A cloud edge service component

Abstract

The present invention relates to a cloud edge service component, comprising a service trigger module, an interface configuration module, and a data processing module; by configuring a corresponding relay interface in the cloud edge service component, the data format required by the client can be fed back from the cloud edge server, so that the cloud server does not need to perform data processing again according to the needs of the client, thereby improving transmission efficiency, and there is no need to configure a corresponding data processing plug-in on the client, thereby reducing the load on the client, and all plug-ins of the cloud edge service component are pre-configured, so it is more convenient to call, and its plug-in resources can be shared with different clients.

Description

Technical Field

[0001] The present invention relates to the field of network application services, and more specifically, to a cloud edge service component. Background Art

[0002] The two parts of the public component are the cloud edge service component and the cloud chain service component, which are responsible for data conversion and database encapsulation respectively. When the user calls the cloud service data, the data type, data encryption requirements and number of data column information of the cloud service data may be inconsistent with the actual application requirements of the user server. Therefore, for different applications, the user server needs to process the data by itself through the microserver of the corresponding application to obtain the required data, which has a great impact on the data response efficiency. The routing provided by the cloud service component in general, the cloud service data and the corresponding cloud edge provide functions such as data forwarding and processing capabilities and functions in a broad state, which cannot meet the usage scenarios with a large number of application types. Although the efficiency problem is alleviated to a certain extent through unified data processing, the pertinence is still poor, and it is impossible to respond to the different needs of the user side in the first time to obtain matching data. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a cloud edge service component.

[0004] In order to solve the above technical problems, the technical solution of the present invention is:

[0005] A cloud edge service component, including a service trigger module, an interface configuration module, and a data processing module;

[0006] The service trigger module is used to receive access request information from the client, and generate interface trigger information and required content information according to the access request information;

[0007] The interface configuration module is configured with a service routing table, the service routing table stores a number of service routing addresses, each service routing address corresponds to a service interface of a cloud server, the service trigger module retrieves the corresponding service routing address according to the interface trigger information, and establishes a relay interface between the corresponding service interface and the user end according to the service routing address;

[0008] The data processing module includes a global filtering unit and a dynamic configuration unit; the dynamic configuration unit is configured with a demand feature index table, the demand feature index table stores demand features, each demand feature corresponds to a logic plug-in, the dynamic configuration unit configures the demand comparison strategy to generate demand features according to the demand content information, calls the corresponding logic plug-in according to the demand features, and configures the logic plug-in to the global filtering unit; the global filtering unit is configured at the relay interface, for receiving initial service data from the corresponding service interface, and processing the initial service data through the logic plug-in to generate filtered service data and output it through the output end of the relay interface.

[0009] Furthermore, the global filtering unit includes an encryption global filter, a format global filter and a permission global filter, and the corresponding logic plug-in is configured with a decryption logic plug-in, an encryption logic plug-in, a format logic plug-in and a permission logic plug-in. The encryption global filter decrypts the corresponding initial service data through the decryption logic plug-in and then encrypts it through the encryption logic plug-in; the format global filter is used to convert the data format of the initial service data from the initial format to the target format corresponding to the format logic plug-in, and the permission global filter filters the data that does not match the permission requirements in the initial service data through the permission logic plug-in.

[0010] Furthermore, it also includes a solid-state configuration module, wherein the solid-state configuration module is configured with a solid-state trigger strategy, wherein the solid-state trigger strategy includes

[0011] An association marking step, when any service interface matches any logic plug-in, an initial association group is established based on the service interface, and the initial association group is a set of numbers of the logic plug-ins;

[0012] a type division step, dividing each initial association group according to the type of the logic plug-in to obtain a logic classification group;

[0013] The trigger calculation step calculates the trigger frequency weight, efficiency effective value and plug-in reliability value corresponding to each logical classification group;

[0014] The trigger judgment step is as follows: when the trigger frequency weight, the efficiency effective value and the plug-in reliability value meet the preset solid-state trigger conditions, a plug-in configuration request is generated according to the corresponding logic plug-in to the corresponding cloud server; when the cloud server receives the plug-in configuration request, it updates its corresponding logic plug-in.

[0015] Furthermore, the trigger calculation step is configured with a frequency calculation formula for calculating the trigger frequency weight:

[0016] Among them A m is the frequency weight corresponding to the mth logic plug-in, αm is the usage frequency value corresponding to the mth logical plug-in, t m is the time when the logic plug-in was last called, t0 is the current time, and q nm is the time weight corresponding to the nth call of the mth logic plug-in. When the moment when the logic plug-in is called falls into the preset time range, different time weights will be assigned according to the time range. p is the number of logic plug-ins in the logic classification group. k m The number of times the mth logic plug-in is called.

[0017] Furthermore, the trigger calculation step is configured with an efficiency calculation formula for calculating the effective efficiency value:

[0018] Among them B m is the effective value of the efficiency corresponding to the mth logic plug-in, Δβ is the average processing efficiency of the logic plug-in, β nm is the processing efficiency of the mth logic plug-in when it is called for the nth time, β v is the preset ideal processing efficiency of this logic plug-in, t x is the time taken to load the xth logic plug-in, c is the number of times the logic plug-in is loaded, and t v is the ideal loading time for loading the logic plug-in, s1 is the preset processing efficiency weight parameter, s2 is the preset loading time weight parameter, and s1+s2=1.

[0019] Furthermore, the trigger calculation step is configured with a reliable calculation formula for calculating the plug-in reliability value: C m =s3k m -s4(χ1+χ2+K+χ d ), where C m is the plug-in reliability value corresponding to the mth logical plug-in, χ d is the anomaly weight corresponding to the dth data anomaly. Different types of data anomalies have different corresponding anomaly weights. d is the number of historical data anomalies of the logic plug-in. s3 is the preset reliable adjustment parameter. s4 is the preset abnormal adjustment parameter. s3=rs4, where r is the data anomaly occurrence rate.

[0020] Furthermore, the demand comparison strategy includes

[0021] A virtual request step, extracting target features according to the demand content information and generating a virtual call request, and sending the virtual call request to the service interface;

[0022] A response receiving step, receiving virtual response information returned by the service interface;

[0023] A response extraction step, extracting response features from a preset virtual response information table according to the virtual response information;

[0024] The feature generation step compares the target feature and the response feature to generate the demand feature.

[0025] Furthermore, the demand characteristics include security requirement characteristics, each encryption logic plug-in is configured with an encryption priority, and the dynamic configuration unit calls the encryption logic plug-in that meets the security requirement characteristics and has the highest encryption priority for configuration.

[0026] Furthermore, the dynamic configuration unit has a first superposition value and a second superposition value. If there is a security requirement feature that belongs to the same client and meets the corresponding encryption logic plug-in, the encryption priority corresponding to the encryption logic plug-in increases by the first superposition value. If there is a security requirement feature that belongs to a different client and meets the corresponding encryption logic plug-in, the encryption priority corresponding to the encryption logic plug-in increases by the second superposition value. The first superposition value is greater than the second superposition value.

[0027] The technical effects of the present invention are mainly reflected in the following aspects: by configuring the corresponding relay interface in the cloud edge service component, the data format required by the client can be fed back from the cloud edge server, so that the cloud server does not need to process the data again according to the client's needs, thereby improving transmission efficiency, and there is no need to configure the corresponding data processing plug-in on the client, thereby reducing the client's load. Moreover, all plug-ins of the cloud edge service component are pre-configured, so it is more convenient to call, and its plug-in resources can be shared with different clients. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 : Schematic diagram of the communication service system architecture of the present invention;

[0029] Figure 2 : Schematic diagram of cloud edge service components of the present invention;

[0030] Figure 3 : Schematic diagram of cloud edge data filtering principle of the present invention.

[0031] Figure numerals: 100, client; 200, cloud edge service component; 210, service trigger module; 220, interface configuration module; 230, data processing module; 231, global filtering unit; 31, encryption global filter; 32, decryption global filter; 33, format global filter; 34, permission global filter; 232, dynamic configuration unit; 240, solid configuration module; 300, cloud server. DETAILED DESCRIPTION

[0032] The specific implementation modes of the present invention are further described below in conjunction with the accompanying drawings to make the technical solutions of the present invention easier to understand and grasp.

[0033] First, refer to Figure 1 As shown, for the relationship between the cloud edge server, the client and the cloud server, according to the normal data interaction requirements, the client's web, APP, applet or other client applications all interact directly with the cloud server for data, and different applications have different requirements for data. For example, the app calls the interface, but the interface provides more data columns, and the app does not need so many, only a few specific columns of data; or the app has requirements for the return data format, such as it must be in XML format; or the app has a unified processing encryption and decryption for the return data. The same client may have different data requirements due to different applications. Generally speaking, there are two ways. One is to pre-process the cloud server in response to different data requirements. In this way, the data requirements are identified, and different data retrieval and processing strategies are configured according to the data requirements, which will cause a large load on the cloud server and may involve the client's information authority issues. The other is to process on the client. This method requires the installation of advance plug-ins based on the data differences between the client and the cloud server, and the number of plug-ins will be increased according to the data requirements, and this method has high requirements for client storage and operation. Therefore, the present invention provides the following solution:

[0034] Reference Figure 1 As shown, a cloud edge service component overcomes the above problems by establishing a virtual interface. Figure 2 As shown, it includes a service trigger module, an interface configuration module, and a data processing module;

[0035] The service trigger module is used to receive the access request information of the client, and generate interface trigger information and demand content information according to the access request information; the interface trigger information reflects the interface routing information of the cloud service corresponding to the access request of the client: ur l, forwarding path, forwarding rules, etc.; and the demand takes specific routing information as an example, referring to Table 1, the routing information table:

[0036] Field Name Field Code Data Types Data length Primary Key id BIGINT Route Name name VARCHAR 50 Services service_name VARCHAR 50 Request Type request_type VARCHAR 4 Uri Path uri VARCHAR 255 Encryption is_encrypt VARCHAR 1 Service Interface service_interface VARCHAR 255 state status VARCHAR 1 Data Format data_format VARCHAR 50 Encryption Type encrypt_type VARCHAR 50 Founder create_user VARCHAR 20 Creation time create_date DATETIME Modified by modify_user VARCHAR 20 Modification time modify_date DATETIME

[0037] Table 1

[0038] The interface configuration module is configured with a service routing table, which stores a number of service routing addresses, each service routing address corresponds to a service interface of a cloud server, and the service trigger module retrieves the corresponding service routing address according to the interface trigger information, and establishes a relay interface between the corresponding service interface and the user end according to the service routing address; the logic of the interface configuration module is to generate a relay interface between the cloud server and the client according to the data requirements of the client, and the generation of the relay interface is realized through the gateway, so that the data of the cloud server can be directly sent to the client, and the corresponding data can be processed by the plug-in loaded by the relay interface, that is, the relay interface receives the return data from the cloud server and outputs it to the corresponding user end.

[0039] The data processing module includes a global filtering unit and a dynamic configuration unit;

[0040] The dynamic configuration unit is configured with a demand feature index table, the demand feature index table stores demand features, each demand feature corresponds to a logic plug-in, the dynamic configuration unit configures a demand comparison strategy to generate demand features according to demand content information, retrieves the corresponding logic plug-in according to the demand features, and configures the logic plug-in in the global filtering unit; the dynamic configuration unit configures different logic plug-ins for each interface, and the corresponding logic plug-in can be one or more, and the demand comparison strategy includes

[0041] The virtual request step extracts the target features according to the demand content information and generates a virtual call request, and sends the virtual call request to the service interface. Since the demand content information is known, including the target data content actually extracted, the first step is to extract the target features according to the demand content information. For example, the target feature is the demand data content feature. According to this data content feature, the cloud server knows the returned data content and which cloud server to call for service. The target feature is sent to the service interface in the form of a virtual call request instead of directly sending the actual demand content information. The difference is that the user's data security can be guaranteed to the greatest extent. After receiving the virtual call request, the cloud server will generate virtual response information according to the virtual call request.

[0042] In the response receiving step, the virtual response information returned by the service interface is received; after the virtual response information is successfully received, the data characteristics of the return data of the cloud server can be known.

[0043] The response extraction step extracts the response features from the preset virtual response information table according to the virtual response information; extracts the elements in the virtual response information by extracting the response features. When extracting, pre-edit a virtual response information table, and the virtual response information table indexes the corresponding response features through the virtual response information, such as obtaining the encryption method, data permission range, data type and other information of the cloud server. It should be noted that the virtual response information is neither specific return data nor directly refers to the corresponding data features, but is based on the "consensus" of the cloud edge component and the cloud server. Therefore, if there is no consensus algorithm for the cloud edge component, simply intercepting the virtual response information cannot obtain any data content, which can ensure security. This formula is synchronized through the virtual response information table, and the virtual response information table is configured in advance according to the relationship between the virtual response information and the response features.

[0044] The feature generation step compares the target feature and the response feature to generate the demand feature. The demand feature includes a security requirement feature. Each encryption logic plug-in is configured with an encryption priority. The dynamic configuration unit calls the encryption logic plug-in that meets the security requirement feature and has the highest encryption priority for configuration. The demand feature is implemented according to the target feature and the virtual response feature; for example, if the encryption method of the target feature is A and the encryption method in the response feature is B, then it can be obtained that the demand feature is BA, that is, B is decrypted and A is encrypted. If the target feature and the response feature are the same, it means that the data meets the requirements and no processing is required. The purpose of configuring the encryption priority for each encryption logic plug-in is that the encryption features in the general target features are empty, but there will be encryption security requirements. For example, the encryption method in the response feature is still B, and the security requirements in the target feature are 4-6. If B does not meet the security requirements, it is necessary to call an encryption method that meets the conditions, which can be A or C. Therefore, the encryption priority is configured for each logic plug-in, and the encryption method with the highest priority is selected for encryption.

[0045] Preferably, the dynamic configuration unit has a first superposition value and a second superposition value. If there is a security requirement feature that belongs to the same client and meets the corresponding encryption logic plug-in, the encryption priority corresponding to the encryption logic plug-in increases by the first superposition value. If there is a security requirement feature that belongs to a different client and meets the corresponding encryption logic plug-in, the encryption priority corresponding to the encryption logic plug-in increases by the second superposition value. The first superposition value is greater than the second superposition value. The encryption priority is updated by superposition value, so that the encryption logic plug-in that is frequently called can be called more frequently, so that the data redundancy of the cloud edge component is reduced, and it is easier to optimize the encryption logic of the cloud edge component.

[0046] The global filtering unit is configured at the relay interface, and is used to receive the initial service data from the corresponding service interface, and process the initial service data through the logic plug-in to generate the filtered service data and output it through the output end of the relay interface. Preferably, the global filtering unit includes an encryption global filter, a format global filter and a permission global filter, and the corresponding logic plug-in is configured with a decryption logic plug-in, an encryption logic plug-in, a format logic plug-in and a permission logic plug-in. The encryption global filter decrypts the corresponding initial service data through the decryption logic plug-in and then encrypts it through the encryption logic plug-in; the format global filter is used to convert the data format of the initial service data from the initial format to the target format corresponding to the format logic plug-in, and the permission global filter filters the data that does not match the permission requirements in the initial service data through the permission logic plug-in. Preferably, the returned data first passes through the permission global filter, then the format global filter, then the decryption global filter and the encryption global filter, and then the encryption global filter. In this way, the amount of data processed by the subsequent filters is less, which improves the system efficiency.

[0047] As another design element of the present invention, the solid configuration module is configured with a solid trigger strategy, which is used to coordinate the balance between the cloud edge component and the cloud server. For example, for a cloud server, a certain application it serves basically needs to be served by a fixed logic plug-in of the cloud edge component. In fact, it is a better way to directly replace this plug-in with the cloud server and then configure the plug-in of the cloud server to the corresponding global filtering unit. The solid trigger strategy includes

[0048] The association marking step is to establish an initial association group based on the service interface when any service interface matches any logic plug-in. The initial association group is a collection of logic plug-in numbers. The purpose of the association marking step is to determine the degree of association between the logic plug-in and the service interface, and to record each match between the logic plug-in and the service interface, which reflects the frequency of use of the logic plug-in.

[0049] The type division step divides each initial association group according to the type of the logic plug-in to obtain a logical classification group; and the type division is to divide each group again according to the classification of the logic plug-in, for example, the permission logic plug-in and the encryption logic plug-in belong to different groups.

[0050] The trigger calculation step calculates the trigger frequency weight, efficiency effective value, and plug-in reliability value corresponding to each logical classification group; and calculates the usage of the logical plug-in from three dimensions. Specifically, the formula for calculating the trigger frequency weight is: The trigger calculation step is configured with a frequency calculation formula for calculating the trigger frequency weight:

[0051] Among them A m is the frequency weight corresponding to the mth logic plug-in, α m is the usage frequency value corresponding to the mth logical plug-in, t m is the time when the logic plug-in was last called, t0 is the current time, and q nm is the time weight corresponding to the nth call of the mth logic plug-in. When the moment when the logic plug-in is called falls into the preset time range, different time weights will be assigned according to the time range. p is the number of logic plug-ins in the logic classification group. k m The number of times the mth logic plug-in is called.

[0052] The formula for calculating the effective value of efficiency is: Specifically, the trigger calculation step is configured with an efficiency calculation formula for calculating the effective value of efficiency:

[0053] Among them B m is the effective value of the efficiency corresponding to the mth logic plug-in, Δβ is the average processing efficiency of the logic plug-in, β nm is the processing efficiency of the mth logic plug-in when it is called for the nth time, β v is the preset ideal processing efficiency of this logic plug-in, t x is the time taken to load the xth logic plug-in, c is the number of times the logic plug-in is loaded, and t v is the ideal loading time for loading the logic plug-in, s1 is the preset processing efficiency weight parameter, s2 is the preset loading time weight parameter, and s1+s2=1.

[0054] The formula for calculating the plug-in reliability value is: Specifically, the trigger calculation step is configured with a reliable calculation formula for calculating the plug-in reliability value: C m =s3k m -s4(χ1+χ2+K+χ d ), where C m is the plug-in reliability value corresponding to the mth logical plug-in, χ d is the anomaly weight corresponding to the dth data anomaly. Different types of data anomalies have different corresponding anomaly weights. d is the number of historical data anomalies of the logic plug-in. s3 is the preset reliable adjustment parameter. s4 is the preset abnormal adjustment parameter. s3=rs4, where r is the data anomaly occurrence rate.

[0055] The trigger judgment step is that when the trigger frequency weight, efficiency effective value and plug-in reliability value meet the preset solid trigger conditions, a plug-in configuration request is generated according to the corresponding logic plug-in to the corresponding cloud server; when the cloud server receives the plug-in configuration request, it updates its corresponding logic plug-in. The solid trigger condition is the threshold of the corresponding value. If the threshold is met, it means that the frequency of use is high, and the replacement efficiency and security meet the conditions, then the corresponding logic plug-in can be configured in the cloud server.

[0056] Of course, the above are only typical examples of the present invention. In addition, the present invention may also have many other specific implementations. All technical solutions formed by equivalent replacement or equivalent transformation fall within the scope of protection required by the present invention.

Claims

1. A cloud edge service component, characterized in that: Including service trigger module, interface configuration module, and data processing module; The service trigger module is used to receive access request information from the client, and generate interface trigger information and required content information according to the access request information; The interface configuration module is configured with a service routing table, the service routing table stores a number of service routing addresses, each service routing address corresponds to a service interface of a cloud server, the service trigger module retrieves the corresponding service routing address according to the interface trigger information, and establishes a relay interface between the corresponding service interface and the user end according to the service routing address; The data processing module includes a global filtering unit and a dynamic configuration unit; the dynamic configuration unit is configured with a demand feature index table, the demand feature index table stores demand features, each demand feature corresponds to a logic plug-in, the dynamic configuration unit configures the demand comparison strategy to generate demand features according to the demand content information, calls the corresponding logic plug-in according to the demand features, and configures the logic plug-in to the global filtering unit; the global filtering unit is configured at the relay interface, for receiving initial service data from the corresponding service interface, and processing the initial service data through the logic plug-in to generate filtered service data and output it through the output end of the relay interface.

2. A cloud edge service component according to claim 1, characterized in that: The global filtering unit includes an encryption global filter, a format global filter and a permission global filter. The corresponding logic plug-in is configured with a decryption logic plug-in, an encryption logic plug-in, a format logic plug-in and a permission logic plug-in. The encryption global filter decrypts the corresponding initial service data through the decryption logic plug-in and then encrypts it through the encryption logic plug-in; the format global filter is used to convert the data format of the initial service data from the initial format to the target format corresponding to the format logic plug-in, and the permission global filter filters the data that does not match the permission requirements in the initial service data through the permission logic plug-in.

3. A cloud edge service component according to claim 1, characterized in that: The solid-state configuration module is configured with a solid-state trigger strategy. The solid-state trigger strategy includes An association marking step, when any service interface matches any logic plug-in, an initial association group is established based on the service interface, and the initial association group is a set of numbers of the logic plug-ins; a type division step, dividing each initial association group according to the type of the logic plug-in to obtain a logic classification group; The trigger calculation step calculates the trigger frequency weight, efficiency effective value and plug-in reliability value corresponding to each logical classification group; The trigger judgment step is as follows: when the trigger frequency weight, the efficiency effective value and the plug-in reliability value meet the preset solid-state trigger conditions, a plug-in configuration request is generated according to the corresponding logic plug-in to the corresponding cloud server; when the cloud server receives the plug-in configuration request, it updates its corresponding logic plug-in.

4. A cloud edge service component as claimed in claim 3, characterized in that: The trigger calculation step is configured with a frequency calculation formula for calculating the trigger frequency weight: Among them A m is the frequency weight corresponding to the mth logic plug-in, α m is the usage frequency value corresponding to the mth logical plug-in, t m is the time when the logic plug-in was last called, t0 is the current time, and q nm is the time weight corresponding to the nth call of the mth logic plug-in. When the moment when the logic plug-in is called falls into the preset time range, different time weights will be assigned according to the time range. p is the number of logic plug-ins in the logic classification group. k m The number of times the mth logic plug-in is called.

5. A cloud edge service component as claimed in claim 4, characterized in that: The trigger calculation step is configured with an efficiency calculation formula for calculating the effective efficiency value: Among them B m is the effective value of the efficiency corresponding to the mth logic plug-in, Δβ is the average processing efficiency of the logic plug-in, β nm is the processing efficiency of the mth logic plug-in when it is called for the nth time, β v is the preset ideal processing efficiency of this logic plug-in, t x is the time taken to load the xth logic plug-in, c is the number of times the logic plug-in is loaded, and t v is the ideal loading time for loading the logic plug-in, s1 is the preset processing efficiency weight parameter, s2 is the preset loading time weight parameter, and s1+s2=1.

6. A cloud edge service component as claimed in claim 5, characterized in that: The trigger calculation step is configured with a reliable calculation formula for calculating the plug-in reliability value: C m =s3k m -s4(χ1+χ2+K+χ d ), where C m is the plug-in reliability value corresponding to the mth logical plug-in, χ d is the anomaly weight corresponding to the dth data anomaly. Different types of data anomalies have different corresponding anomaly weights. d is the number of historical data anomalies of the logic plug-in. s3 is the preset reliable adjustment parameter. s4 is the preset abnormal adjustment parameter. s3=rs4, where r is the data anomaly occurrence rate.

7. A cloud edge service component according to claim 1, characterized in that: The demand matching strategy includes A virtual request step, extracting target features according to the demand content information and generating a virtual call request, and sending the virtual call request to the service interface; A response receiving step, receiving virtual response information returned by the service interface; A response extraction step, extracting response features from a preset virtual response information table according to the virtual response information; The feature generation step compares the target feature and the response feature to generate the demand feature.

8. A cloud edge service component as claimed in claim 2, characterized in that: The demand characteristics include security requirement characteristics, each encryption logic plug-in is configured with an encryption priority, and the dynamic configuration unit calls the encryption logic plug-in that meets the security requirement characteristics and has the highest encryption priority for configuration.

9. A cloud edge service component as claimed in claim 8, characterized in that: The dynamic configuration unit has a first superposition value and a second superposition value. If there is a security requirement feature that belongs to the same client and meets the corresponding encryption logic plug-in, the encryption priority corresponding to the encryption logic plug-in increases by the first superposition value. If there is a security requirement feature that belongs to a different client and meets the corresponding encryption logic plug-in, the encryption priority corresponding to the encryption logic plug-in increases by the second superposition value. The first superposition value is greater than the second superposition value.

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