CN113610156A

CN113610156A - Artificial intelligence model machine learning method and server for big data analysis

Info

Publication number: CN113610156A
Application number: CN202110904996.9A
Authority: CN
Inventors: 廖彩红
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-01-20
Filing date: 2021-01-20
Publication date: 2021-11-05
Also published as: CN112801155B; CN112801155A; CN113610157A

Abstract

According to the artificial intelligence model machine learning method and the server for big data analysis provided by the embodiment of the invention, a plurality of service data flow samples obtained by pre-collecting service big data aiming at a data collection range are obtained to obtain a service data flow sample set, and then machine learning is carried out on a preset anti-interference feature detection network according to the service data flow sample set, so that the anti-interference feature detection network after learning can be used for carrying out feature detection on the service data flow collected by each service collection terminal to obtain service feature information of the corresponding service data flow. Therefore, the influence of the interference data on the target feature detection accuracy of the acquired service data stream can be reduced as much as possible, and the target feature detection accuracy of the service data stream is improved.

Description

Artificial intelligence model machine learning method and server for big data analysis

The application is a divisional application of an invention patent application with the application number of 202110077113.1, the application date of 2021, 20.01.1, and the invention name of a service big data analysis method and a server based on artificial intelligence.

Technical Field

The embodiment of the invention relates to the technical field of artificial intelligence and big data analysis, in particular to a service big data analysis method and a server based on artificial intelligence.

Background

Artificial Intelligence (AI) is a discipline that simulates the process of human consciousness and thinking. Machine Learning (Machine Learning) and Deep Learning (Deep Learning) have also made a significant breakthrough as the core of artificial intelligence, and machines are endowed with powerful cognitive and predictive capabilities. The artificial intelligence platform based on various practical service application scenarios, such as e-commerce shopping and online education, has been widely used for feature detection of generated service data streams by artificial intelligence technology. In the practical application of big data analysis and feature detection based on machine learning, a large amount of interference data or useless data can be generated by sample data used for learning or data to be detected, even intrusion interference data specially added by an external malicious attack object, so that the big data analysis result based on machine learning cannot be well applied or the application effect is poor and the like.

Disclosure of Invention

In view of this, an embodiment of the present invention provides a service big data analysis method based on artificial intelligence, where the method includes:

acquiring a plurality of service data stream samples obtained by acquiring service big data in advance according to a data acquisition range to obtain a service data stream sample set;

and performing machine learning on a preset anti-interference characteristic detection network according to the service data flow sample set, and performing characteristic detection on the service data flows acquired by each service acquisition terminal through the learned anti-interference characteristic detection network to obtain service characteristic information of the corresponding service data flows.

In the embodiment of the present invention, the performing machine learning on a preset interference prevention feature detection network according to the service data flow sample set, and performing feature detection on the service data flows acquired by each service acquisition terminal through the learned interference prevention feature detection network to obtain service feature information of corresponding service data flows includes:

inputting the service data stream sample set into a preset target service characteristic network for machine learning to obtain a learned target service characteristic network;

performing target service characteristic detection on the service data flow sample set through the learned target service characteristic network to obtain an initial target service characteristic set of the service data flow sample set;

inputting the initial target service feature set into a preset first anti-interference feature detection network for machine learning to obtain a first target anti-interference feature detection network;

performing machine learning on a preset second anti-interference feature detection network based on a joint model training strategy and the first target anti-interference feature detection network to obtain a second target anti-interference feature detection network, so that the parameter quantity of the trained second target anti-interference feature detection network is smaller than that of the first target anti-interference feature detection network;

and sending a second target anti-interference characteristic detection network to the service acquisition terminal, and performing target characteristic detection on the acquired service data stream through the service acquisition terminal according to the second target anti-interference characteristic detection network to obtain service characteristic information of the acquired service data stream.

The embodiment of the invention also provides a server, which comprises a processor and a memory, wherein the processor executes the computer program stored in the memory to realize the service big data analysis method based on artificial intelligence.

The service big data analysis method and the server based on artificial intelligence provided by the embodiment of the invention obtain a service data flow sample set by extracting the target service characteristics of the service data flow sample, then perform machine learning on a preset anti-interference characteristic detection network according to the service data flow sample set, and perform characteristic detection on the service data flow acquired by each service acquisition terminal through the learned anti-interference characteristic detection network to obtain the service characteristic information of the corresponding service data flow. The influence of interference data on the accuracy of target feature detection of the acquired service data stream can be reduced as much as possible, so that the accuracy of target feature detection of the service data stream is improved.

In addition, a preset target service characteristic network is further learned by adopting a service data flow sample set to obtain a learned target service characteristic network, target service characteristic detection is carried out on the service data flow sample set to obtain an initial target service characteristic set, then a preset first anti-interference characteristic detection network is learned by adopting the initial target service characteristic set to obtain a first target anti-interference characteristic detection network, machine learning is carried out on a preset second anti-interference characteristic detection network based on a joint model training strategy and the first target anti-interference characteristic detection network to obtain a second target anti-interference characteristic detection network, thus the second target anti-interference characteristic detection network can be sent to a service acquisition terminal, and target characteristic detection is carried out on the acquired service data flow by the service acquisition terminal and the second target anti-interference characteristic detection network to obtain a target characteristic detection result, and determining the service characteristic information of the collected service data stream based on the target characteristic detection result. And training based on a joint model training strategy to obtain a second anti-interference feature detection network, wherein the joint model training strategy is based on a large network to train a small network essentially, so that the parameter quantity of the small network is prevented from being enlarged on the premise of ensuring the prediction accuracy of the small network. Therefore, the obtained parameter quantity of the second anti-interference characteristic detection network is reduced relative to the parameter quantity of the first anti-interference characteristic detection network, so that the second anti-interference characteristic detection network can directly run in the service acquisition terminal to realize the characteristic detection of the service data stream on the service acquisition terminal, the detection work of the server is dispersed on each service acquisition terminal to be realized, the burden of the server can be reduced, and the operational capability of each service acquisition terminal is fully exerted. Meanwhile, when the second anti-interference feature detection network is deployed at the service acquisition terminal, the real-time performance of the service data stream target feature detection of the service acquisition terminal can be ensured.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic structural diagram of a network architecture of a service big data analysis system based on artificial intelligence according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of a server according to an embodiment of the present invention.

Fig. 3 is a schematic flow chart of a service big data analysis method based on artificial intelligence according to an embodiment of the present invention.

Fig. 4 is a flowchart illustrating the sub-steps of step S20 in fig. 3.

Detailed Description

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

The service big data analysis method and the server based on artificial intelligence provided by the invention will be described in detail with reference to the accompanying drawings.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a network architecture of a big data acquisition system according to an embodiment of the present invention. As shown in fig. 1, the big data collecting system may include a server 100 and a service data terminal cluster, and the service data terminal cluster may include a plurality of service data terminals 200. The server 100 is in communication connection with the service data terminals 200, and is configured to collect service data generated by each service data terminal 200 from the service data terminal 200, to realize collection of big data, and perform data analysis based on the collected service big data, and perform corresponding application. The number of the service data terminals 200 is not limited, and each service data terminal 200 may be in communication connection with the server 100 to facilitate data interaction with the server 100.

The server 100 shown in fig. 1 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud computing services such as cloud services, cloud storage, cloud computing, cloud communication, cloud security services, and big data and artificial intelligence platforms. The service data terminal may be a smart phone, a tablet computer, a notebook computer, a personal computer, or the like, which can generate corresponding service data using the service provided by the server 100 or other third party platform.

Referring to fig. 2, fig. 2 is a schematic diagram of the server 100. In this embodiment, the server 100 is configured to implement the service big data analysis method based on artificial intelligence provided in the embodiment of the present invention. In this embodiment, the server 100 may include a traffic big data analysis device 110, a machine-readable storage medium 120, and a processor 130.

Alternatively, the machine-readable storage medium 120 and the processor 130 may be located in the server 100 and separately provided, or the machine-readable storage medium 120 and the processor 130 may be independent of the server 100. The machine-readable storage medium 120 may be accessed by the processor 130 through a bus interface. Alternatively, the machine-readable storage medium 120 may be integrated into the processor 130, e.g., may be a cache and/or general purpose registers.

The processor 130 is a control center of the server 100, connects various parts of the entire server 100 using various interfaces and lines, performs various functions of the server 100 and processes data by running or executing software programs and/or modules stored in the machine-readable storage medium 120 and calling data stored in the machine-readable storage medium 120, thereby performing overall monitoring of the server 100. Optionally, processor 130 may include one or more processing cores. For example, the processor 130 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.

The processor 130 may be a general-purpose Central Processing Unit (CPU), a microprocessor, an Application-Specific Integrated Circuit (ASIC), or the like.

The machine-readable storage medium 120 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an Electrically Erasable programmable Read-Only MEMory (EEPROM), a compact disc Read-Only MEMory (CD-ROM) or other optical disk storage, optical disk storage (including compact disc, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The machine-readable storage medium 120 may be self-contained and coupled to the processor 130 via a communication bus. The machine-readable storage medium 120 may also be integrated with the processor. The machine-readable storage medium 120 is used for storing machine-executable instructions for performing aspects of the present application. The processor 130 is configured to execute machine-executable instructions stored in the machine-readable storage medium 120 to implement the big data collecting method provided by the present invention.

Referring to fig. 3, fig. 3 is a schematic flowchart of a service big data analysis method based on artificial intelligence according to an embodiment of the present invention, where the service big data analysis method based on artificial intelligence may be executed by the server 100. The method steps defined by the flow related to the method are applicable to the server 100 and can be implemented by the processor 130, and the method includes the following steps S10-S20. It can be understood that, in the method described in the embodiment of the present invention, the order of some steps may be interchanged according to actual needs, or some steps may be omitted or deleted, and each implementation step of the artificial intelligence based business big data analysis method is described below.

Step S10, obtaining a plurality of service data stream samples obtained by performing service big data acquisition in advance for the data acquisition range, and obtaining a service data stream sample set.

And step S20, performing machine learning on a preset anti-interference feature detection network according to the service data flow sample, and performing feature detection on the service data flow acquired by each service acquisition terminal through the learned anti-interference feature detection network to obtain service feature information of the corresponding service data flow.

The specific implementation methods and related contents of the above steps S10 and S20 will be described in detail below.

In the above step S10, the method for obtaining the service data stream sample set may be implemented with reference to the following sub-steps S101 to S102, which are described in detail below.

And a substep S101 of respectively acquiring node data of a time domain node and a space domain node according to service data generated in a preset service data acquisition range for acquiring large service data to obtain time-space domain node data. In this embodiment, the time-space domain node data includes time domain node data and space domain node data generated in the service data acquisition range.

In this embodiment, it can be understood that the service data acquisition range may be a data acquisition range predetermined according to a big data acquisition task, for example, a geographical range for each service data terminal, and for example, service data generated by each service data terminal located in the preset geographical range all belong to the corresponding data acquisition range; or, the service data may also be preset service data belonging to a specific service type range, and is not limited specifically.

Secondly, each service data carrying time domain information can be positioned as time domain node data, and the time domain information can be, but is not limited to, time nodes generated by the service data, service execution flow time sequences, the sequence among the service flows and the time topological relation among the service flows. After the time domain node data are topologically associated through the corresponding time domain relationship association rule, a time domain topology network among the service data can be formed, and each node in the time domain topology network can be defined as a time domain node. For some time-sensitive service data, the service data acquired by the service data acquisition of the time domain nodes carries corresponding time domain information, and the time domain information of each service data can be considered when machine learning and model training are carried out in the later period, so that the result obtained by the machine learning or model training in the later period can be better applied. For example, in business data for analyzing user behaviors in the fields of internet finance, digital network and the like, when a user is portrayed, a factor that a behavior interest characteristic in each user behavior data is attenuated along with time needs to be considered, so that data acquisition needs to be performed on each data in a time domain dimension.

Accordingly, each service data carrying airspace information may be located as airspace node data, and the airspace information may be, but is not limited to, a spatial node (which may specifically correspond to location information of the data acquisition terminal, such as an IP address, an equipment identifier, and the like) generated by the service data, a geographic location range corresponding to the generated service data, a service range corresponding to the data service, and the like. And performing topological correlation on the node data of each airspace domain through a corresponding airspace relation correlation rule to form an airspace topological network among the service data, wherein each node in the airspace topological network can be defined as an airspace node. For some service data sensitive to space, the service data acquired by the service data acquisition through the airspace nodes carries corresponding airspace information, and the airspace information of each service data can be considered when machine learning and model training are carried out in the later period, so that the result obtained by the machine learning or model training in the later period can be better applied. For example, in the related field, it is necessary to consider the service data that analyzes the user behavior, such as the frequent activity range of the user corresponding to the service data, the general application scenario of each service type, and the user terminal corresponding to each service type, and when the user is portrayed, it is necessary to consider the correlation factor between the behavior interest feature in each user behavior data and the space, so that data acquisition in the airspace dimension is necessary for each data. Therefore, business data can be acquired through two data dimensions of time and space domains, and the business data flow and relevant information acquired through data acquisition can reflect the relevant data characteristics of the business data more accurately through the correlation of the two dimensions, so that the learning effect and the application effect of machine learning or artificial intelligence model training in the later period can be improved.

And a substep S102, performing topology fusion analysis on the time-space domain node data to obtain a plurality of service data flows in the service data acquisition range and service characteristic information corresponding to the service data flows, and obtaining a service data flow sample set according to the service data flows and the corresponding service characteristic information for machine learning as a service data learning sample for machine learning.

In the substep S102, the topology fusion analysis is performed on the time-space domain node data to obtain a plurality of service data flows in the service data acquisition range and service characteristic information corresponding to the service data flows, and a specific implementation scheme is implemented with reference to the following substeps S1021-S1029.

And a substep S1021, forming a plurality of time domain data topological distributions and a plurality of space domain data topological distributions according to the time domain node data and the space domain node data in the time-space domain node data respectively.

In this embodiment, the time domain node data included in the time-space domain node data may be topologically associated according to a preset time domain information association rule, so as to form a plurality of time domain data topological distributions. For example, the time domain information association rule may be set in advance according to the execution flow, generation timing, and the like of each service data. In a large amount of time domain node data carrying time domain information, according to a corresponding time domain information association rule, the time domain node data carrying different time domain information can be topologically associated to different distribution groups, and the different distribution groups have different topological distribution nodes to form a plurality of different time domain data topological distributions.

Correspondingly, the spatial domain node data included in the time-spatial domain node data can also be topologically associated according to a preset spatial domain information association rule to form a plurality of spatial domain data topological distributions. For example, the spatial domain information association rule may be set in advance according to the generation space node of each service data, the belonging service space range, the positional relationship between the ranges, and the like. According to the corresponding airspace information association rule, the airspace node data carrying different airspace information can be topologically associated to different distribution groups, and the different distribution groups have different topological distribution nodes to form a plurality of different airspace data topological distributions. The different topological distributions may be expressed by means of a topological graph.

And a substep S1022, performing topology fusion on each time domain data topology distribution and each space domain data topology distribution generated in the service data acquisition range based on the service topology relation between the time domain data topology distribution and the space domain data topology distribution to obtain a plurality of topology distribution fusion groups.

In this embodiment, the spatial domain data topology distribution in each topology distribution fusion group respectively includes the second spatial domain node service data in the service data acquisition range. The service topological relation may be a service association relation between service data corresponding to each node of time domain data topological distribution and spatial domain data topological distribution, for example, the service topological relation may be obtained according to a service type, user information, and user identity information corresponding to the service data of each node in the topological distribution. Therefore, the time domain data topological distribution and the space domain data topological distribution with the service topological relation can be fused to obtain the corresponding topological distribution fusion group. A topological distribution fusion set includes at least one time domain data topological distribution and at least one spatial domain data topological distribution.

In an alternative manner, the sub-step S1022 may be implemented by:

firstly, determining each spatial domain data topological distribution generated in the service data acquisition range as local spatial domain topological distribution, and determining each time domain data topological distribution generated in the service data acquisition range as local time domain topological distribution; the airspace node service data in the local airspace topological distribution are obtained by carrying out data acquisition on a target service node in the service data acquisition range;

then, acquiring time domain node service data in the target service node; calculating a service data association parameter between time domain node service data in the target service node and each time domain node service data in the local time domain topological distribution, and determining a service topological relation between the local space domain topological distribution and the local time domain topological distribution according to the calculated service data association parameter;

and finally, when the business data correlation parameter is not less than a preset correlation parameter threshold value, performing topological fusion on the local spatial domain topological distribution and the local time domain topological distribution to obtain a plurality of topological distribution fusion groups. Therefore, the local time domain topological distribution can be respectively associated and matched with the time domain data generated by each service node (spatial domain feature), and topology fusion is performed if the association and matching are performed, so that a topology fusion group is generated.

And S1023, determining the spatial domain data topological distribution without topological fusion as the spatial domain data topological distribution to be processed, and acquiring first topological distribution description information of the spatial domain data topological distribution to be processed according to first spatial domain node service data contained in the spatial domain data topological distribution to be processed.

In this embodiment, time domain information may be lost or carried in the spatial domain node data generated by some abnormal data nodes, and the spatial domain data topology distribution generated by the spatial domain node data may not be matched with the corresponding time domain node topology distribution for topology fusion, so that the partial spatial domain data topology distribution is listed as the spatial domain data topology distribution to be processed for subsequent processing. For example, the airspace node service data included in the to-be-processed airspace data topological distribution may be referred to as first airspace node service data, and the to-be-processed airspace data topological distribution may include a plurality of first airspace node service data. Then, a service data recognition model can be obtained through pre-training, and the service data feature of each first airspace node service data is extracted, wherein the service data feature can be a service data description information. Then, the service data description information corresponding to each first airspace node service data can be combined to obtain the global service characteristic information corresponding to all the first airspace node service data. Finally, the global service characteristic information corresponding to the first airspace node service data may be referred to as first global service characteristic information. The first global service feature information is the topology distribution feature of the spatial domain data to be processed, so the first global service feature information may be referred to as first topology distribution description information of the spatial domain data to be processed.

And step S1024, respectively acquiring second topological distribution description information of the spatial domain data topological distribution in each topological distribution fusion group according to the second spatial domain node service data included in each topological distribution fusion group.

In this embodiment, the manner of acquiring the second topology distribution description information may refer to the manner of acquiring the first topology distribution description information. For example, the plurality of topologically distributed fused groups may include a topologically distributed fused group Ri, i not greater than a total number of the plurality of topologically distributed fused groups; and the topology distribution fusion group Ri comprises a plurality of service data fragments of the second airspace node service data. Based on this, firstly, service data description information corresponding to each second airspace node service data in a plurality of second airspace node service data included in the topology distribution fusion group Ri can be obtained; then acquiring second global service characteristic information corresponding to the plurality of second airspace node service data according to the service data description information corresponding to each second airspace node service data; and finally, determining the second global service characteristic information as second topological distribution description information of the spatial domain data topological distribution in the topological distribution fusion group Ri.

And a substep S1025 of obtaining the characteristic difference between the first topology distribution description information and the second topology distribution description information corresponding to each topology distribution fusion group.

And a substep S1026 of determining topology association parameters between the spatial domain data topology distribution in each topology distribution fusion group and the spatial domain data topology distribution to be processed respectively according to the characteristic difference corresponding to each topology distribution fusion group.

For example, in this embodiment, after obtaining the first topology distribution description information of the spatial domain data topology distribution to be processed and the second topology distribution description information of the spatial domain data topology distribution in each topology distribution fusion group, the feature difference between the first topology distribution description information and each second topology distribution description information may be obtained, and the topology association parameters between the spatial domain data topology distribution to be processed and the spatial domain data topology distribution in each topology distribution fusion group may be obtained through the feature difference corresponding to each topology distribution fusion group. For example, the larger the feature difference, the smaller the topology relation parameter, and the smaller the feature difference, the larger the topology relation parameter. Therefore, the reciprocal of the feature difference corresponding to each topology distribution fusion group can be used as the topology association parameter between the spatial domain data topology distribution to be processed and the spatial domain data topology distribution in each topology distribution fusion group, and the topology association parameter can be obtained by other methods besides obtaining the reciprocal according to the feature difference, which is not limited herein.

And step S1027, counting a target topological distribution fusion group with topological correlation parameters not less than a preset correlation parameter threshold value, and determining service characteristic information contained in time domain data topological distribution in the target topological distribution fusion group as service characteristic information associated with the spatial domain data topological distribution to be processed.

And a substep S1028 of performing topological fusion on the service characteristic information associated with the spatial domain data topological distribution to be processed and the spatial domain data topological distribution to be processed to obtain a characteristic topological fusion group corresponding to the spatial domain data topological distribution to be processed.

And a substep S1029, determining the service data stream in the service data acquisition range and the service characteristic information corresponding to the service data stream according to the characteristic topology fusion group and the plurality of topology distribution fusion groups. In this embodiment, each service data included in one topology distribution fusion group or one topology distribution fusion group may be used as one corresponding service data stream, and each feature information related to a time-space domain included in each service data in the service data stream may be extracted as corresponding service feature information. Therefore, the related information of the service data in the to-be-processed airspace data topological distribution without topology fusion can be extracted, so that the data acquisition is more comprehensive and accurate.

Further, in the present embodiment, please refer to fig. 4, which is a schematic flow chart illustrating the sub-step of the step S20. The step S20 may include sub-steps S201-S204, as described in more detail below.

And a substep S201, inputting the service data stream sample set into a preset target service characteristic network for machine learning, and obtaining a learned target service characteristic network.

In this embodiment, the preset target service feature Network may be a reduced-size neural Network (e.g., a Visual Geometry Group Network, VGG), and the learning manner of the neural Network is not described herein again.

And a substep S202, performing target service characteristic detection on the service data flow sample set through the learned target service characteristic network to obtain an initial target service characteristic set of the service data flow sample set.

In this embodiment, the step S202 of performing target service feature detection on the service data stream sample set through the learned target service feature network to obtain an initial target service feature set may be obtained in the following manner.

(1) And aiming at each sample service data flow in the service data flow sample set, acquiring the time-space domain topological distribution of each data segment of the sample service data flow and the time-space domain characteristics of each data segment.

In this embodiment, the time-space domain topology distribution of the data segments may refer to the foregoing corresponding description for step S10, and is not described herein again. The time-space domain features of the data segment may include time-domain features and space-domain features, and the corresponding definitions of the time-domain features and space-domain features may also refer to the above description for step S10.

(2) When an interference data block is determined in the sample service data stream according to the time-space domain topological distribution of the data segments, determining the characteristic difference between the time-space domain characteristic of each data segment corresponding to the non-interference data block of the sample service data stream and the time-space domain characteristic of each data segment corresponding to the interference data block of the sample service data stream according to the time-space domain characteristic of the data segment corresponding to the interference data block of the sample service data stream and the target characteristic detection weight of the data segment corresponding to the interference data block of the sample service data stream, and dividing the time-space domain characteristic of the data segment corresponding to the non-interference data block of the sample service data stream and matched with the time-space domain characteristic of the data segment corresponding to the interference data block into the interference data block. In this embodiment, when the current non-interference data block of the sample service data stream corresponds to a time-space domain feature having a plurality of data segments, the feature difference between the time-space domain features of the data segments corresponding to the current non-interference data block of the sample service data stream is determined according to the time-space domain feature of the data segments corresponding to the interference data block of the sample service data stream and the target feature detection weight thereof, and the time-space domain features of the data segments corresponding to the current non-interference data block are subjected to feature fusion according to the feature difference between the time-space domain features of the data segments. And then, configuring a feature identifier for the data fragment fusion feature obtained by the feature fusion according to the time-space domain feature of the data fragment corresponding to the interference data block of the sample service data stream and the target feature detection weight thereof, and dividing the data fragment fusion feature into the interference data block according to the feature identifier.

In this embodiment, the interference data blocks and the non-interference data blocks may include irregular data blocks and/or regular data blocks, the target feature detection weight is used to characterize a target feature detection degree of time-space domain features of the data segment, and the higher the target feature detection weight is, the greater the target feature detection degree of the time-space domain features of the data segment is, the greater the degree of distinction of contained information is. The feature identifier may be configured to characterize a block adjustment priority of the data segment fusion feature, and the dividing the data segment fusion feature into the interference data blocks according to the feature identifier may be dividing part of the data segment fusion features corresponding to the block adjustment priority corresponding to the feature identifier into the interference data blocks in descending order. The feature difference may be represented by a vector distance (e.g., cosine distance, euclidean distance, etc.) of the feature vector.

In some possible embodiments, for example, the determining, according to the time-space domain characteristics of the data segment corresponding to the interference data block of the sample service data stream and the target characteristic detection weight thereof, the characteristic difference between the time-space domain characteristics of each data segment in the non-interference data block of the sample service data stream and the time-space domain characteristics of each data segment corresponding to the interference data block of the sample service data stream, and dividing the time-space domain characteristics of the data segment corresponding to the non-interference data block of the sample service data stream, which are matched with the time-space domain characteristics of the data segment corresponding to the interference data block, into the interference data blocks may be implemented by:

firstly, calculating the correlation parameters between the time-space domain characteristics of each data segment corresponding to the non-interference data block of the sample service data stream and the characteristic vectors of the time-space domain characteristics of each data segment corresponding to the interference data block of the sample service data stream;

then, respectively judging whether each correlation parameter reaches a first set parameter threshold value, and dividing the time-space domain characteristics of the data segment corresponding to the non-interference data block of which the correlation parameter reaches the first set parameter threshold value into the interference data block; and the characteristic vector of the time-space domain characteristic of the data segment is a matching result of the time-space domain characteristic and the characteristic identifier of the data segment counted according to the time-space domain characteristic of the data segment corresponding to the interference data block of the sample service data stream and the target characteristic detection weight thereof.

In some possible embodiments, the feature difference between the time-space domain features of the data segments corresponding to the current non-interference data block of the sample service data stream is determined according to the time-space domain features of the data segments corresponding to the interference data block of the sample service data stream and the target feature detection weight thereof, and the time-space domain features of the data segments corresponding to the current non-interference data block are feature fused according to the feature difference between the time-space domain features of the data segments, and the specific implementation manner is as follows:

firstly, calculating the correlation parameters among the characteristic vectors of the time-space domain characteristics of each data segment corresponding to the current non-interference data block of the sample service data stream;

then, aiming at the time-space domain characteristics of a data segment corresponding to the current non-interference data block of the sample service data stream, performing characteristic fusion on the time-space domain characteristics of the data segment and the time-space domain characteristics of all the data segments of which the associated parameters with the characteristic vectors reach a second set parameter threshold value to obtain a data segment fusion characteristic sequence.

(3) And determining sample business data stream segments based on the time-space domain characteristics of the target data segments in the interference data blocks corresponding to the sample business data streams, and integrating the determined sample business data stream segments to obtain an initial target business characteristic set. In this embodiment, the sample service data stream segment may be a sample service data stream segment corresponding to interference data.

Therefore, based on the contents described in (1) to (3), the time-space domain characteristics of the data segments in the interference data block and the non-interference data block can be re-divided, so that the interference data block and the non-interference data block can be considered, and the accuracy of analyzing the business characteristics of the business data stream acquired at the later stage can be improved.

And a substep S203, inputting the initial target service characteristic set into a preset first anti-interference characteristic detection network for machine learning, so as to obtain a first target anti-interference characteristic detection network.

In this embodiment, the first interference prevention feature detection network may be understood as a network having a large parameter amount, and may be understood as a large network. Further, inputting the initial target service feature set into a preset first anti-interference feature detection network for machine learning, to obtain a first target anti-interference feature detection network, wherein an implementation manner is as follows:

and performing machine iteration learning on a preset first anti-interference feature detection network by adopting the initial target service feature set, and determining the first anti-interference feature detection network obtained by the Nth learning as the first target anti-interference feature detection network when a target feature detection result obtained by performing target feature detection on test service data by adopting the first anti-interference feature detection network obtained by the Nth learning reaches a set condition. In this embodiment, the setting result may be preset according to actual requirements, for example, may be 90% to 99%, for example, may be preferably 95%, and is not limited herein.

And a substep S204, performing machine learning on a preset second anti-interference feature detection network based on a joint model training strategy and the first target anti-interference feature detection network to obtain a second target anti-interference feature detection network, so that the parameter quantity of the trained second target anti-interference feature detection network is smaller than that of the first target anti-interference feature detection network.

In this embodiment, the second tamper-proof feature detection network may be understood as a network (small network) having a smaller parameter than the first tamper-proof feature detection network. Based on the above, machine learning is performed on a preset second anti-interference feature detection network based on a joint model training strategy and the first target anti-interference feature detection network to obtain a second target anti-interference feature detection network, which can be implemented in the following ways:

and performing machine learning on a preset second anti-interference feature detection network based on a preset model training evaluation index and the first target anti-interference feature detection network to obtain a second target anti-interference feature detection network.

In this embodiment, the preset model training evaluation index may be a preset loss function, which is not limited herein.

Further, in the process of performing machine learning on a preset second anti-interference feature detection network based on a preset model training evaluation index and the first target anti-interference feature detection network to obtain a second target anti-interference feature detection network: and when the value of the preset model training evaluation index obtained by the ith learning is located in a set numerical value interval, determining a second anti-interference feature detection network obtained by the ith learning as a second target anti-interference feature detection network. It is understood that the set value interval may be an interval close to 0, for example, 0.01 to 0.03, and is not limited herein. In some examples, the learning termination condition of the second interference-free feature detection network may be that a model training evaluation index (e.g., a loss function value) approaches 0.

In this embodiment, the second interference-prevention feature detection network is obtained based on a joint model training strategy, which is essentially based on a large network (large model) to train a small network (small model), so that the parameter of the small network is prevented from being expanded on the premise of ensuring the prediction accuracy of the small network. Therefore, the obtained parameter quantity of the second anti-interference characteristic detection network is reduced relative to the parameter quantity of the first anti-interference characteristic detection network, so that the second anti-interference characteristic detection network can directly run in the service acquisition terminal to realize the characteristic detection of the service data stream on the service acquisition terminal, the detection work of the server is dispersed on each service acquisition terminal to be realized, the burden of the server can be reduced, and the operational capability of each service acquisition terminal is fully exerted. Meanwhile, when the second anti-interference feature detection network is deployed at the service acquisition terminal, the real-time performance of the service data stream target feature detection of the service acquisition terminal can be ensured.

And a substep S205, sending a second target anti-interference feature detection network to the service acquisition terminal, and performing target feature detection on the acquired service data stream through the service acquisition terminal according to the second target anti-interference feature detection network to obtain service feature information of the acquired service data stream.

In this embodiment, the service acquisition terminal may be a mobile phone, a tablet computer, a notebook computer, or other portable terminals, which is not limited herein. In an actual implementation process, the determining of the service characteristic information may be performed by a service acquisition terminal and a server in cooperation, and to achieve this purpose, the performing of the target characteristic detection on the acquired service data stream by the service acquisition terminal and the second target anti-interference characteristic detection network to obtain the service characteristic information of the acquired service data stream, which is described in the substep S205, may be implemented in the following manner.

Firstly, the service acquisition terminal detects the data stream characteristics to be identified corresponding to the target block of the acquired service data stream by the network based on the second target anti-interference characteristics; wherein the target block may be a block in which the collected traffic data stream does not have interference data.

Then, the characteristics of the data stream to be identified sent by the service acquisition terminal are acquired, target service characteristic information matched with the characteristics of the data stream to be identified is acquired in a preset storage space, and the target service characteristic information is determined as the service characteristic information of the acquired service data stream.

In some examples, in order to ensure the accuracy of detecting the target feature of the service data stream, further mining needs to be performed on the feature of the data stream to be identified, and for this purpose, the above-mentioned obtaining, in the preset storage space, the target service feature information that matches the feature of the data stream to be identified may include the following contents.

(a1) Decomposing the data stream features to be identified to obtain a plurality of sub-data stream features, and obtaining space domain feature description information of the plurality of sub-data stream features, and m undetermined feature description sequences corresponding to m continuous target feature detection moments of the plurality of sub-data stream features before the current target feature detection moment, wherein the undetermined feature description sequence of each target feature detection moment comprises undetermined feature descriptions of the sub-data stream features under a plurality of feature identification categories.

(a2) And respectively obtaining a characteristic identification degree offset sequence corresponding to each undetermined characteristic description sequence in the m undetermined characteristic description sequences of the characteristics of each sub-data stream. Each characteristic recognition degree offset sequence comprises characteristic recognition degree offsets of the sub-data stream characteristics under a plurality of characteristic identification categories, and each characteristic recognition degree offset represents an offset between the current characteristic recognition degree and the offset characteristic recognition degree under one characteristic identification category.

(a3) And acquiring the characteristic identification degree offset of each sub-data stream characteristic at the current target characteristic detection moment by utilizing the learned characteristic identification degree adjusting network according to the spatial domain characteristic description information of each sub-data stream characteristic and m characteristic identification degree offset sequences corresponding to the m undetermined characteristic description sequences. The characteristic identification degree adjusting network is obtained by learning a plurality of network learning samples, and each network learning sample comprises space domain characteristic description information of a sub-stream characteristic and a characteristic identification degree offset sequence of m +1 continuous target characteristic detection moments. The characteristic recognition degree offset represents an offset between a current characteristic recognition degree and an offset characteristic recognition degree of the sub-data stream characteristic.

In this embodiment, the feature recognition degree adjustment network may be obtained through learning in the following learning process:

firstly, acquiring a large number of network learning samples from a network learning sample library;

then, through the obtained network learning sample, the characteristic identification degree adjustment network is subjected to multiple times of learning according to set learning parameters, and each learning process comprises the following steps: according to the airspace feature description information and the feature recognition degree offset sequence of the previous m target feature detection moments in the m +1 continuous target feature detection moments, acquiring the feature recognition degree offset of the subdata stream feature of each network learning sample at the m +1 target feature detection moment through the feature recognition degree adjusting network; acquiring a network evaluation index of the characteristic identification degree adjusting network according to the characteristic identification degree offset of the subdata stream characteristic of the network learning sample at the (m + 1) th target characteristic detection moment and the characteristic identification degree offset sequence of the (m + 1) th target characteristic detection moment in the network learning sample; determining whether to continuously learn the characteristic recognition degree adjusting network according to the network evaluation index; and if the learning of the characteristic recognition degree adjusting network is determined to be continued, modifying the network parameters of the characteristic recognition degree adjusting network, and continuing the next learning process through the modified characteristic recognition degree adjusting network.

In this embodiment, for example, the feature recognition degree adjusting network may include a feature noise recognition network layer and a feature segment splicing network layer. Based on this, for each sub-data stream feature, obtaining the feature recognition offset by using the feature recognition degree adjusting network may include: according to the m characteristic identification degree offset sequences, acquiring characteristic noise identification indexes of the characteristics of the sub-data stream through the characteristic noise identification network layer; acquiring a characteristic segment splicing index of the sub-data stream characteristic through the characteristic segment splicing network layer according to the airspace characteristic description information; and based on the network layer transmission parameters of the characteristic noise identification network layer and the characteristic segment splicing network layer, obtaining the characteristic identification degree offset at the current target characteristic detection moment according to the characteristic noise identification index and the characteristic segment splicing index.

(a4) Respectively adjusting the current characteristic identification degree of each sub-data stream characteristic through the characteristic identification degree offset of each sub-data stream characteristic at the current target characteristic detection moment; and determining target sub-data stream characteristics from the plurality of sub-data stream characteristics according to the adjusted current characteristic identification degree of each sub-data stream characteristic, and performing characteristic combination on the data stream characteristics to be identified according to the target sub-data stream characteristics to obtain characteristics to be analyzed for data characteristic analysis.

(a5) And acquiring the pre-stored data stream characteristics with the minimum characteristic difference with the characteristics to be analyzed in a preset storage space, and determining the associated service characteristic information of the pre-stored data stream characteristics as target service characteristic information matched with the characteristics of the data stream to be identified. The preset storage space may set in advance a storage location for storing the relevant service characteristic information of the service data stream according to the specified path.

Therefore, by the above method, the features of the data stream to be recognized can be further mined, so that the features of the data stream to be recognized are combined to obtain features to be analyzed for data feature analysis, and then the target service feature information matched with the features of the data stream to be recognized is determined based on the features to be analyzed, so that the accuracy of detecting the target features of the data stream can be ensured as much as possible.

As further shown in fig. 2, the service big data analysis device 110 included in the server 100 may include a plurality of software functional modules for implementing each corresponding step of the service big data analysis method based on artificial intelligence. In detail, in this embodiment, the business big data analysis apparatus 110 may include a sample set obtaining module 111 and a business data analysis module 112.

The sample set obtaining module 111 is configured to obtain a plurality of service data stream samples obtained by performing service big data collection in advance for a data collection range, so as to obtain a service data stream sample set.

The service data analysis module 112 is configured to perform machine learning on a preset interference prevention feature detection network according to the service data flow sample, and perform feature detection on the service data flow acquired by each service acquisition terminal through the learned interference prevention feature detection network to obtain service feature information of the corresponding service data flow.

The service data analysis module 112 is specifically configured to:

It should be understood that the sample set obtaining module 111 and the service data analyzing module 112 may be configured to respectively perform the method steps corresponding to step S10 and step S20 shown in fig. 3, and for details and specific implementation of the two modules, reference may be made to the corresponding contents of step S10 and step S20, which is not described herein again.

In summary, the service big data analysis method and the server based on artificial intelligence provided in the embodiments of the present invention obtain a service data flow sample set by performing target service feature extraction on a service data flow sample, then perform machine learning on a preset interference prevention feature detection network according to the service data flow sample set, and perform feature detection on a service data flow acquired by each service acquisition terminal through the learned interference prevention feature detection network, so as to obtain service feature information of a corresponding service data flow. The influence of interference data on the accuracy of target feature detection of the acquired service data stream can be reduced as much as possible, so that the accuracy of target feature detection of the service data stream is improved.

In addition, a preset target service characteristic network is further learned by adopting a service data flow sample set to obtain a learned target service characteristic network, target service characteristic detection is carried out on the service data flow sample set to obtain an initial target service characteristic set, then a preset first anti-interference characteristic detection network is learned by adopting the initial target service characteristic set to obtain a first target anti-interference characteristic detection network, machine learning is carried out on a preset second anti-interference characteristic detection network based on a joint model training strategy and the first target anti-interference characteristic detection network to obtain a second target anti-interference characteristic detection network, thus the second target anti-interference characteristic detection network can be sent to a service acquisition terminal, and target characteristic detection is carried out on the acquired service data flow by the service acquisition terminal and the second target anti-interference characteristic detection network to obtain a target characteristic detection result, and determining the service characteristic information of the collected service data stream based on the target characteristic detection result. And training based on a joint model training strategy to obtain a second anti-interference characteristic detection network, wherein the joint model training strategy is to train the small model based on the large model essentially, so that the parameter quantity of the small model is prevented from being enlarged on the premise of ensuring the prediction accuracy of the small model. Therefore, the obtained parameter quantity of the second anti-interference characteristic detection network is reduced relative to the parameter quantity of the first anti-interference characteristic detection network, so that the second anti-interference characteristic detection network can directly run in the service acquisition terminal to realize the characteristic detection of the service data stream on the service acquisition terminal, the detection work of the server is dispersed on each service acquisition terminal to be realized, the burden of the server can be reduced, and the operational capability of each service acquisition terminal is fully exerted. Meanwhile, when the second anti-interference feature detection network is deployed at the service acquisition terminal, the real-time performance of the service data stream target feature detection of the service acquisition terminal can be ensured.

The embodiments described above are only a part of the embodiments of the present invention, and not all of them. The components of embodiments of the present invention generally described and illustrated in the figures can be arranged and designed in a wide variety of different configurations. Therefore, the detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the present invention, but is merely representative of selected embodiments of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims. Moreover, all other embodiments that can be made available by a person skilled in the art without inventive step based on the embodiments of the present invention shall fall within the scope of protection of the present invention.

Claims

1. An artificial intelligence model machine learning method for big data analysis, which is applied to a server, and comprises the following steps:

respectively acquiring node data of a time domain node and a space domain node aiming at service data generated in a preset service data acquisition range for acquiring big service data to obtain time-space domain node data; the time-space domain node data comprises time domain node data and space domain node data corresponding to the service data generated in the service data acquisition range;

performing topology fusion analysis on the time-space domain node data to obtain a plurality of service data streams in the service data acquisition range and service characteristic information corresponding to the service data streams, and obtaining a service data stream sample set according to the service data streams and the corresponding service characteristic information;

2. The method according to claim 1, wherein the performing topology fusion analysis on the time-space domain node data to obtain a plurality of service data flows in the service data acquisition range and service feature information corresponding to the service data flows comprises:

respectively forming a plurality of time domain data topological distributions and a plurality of space domain data topological distributions on the time domain node data and the space domain node data in the time-space domain node data;

performing topological fusion on each time domain data topological distribution and each space domain data topological distribution generated in the service data acquisition range according to a service topological relation between the time domain data topological distribution and the space domain data topological distribution to obtain a plurality of topological distribution fusion groups; the spatial domain data topological distribution in each topological distribution fusion group respectively comprises second spatial domain node service data in the service data acquisition range;

determining the spatial domain data topological distribution which is not subjected to topological fusion as to-be-processed spatial domain data topological distribution, and acquiring first topological distribution description information of the to-be-processed spatial domain data topological distribution according to first spatial domain node service data contained in the to-be-processed spatial domain data topological distribution; the first airspace node service data is contained in the service data acquisition range;

respectively acquiring second topological distribution description information of the spatial domain data topological distribution in each topological distribution fusion group according to second spatial domain node service data included in each topological distribution fusion group;

acquiring characteristic differences between the first topological distribution description information and second topological distribution description information corresponding to each topological distribution fusion group;

determining topological correlation parameters between the spatial domain data topological distribution in each topological distribution fusion group and the spatial domain data topological distribution to be processed respectively according to the characteristic difference corresponding to each topological distribution fusion group;

counting the number of target topological distribution fusion groups with topological correlation parameters not less than a preset correlation parameter threshold, and determining service characteristic information contained in time domain data topological distribution in the target topological distribution fusion groups as service characteristic information associated with the spatial domain data topological distribution to be processed;

performing topological fusion on the service characteristic information associated with the spatial domain data topological distribution to be processed and the spatial domain data topological distribution to be processed to obtain a characteristic topological fusion group;

and determining the service data stream in the service data acquisition range and the service characteristic information corresponding to the service data stream according to the characteristic topology fusion group and the plurality of topology distribution fusion groups.

3. The method of claim 1, wherein the performing machine learning on a preset interference-prevention feature detection network according to the service data flow sample set to perform feature detection on the service data flows acquired by the service acquisition terminals through the learned interference-prevention feature detection network to obtain service feature information of the corresponding service data flows comprises:

4. The method of claim 3, wherein inputting the initial target service feature set into a preset first anti-interference feature detection network for machine learning to obtain a first target anti-interference feature detection network comprises:

and performing machine iteration learning on a preset first anti-interference feature detection network by adopting the initial target service feature set, and determining the first anti-interference feature detection network obtained by the Nth learning as the first target anti-interference feature detection network when a target feature detection result obtained by performing target feature detection on the predetermined test service data by adopting the first anti-interference feature detection network obtained by the Nth learning reaches a set condition.

5. The method of claim 3, wherein performing machine learning on a preset second anti-interference feature detection network based on a joint model training strategy and the first target anti-interference feature detection network to obtain a second target anti-interference feature detection network comprises:

performing machine learning on a preset second anti-interference feature detection network based on a preset model training evaluation index and the first target anti-interference feature detection network to obtain a second target anti-interference feature detection network;

the method includes the following steps that machine learning is carried out on a preset second anti-interference feature detection network based on preset model training evaluation indexes and the first target anti-interference feature detection network, and the second target anti-interference feature detection network is obtained, and includes:

and when the value of the preset model training evaluation index obtained by the ith learning is located in a set numerical value interval, determining a second anti-interference feature detection network obtained by the ith learning as a second target anti-interference feature detection network.

6. The method of claim 3, wherein performing target traffic feature detection on the traffic data stream sample set through the learned target traffic feature network to obtain an initial target traffic feature set of the traffic data stream sample set, comprises:

aiming at each sample service data flow in the service data flow sample set, acquiring the time-space domain topological distribution of the data segments of the sample service data flow and the time-space domain characteristics of each data segment;

when an interference data block is determined in the sample service data stream according to the time-space domain topological distribution of the data segments, determining the characteristic difference between the time-space domain characteristic of each data segment corresponding to the non-interference data block of the sample service data stream and the time-space domain characteristic of each data segment corresponding to the interference data block of the sample service data stream according to the time-space domain characteristic of the data segment corresponding to the interference data block of the sample service data stream and the target characteristic detection weight of the data segment, and dividing the time-space domain characteristic of the data segment matched with the time-space domain characteristic of the data segment corresponding to the interference data block in the non-interference data block of the sample service data stream into the interference data block; when the current non-interference data block of the sample service data flow has the time-space domain characteristics of a plurality of data fragments, determining the characteristic difference between the time-space domain characteristics of the data fragments corresponding to the current non-interference data block of the sample service data flow according to the time-space domain characteristics of the data fragments corresponding to the interference data block of the sample service data flow and the target characteristic detection weight thereof, and performing characteristic fusion on the time-space domain characteristics of the data fragments corresponding to the current non-interference data block according to the characteristic difference between the time-space domain characteristics of the data fragments; configuring a feature identifier for the data fragment fusion feature obtained by the feature fusion according to the time-space domain feature of the data fragment corresponding to the interference data block of the sample service data stream and the target feature detection weight thereof, and dividing the data fragment fusion feature into the interference data block according to the feature identifier;

determining sample business data flow segments based on the time-space domain characteristics of target data segments in interference data blocks corresponding to the sample business data flows, and integrating the determined sample business data flow segments to obtain an initial target business characteristic set; the sample service data stream fragment is a sample service data stream fragment corresponding to interference data;

determining a feature difference between the time-space domain feature of each data segment corresponding to the non-interference data block of the sample service data stream and the time-space domain feature of each data segment corresponding to the interference data block of the sample service data stream according to the time-space domain feature of the data segment corresponding to the interference data block of the sample service data stream and the target feature detection weight thereof, and dividing the time-space domain feature of the data segment corresponding to the non-interference data block of the sample service data stream, which is matched with the time-space domain feature of the data segment corresponding to the interference data block, into the interference data block includes:

calculating correlation parameters between the characteristic vectors of the time-space domain characteristics of the data segments corresponding to the non-interference data blocks of the sample service data stream and the characteristic vectors of the time-space domain characteristics of the data segments corresponding to the interference data blocks of the sample service data stream;

respectively judging whether the calculated correlation parameters reach a first set parameter threshold value or not, and dividing the time-space domain characteristics of the data segments corresponding to the non-interference data blocks of which the correlation parameters reach the first set parameter threshold value into the interference data blocks; wherein, the characteristic vector of the time-space domain characteristic of the data segment is as follows: according to the time-space domain characteristics of the data segments corresponding to the interference data blocks of the sample service data stream and the target characteristic detection weight thereof, counting the matching results of the time-space domain characteristics and the characteristic identification of the data segments;

determining the characteristic difference between the time-space domain characteristics of each data segment corresponding to the current non-interference data block of the sample service data stream according to the time-space domain characteristics of the data segment corresponding to the interference data block of the sample service data stream and the target characteristic detection weight thereof, and performing characteristic fusion on the time-space domain characteristics of each data segment corresponding to the current non-interference data block according to the characteristic difference between the time-space domain characteristics of each data segment comprises:

calculating the correlation parameters among the characteristic vectors of the time-space domain characteristics of each data segment corresponding to the current non-interference data block of the sample service data stream; and performing feature fusion on the time-space domain features of a data segment corresponding to the current non-interference data block of the sample service data stream and the time-space domain features of all the data segments of which the associated parameters with the feature vectors reach a second set parameter threshold to obtain a data segment fusion feature sequence.

7. The method of claim 4, wherein performing target feature detection on the collected service data stream by the service collection terminal according to the second target anti-interference feature detection network to obtain the service feature information of the collected service data stream comprises:

extracting the data stream characteristics to be identified corresponding to the target block of the acquired service data stream by the service acquisition terminal based on the second target anti-interference characteristic detection network; wherein the target block is a block in which the collected service data stream does not have interference data;

acquiring the characteristics of the data stream to be identified sent by the service acquisition terminal;

and acquiring target service characteristic information matched with the characteristics of the data stream to be identified in a preset storage space, and determining the target service characteristic information as the service characteristic information of the acquired service data stream.

8. The method according to claim 7, wherein obtaining the target service characteristic information matched with the characteristic of the data stream to be identified in a preset storage space comprises:

decomposing the data stream characteristics to be identified to obtain a plurality of sub data stream characteristics; obtaining space domain feature description information of a plurality of sub-data stream features and m undetermined feature description sequences corresponding to m continuous target feature detection moments of the plurality of sub-data stream features before a current target feature detection moment, wherein the undetermined feature description sequence of each target feature detection moment comprises undetermined feature descriptions of the sub-data stream features under a plurality of feature identification categories;

respectively obtaining a characteristic identification degree offset sequence corresponding to each undetermined characteristic description sequence in m undetermined characteristic description sequences of the characteristics of each sub-data stream; each characteristic recognition degree offset sequence comprises characteristic recognition degree offsets of the sub-data stream characteristics under a plurality of characteristic identification categories, and each characteristic recognition degree offset represents an offset between the current characteristic recognition degree and the offset characteristic recognition degree under one characteristic identification category;

acquiring the characteristic identification degree offset of each sub-data stream characteristic at the current target characteristic detection moment by utilizing a learned characteristic identification degree adjusting network according to the spatial domain characteristic description information of each sub-data stream characteristic and m characteristic identification degree offset sequences corresponding to m undetermined characteristic description sequences; the characteristic identification degree adjusting network is obtained by utilizing a plurality of network learning samples in advance, and each network learning sample comprises space domain characteristic description information of a sub-data stream characteristic and a characteristic identification degree offset sequence of m +1 continuous target characteristic detection moments; the characteristic identification degree offset represents the offset between the current characteristic identification degree and the offset characteristic identification degree of the sub-data stream characteristic;

respectively adjusting the current characteristic identification degree of each sub-data stream characteristic through the characteristic identification degree offset of each sub-data stream characteristic at the current target characteristic detection moment; determining target sub-data stream characteristics from the plurality of sub-data stream characteristics according to the adjusted current characteristic identification degree of each sub-data stream characteristic, and performing characteristic combination on the data stream characteristics to be identified according to the target sub-data stream characteristics to obtain characteristics to be analyzed for data characteristic analysis;

acquiring a pre-stored data stream characteristic with the minimum characteristic difference with the characteristic of the characteristic to be analyzed in a preset storage space, and determining associated service characteristic information of the pre-stored data stream characteristic as target service characteristic information matched with the characteristic of the data stream to be identified;

the feature recognition degree adjusting network is obtained by learning through the following learning process: acquiring a large number of online learning samples from an online learning sample library; and performing the following learning processes on the characteristic recognition degree adjusting network for multiple times according to the set learning parameters through the acquired network learning samples:

according to the airspace feature description information and the feature recognition degree offset sequence of the previous m target feature detection moments in the m +1 continuous target feature detection moments, acquiring the feature recognition degree offset of the subdata stream feature of each network learning sample at the m +1 target feature detection moment through the feature recognition degree adjusting network;

acquiring a network evaluation index of the characteristic identification degree adjusting network according to the characteristic identification degree offset of the subdata stream characteristic of the network learning sample at the (m + 1) th target characteristic detection moment and the characteristic identification degree offset sequence of the (m + 1) th target characteristic detection moment in the network learning sample;

determining whether to continuously learn the characteristic recognition degree adjusting network according to the network evaluation index; and if the learning of the characteristic recognition degree adjusting network is determined to be continued, modifying the network parameters of the characteristic recognition degree adjusting network, and continuing the next learning process through the modified characteristic recognition degree adjusting network.

9. A server, comprising a processor and a memory, the processor executing a computer program stored in the memory to implement the method of any one of claims 1-8.