CN115086172A

CN115086172A - Data gateway plug-in updating method and device, electronic equipment and storage medium

Info

Publication number: CN115086172A
Application number: CN202210879269.6A
Authority: CN
Inventors: 邢旭军; 林绪辉; 袁怡
Original assignee: Guangdong One Kilometer Data Intelligence Technology Co ltd
Current assignee: Guangdong One Kilometer Data Intelligence Technology Co ltd
Priority date: 2022-07-25
Filing date: 2022-07-25
Publication date: 2022-09-20
Anticipated expiration: 2042-07-25
Also published as: CN115086172B

Abstract

The application belongs to the technical field of computers and discloses a method, a device, electronic equipment and a storage medium for updating a data gateway plug-in, wherein the data gateway screens characteristic data according to the processing speed of the existing plug-in on actual data, stores the characteristic data in a cloud platform to form a test data set, when the cloud platform finds a new plug-in adaptive to an object system, the new plug-in is tested according to the test data set to preliminarily judge whether the new plug-in is more optimal, if so, the cloud platform sends update recommendation information to the data gateway, the data gateway downloads the new plug-in and then carries out actual measurement, and the new plug-in is replaced or deleted according to the actual measurement result, so that the plug-in of the data gateway can be replaced in time when the new plug-in more suitable for a user to use appears, and the data processing effect of the data gateway is optimal.

Description

Data gateway plug-in updating method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for updating a data gateway plug-in, an electronic device, and a storage medium.

Background

At present, some enterprises may access a plurality of production management systems used by the enterprises to the same data gateway, and the data gateway provides data services such as data analysis, data backup, information interaction between systems and the like for each system. For different data services, the data gateway can automatically download corresponding plug-ins through the cloud platform according to the types of the object systems to realize corresponding functions, so that a user does not need to configure a service program and plug and play of the system are realized.

After the data gateway has downloaded and used a certain plug-in from the cloud platform, a new plug-in may appear in the plug-in market subsequently, and the plug-in may be more suitable for a user to use than a currently used plug-in, and may also be less suitable for the user to use, and is specifically related to factors such as a hardware environment, a software environment, and a use environment of each production management system of the user.

Disclosure of Invention

The application aims to provide a method and a device for updating a data gateway plug-in, an electronic device and a storage medium, which can replace the plug-in of the data gateway in time when a new plug-in more suitable for a user appears.

In a first aspect, the present application provides a method for updating a plug-in of a data gateway, which is applied to the data gateway to update the plug-in the data gateway, and includes the steps of:

A1. when the existing plug-in is applied to process data transmitted by an object system, judging whether the currently processed data is characteristic data or not according to the data processing speed;

A2. if so, sending the feature data to a cloud platform so that the cloud platform can update the test data set of the object system;

A3. when receiving update recommendation information sent by the cloud platform according to the test data set to find a new plug-in which is better than the existing plug-in, downloading the new plug-in through the cloud platform;

A4. carrying out actual operation test on the new plug-in unit so as to compare the operation effects of the existing plug-in unit and the new plug-in unit;

A5. and replacing the existing plug-in with the new plug-in according to the comparison result, or deleting the new plug-in.

The data gateway plug-in updating method includes the steps of screening feature data according to the processing speed of an existing plug-in to actual data, storing the feature data to a cloud platform to form a test data set, testing the new plug-in according to the test data set when the cloud platform finds the new plug-in matched with an object system to preliminarily judge whether the new plug-in is better or not, sending update recommendation information to a data gateway by the cloud platform if the new plug-in is better, carrying out actual measurement after the new plug-in is downloaded by the data gateway, and replacing or deleting the new plug-in according to actual measurement results, so that the plug-in of the data gateway can be replaced timely when the new plug-in which is more suitable for a user appears, and the data processing effect of the data gateway is optimal.

Preferably, step a1 includes:

when the existing plug-in is applied to process the data transmitted by the object system, calculating the historical average speed and the current average speed of data processing;

and comparing the current average speed with the historical average speed to judge whether the data processed currently is characteristic data.

Preferably, the update recommendation information is sent by the cloud platform after a new plug-in adapted to the object system is found and the new plug-in is tested according to the test data set of the object system and judged to be better than the existing plug-in.

The cloud platform firstly tests according to the test data set after finding the new plug-in adapted to the object system, and only when the test judges that the new plug-in is better than the existing plug-in, the cloud platform recommends to the data gateway, so that the problem that the data gateway downloads and actually measures the new plug-in too frequently to influence the processing efficiency of normal tasks can be avoided, meanwhile, the probability that the recommended new plug-in is really the better plug-in can be improved, and the probability that the downloading and actually measuring processes of the new plug-in are useless processes is reduced.

In a second aspect, the present application provides a data gateway plug-in updating apparatus, which is applied to a data gateway to update a plug-in the data gateway, and includes:

the system comprises a first screening module, a second screening module and a third screening module, wherein the first screening module is used for judging whether the currently processed data is characteristic data or not according to the data processing speed when the existing plug-in is applied to process the data transmitted by the object system;

the first sending module is used for sending the feature data to a cloud platform when the currently processed data is the feature data so that the cloud platform can update the test data set of the object system;

the downloading module is used for downloading a new plug-in through the cloud platform when receiving update recommendation information sent by the cloud platform according to the test data set and finding the new plug-in which is better than the existing plug-in;

the first testing module is used for carrying out actual operation testing on the new plug-in unit so as to compare the operation effects of the existing plug-in unit and the new plug-in unit;

and the first execution module is used for replacing the existing plug-in with the new plug-in or deleting the new plug-in according to the comparison result.

The data gateway plug-in updating device screens characteristic data according to the processing speed of the existing plug-in to actual data, stores the characteristic data to a cloud platform to form a test data set, when the cloud platform finds a new plug-in adaptive to an object system, the new plug-in can be tested according to the test data set to preliminarily judge whether the new plug-in is more optimal, if so, the cloud platform sends update recommendation information to the data gateway, the data gateway downloads the new plug-in and then carries out actual measurement, and the plug-in is replaced or the new plug-in is deleted according to the actual measurement result, so that the plug-in of the data gateway can be replaced in time when the new plug-in which is more suitable for a user appears, and the data processing effect of the data gateway is optimal.

In a third aspect, the present application provides a method for updating a plug-in of a data gateway, which is applied to a cloud platform to update the plug-in the data gateway, and includes the steps of:

B1. when receiving characteristic data from an object system sent by the data gateway, updating a test data set of the object system by using the characteristic data;

B2. when a new plug-in which is adapted to the object system and is more optimal than the existing plug-ins corresponding to the object system in the data gateway is found, sending update recommendation information to the data gateway;

B3. and after receiving a downloading request sent by the data gateway, sending the installation package of the new plug-in to the data gateway.

According to the data gateway plug-in updating method, a test data set is formed according to feature data uploaded by a data gateway, when a new plug-in adaptive to an object system is found by a cloud platform, the new plug-in can be tested according to the test data set to preliminarily judge whether the new plug-in is more optimal or not, if the new plug-in is more optimal, update recommendation information is sent to the data gateway by the cloud platform, when the data gateway receives the information, the new plug-in can be downloaded and then actually measured, and plug-in replacement or new plug-in deletion is carried out according to the actually measured result, so that when the new plug-in more suitable for a user to use appears, the plug-in of the data gateway can be replaced in time, and the data processing effect of the data gateway is ensured to be optimal.

Preferably, step B1 includes:

when receiving the characteristic data from the object system sent by the data gateway, adding the characteristic data as a new sample into the test data set of the object system;

determining whether the test data set of the subject system is too large;

and if the sample size is too large, performing screening and removing processing on an old sample of the test data set of the object system.

By the method, the test data set is not excessively large, and the test efficiency of the new plug-in is prevented from being influenced by the excessively large test data set.

Preferably, step B2 includes:

when a new plug-in is detected, judging whether the new plug-in is matched with the object system or not according to the pairing information of the object system; the pairing information comprises system version information and interface type information;

if the plug-in is adaptive, testing the new plug-in by using the test data set of the object system to judge whether the new plug-in is better than the existing plug-in or not;

and if the update recommendation information is more optimal, sending the update recommendation information to the data gateway.

In a fourth aspect, the present application provides a data gateway plug-in updating apparatus, which is applied to a cloud platform to update a plug-in a data gateway, and includes:

the first updating module is used for updating the test data set of the object system by using the characteristic data when the characteristic data from the object system sent by the data gateway is received;

the recommendation module is used for sending update recommendation information to the data gateway when a new plug-in which is adapted to the object system and is more optimal than the existing plug-ins corresponding to the object system in the data gateway is found;

and the issuing module is used for sending the installation package of the new plug-in to the data gateway after receiving the downloading request sent by the data gateway.

The data gateway plug-in updating device forms a test data set according to feature data uploaded by a data gateway, when a cloud platform finds a new plug-in adaptive to an object system, the new plug-in can be tested according to the test data set to preliminarily judge whether the new plug-in is more optimal or not, if the new plug-in is more optimal, update recommendation information is sent to the data gateway by the cloud platform, when the data gateway receives the information, the data gateway can download the new plug-in and then carry out actual measurement, and the plug-in is replaced or the new plug-in is deleted according to the actual measurement result, so that the plug-in of the data gateway can be replaced in time when the new plug-in more suitable for a user appears, and the data processing effect of the data gateway is optimal.

In a fifth aspect, the present application provides an electronic device, comprising a processor and a memory, where the memory stores a computer program executable by the processor, and the processor executes the computer program to execute the steps in the data gateway plug-in updating method as described above.

In a sixth aspect, the present application provides a storage medium having a computer program stored thereon, where the computer program is executed by a processor to perform the steps of the data gateway plug-in updating method as described above.

Has the advantages that:

according to the data gateway plug-in updating method, the data gateway conducts characteristic data screening according to the processing speed of the existing plug-ins on actual data, the characteristic data are stored in the cloud platform to form a test data set, when the cloud platform finds the new plug-ins adaptive to the object system, the new plug-ins are tested according to the test data set to preliminarily judge whether the new plug-ins are more optimal, if the new plug-ins are more optimal, update recommendation information is sent to the data gateway by the cloud platform, the data gateway downloads the new plug-ins and then conducts actual measurement, and plug-ins are replaced or the new plug-ins are deleted according to actual measurement results, so that when the new plug-ins more suitable for users to use appear, the plug-ins of the data gateway are replaced in time, and the data processing effect of the data gateway is optimal.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application.

Drawings

Fig. 1 is a flowchart of a data gateway plug-in updating method according to an embodiment of the present application.

Fig. 2 is a schematic structural diagram of a data gateway plug-in updating apparatus according to an embodiment of the present application.

Fig. 3 is a flowchart of another data gateway plug-in updating method according to an embodiment of the present application.

Fig. 4 is a schematic structural diagram of another data gateway plug-in updating apparatus according to an embodiment of the present application.

Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Fig. 6 is a schematic diagram of an exemplary data gateway plug-in update system.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a data gateway plug-in updating method in some embodiments of the present application, applied to a data gateway to update a plug-in the data gateway, including the steps of:

A1. when the existing plug-in is applied to process the data transmitted by the object system, judging whether the currently processed data is characteristic data or not according to the data processing speed;

A2. if so, sending the characteristic data to the cloud platform so that the cloud platform can update the test data set of the object system;

A5. and replacing the existing plug-in with the new plug-in or deleting the new plug-in according to the comparison result.

Wherein, the data gateway plug-in updating method can be applied to the data gateway 90 of the data gateway plug-in updating system shown in fig. 6, which comprises the data gateway 90, the cloud platform 91 and at least one object system 92, wherein the data gateway 90 is a device with data processing function and network connection function, which has a plurality of communication interfaces, and can be in communication connection with the plurality of object systems 92 through the communication interfaces, wherein, the object system 92 can be but is not limited to a production management system, such as an MES system (manufacturing enterprise production process execution management system), an EPR system (enterprise resource planning management system), a DNC system (production equipment and station intelligent networking management system), an MDC system (production data and equipment status information acquisition and analysis management system), a PDM system (manufacturing process data document management system) and the like, in fig. 6, each object system 92 is generally a different system, but is not limited thereto.

The processing of the data transmitted by the object system may be one or more of data analysis, data backup, and intersystem information interaction (the intersystem information interaction includes data reception, data conversion, and data transmission), but is not limited thereto.

For inter-system information interaction processing, the new plug-in needs to be matched with an object system for sending data and an object system for receiving data. The data gateway plug-in can install different communication plug-ins according to the communication requirements between different object systems, so that when communication is required between two object systems, the corresponding communication plug-ins are called to perform data processing. Therefore, a corresponding test data set is formed between every two object systems needing communication on the cloud platform, and when a new plug-in corresponding to one communication plug-in is found, the cloud platform can test the new plug-in by using the corresponding test data set so as to update the corresponding existing communication plug-ins in time.

The feature data refers to data meeting a preset condition, and the preset condition may be set according to actual needs, for example, the preset condition is that a processing speed of the data is higher, the processing speed is lower, or the processing speed is close to a historical average speed, but not limited thereto.

In some embodiments, step a1 includes:

and comparing the current average speed with the historical average speed to judge whether the currently processed data is the characteristic data.

The current average speed refers to an average processing speed when processing data currently transmitted by the object system; the historical average speed is an average value of the average speeds of the processing tasks in a preset time period (which can be set according to actual needs) taking the current time as an end point (namely, the current average speed calculated each time the data processing task is executed).

In some embodiments, comparing the current average speed with the historical average speed to determine whether the currently processed data is the feature data specifically includes: and calculating the absolute value deviation of the current average speed and the historical average speed, and if the absolute value deviation is greater than a preset first deviation threshold (which can be set according to actual needs), judging the currently processed data as the characteristic data. The characteristic data obtained by the method is data with larger or smaller processing speed, has certain particularity, can better reflect the boundary range of the processing speed of the plug-in, and can reliably compare the processing efficiency of the new plug-in and the old plug-in as a sample of the test data set. On this basis, can also include: if the absolute value deviation is smaller than a preset second deviation threshold (which can be set according to actual needs and is smaller than the first deviation threshold), the currently processed data is determined to be the feature data. The characteristic data obtained by the step is relatively close to the historical average speed, the average processing efficiency of the plug-ins can be reflected, and the comprehensive performance of the plug-ins can be better reflected by combining the characteristic data obtained by the previous step.

In which a historical average speed and a current average speed may be calculated each time a data processing task is performed and it is determined whether the currently processed data is feature data. However, in practical applications, since the processes of calculating the historical average speed and the current average speed and determining whether the currently processed data is the feature data require a certain processing time and occupy a certain amount of computing resources of the data gateway, if the operations are performed too frequently, the processing efficiency of the data gateway on the normal work task is easily affected. Therefore, the operation can be executed every N times of data processing tasks, wherein N is a preset positive integer greater than 1, so that the frequency of executing the operation is reduced, and the influence on the process of executing the normal tasks by the data gateway is reduced. Or, each time a data processing task is executed, detecting whether the proportion of the currently remaining computing resources (i.e. the proportion of the currently remaining computing resources to the total computing resources) of the data gateway is higher than a preset proportion threshold, if so, executing the operation, if not, executing the data processing task when the number of times of executing the data processing task reaches N from the last time the operation is executed to the current time, and if not, executing the data processing task when the number of times of executing the data processing task does not reach N from the last time the operation is executed to the current time, not executing the operation. Therefore, when the computing resources are sufficient, the feature parameters can be screened relatively frequently, and when the computing resources are insufficient, the feature parameters can be prevented from being screened for too long time intervals and missing typical feature parameters (such as the corresponding feature parameters with large absolute value deviation).

In practical applications, data processed by the data gateway often relates to security information inside an enterprise, and therefore, in order to avoid leakage of the security information, when the feature data is uploaded to the cloud platform, the feature data may be encrypted, that is, in some embodiments, step a2 includes: and encrypting the characteristic data, and sending the encrypted characteristic data to the cloud platform so that the cloud platform can update the test data set of the object system. Wherein, the existing encryption processing method can be selected according to actual needs for encryption processing.

In fact, because the sample in the test data set is only used for testing the processing speed of the plug-in, and does not care about the data content in the sample, it is only necessary to ensure that the length of the data content therein is equal to the length of the corresponding data content in the original data, and how to decrypt the data content after the encryption processing to obtain the original information does not need to be considered, the feature data can also be encrypted according to the following method:

each bit of a data field (data field is a field for recording data content) in a data frame of the feature data is replaced by a randomly selected 0 or 1 (i.e., at each bit, one of 0 and 1 is randomly selected to replace the original value), and the other fields in the data frame are kept unchanged.

By the method, the size and the data structure of the feature data after encryption processing are the same as those of the original feature data, the test effect of testing the plug-in processing data is the same, and each bit in the data field is replaced by the randomly selected 0 or 1, so that the content information cannot be restored by a decryption means, and the leakage of confidential information is effectively avoided.

Preferably, the update recommendation information is sent by the cloud platform after a new plug-in adapted to the object system is found and the new plug-in is tested according to the test data set of the object system and judged to be better than the existing plug-in (the specific test method may refer to the following).

In some embodiments, step a3 includes:

when receiving the update recommendation information, carrying out identity authentication on a sender of the update recommendation information so as to judge the validity of the update recommendation information;

if the update recommendation information is valid, sending a download request to the cloud platform;

and receiving an installation package of the new plug-in sent by the cloud platform, and installing.

In practical application, a data gateway may download the trojan program by sending the dummy information, and for this reason, before downloading a new plug-in, the sender of the update recommendation information is authenticated to ensure the validity of the update recommendation information. The specific authentication method may be an existing authentication method as needed, and is not limited herein.

In some embodiments, step a4 includes:

A401. alternately executing the actual data processing task by using the new plug-in and the existing plug-in, and calculating the respective average processing speed and processing speed variance of the new plug-in and the existing plug-in;

A402. respectively calculating the running effect evaluation values of the new plug-in unit and the existing plug-in unit according to the average processing speed and the processing speed variance;

A403. and comparing the running effect evaluation values of the new plug-in unit and the existing plug-in unit to judge whether the running effect of the new plug-in unit is better or not.

Wherein, in a first preset test time period (which can be set according to actual needs), each time of actual data processing tasks are executed by using new plug-ins and existing plug-ins alternately (for example, the first time of actual data processing task is executed by the new plug-ins, the second time of actual data processing task is executed by the existing plug-ins, the third time of actual data processing task is executed by the new plug-ins, the fourth time of actual data processing task is executed by the existing plug-ins, and so on), the single processing speed of the current task is calculated when the task is executed each time, and finally, the average value of the single processing speed is calculated as the corresponding average processing speed, and calculating the variance value of the single processing speed to obtain a corresponding processing speed variance (calculating the average processing speed and the processing speed variance of the new plug-in unit by using the single processing speed corresponding to the new plug-in unit, and calculating the average processing speed and the processing speed variance of the existing plug-in unit by using the single processing speed corresponding to the existing plug-in unit).

Alternatively, all the actual data processing tasks in two second preset test time periods (which can be set according to actual requirements) can be executed by the new plug-in and the existing plug-in (for example, all the actual data processing tasks in the first second preset test time period are executed by the new plug-in, and all the actual data processing tasks in the second preset test time period are executed by the existing plug-in), the single processing speed of the current task is calculated when the task is executed each time, and finally, the average value of the single processing speed is calculated as the corresponding average processing speed, and calculating the variance value of the single processing speed to obtain a corresponding processing speed variance (calculating the average processing speed and the processing speed variance of the new plug-in unit by using the single processing speed corresponding to the new plug-in unit, and calculating the average processing speed and the processing speed variance of the existing plug-in unit by using the single processing speed corresponding to the existing plug-in unit).

Wherein, the operation effect evaluation value of the new plug-in and the existing plug-in can be calculated according to the following formula:

P1=aX1+bY1；

P2=aX2+bY2；

p1 is the evaluation value of the running effect of the new card, X1 is the average processing speed of the new card, Y1 is the variance of the processing speed of the new card, P2 is the evaluation value of the running effect of the existing card, X2 is the average processing speed of the existing card, Y2 is the variance of the processing speed of the existing card, and a and b are two preset influence coefficients. The operation effect evaluation value obtained by the method comprehensively reflects the average processing efficiency and the fluctuation condition of the processing efficiency of the plug-in, and whether the operation effect is better or not is judged according to the operation effect evaluation value, so that the reliability of the judgment result is higher.

And if the operation effect evaluation value of the new plug-in is larger than that of the existing plug-in, judging that the operation effect of the new plug-in is more optimal, otherwise, judging that the operation effect of the new plug-in is not more optimal.

Specifically, step a5 includes: and if the operation effect of the new plug-in is better, replacing the existing plug-in with the new plug-in, otherwise, deleting the new plug-in.

Referring to fig. 2, the present application provides a data gateway plug-in updating apparatus, applied to a data gateway, for updating a plug-in the data gateway, including:

the first screening module 1 is used for judging whether the currently processed data is the characteristic data or not according to the data processing speed when the data transmitted by the object system is processed by applying the existing plug-in;

the first sending module 2 is configured to send the feature data to the cloud platform when the currently processed data is the feature data, so that the cloud platform updates the test data set of the object system;

the downloading module 3 is used for downloading a new plug-in through the cloud platform when receiving update recommendation information sent by the cloud platform according to the test data set to find the new plug-in which is better than the existing plug-in;

the first test module 4 is used for carrying out actual operation test on the new plug-in unit so as to compare the operation effects of the existing plug-in unit and the new plug-in unit;

and the first execution module 5 is used for replacing the existing plug-in with the new plug-in or deleting the new plug-in according to the comparison result.

Wherein, the data gateway plug-in updating apparatus can be applied in a data gateway 90 of a data gateway plug-in updating system shown in fig. 6, the data gateway plug-in updating system includes a data gateway 90, a cloud platform 91 and at least one object system 92, wherein, the data gateway 90 is a device having a data processing function and a network connection function, and has a plurality of communication interfaces, and can be communicatively connected with the plurality of object systems 92 through the communication interfaces, wherein, the object system 92 can be but not limited to a production management system, such as an MES system (manufacturing enterprise production process execution management system), an EPR system (enterprise resource planning management system), a DNC system (production equipment and station intelligent networking management system), an MDC system (production data and equipment status information collection and analysis management system), a PDM system (manufacturing process data document management system) and the like, in fig. 6, each object system 92 is generally a different system, but is not limited thereto.

The feature data refers to data meeting a preset condition, and the preset condition may be set according to actual needs, for example, the preset condition is that the processing speed of the data is higher, the processing speed is lower, or the processing speed is close to the historical average speed, but not limited thereto.

In some embodiments, when the existing plug-in is applied to process data transmitted by the object system, the first screening module 1 determines whether the currently processed data is feature data according to the data processing speed, and specifically includes:

Wherein, the current average speed refers to the average processing speed when processing the data currently transmitted by the target system (in this document, the processing speed of the data refers to the size of the processed data per unit time, and the unit can be but is not limited to B/s, KB/s, MB/s, etc.); the historical average speed is an average value of the average speeds of the processing tasks in a preset time period (which can be set according to actual needs) taking the current time as an end point (namely, the current average speed calculated each time the data processing task is executed).

In some embodiments, comparing the current average speed with the historical average speed to determine whether the currently processed data is the feature data specifically includes: and calculating the absolute value deviation of the current average speed and the historical average speed, and if the absolute value deviation is greater than a preset first deviation threshold (which can be set according to actual needs), judging the currently processed data as the characteristic data. The characteristic data obtained by the method is data with larger or smaller processing speed, has certain particularity, can better reflect the boundary range of the processing speed of the plug-in, and can reliably compare the processing efficiency of the new plug-in and the old plug-in as a sample of the test data set. On this basis, can also include: if the absolute value deviation is smaller than a preset second deviation threshold (which can be set according to actual needs and is smaller than the first deviation threshold), the currently processed data is determined to be the feature data. The feature data obtained through the processing is relatively close to the historical average speed, the average processing efficiency of the plug-ins can be reflected, and the comprehensive performance of the plug-ins can be better reflected by combining the feature data obtained through the previous processing.

In this case, the historical average speed and the current average speed may be calculated each time a data processing task is executed, and it may be determined whether the currently processed data is characteristic data. However, in practical applications, since the processes of calculating the historical average speed and the current average speed and determining whether the currently processed data is the feature data require a certain processing time and occupy a certain amount of computing resources of the data gateway, if the operations are performed too frequently, the processing efficiency of the data gateway on the normal work task is easily affected. Therefore, the operation can be executed every N times of data processing tasks, wherein N is a preset positive integer greater than 1, so that the frequency of executing the operation is reduced, and the influence on the process of executing the normal tasks by the data gateway is reduced. Or, each time the data processing task is executed, detecting whether the proportion of the currently remaining computing resources (i.e. the proportion of the currently remaining computing resources to the total computing resources) of the data gateway is higher than a preset proportion threshold, if so, executing the operation, if not, executing the data processing task when the number of times of executing the data processing task reaches N from the last time the operation is executed to the current time, and if not, executing the data processing task when the number of times of executing the data processing task does not reach N from the last time the operation is executed to the current time the operation is not executed. Therefore, when the computing resources are sufficient, the feature parameters can be screened relatively frequently, and when the computing resources are insufficient, the feature parameters can be prevented from being screened for too long time intervals and missing typical feature parameters (such as the corresponding feature parameters with large absolute value deviation).

In practical application, data processed by the data gateway often relates to confidential information inside an enterprise, and therefore, in order to avoid leakage of the confidential information, when uploading the feature data to the cloud platform, encryption processing may be performed on the feature data first, that is, in some embodiments, the first sending module 2 performs, when sending the feature data to the cloud platform so that the cloud platform updates the test data set of the object system: and encrypting the characteristic data, and sending the encrypted characteristic data to the cloud platform so that the cloud platform can update the test data set of the object system. Wherein, the existing encryption processing method can be selected according to actual needs for encryption processing.

In some embodiments, when receiving update recommendation information sent by the cloud platform according to the test data set to find a new plug-in that is better than an existing plug-in, the downloading module 3 downloads the new plug-in through the cloud platform, which specifically includes:

In practical application, a third party may send dummy information to enable the data gateway to download the trojan horse program, and therefore, before downloading a new plug-in, the identity of a sender of the update recommendation information is verified to ensure the validity of the update recommendation information. The specific authentication method may be an existing authentication method as needed, and is not limited herein.

In some embodiments, the first testing module 4 performs, when performing an actual operation test on the new plug-in to compare the operation effect of the existing plug-in with the operation effect of the new plug-in:

alternately executing the actual data processing task by using the new plug-in and the existing plug-in, and calculating the respective average processing speed and processing speed variance of the new plug-in and the existing plug-in;

respectively calculating the running effect evaluation values of the new plug-in and the existing plug-in according to the average processing speed and the processing speed variance;

and comparing the running effect evaluation values of the new plug-in unit and the existing plug-in unit to judge whether the running effect of the new plug-in unit is better or not.

Alternatively, all the actual data processing tasks in two second preset test time periods (which can be set according to actual requirements) can be executed by the new plug-in and the existing plug-in (for example, all the actual data processing tasks in the first second preset test time period are executed by the new plug-in, and all the actual data processing tasks in the second preset test time period are executed by the existing plug-in), the single processing speed of the current task is calculated when the task is executed each time, and finally, the average value of the single processing speed is calculated as the corresponding average processing speed, and calculating the variance value of the single processing speed to obtain the corresponding processing speed variance (calculating the average processing speed and the processing speed variance of the new plug-in unit by using the single processing speed corresponding to the new plug-in unit, and calculating the average processing speed and the processing speed variance of the existing plug-in unit by using the single processing speed corresponding to the existing plug-in unit).

P1=aX1+bY1；

P2=aX2+bY2；

Specifically, when replacing an existing plug-in with a new plug-in or deleting the new plug-in according to the comparison result, the first execution module 5 executes: and if the operation effect of the new plug-in is better, replacing the existing plug-in with the new plug-in, otherwise, deleting the new plug-in.

Referring to fig. 3, the present application provides a method for updating a plug-in of a data gateway, which is applied to a cloud platform to update the plug-in of the data gateway, and includes the steps of:

B1. when receiving characteristic data from the object system sent by the data gateway, updating a test data set of the object system by using the characteristic data;

B2. when a new plug-in which is adapted to the object system and is more optimal than the existing plug-ins of the corresponding object system in the data gateway is found, sending update recommendation information to the data gateway;

B3. and after receiving a downloading request sent by the data gateway (according to the updated recommendation information), sending an installation package of the new plug-in to the data gateway.

The data gateway plug-in updating method can be applied to the cloud platform 91 of the data gateway plug-in updating system shown in fig. 6, so as to update the plug-ins in the data gateway 90.

In some embodiments, step B1 includes:

when receiving characteristic data from an object system sent by a data gateway, adding the characteristic data serving as a new sample into a test data set of the object system;

judging whether the test data set of the object system is too large;

if the size is too large, the old samples of the test data set of the object system are subjected to screening and rejecting treatment.

For example, in some examples, the step of determining whether the test data set of the subject system is too large comprises: and judging whether the sample number of the test data set of the object system exceeds a preset number threshold, if so, judging that the test data set is too large, and otherwise, judging that the test data set is not too large. Whether the test data set is too large is judged through the sample size, so that the test data set is ensured to have enough sample size, and the plug-in is ensured to be more sufficiently tested.

Or for example, in some examples, determining whether the test data set of the subject system is too large includes: and judging whether the data size (the size of the occupied storage space) of the test data set of the object system exceeds a preset data size threshold, if so, judging that the test data set is too large, and otherwise, judging that the test data set is not too large. Whether the test data set is too large or not is judged according to the data size, so that the data size of the test data set is favorably avoided being too large, and the test time is avoided being too long.

For another example, in some examples, the step of determining whether the test data set for the subject system is too large comprises: judging whether the data size (which refers to the size of occupied storage space) of a test data set of the object system exceeds a preset data size threshold value or not, judging whether the number of samples of the test data set exceeds a preset number threshold value or not, if the data size of the test data set does not exceed the preset data size threshold value and the number of samples of the test data set does not exceed the preset number threshold value, judging that the test data set is not too large, and otherwise, judging that the test data set is too large. Whether the test data set is too large or not is judged by integrating the data size and the sample size, so that the test data set is ensured to have enough sample size, the data size of the test data set is avoided being too large, and the plug-in unit is ensured to be fully tested and the test time is avoided being too long.

When the data gateway uploads the feature data, the processing speed of the feature data (that is, the current average speed calculated when the data gateway processes the feature data by using the existing plug-in) can be uploaded together, and when the old samples of the test data set of the object system are screened and removed, the old sample with the processing speed similar to the processing speed of the feature data (new sample) and the old sample with the largest data size can be deleted. For example, in some embodiments, the step of culling old samples of the test data set of the subject system comprises:

calculating a speed difference between the processing speed of each old sample and the processing speed of the new sample in the test data set of the object system;

and (3) taking the old sample with the corresponding speed difference within a preset speed difference range (which can be set according to actual needs) as a candidate sample, and deleting the largest data size in the candidate sample.

Therefore, the data size of the test data set is small while the number of the samples is ensured to be sufficient, and the probability that the test effect on the new plug-in is close to that of the new plug-in is high due to the fact that the processing speed of the deleted old sample is close to that of the new sample, and therefore the influence on the accuracy of the test result is small when the new sample is used for replacing the old sample.

In fact, if the data size deviation between the sample with the largest data size and the sample with the second largest data size in the candidate samples is within a preset tolerance range (which can be set according to actual needs), the oldest one of the two candidate samples can be deleted, otherwise, the largest one of the two candidate samples is deleted. Therefore, the test data set is more adaptive to the current use environment of the data gateway, and the cloud platform judges a better new plug-in through testing, so that the data gateway has higher probability of being better.

In this embodiment, step B2 includes:

if the plug-ins are matched, testing the new plug-ins by using the test data set of the object system to judge whether the new plug-ins are better than the existing plug-ins;

and if the preference is better, sending update recommendation information to the data gateway.

It should be noted that, if the new plug-in is used to implement information interaction between systems, the step of determining whether the new plug-in is adapted to the object system according to the pairing information of the object system includes: and judging whether the new plug-in is matched with the two object systems or not according to the matching information of the two object systems for information interaction.

Since the hardware environment of the cloud platform is different from the hardware environment of the data gateway, even if the same plug-in is used to process the same data, the processing speeds of the cloud platform and the data gateway are also different, so that when the new plug-in is tested by using the test data set of the object system, the new plug-in and the existing plug-in need to be operated respectively to process the sample of the test data set so as to compare the processing effects according to the respective processing effects. In some embodiments, the step of testing the new plug-in using the test dataset of the subject system to determine whether the new plug-in is more optimal than the existing plug-in comprises:

processing each sample of the test data set of the object system by using a new plug-in and an existing plug-in respectively, and recording the processing speed of each sample to obtain the sample processing speed of each sample;

calculating a first average sample processing speed (dividing the sum of the sample processing speeds corresponding to the new plug-ins by the number of samples) and a first sample processing speed variance according to the sample processing speed corresponding to the new plug-ins;

calculating a second average sample processing speed (the sum of the sample processing speeds corresponding to the existing plug-ins is divided by the number of samples) and a second sample processing speed variance according to the sample processing speed corresponding to the existing plug-ins;

calculating the operation effect measurement and evaluation values of the new plug-in and the existing plug-in according to the following formula:

Q1=cK1+dM1；

Q2=cK2+dM2；

q1 is a running effect measurement and evaluation value of a new plug-in, K1 is a first average sample processing speed of the new plug-in, M1 is a first sample processing speed variance of the new plug-in, Q2 is a running effect measurement and evaluation value of an existing plug-in, K2 is a second average sample processing speed of the existing plug-in, M2 is a second sample processing speed variance of the existing plug-in, and c and d are two preset weights;

and if the operation effect measurement evaluation value Q1 of the new plug-in is greater than the operation effect measurement evaluation value Q2 of the existing plug-in, judging that the new plug-in is more excellent than the existing plug-in, and otherwise, judging that the new plug-in is not more excellent than the existing plug-in.

The test result obtained by the method comprehensively reflects the average processing efficiency and the fluctuation condition of the processing efficiency of the plug-in, and whether the running effect is better or not is judged according to the running effect test and evaluation value, so that the reliability of the judgment result is higher.

In order to facilitate the data gateway to confirm the validity of the update recommendation information, the update recommendation information sent to the data gateway includes authentication information, so that the data gateway can authenticate the sender of the update recommendation information.

Referring to fig. 4, the present application provides a data gateway plug-in updating apparatus, which is applied to a cloud platform to update a plug-in a data gateway, and includes:

a first updating module 10, configured to update a test data set of a target system with feature data when the feature data sent by a data gateway from the target system is received;

a recommending module 20, configured to send update recommendation information to the data gateway when a new plug-in that is adapted to the object system and is better than an existing plug-in of a corresponding object system in the data gateway is found;

and the issuing module 30 is configured to send an installation package of a new plug-in to the data gateway after receiving the download request sent by the data gateway.

The data gateway plug-in updating apparatus can be applied to the cloud platform 91 of the data gateway plug-in updating system shown in fig. 6, so as to update the plug-in the data gateway 90.

In some embodiments, when receiving the feature data from the object system sent by the data gateway, the first updating module 10 updates the test data set of the object system with the feature data, which specifically includes:

judging whether the test data set of the object system is too large;

For example, in some examples, first update module 10, when determining whether the test data set for the subject system is too large, performs: and judging whether the sample number of the test data set of the object system exceeds a preset number threshold, if so, judging that the test data set is too large, and otherwise, judging that the test data set is not too large. Whether the test data set is too large is judged through the sample size, so that the test data set is ensured to have enough sample size, and the plug-in is ensured to be more sufficiently tested.

Or for example, in some examples, first update module 10, when determining whether the test data set of the subject system is too large, performs: and judging whether the data size (the size of the occupied storage space) of the test data set of the object system exceeds a preset data size threshold, if so, judging that the test data set is too large, and otherwise, judging that the test data set is not too large. Whether the test data set is too large or not is judged according to the data size, so that the data size of the test data set is favorably avoided being too large, and the test time is avoided being too long.

For another example, in some examples, first update module 10, when determining whether the test data set of the subject system is too large, performs: judging whether the data size (the size of occupied storage space) of a test data set of the object system exceeds a preset data size threshold value or not, judging whether the number of samples of the test data set exceeds a preset number threshold value or not, if the data size of the test data set does not exceed the preset data size threshold value and the number of samples of the test data set does not exceed the preset number threshold value, judging that the test data set is not overlarge, and otherwise, judging that the test data set is overlarge. Whether the test data set is too large or not is judged by integrating the data size and the sample size, so that the test data set is ensured to have enough sample size, the data size of the test data set is avoided being too large, and the plug-in unit is ensured to be fully tested and the test time is avoided being too long.

When the data gateway uploads the feature data, the processing speed of the feature data (namely, the current average speed calculated by the data gateway when the feature data is processed by using the existing plug-in) can be uploaded together, and when the old samples of the test data set of the object system are screened and removed, the old sample with the processing speed similar to the processing speed of the feature data (new sample) and with the largest data size can be deleted. For example, in some embodiments, the first update module 10 performs, while performing a culling process on old samples of the test data set of the subject system:

In this embodiment, when finding a new plug-in that is adapted to the object system and is better than an existing plug-in of a corresponding object system in the data gateway, the recommending module 20 sends update recommendation information to the data gateway, which specifically includes:

if the plug-ins are adaptive, testing the new plug-ins by using the test data set of the object system to judge whether the new plug-ins are better than the existing plug-ins or not;

It should be noted that, if the new plug-in is used to implement information interaction between systems, when determining whether the new plug-in is adapted to the object system according to the pairing information of the object system, the recommending module 20 executes: and judging whether the new plug-in is matched with the two object systems or not according to the matching information of the two object systems for information interaction.

Since the hardware environment of the cloud platform is different from the hardware environment of the data gateway, even if the same plug-in is used to process the same data, the processing speeds of the cloud platform and the data gateway are also different, so that when the new plug-in is tested by using the test data set of the object system, the new plug-in and the existing plug-in need to be operated respectively to process the sample of the test data set so as to compare the processing effects according to the respective processing effects. In some embodiments, recommendation module 20 performs, when testing the new plug-in using the test dataset of the subject system to determine whether the new plug-in is more optimal than the existing plug-in:

calculating a second average sample processing speed (dividing the sum of the sample processing speeds corresponding to the existing plug-ins by the number of samples) and a second sample processing speed variance according to the sample processing speed corresponding to the existing plug-ins;

Q1=cK1+dM1；

Q2=cK2+dM2；

Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, where the present disclosure provides an electronic device including: the processor 301 and the memory 302, the processor 301 and the memory 302 are interconnected and communicate with each other through the communication bus 303 and/or other types of connection mechanisms (not shown), the memory 302 stores a computer program executable by the processor 301, and when the electronic device runs, the processor 301 executes the computer program to execute the data gateway plug-in updating method in any optional implementation manner of the above embodiments to realize the following functions: when the existing plug-in is applied to process the data transmitted by the object system, judging whether the currently processed data is characteristic data or not according to the data processing speed; if so, sending the characteristic data to the cloud platform so that the cloud platform can update the test data set of the object system; when receiving update recommendation information sent by the cloud platform according to the test data set to find a new plug-in which is better than the existing plug-in, downloading the new plug-in through the cloud platform; carrying out actual operation test on the new plug-in unit so as to compare the operation effects of the existing plug-in unit and the new plug-in unit; and replacing the existing plug-in with the new plug-in or deleting the new plug-in according to the comparison result. Or to implement the following functions: when receiving characteristic data from an object system sent by a data gateway, updating a test data set of the object system by using the characteristic data; when a new plug-in which is adapted to the object system and is more optimal than the existing plug-ins of the corresponding object system in the data gateway is found, sending update recommendation information to the data gateway; and after receiving a downloading request sent by the data gateway, sending an installation package of the new plug-in to the data gateway.

An embodiment of the present application provides a storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the data gateway plug-in updating method in any optional implementation manner of the foregoing embodiment is executed, so as to implement the following functions: when the existing plug-in is applied to process the data transmitted by the object system, judging whether the currently processed data is the characteristic data or not according to the data processing speed; if so, sending the characteristic data to the cloud platform so that the cloud platform can update the test data set of the object system; when receiving update recommendation information sent by the cloud platform according to the test data set to find a new plug-in which is better than the existing plug-in, downloading the new plug-in through the cloud platform; carrying out actual operation test on the new plug-in unit so as to compare the operation effects of the existing plug-in unit and the new plug-in unit; and replacing the existing plug-in with the new plug-in or deleting the new plug-in according to the comparison result. Or to implement the following functions: when receiving characteristic data from the object system sent by the data gateway, updating a test data set of the object system by using the characteristic data; when a new plug-in which is adapted to the object system and is more optimal than the existing plug-ins of the corresponding object system in the data gateway is found, sending update recommendation information to the data gateway; and after receiving a downloading request sent by the data gateway, sending an installation package of the new plug-in to the data gateway. The storage medium may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data gateway plug-in updating method is applied to a data gateway to update plug-ins in the data gateway, and is characterized by comprising the following steps:

2. The data gateway plug-in updating method according to claim 1, wherein step a1 comprises:

3. The data gateway plug-in updating method according to claim 1, wherein the update recommendation information is sent by the cloud platform after a new plug-in adapted to the object system is discovered and tested according to a test data set of the object system to determine that the new plug-in is better than the existing plug-in.

4. A data gateway plug-in updating device is applied to a data gateway to update plug-ins in the data gateway, and is characterized by comprising the following components:

the downloading module is used for downloading a new plug-in through the cloud platform when receiving update recommendation information sent by the cloud platform according to the test data set to find the new plug-in which is better than the existing plug-in;

5. A data gateway plug-in updating method is applied to a cloud platform to update plug-ins in a data gateway, and is characterized by comprising the following steps:

6. The data gateway plug-in updating method according to claim 5, wherein the step B1 comprises:

determining whether the test data set of the subject system is too large;

and if the size of the sample is too large, screening and rejecting the old sample of the test data set of the object system.

7. The data gateway plug-in updating method according to claim 5, wherein the step B2 comprises:

if the plug-in is matched with the target system, testing the new plug-in by using the test data set of the target system so as to judge whether the new plug-in is better than the existing plug-in;

8. The utility model provides a data gateway plug-in components update device, is applied to the cloud platform to update the plug-in components in the data gateway, its characterized in that includes:

9. An electronic device, comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor executes the computer program to perform the steps of the data gateway plug-in updating method according to any one of claims 1-3 and 5-7.

10. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the data gateway plug-in updating method according to any of claims 1-3, 5-7.