize the subject from the user information to be detected, such as account information. It compares whether the user information in the preset correspondence contains the account information in the user information to be detected. If it does, for the user corresponding to the account information, it matches the business scenario of the user in the user information to be detected with the business scenarios shown in Table 1 to generate a feature value string.

[0079] In one example, if the user service subscription information of user A in the user information to be detected has not changed compared to the user service subscription information of user A in the historical records, then the feature value in the user service subscription scenario is 0; if the user star rating change information of user A in the user information to be detected has not changed compared to the user star rating change information of user A in the historical records, then the feature value in the user star rating change scenario is 0; if the user brand change information of user A in the user information to be detected has changed from Platinum Card to Gold Card compared to the user brand change information of user A in the historical records, then the feature value in the user brand change scenario is 2, and the feature value string corresponding to the changing subject user A is 002.

[0080] Specifically, it can be represented as the feature value string = [X1, X2, ..., X...]. i ] T ·[Y1,Y2……Y j ].

[0081] Where X1, X2……X iIndicates different change subjects, Y1, Y2...Y j This represents different business scenarios. Specifically, after the server obtains the user information to be detected, it iterates through the business scenarios corresponding to each changing subject in the user information, determines the feature value of each changing subject in each business scenario, and thus generates a feature value string corresponding to each changing subject.

[0082] It should be noted that Table 1 above is only an example. In actual implementation, the number and types of business scenarios in the preset correspondence are not limited to this.

[0083] Step B: Determine whether the sum of the feature values ​​included in the feature value string is greater than a preset value.

[0084] Specifically, it can be divided into the following two situations:

[0085] Case 1: If the sum of the feature values ​​in the feature value string is greater than a preset value, it is determined that the user information to be detected is inconsistent with the user information stored in the historical records.

[0086] The size of the preset value is determined based on the feature value corresponding to each business scenario in the preset correspondence.

[0087] Specifically, the server determines the minimum feature value for each business scenario, the sum of the minimum feature values ​​for each business scenario, and uses this sum as a preset value.

[0088] Taking Table 1 above as an example, the minimum feature value in the user business subscription scenario is 0, the minimum feature value in the user star rating change scenario is 0, and the minimum feature value in the user brand change scenario is 0. Therefore, the preset value = 0 + 0 + 0 = 0, so the preset value is set to 0.

[0089] Therefore, if the sum of the feature values ​​in the feature string is greater than 0, it is determined that the user information to be detected is inconsistent with the user information stored in the historical records.

[0090] Case 2: If the sum of the feature values ​​in the feature value string is less than or equal to a preset value, it is determined that the user information to be detected is consistent with the user information stored in the historical records.

[0091] Thus, by matching the subject and business scenario in the user information to be detected with the changed subject and business scenario in the preset correspondence, a feature value string is generated. This feature value string can accurately reflect whether the user information to be detected has changed compared to the user information in the historical records. Then, by summing the feature values ​​in the feature value string, the sum of the feature values ​​included in the feature value string is used to determine whether the user information to be detected has changed compared to the user information in the historical records. This allows for a quick determination of whether the user information to be detected has changed, thereby determining whether the QCI of the user information to be detected should be updated.

[0092] In some embodiments of this application, when it is determined that the user information to be detected is inconsistent with the user information stored in the historical records, a target rule corresponding to the user information to be detected can be determined from preset rules, and the QCI value of the user information to be detected can be calculated using the target rule. Based on this, the above-mentioned determination of the target rule corresponding to the user information to be detected from preset rules can be implemented in the following two ways:

[0093] Implementation method 1 includes steps 1 to 4:

[0094] Step 1: Find the entities included in the user information to be detected in the preset rules.

[0095] Step 2: If the preset rules include the subjects in the user information to be detected, for each subject found, according to the business scenario in which the subject in the user information to be detected changes, search for the target business scenario in the preset rules.

[0096] Among them, the preset rules are set in advance based on experience.

[0097] Specifically, as shown in Table 2, Table 2 exemplifies the preset rules for the change subject under different business scenarios:

[0098] Table 2

[0099]

[0100] The business set refers to the business scenarios shown in Table 1 above. For example, business set Y1 is the user ordering scenario and the feature values ​​corresponding to different change states under the scenario.

[0101] Based on this, the server matches the user information to be detected with historical records to determine the subject of the change whose feature value has changed, and then finds the QCI calculation rule corresponding to the subject of the change according to Table 2 above.

[0102] In one example, the subject of the change is X3, and the user information of X3 under the business set Y1 changes. Then the server determines the QCI calculation rule for the subject X3 to be QCI change rule C. 31 .

[0103] The contents shown in Table 2 above can be transformed into a QCI calculation rule matrix using Pij = [X1, X2……Xi]T·[Y1, Y2……Yj].

[0104] Wherein, Pij is the QCI calculation rule matrix, which includes the QCI calculation rules for each change subject under different business sets as shown in Table 2 above.

[0105] Step 3: Determine the QCI calculation rules corresponding to the target business scenario to obtain the set of QCI calculation rules corresponding to the user information to be detected.

[0106] It should be noted that there may be situations where user information changes across multiple business scenarios in the user information to be detected. Therefore, the server determines the QCI calculation rules corresponding to each business scenario.

[0107] Taking Table 2 above as an example, for the changed subject X3, the user information in business sets Y1 and Y2 has changed. Therefore, the server can determine that the QCI calculation rule corresponding to the changed subject X3 is QCI change rule C. 31 QCI Change Rules C 32 .

[0108] It should be noted that Table 2 above is only an example, and in actual implementation, the number and types of QCI change rules are not limited to this.

[0109] Step 4: In the QCI calculation rule set, randomly select a target QCI calculation rule and use it as the target rule.

[0110] In implementation method 1 described above, preset rules can be used to determine the QCI calculation rules for each changing entity under different business scenarios. When multiple QCI calculation rules are obtained, one can be randomly selected from the set of multiple QCI calculation rules as the target QCI calculation rule. Thus, by determining the target QCI calculation rule based on the changing entity and its changes under different business scenarios, the changes under different business scenarios are fully considered, making the determined target QCI calculation rule more closely aligned with the user's actual usage scenario, thereby improving the accuracy of the QCI calculation results.

[0111] Implementation method 2 includes steps 1 to 3:

[0112] Step 1: Find the entities included in the user information to be detected in the preset rules.

[0113] Step 2: If the preset rules include the subjects in the user information to be detected, for each subject found, according to the business scenario in which the subject in the user information to be detected changes, search for the target business scenario in the preset rules.

[0114] Step 3: Determine the highest preset priority for each QCI calculation rule in the QCI calculation rule set according to the preset priority corresponding to the business scenario, and take the QCI calculation rule corresponding to the highest preset priority as the target rule.

[0115] In this preset mapping, each business scenario corresponds to a preset priority. The preset priority is set in advance based on actual needs.

[0116] Taking Tables 1 and 2 above as examples, assuming that the preset priority of the user brand change business scenario is higher than the priority of the user star rating change business scenario, when the server determines that both the user brand change and the user star rating change have occurred simultaneously, and determines the QCI calculation rule A for this business scenario based on the user brand change information in the user information to be detected, and determines the QCI calculation rule B for this business scenario based on the user star rating change information, since the preset priority of the user brand change business scenario is higher than the priority of the user star rating change business scenario, that is, the preset priority of the user brand change is the highest preset priority, QCI calculation rule A is taken as the target rule.

[0117] Using implementation method 2 described above, target rules are determined by preset priorities for each business scenario. Since these preset priorities are based on actual business needs, the target rules determined using these preset priorities are closer to real-world application scenarios, thus meeting actual user requirements. Furthermore, the QCI values ​​calculated using these target rules are more accurate.

[0118] The following combination Figure 2 This application introduces a user-differentiated network service computing device provided by an embodiment, such as... Figure 2 As shown:

[0119] The device includes a user-differentiated network service computing system, a policy control system, a policy and charging rules function (PCRF), and a charging system.

[0120] The user-differentiated network service computing system includes a user information collection module, a billing rule configuration module, a user change rule (UCR) string processing module, and a user policy sending module.

[0121] The strategy control system includes a strategy signing module, a strategy change module, a service ordering module, and a strategy instruction sending module.

[0122] PCRF includes a policy instruction receiving module, a perception information generation module, an over-the-top (OTT) content perception module, a policy control module, and a redirection control module.

[0123] The billing system includes a call detail record (CDR) acquisition module, a CDR preprocessing module, a billing batch module, and a billing second batch module.

[0124] Specifically, after detecting the user information to be detected, the User Differentiation Network Service (PCRF) system converts the corresponding QCI value into data information recognizable by the policy control system and sends it to the policy control system. The policy control system is connected to both the PCRF and the PCRF via general network elements. Based on the calculated QCI value, the policy control system generates a QCI adjustment policy and sends it to the PCRF. The PCRF updates the QCI value using the received QCI adjustment policy and controls the user's QoS (Quality of Service) to provide differentiated services.

[0125] Furthermore, the billing system can combine personalized user subscription services and calculate the user's call detail record (CDR) charges based on key billing fields in the user's CDR file. These key fields may include duration, traffic, and number of communication messages. This generates a bill, which is then sent to the PCRF. Based on the received bill, the PCRF provides the user with service flow support resource guarantees and flow billing policies, enabling more granular service control and billing.

[0126] exist Figure 2 Based on the user-differentiated network service computing device shown, combined with Figure 3 This section describes how a user-differentiated network service computing system determines target rules, such as... Figure 3 As shown, the method includes:

[0127] S301. Load the rule configuration information into the cache.

[0128] The rule configuration information includes the aforementioned preset corresponding rules and the aforementioned preset correspondence. After receiving the rule configuration information configured by the user, the rule configuration module stores the rule configuration information in a local cache.

[0129] S302. Collect the subject and detailed information of the changes.

[0130] Among them, detailed information refers to information about different entities in different business scenarios, such as user star rating change information.

[0131] Specifically, the user information to be detected is collected through the user information collection module in the user-differentiated network service computing system. The user information to be detected includes the subject of the change and detailed information.

[0132] S303. Obtain and identify information regarding the subject of the change and related details.

[0133] Among them, information acquisition and identification refers to data transformation and processing of the acquired change subject and detailed information, converting the data format of the change subject and detailed information into a format that can be recognized and processed by the user-differentiated network service computing system.

[0134] S304. Is the subject of the change within the configuration scope?

[0135] If yes, execute S305; otherwise, end the process.

[0136] The configuration scope refers to the range of subjects included in the above rule configuration information.

[0137] Specifically, after the user information collection module identifies the changed subject in the user information to be detected, it matches it with the subjects included in the above rule configuration information. If the changed subject matches the subjects included in the above rule configuration information, then S305 is executed; if the changed subject does not match the subjects included in the above rule configuration information, then the process ends.

[0138] In this way, if the changed user information in the user information to be detected is not within the configuration range, the process will end directly, avoiding triggering the subsequent calculation of QCI value and reducing the interaction of network signaling.

[0139] S305, match with the rule configuration information, and record the information in the UCR string.

[0140] The rule configuration information is the aforementioned preset correspondence, and the UCR string is the aforementioned feature value string.

[0141] Specifically, after the user UCR string processing module reads the preset correspondence in the calculation rule configuration module, it searches for the corresponding change subject in the preset correspondence according to the user information to be detected.

[0142] S306. Is the change details within the configuration range?

[0143] If yes, then execute S307; otherwise, end the process.

[0144] S307. Match the information with the rule configuration information and record the information in the UCR string.

[0145] Specifically, based on the aforementioned determination of the preset correspondence relationship including the changed subject in the user information to be detected, the business scenario in the preset correspondence relationship and the business scenario corresponding to the changed subject in the user information to be detected are matched to generate a feature value string.

[0146] Then, the generated feature value string is used to determine whether the user information to be detected has changed. If the user information to be detected has changed, the QCI change rule is determined in the preset rules. The specific method for determining the QCI change rule is described in the relevant embodiments above, and will not be repeated here.

[0147] S308. Determine the change rule based on the rule priority and calculate the QCI value after the change.

[0148] The rule priority is determined based on the preset priority corresponding to the aforementioned business scenario, and the preset priority corresponding to the business scenario is equal to the rule priority. The changed rule is the aforementioned target rule.

[0149] Specifically, the user UCR string processing module determines the change rule based on the rule priority and uses the change rule to calculate the changed QCI value.

[0150] S309. Does the user's QCI need to be changed?

[0151] If yes, then execute S310; otherwise, end the process.

[0152] It should be noted that after calculating the modified QCI value using the change rules described above, the calculated QCI value may be the same as the original QCI value. Therefore, it is necessary to determine whether to change the user policy based on the QCI value. If the QCI value changes, the user policy sending module sends policy control information to the policy control system. This policy control information includes the modified QCI value.

[0153] S310, Send policy change information to the policy control system.

[0154] The user policy sending module converts the changed QCI value into a format that the policy control system can recognize and process, and then sends it to the policy control system.

[0155] After executing S310, the process ends.

[0156] Using the above method, the collected user information to be detected is identified and matched using a preset correspondence, resulting in a UCR string, or feature value string, corresponding to the user information. This UCR string is then used to determine if the user information has changed. If it has, a target rule is determined in the QCI calculation rules based on rule priority, allowing the QCI value to be updated using the target rule. Thus, when the user information changes, the corresponding target rule can be matched promptly using the user information, and the QCI value can be updated based on the acquired user data, improving the accuracy of QCI value calculation and enabling better differentiated services for different users.

[0157] Based on the same concept, embodiments of this application provide a device for QCI calculation, such as... Figure 4 As shown, the device includes:

[0158] The acquisition module 401 is used to acquire the user information to be detected, which includes information of different subjects in different business scenarios;

[0159] The judgment module 402 is used to determine whether the user information to be detected is consistent with the user information stored in the historical records;

[0160] The determination module 403 is used to determine, in the case that the user information to be detected and the user information stored in the historical records are inconsistent, a target rule corresponding to the user information to be detected is determined in a preset rule according to the subject and business scenario information in the user information to be detected. The preset rule includes QCI calculation rules for different change subjects in different business scenarios.

[0161] The calculation module 404 is used to calculate the QCI value using the target rule.

[0162] In one possible implementation, the judgment module 402 is specifically used for:

[0163] The feature value string corresponding to the user information to be detected is generated according to the preset correspondence relationship. The preset correspondence relationship includes the feature values ​​corresponding to different change subjects in different business scenarios. The feature value string represents the change status of the change subject included in the user information to be detected in the corresponding business scenario.

[0164] Determine whether the sum of the feature values ​​included in the feature value string is greater than a preset value;

[0165] If the sum of the feature values ​​included in the feature value string is greater than a preset value, it is determined that the user information to be detected is inconsistent with the user information stored in the historical records;

[0166] If the sum of the feature values ​​included in the feature value string is less than or equal to a preset value, it is determined that the user information to be detected is consistent with the user information stored in the historical record.

[0167] In one possible implementation, the detected user information includes at least one subject; the judgment module 402 is specifically used for:

[0168] Search for the subject included in the user information to be detected in the preset correspondence, and take the found subject as the target change subject;

[0169] Search the preset correspondence for the business scenario corresponding to the target change subject;

[0170] Based on the characteristic values ​​of the target change subject in each business scenario, a characteristic value string corresponding to the target change subject is generated.

[0171] In one possible implementation, module 403 is specifically used for:

[0172] The system searches for the entities included in the user information to be detected within the preset rules.

[0173] When the preset rules include the subjects in the user information to be detected, for each subject found, according to the business scenario in which the subject in the user information to be detected changes, the target business scenario is searched in the preset rules;

[0174] Determine the QCI calculation rules corresponding to the target business scenario to obtain the QCI calculation rule set corresponding to the user information to be detected;

[0175] From the set of QCI calculation rules, a target QCI calculation rule is randomly selected and used as the target rule.

[0176] In one possible implementation, each business scenario corresponds to a preset priority, and module 403 is specifically used for:

[0177] The system searches for the entities included in the user information to be detected within the preset rules.

[0178] When the preset rules include the subjects in the user information to be detected, for each subject found, according to the business scenario in which the subject in the user information to be detected changes, the target business scenario is searched in the preset rules;

[0179] The highest preset priority corresponding to each QCI calculation rule in the QCI calculation rule set is determined according to the preset priority corresponding to the business scenario, and the QCI calculation rule corresponding to the highest preset priority is taken as the target rule.

[0180] In one possible implementation, module 401 is specifically used for:

[0181] Real-time acquisition of user information;

[0182] The acquired user information is processed and transformed, and the transformed user information is used as the user information to be tested.

[0183] It should be noted that the QCI calculation device is the same as the QCI calculation method applied to the server described above. All implementation methods in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effect.

[0184] Figure 5 A schematic diagram of the hardware structure of the terminal device provided in an embodiment of this application is shown.

[0185] The terminal device may include a processor 501 and a memory 502 storing computer program instructions.

[0186] Specifically, the processor 501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0187] Memory 502 may include mass storage for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 502 is non-volatile solid-state memory.

[0188] In certain embodiments, memory 502 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0189] The processor 501 implements any of the data processing methods described in the above embodiments by reading and executing computer program instructions stored in the memory 502.

[0190] In one example, the terminal device may also include a communication interface 503 and a bus 504. Wherein, for example... Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 504 and complete communication with each other.

[0191] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0192] Bus 504 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 504 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0193] Furthermore, in conjunction with the QCI calculation method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the QCI calculation methods in the above embodiments.

[0194] In addition, in conjunction with the QCI calculation method in the above embodiments, this application embodiment can provide a computer program product, the computer product including a computer program, which, when executed by a processor, implements any one of the QCI calculation methods in the above embodiments.

[0195] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0196] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0197] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0198] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0199] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for calculating the Quality of Service Class Identifier (QCI), characterized in that, include: The system obtains user information to be detected, which includes information about different entities in different business scenarios. The user information to be detected includes at least one entity. The user information to be detected is obtained by the server from various source systems through message middleware. The subject included in the user information to be detected is searched in the preset correspondence, and the found subject is taken as the target change subject. The preset correspondence includes the feature values ​​corresponding to different change subjects in different business scenarios. Search the preset correspondence for the business scenario corresponding to the target change subject; Based on the feature values ​​of the target change subject in each business scenario, a feature value string corresponding to the target change subject is generated. The feature value string represents the change status of the change subject included in the user information to be detected in the corresponding business scenario. Determine whether the sum of the feature values ​​included in the feature value string is greater than a preset value; If the sum of the feature values ​​included in the feature value string is greater than a preset value, it is determined that the user information to be detected is inconsistent with the user information stored in the historical records; If the sum of the feature values ​​included in the feature value string is less than or equal to a preset value, it is determined that the user information to be detected is consistent with the user information stored in the historical record; In the case where the user information to be detected is inconsistent with the user information stored in the historical records, a target rule corresponding to the user information to be detected is determined in a preset rule according to the subject and business scenario information in the user information to be detected. The preset rule includes QCI calculation rules for different change subjects in different business scenarios. The QCI value is calculated using the target rule.

2. The method according to claim 1, characterized in that, The step of determining the target rule corresponding to the user information to be detected from the preset rules includes: The system searches for the entities included in the user information to be detected within the preset rules. When the preset rules include the subjects in the user information to be detected, for each subject found, according to the business scenario in which the subject in the user information to be detected changes, the target business scenario is searched in the preset rules; Determine the QCI calculation rules corresponding to the target business scenario to obtain the QCI calculation rule set corresponding to the user information to be detected; From the set of QCI calculation rules, a target QCI calculation rule is randomly selected and used as the target rule.

3. The method according to claim 1, characterized in that, Each business scenario corresponds to a preset priority. The step of determining the target rule corresponding to the user information to be detected from the preset rules includes: The system searches for the entities included in the user information to be detected within the preset rules. When the preset rules include the subjects in the user information to be detected, for each subject found, according to the business scenario in which the subject in the user information to be detected changes, the target business scenario is searched in the preset rules; The highest preset priority corresponding to each QCI calculation rule in the QCI calculation rule set is determined according to the preset priority corresponding to the business scenario, and the QCI calculation rule corresponding to the highest preset priority is taken as the target rule.

4. The method according to claim 1, characterized in that, The process of obtaining the user information to be detected includes: Real-time acquisition of user information; The acquired user information is processed and transformed, and the transformed user information is used as the user information to be tested.

5. An apparatus for calculating a Quality of Service Class Identifier (QCI), characterized in that, include: The acquisition module is used to acquire user information to be detected. The user information to be detected includes information of different subjects in different business scenarios. The user information to be detected includes at least one subject. The user information to be detected is acquired by the server from various source systems through message middleware. The judgment module is used to find the subject included in the user information to be detected in the preset correspondence, and take the found subject as the target change subject. The preset correspondence includes the feature values ​​corresponding to different change subjects in different business scenarios. The system searches for the business scenario corresponding to the target change subject in the preset correspondence; it generates a feature value string corresponding to the target change subject based on the feature values ​​of the target change subject in each business scenario, the feature value string representing the change status of the change subject included in the user information to be detected in the corresponding business scenario; it determines whether the sum of the feature values ​​included in the feature value string is greater than a preset value; if the sum of the feature values ​​included in the feature value string is greater than the preset value, it determines that the user information to be detected is inconsistent with the user information stored in the historical record; if the sum of the feature values ​​included in the feature value string is less than or equal to the preset value, it determines that the user information to be detected is consistent with the user information stored in the historical record. The determination module is used to determine, in the case where the user information to be detected and the user information stored in the historical records are inconsistent, a target rule corresponding to the user information to be detected is determined in a preset rule according to the subject and business scenario information in the user information to be detected. The preset rule includes QCI calculation rules for different change subjects under different business scenarios. The calculation module is used to calculate the QCI value using the target rule.

6. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the method as described in any one of claims 1-4.

8. A computer program product, characterized in that, The computer product includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-4.

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