CN111144495B - Service distribution method, device and medium - Google Patents
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Abstract
The invention discloses a service distribution method, a device and a medium. Wherein the method comprises the following steps: classifying the services to be distributed according to equipment codes, algorithm models and distribution control states of the services to be distributed based on a decision tree model so as to divide the services to be distributed into service groups to be distributed; and the service group to be distributed is issued to a target analysis unit, and the target analysis unit is used for indicating the target analysis unit to analyze the received service group. According to the technical scheme, automatic sorting and combination of the to-be-distributed services are realized based on the classification tree feature selection, so that the combination efficiency of the to-be-distributed services is improved, batch distribution of the to-be-distributed services is realized, and the labor cost is saved.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a service distribution method, a device and a medium.
Background
Along with the development of the age and the intellectualization of the security system, the demand of intelligent video analysis on the more concentrated activity places of various personnel is also increasing, such as a monitoring center, an airport, a subway and the like, and the information analyzed by the intelligent analysis system is used for timely processing the existing potential safety hazards, so that the stability of the society can be effectively ensured.
The analysis unit in the intelligent analysis system is only used for analyzing the business of a certain appointed algorithm model, when a single camera is needed for simultaneously analyzing a plurality of businesses in certain intelligent analysis scenes, the algorithm model to which the business belongs is usually needed to be judged manually, and the configuration and combination tasks of one way and one way are issued.
Disclosure of Invention
The invention provides a service distribution method, a device and a medium, which are used for realizing automatic batch distribution of services, greatly reducing repeated workload and being beneficial to improving the efficiency of service combination.
In a first aspect, an embodiment of the present invention provides a service distribution method, where the method includes:
classifying the services to be distributed according to equipment codes, algorithm models and distribution control states of the services to be distributed based on a decision tree model so as to divide the services to be distributed into service groups to be distributed;
and the service group to be distributed is issued to a target analysis unit, and the target analysis unit is used for indicating the target analysis unit to analyze the received service group.
In a second aspect, an embodiment of the present invention further provides a service distribution apparatus, where the apparatus includes:
the service classification module is used for classifying the service to be distributed according to the equipment codes, the algorithm model and the distribution control state of the service to be distributed based on the decision tree model so as to divide the service to be distributed into service groups to be distributed;
and the service issuing module is used for issuing the service group to be distributed to a target analysis unit and indicating the target analysis unit to analyze the received service group.
In a third aspect, an embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a service distribution method according to any of the embodiments of the present invention.
Based on a decision tree model, classifying the to-be-distributed service according to equipment codes, algorithm models and distribution control states of the to-be-distributed service so as to divide the to-be-distributed service into to-be-distributed service groups; and then the service group to be distributed is issued to a target analysis unit for indicating the target analysis unit to analyze the received service group. According to the technical scheme, automatic sorting and combination of the services are realized based on the classification tree feature selection, so that the combination efficiency of the services to be distributed is improved, the batch distribution of the services to be distributed is realized, and the labor cost is saved.
Drawings
Fig. 1 is a flowchart of a service distribution method provided in an embodiment of the present invention;
fig. 2 is a flowchart of another service distribution method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a decision tree model according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a service distribution device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a service distribution device according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Fig. 1 is a flowchart of a service data method according to an embodiment of the present invention, where the embodiment is applicable to a service data issuing situation, and typically, the method may be applied to intelligent analysis based on video streaming. The method may be performed by a business data device, which may be implemented in software and/or hardware. Referring to fig. 1, the method specifically includes the steps of:
step 110, classifying the service to be distributed according to the equipment codes, the algorithm model and the distribution control state of the service to be distributed based on the decision tree model so as to divide the service to be distributed into service groups to be distributed.
The service to be distributed is various services configured by users in batches, and the configuration information of the service comprises related information such as equipment codes, algorithm models, distribution control states and the like.
The decision tree is a basic classification and regression method, and in this embodiment, a classification model of the decision tree is used. The model is a tree structure for classifying the examples, and the data is classified and summarized through the sequence of feature selection.
In general, in order to save analysis resources in an intelligent analysis system, analysis units in the intelligent analysis system are divided according to different algorithm models, and each analysis unit only loads a corresponding algorithm model and only supports services associated with the algorithm model. In addition, in an actual application scenario, different services need to be analyzed in different time periods, that is, time-division control needs to be performed, so in order to reduce the influence caused by switching of control time, services in the same control state need to be distributed in the same group for processing.
Therefore, in this embodiment, the device code, the algorithm model, and the distribution state are used as feature selection points for sorting and combining the services to be distributed.
Specifically, to-be-distributed services configured by users in batches are obtained, configuration information such as equipment codes, algorithm models and distribution control states of the to-be-distributed services are summarized and arranged in a container queue, the equipment codes, the algorithm models and the distribution control states are respectively used as first, second and third word classical sequences based on a decision tree model, and the services in the container queue are classified and ordered according to dictionary sequences.
Illustratively, table 1 is the traffic to be distributed before sorting:
| service ID | Camera coding | Algorithm model | Whether or not to control |
| ruletype1 | Camera3 | Model1 | Is that |
| ruletype2 | Camera2 | Model2 | Whether or not |
| ruletype3 | Camera1 | Model3 | Is that |
| ruletype4 | Camera2 | Model2 | Whether or not |
| ruletype5 | Camera3 | Model1 | Is that |
Based on a decision tree model, classifying the to-be-distributed service according to equipment codes, algorithm models and distribution control states of the to-be-distributed service, wherein the to-be-distributed service comprises the following table:
in the above table, two to-be-distributed services with the service IDs of rule type1 and rule type5 may be divided into the same to-be-distributed service group, the service IDs of rule type2 and rule type4 are used as the same to-be-distributed service group, and the to-be-distributed service with the service ID of rule type3 is independently used as a group to-be-distributed service group. By utilizing the decision tree model, the automatic combination of the services to be distributed is realized according to the equipment codes, the algorithm model and the distribution control state of the services to be distributed, and the combination efficiency of the services to be distributed is improved.
And 120, issuing the service group to be distributed to a target analysis unit, wherein the target analysis unit is used for indicating the target analysis unit to analyze the received service group.
The target analysis units are actual calculation units for performing intelligent analysis, and each target analysis unit is loaded with a corresponding algorithm model. And distributing the service group to be distributed to a target analysis unit which is the same as the algorithm model of the service group to be distributed, and analyzing the received service group through the target analysis unit.
According to the technical scheme of the embodiment, the service to be distributed is classified according to the equipment codes, the algorithm model and the distribution control state of the service to be distributed based on the decision tree model, so that the service to be distributed is divided into service groups to be distributed; and then the service group to be distributed is issued to a target analysis unit for indicating the target analysis unit to analyze the received service group. The automatic sorting and combining of the businesses are realized based on the feature selection of the classification tree, so that the combining efficiency of the businesses to be distributed is improved, the batch distribution of the businesses to be distributed is realized, and the labor cost is saved.
Fig. 2 is a flowchart of another service distribution method according to an embodiment of the present invention. The embodiment of the present invention further refines step 210 based on the above embodiment.
With further reference to fig. 3, in this embodiment, the decision tree model includes a device coding feature selection point, an algorithm model feature selection point, and a distribution control state feature selection point, where the device coding feature selection point is a parent node of the algorithm model feature selection point, and the algorithm model feature selection point is a parent node of the distribution control state feature selection point.
Specifically, the order of feature selection points in the decision tree is determined according to the information gain of the variable, and the higher the information gain, the higher the hierarchical order of feature selection points in the decision number. In this embodiment, the order of the information gain of the feature selection points calculated by the training data set is the camera coding > algorithm model > control time. Therefore, in this embodiment, the device code feature selection point is used as a parent node of the algorithm model feature selection point, and the algorithm model feature selection point is used as a parent node of the control state feature selection point.
Wherein, the information entropy represents uncertainty of random variables; conditional entropy represents the uncertainty of random variables under certain conditions; the information gain is equal to the information entropy minus the conditional entropy, indicating the degree to which the information uncertainty is reduced under certain conditions.
It should be noted that, the sorting manner of the feature selection points in the embodiment is only an example, and different hierarchical sorting manners of the feature selection points of the decision tree can be obtained for different training data sets.
Referring to fig. 2, the method specifically includes:
step 210, classifying the service to be distributed according to the device code of the service to be distributed based on the device code feature selection point, so as to obtain a service group to be distributed associated with the device code.
In this embodiment, first, based on the device code feature selection point of the decision tree model, the to-be-distributed service is classified according to the device codes of the to-be-distributed service, so that the to-be-distributed service with the same device code is divided into a group, so as to obtain the to-be-distributed service group associated with the device codes.
And 220, classifying the service groups to be distributed associated with the equipment codes according to the algorithm model of the service to be distributed based on the algorithm model feature selection points so as to obtain the service groups to be distributed associated with the algorithm model.
After the service groups to be distributed with the same equipment codes are obtained, the service groups to be distributed with the same equipment codes are classified according to the algorithm model of the service to be distributed based on the algorithm model feature selection points of the decision tree model, so that the service groups to be distributed with the same equipment codes and the same algorithm model are divided into a group, and the service groups to be distributed associated with the algorithm model are obtained.
And 230, grouping the service groups to be distributed associated with the algorithm model according to the distribution state of the service to be distributed based on the distribution state feature selection point so as to obtain the service groups to be distributed associated with the distribution state.
After the service groups to be distributed with the same equipment codes and the same algorithm model are obtained, classifying the service groups to be distributed with the same equipment codes and the same algorithm model according to the distribution state characteristic selection points of the decision tree model, so that the service groups to be distributed with the same equipment codes, the same algorithm model and the same distribution state are divided into a group, and the service groups to be distributed with the same distribution state are obtained.
And 240, issuing the service group to be distributed to a target analysis unit, wherein the target analysis unit is used for indicating the target analysis unit to analyze the received service group.
Specifically, the service group to be distributed is issued to a target analysis unit, which includes:
issuing the service groups to be distributed associated with different distribution states to different target analysis units;
and issuing the service groups to be distributed, which are associated with the same control state, to the same target analysis unit.
In this embodiment, the camera code, the algorithm model and the distribution state of each service to be distributed in the service group to be distributed associated with the distribution state are the same, so that the service groups to be distributed with the same camera code, algorithm model and distribution state are formed into the same service group to be distributed, and the service groups to be distributed are distributed to the target analysis unit.
Optionally, the issuing the service group to be distributed to a target analysis unit includes:
if the quantity of the to-be-distributed services in any distribution state associated to the to-be-distributed service group is detected to be smaller than the grouping service quantity threshold, continuing to acquire the to-be-distributed services, classifying the acquired to-be-distributed services until the quantity of the to-be-distributed services in the distribution state associated to the to-be-distributed service group is detected to be greater than or equal to the grouping service quantity threshold, and issuing the distribution state associated to the to-be-distributed service group to a target analysis unit.
For example, when the classified services to be distributed are combined, if the number of the services in the service group to be distributed is 1, the services to be distributed can be continuously combined with the newly added services to be distributed, and the recombined services to be distributed are issued to the target analysis unit.
According to the technical scheme, the equipment codes, the algorithm model and the distribution control states are used as feature selection points of the decision tree model, the services to be distributed are distributed and ordered, the combination efficiency of the services to be distributed is improved, and efficient batch distribution of the services to be distributed is achieved.
On the basis of the foregoing embodiment, before the delivering the to-be-distributed service groups associated with the same distribution state to the same target analysis unit, the method further includes:
if the quantity of the to-be-distributed services in any one of the service groups to be distributed associated with the distribution state is larger than a single-path service quantity threshold, grouping the to-be-distributed services so that the quantity of the to-be-distributed services in each group after grouping is smaller than or equal to the single-path service quantity threshold.
The single-path service quantity threshold value is the upper limit of the service quantity which can be analyzed by a single target analysis unit.
For example, if the single-path service number threshold is 3, the number of to-be-distributed services in the to-be-distributed service group associated with any one of the distribution states is 5, the to-be-distributed service group may be divided into a to-be-distributed service group with a service number of 3 and a service number of 2, so that the number of to-be-distributed services in each group after grouping is less than or equal to the single-path service number threshold, so as to ensure that the target analysis unit can provide sufficient analysis capability for the to-be-distributed service group.
Fig. 4 is a schematic structural diagram of a service distribution device according to an embodiment of the present invention, where the device may execute a service distribution method according to an embodiment of the present invention, and the service distribution device has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 4, the apparatus may specifically include:
the service classification module 310 is configured to classify the service to be distributed according to the device code, the algorithm model and the distribution state of the service to be distributed based on the decision tree model, so as to divide the service to be distributed into service groups to be distributed.
And the service issuing module 320 is configured to issue the service group to be distributed to a target analysis unit, and instruct the target analysis unit to analyze the received service group.
Specifically, the decision tree model comprises equipment coding feature selection points, algorithm model feature selection points and distribution control state feature selection points, wherein the equipment coding feature selection points are father nodes of the algorithm model feature selection points, and the algorithm model feature selection points are father nodes of the distribution control state feature selection points;
accordingly, the service classification module 310 is specifically configured to: classifying the service to be distributed according to the equipment codes of the service to be distributed based on the equipment code characteristic selection points so as to obtain a service group to be distributed associated with the equipment codes;
classifying the service groups to be distributed associated with the equipment codes according to the algorithm model of the service to be distributed based on the algorithm model feature selection points so as to obtain the service groups to be distributed associated with the algorithm model;
and classifying the service groups to be distributed, which are associated with the algorithm model, according to the distribution state of the service to be distributed based on the distribution state feature selection points so as to obtain the service groups to be distributed, which are associated with the distribution state.
The service delivery module 320 is specifically configured to: issuing the service groups to be distributed associated with different distribution states to different target analysis units;
and issuing the service groups to be distributed, which are associated with the same control state, to the same target analysis unit.
Optionally, the device further comprises a judging module and a service grouping module, wherein the judging module is used for judging whether the number of the to-be-distributed services in the to-be-distributed service group associated with any one of the distribution states is larger than a single-path service number threshold;
the grouping module is used for grouping the to-be-distributed service groups if the number of to-be-distributed services in any to-be-distributed service group associated with the distribution state is larger than a single-path service number threshold value, so that the number of to-be-distributed services in each group after grouping is smaller than or equal to the single-path service number threshold value.
Optionally, the judging module is further configured to judge whether the number of the to-be-distributed services in the to-be-distributed service group associated with any one of the control states is smaller than a threshold value of the number of the packet services.
The above-mentioned service classification module 310 is further specifically configured to, if the number of to-be-distributed services in the to-be-distributed service group associated with any one of the distribution states is smaller than a packet service number threshold, continue to obtain to-be-distributed services, and classify the obtained to-be-distributed services until the number of to-be-distributed services in the to-be-distributed service group associated with the distribution state is greater than or equal to the packet service number threshold, where the service issuing module 320 is configured to issue the to-be-distributed service group associated with the distribution state to the target analysis unit.
The service distribution device provided by the embodiment of the invention can execute the service distribution method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 5 is a schematic structural diagram of a service distribution device according to an embodiment of the present invention. Fig. 5 shows a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 5 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 5, device 12 is in the form of a general purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures.
Device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with device 12, and/or any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, device 12 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, via network adapter 20. As shown, network adapter 20 communicates with other modules of device 12 over bus 18. The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, to implement a service distribution method provided by an embodiment of the present invention.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the service distribution method according to the above embodiment of the invention. Wherein the method comprises the following steps:
classifying the services to be distributed according to equipment codes, algorithm models and distribution control states of the services to be distributed based on a decision tree model so as to divide the services to be distributed into service groups to be distributed;
and the service group to be distributed is issued to a target analysis unit, and the target analysis unit is used for indicating the target analysis unit to analyze the received service group.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A method of service distribution, the method comprising:
classifying the services to be distributed according to equipment codes, algorithm models and distribution control states of the services to be distributed based on a decision tree model so as to divide the services to be distributed into service groups to be distributed; the service to be distributed is various services configured by users in batches, the equipment codes are camera codes, and the service is intelligent analysis service based on video streams;
and the service group to be distributed is issued to a target analysis unit, and the target analysis unit is used for indicating the target analysis unit to analyze the received service group.
2. The method of claim 1, wherein the decision tree model comprises device encoding feature selection points, algorithm model feature selection points, and deployment state feature selection points, and wherein the device encoding feature selection points are parent nodes of the algorithm model feature selection points, the algorithm model feature selection points being parent nodes of the deployment state feature selection points;
correspondingly, based on the decision tree model, classifying the service to be distributed according to the equipment codes, the algorithm model and the distribution control state of the service to be distributed, and the method comprises the following steps:
classifying the service to be distributed according to the equipment codes of the service to be distributed based on the equipment code characteristic selection points so as to obtain a service group to be distributed associated with the equipment codes;
classifying the service groups to be distributed associated with the equipment codes according to the algorithm model of the service to be distributed based on the algorithm model feature selection points so as to obtain the service groups to be distributed associated with the algorithm model;
and classifying the service groups to be distributed, which are associated with the algorithm model, according to the distribution state of the service to be distributed based on the distribution state feature selection points so as to obtain the service groups to be distributed, which are associated with the distribution state.
3. The method according to claim 2, wherein the delivering the set of to-be-distributed traffic to the target analysis unit comprises:
issuing the service groups to be distributed associated with different distribution states to different target analysis units;
and issuing the service groups to be distributed, which are associated with the same control state, to the same target analysis unit.
4. A method according to claim 3, wherein said issuing the same service group to be distributed associated with the same distribution state, before the issuing to the same target analysis unit, further comprises:
if the quantity of the to-be-distributed services in any one of the to-be-distributed service groups associated with the control state is larger than a single-path service quantity threshold, grouping the to-be-distributed service groups associated with the control state, so that the quantity of the to-be-distributed services in each group after grouping is smaller than or equal to the single-path service quantity threshold.
5. The method according to any of claims 2-4, wherein the delivering the set of traffic to be distributed to a target analysis unit comprises:
if the quantity of the to-be-distributed services in any distribution state associated to the to-be-distributed service group is detected to be smaller than the grouping service quantity threshold, continuing to acquire the to-be-distributed services, classifying the acquired to-be-distributed services until the quantity of the to-be-distributed services in the distribution state associated to the to-be-distributed service group is detected to be greater than or equal to the grouping service quantity threshold, and issuing the distribution state associated to the to-be-distributed service group to a target analysis unit.
6. A service distribution apparatus, the apparatus comprising:
the service classification module is used for classifying the service to be distributed according to the equipment codes, the algorithm model and the distribution control state of the service to be distributed based on the decision tree model so as to divide the service to be distributed into service groups to be distributed; the service to be distributed is various services configured by users in batches, the equipment codes are camera codes, and the service is intelligent analysis service based on video streams;
and the service issuing module is used for issuing the service group to be distributed to a target analysis unit and indicating the target analysis unit to analyze the received service group.
7. The apparatus of claim 6, wherein the decision tree model comprises device encoding feature selection points, algorithm model feature selection points, and deployment state feature selection points, and wherein the device encoding feature selection points are parent nodes of the algorithm model feature selection points, the algorithm model feature selection points being parent nodes of the deployment state feature selection points;
correspondingly, the service classification module is specifically configured to: classifying the service to be distributed according to the equipment codes of the service to be distributed based on the equipment code characteristic selection points so as to obtain a service group to be distributed associated with the equipment codes;
classifying the service groups to be distributed associated with the equipment codes according to the algorithm model of the service to be distributed based on the algorithm model feature selection points so as to obtain the service groups to be distributed associated with the algorithm model;
and classifying the service groups to be distributed, which are associated with the algorithm model, according to the distribution state of the service to be distributed based on the distribution state feature selection points so as to obtain the service groups to be distributed, which are associated with the distribution state.
8. The apparatus of claim 7, wherein the service delivery module is specifically configured to:
issuing the service groups to be distributed associated with different distribution states to different target analysis units;
and issuing the service groups to be distributed, which are associated with the same control state, to the same target analysis unit.
9. The apparatus of claim 7 or 8, further comprising a grouping module: and if the quantity of the to-be-distributed services in any one of the to-be-distributed service groups associated with the control state is larger than the single-path service quantity threshold, grouping the to-be-distributed service groups associated with the control state, so that the quantity of the to-be-distributed services in each group after grouping is smaller than or equal to the single-path service quantity threshold.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a service distribution method according to any of claims 1-5.
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