Dummy resource image distribution method and device, equipment, storage medium and program product

Abstract

The invention relates to the technical field of information processing, and particularly provides a dumb resource image distribution method and device, equipment, a storage medium and a program product. The dumb resource image distribution method comprises the following steps: acquiring a dumb resource image to be distributed; obtaining a channel rule model, wherein the channel rule model is used for indicating a dumb resource feature combination supported by each dumb resource capability module; determining dumb resource features of the dumb resource image; and determining a distribution channel of the dumb resource image based on the channel rule model and the dumb resource feature matched with the dumb resource image, and distributing the dumb resource image according to the distribution channel. By implementing the technical scheme of the invention, the distribution efficiency of the dummy resource image can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing technology, and in particular to a method, device, equipment, storage medium and program product for distributing dumb resource images. Background Art

[0002] With the development of information processing technology, the processing methods for dumb resource images have also varied according to different application scenarios, resulting in nearly 100 dumb resource capability modules such as optical cross-box identification, flange identification, terminal identification, process detection, connectivity analysis, compliance analysis, and idle rate calculation for scenarios such as daily operation and maintenance inspections, system cutovers, and data verification.

[0003] In the prior art, in order to facilitate the use of front-line personnel, R&D personnel will provide a plug-in capability calling interface. Users install the capability calling software in the portable terminal, click the required interface channel, and send the acquired dumb resource image to the corresponding interface channel, which can trigger the data processing logic on the server side and generate the processing result of the capability module. The disadvantage is that the dumb resource image distribution efficiency is low and the labor cost is high. Summary of the invention

[0004] In view of the above problems, the present disclosure is proposed. The present disclosure provides a method, apparatus, device, storage medium and program product for distributing dumb resource images.

[0005] According to one aspect of the present disclosure, a dummy resource image distribution method is provided, comprising:

[0006] Get the dummy resource image to be distributed;

[0007] Acquire a channel rule model, where the channel rule model is used to indicate a combination of dummy resource features supported by each dummy resource capability module;

[0008] Determining a dumb resource feature of the dumb resource image;

[0009] Based on the channel rule model and the dumb resource features of the dumb resource image, a distribution channel of the dumb resource image is determined, and the dumb resource image is distributed according to the distribution channel, wherein the distribution channel is an interface channel of a target dumb resource capability module, and the target dumb resource capability module is a dumb resource capability module whose dumb resource feature combination matches the dumb resource features of the dumb resource image. In addition, according to a dumb resource image distribution method in one aspect of the present disclosure, the channel rule model uses a rule expression based on dumb resource features to indicate the dumb resource feature combinations supported by each of the dumb resource capability modules.

[0010] In addition, according to a dumb resource image distribution method according to one aspect of the present disclosure, each dumb resource feature combination supported by the dumb resource capability module includes multiple OR gate chains, each of the OR gate chain includes an OR operation combination of multiple AND gate chains, and each of the AND gate chain includes an AND operation combination of several dumb resource features, wherein if all the dumb resource features of any AND gate chain in the dumb resource feature combination are included in the dumb resource features of the dumb resource image, then the dumb resource feature combination matches the dumb resource features of the dumb resource image.

[0011] In addition, according to a dummy resource image distribution method in one aspect of the present disclosure, the method further includes:

[0012] A channel rule model is generated based on the description of dummy resource information that can be processed by each of the dummy resource capability modules.

[0013] In addition, according to a dummy resource image distribution method in one aspect of the present disclosure, a channel rule model is generated based on the dummy resource information description that can be processed by each dummy resource capability module, including:

[0014] Based on the description of the dummy resource information that each of the dummy resource capability modules can process, determining the dummy resource features that each of the dummy resource capability modules can process, and constructing a combination of dummy resource features supported by each of the dummy resource capability modules;

[0015] The channel rule model is generated according to the combination of dummy resource features supported by each of the dummy resource capability modules.

[0016] In addition, according to a dummy resource image distribution method in one aspect of the present disclosure, the determining of the dummy resource feature of the dummy resource image includes:

[0017] Based on a label dictionary, all dummy resource features matching the dummy resource image are determined to obtain dummy resource features of the dummy resource image, wherein the label dictionary contains all dummy resource features involved in the channel rule model.

[0018] In addition, according to a dummy resource image distribution method in one aspect of the present disclosure, the determining, based on the tag dictionary, all dummy resource features matching the dummy resource image includes:

[0019] Acquire image features of the dummy resource image;

[0020] According to the image features of the dummy resource image and the respective dummy resource features of the tag dictionary, all dummy resource features matching the dummy resource image are determined.

[0021] In addition, according to one aspect of the present disclosure, a method for distributing a dummy resource image determines a distribution channel of the dummy resource image based on each of the channel rule models and the dummy resource features of the dummy resource image, including:

[0022] Determine an interface channel of the dumb resource capability module supporting the dumb resource image according to the dumb resource feature combinations supported by each of the dumb resource capability modules indicated by the channel rule model and the dumb resource features of the dumb resource image;

[0023] A distribution channel of the dummy resource image is determined according to an interface channel of the dummy resource capability module supporting the dummy resource image.

[0024] According to another aspect of the present disclosure, a dummy resource image distribution device is provided, comprising:

[0025] A first acquisition module, used for acquiring a dummy resource image to be distributed;

[0026] A second acquisition module is used to acquire a channel rule model, where the channel rule model is used to indicate a combination of dummy resource features supported by each dummy resource capability module;

[0027] A determination module, used for determining all dumb resource features of the dumb resource image;

[0028] An image distribution module is used to determine the distribution channel of the dumb resource image based on the channel rule model and the dumb resource features of the dumb resource image, and distribute the dumb resource image according to the distribution channel, wherein the distribution channel is an interface channel of a target dumb resource capability module, and the target dumb resource capability module is a dumb resource capability module whose dumb resource feature combination matches the dumb resource features of the dumb resource image.

[0029] According to another aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above methods.

[0030] According to another aspect of the present disclosure, a computer-readable storage medium stores a computer program / instruction thereon, and the computer program / instruction implements the steps of any of the above-mentioned methods when executed by a processor.

[0031] According to another aspect of the present disclosure, a computer program product is provided, including a computer program / instruction, which implements the steps of any of the above methods when executed by a processor.

[0032] As will be described in detail below, according to a dumb resource image distribution method, apparatus, device, storage medium and program product of an embodiment of the present disclosure, by acquiring a channel rule model, based on a label dictionary, dumb resource features that match the dumb resource image are determined, based on each channel rule model and the dumb resource features of the dumb resource image, the distribution channel of the dumb resource image is determined, and the dumb resource image is distributed according to the distribution channel, the distribution channel of the dumb resource image is automatically determined, and the distribution efficiency of the dumb resource image is improved.

[0033] It is to be understood that both the foregoing general description and the following detailed description are exemplary, and are intended to provide further explanation of the technology as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0035] Figure 1 is a flowchart illustrating a dummy resource image distribution method according to an embodiment of the present disclosure.

[0036] Figure 2 It is a flowchart further illustrating the generation of a channel rule model in the dumb resource image distribution method according to an embodiment of the present disclosure.

[0037] Figure 3 It is a schematic diagram further illustrating the channel rule model in the dumb resource image distribution method of the embodiment of the present disclosure.

[0038] Figure 4 The flowchart further illustrates determining dummy resource features matching a dummy resource image in the dummy resource image distribution method according to an embodiment of the present disclosure.

[0039] Figure 5 The flowchart further illustrates the determination of the distribution channel of the dummy resource image in the dummy resource image distribution method according to the embodiment of the present disclosure.

[0040] Figure 6 is a block diagram illustrating a dummy resource image distribution apparatus according to an embodiment of the present disclosure.

[0041] Figure 7 is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure.

[0042] Figure 8 is a hardware block diagram illustrating an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present disclosure more obvious, the exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described here.

[0044] See also Figure 1 , a dumb resource image distribution method, comprising:

[0045] S101, obtaining a dummy resource image to be distributed.

[0046] In step S101 of the present disclosure, the dummy resource image to be distributed can be uploaded by the user or transmitted by other systems. For example, when the user is preparing to initiate an interface call request of the dummy resource capability module, the user uploads a dummy resource image or a dummy resource video, and the system executing this step obtains the dummy resource image uploaded by the user as the dummy resource image to be distributed or extracts the video frame of the dummy resource video as the dummy resource image to be distributed. When extracting the video frame of the dummy resource video, the feature frame of the dummy resource video can be extracted to better determine the distribution channel.

[0047] S102, obtaining a channel rule model, where the channel rule model is used to indicate a combination of dummy resource features supported by each dummy resource capability module.

[0048] In the implementation of the present disclosure, a label dictionary can also be obtained, and the label dictionary contains all the dummy resource features involved in the channel rule model. The label dictionary can be used to determine the dummy resource features of the dummy resource image. The channel rule model and the label dictionary can be pre-set. Each dummy resource capability module refers to each dummy resource capability module that can be called, and which dummy resource capability modules are specifically used can be set according to actual conditions. If the dummy resource capability module that can be called is changed and updated (for example, the dummy resource features supported by the dummy resource capability module change or a new dummy resource capability module that can be called appears), then the channel rule model and the label dictionary are also updated accordingly.

[0049] In step S102 of the present disclosure, the dummy resource feature combinations supported by each dummy resource capability module may include an OR combination, an AND combination, etc. Taking the dummy resource information description that can be processed by the first dummy resource capability module as "optical cross-connect box made of stainless steel, or an optical cross-connect box with a fiber core number between 48 and 144" as an example, the dummy resource feature combinations supported by the first dummy resource capability module are: the first dummy resource feature "stainless steel" and the second dummy resource feature "optical cross-connect box" form a first AND combination, and the first AND combination and the third dummy resource feature "fiber core number 48 to 144" form a first OR combination, and the channel rule model of the first dummy resource capability module may include the first OR combination. If the dumb resource characteristics of the dumb resource image include the first dumb resource characteristic "stainless steel" and the second dumb resource characteristic "optical cross-connect box", then the dumb resource image matches the channel rule model of the first dumb resource capability module; if the dumb resource characteristics of the dumb resource image include the third dumb resource characteristic "number of fiber cores 48 to 144", then the dumb resource image matches the channel rule model of the first dumb resource capability module; if the dumb resource characteristics of the dumb resource image only include the first dumb resource characteristic "stainless steel" and the fourth dumb resource characteristic "optical fiber splitter box", then the dumb resource characteristics of the dumb resource image do not include the first dumb resource characteristic and the second dumb resource characteristic at the same time, nor do they include the third dumb resource characteristic, then the dumb resource image does not match the channel rule model of the first dumb resource capability module.

[0050] In step S102 of the embodiment of the present disclosure, the channel rule model may use a regular expression based on dummy resource features to indicate the dummy resource feature combinations supported by each dummy resource capability module. For example, an AND or NON expression may be used to indicate the dummy resource feature combinations supported by each dummy resource capability module, a regular expression may be used to indicate the dummy resource feature combinations supported by each dummy resource capability module, and a suffix expression may be used to indicate the dummy resource feature combinations supported by each dummy resource capability module. Taking an AND or NON expression as an example, the dummy resource feature combination supported by each dummy resource capability module includes multiple OR gate chains, each OR gate chain includes an OR operation combination of multiple AND gate chains, and each AND gate chain includes an AND operation combination of several dummy resource features, wherein, if all the dummy resource features of any AND gate chain in the dummy resource feature combination are included in the dummy resource features of the dummy resource image, then the dummy resource feature combination matches the dummy resource features of the dummy resource image.

[0051] In the disclosed embodiment, the label dictionary is a collection of dummy resource features involved in all channel rule models. The dummy resource features involved in all channel rule models can be labelled to obtain a label dictionary under the current status of the external capability cluster.

[0052] In the embodiment of the present disclosure, if the system already has channel rule models or label dictionaries for all dummy resource capability modules targeted by the interface call request, the channel rule model or label dictionary can be directly obtained; it can be known that in this case, before executing step S102, the method includes generating a channel rule model and label dictionary based on the description of dummy resource information that can be processed by each dummy resource capability module. When the system does not have channel rule models and label dictionaries for all dummy resource capability modules targeted by the interface call request, such as when the dummy resource capability module is updated, the first interface call is made (when the channel rule model and label dictionary have not been generated), etc., the channel rule model and label dictionary can be updated or generated based on the description of dummy resource information that can be processed by each dummy resource capability module.

[0053] As an implementation of the generative channel rule model, see Figure 2 , step S102 includes:

[0054] S201, based on the description of dummy resource information that each dummy resource capability module can process, determine the dummy resource features that each dummy resource capability module can process, and construct a combination of dummy resource features supported by each dummy resource capability module.

[0055] Take the example of a first dumb resource capability module being able to process dumb resource information description of "optical cross-connect box made of stainless steel, or optical cross-connect box with a fiber core count between 48 and 144". Determine that the dumb resource features that the first dumb resource capability module can process are the first dumb resource feature "stainless steel", the second dumb resource feature "optical cross-connect box", and the third dumb resource feature "fiber core count between 48 and 144". The dumb resource feature combinations supported by the first dumb resource capability module can be the first AND combination of the first dumb resource feature and the second dumb resource feature, and the first AND combination and the first OR combination of the third dumb resource feature.

[0056] Take the example that the description of dumb resource information that can be processed by a second dumb resource capability module is "wall-mounted optical fiber splitter box, or upright optical cross-connect box". Determine that the dumb resource features that can be processed by the second dumb resource capability module are the fifth dumb resource feature "wall-mounted", the fourth dumb resource feature "optical fiber splitter box", the sixth dumb resource feature "upright" and the second dumb resource feature "optical cross-connect box". The dumb resource feature combination supported by the second dumb resource capability module can be the second AND combination of the fifth dumb resource feature and the fourth dumb resource feature, the third AND combination of the sixth dumb resource feature and the second dumb resource feature, and the second OR combination formed by the second AND combination and the third AND combination.

[0057] S202: Generate a channel rule model according to the combination of dummy resource features supported by each dummy resource capability module.

[0058] Take the example that the description of the dumb resource information that can be processed by a first dumb resource capability module is "a stainless steel optical cross-connect box, or an optical cross-connect box with a core number between 48 and 144", and the description of the dumb resource information that can be processed by a second dumb resource capability module is "a wall-mounted optical fiber splitter box, or a vertical optical cross-connect box". The generated channel rule model includes the first or combination corresponding to the first dumb resource capability module and the second or combination corresponding to the second dumb resource capability module; it can be understood that the first or combination can be used as a dumb resource feature combination of the first channel rule model, and the second or combination can be used as a dumb resource feature combination of the second channel rule model. The generated channel rule model includes the first channel rule model corresponding to the first dumb resource capability module and the second channel rule model corresponding to the second dumb resource capability module.

[0059] In one embodiment, a label dictionary may be generated according to the dummy resource features involved in all channel rule models.

[0060] For example, the description of the dummy resource information supported by a third dummy resource capability module is "optical cross-connect box made of non-stainless steel material, or optical cross-connect box with a fiber core number between 48 and 144", and the channel rule model uses the AND or NOT regular expression based on the dummy resource feature to indicate the dummy resource feature combination supported by each dummy resource capability module as an example:

[0061] The rule expression of the dummy resource feature combination supported by the dummy resource capability module of the channel rule model is:

[0062]

[0063] Assume that the optical cross-connect box is element A (i.e., dummy resource feature A), the stainless steel material is element B (i.e., dummy resource feature B), and the number of fiber cores (48, 144) is element C (i.e., dummy resource feature C), then the above regular expression can be abstracted as:

[0064]

[0065] In the same way, corresponding regular expressions can be generated for all dummy resource capability modules, as shown below:

[0066] Channel number Supported dummy resource information description Regular Expressions Abstract Model ring1 Wall-mounted optical fiber splitter box Optical fiber splitter box∧wall mounted A∧B ring2 Optical cross-connect boxes other than upright Light cross box ∧﹁ upright A∧﹁B ... ringN Electric pole or manhole Electric pole ∨ manhole A∨B

[0067] The AND or NOT operation satisfies the distributive law and the associative law, that is, the abstract model of any channel can be transformed into the following form:

[0068] Primitive 1 ∨ ... ∨ primitive N (N = 1, 2, 3, 4, ...);

[0069] Among them, each group of primitives can be expressed as:

[0070] Element 1 ∧ ... ∧ element M (M = 1, 2, 3, 4, ...);

[0071] Each element is a dummy resource feature.

[0072] Each element can carry attribute tags, feature descriptions, range restrictions, and negation flags, as shown below:

[0073]

[0074] Generate rule models for all dummy resource capability modules, and the complete channel rule model can be obtained. Figure 3 , Figure 3 It is an exemplary channel rule model, which is a channel rule set, including several channel rule models, wherein channel rule model 1 includes an OR gate chain formed by primitive 1∨…∨ primitive N, primitive 1 includes element 11, primitive 2 includes an AND gate chain formed by element 21, element 22…element 2M, primitive N includes an AND gate chain of element N1 and element N2, and each element can carry an attribute label, feature description, range restriction, and negation flag.

[0075] S103: Determine the dumb resource features of the dumb resource image.

[0076] In step S103 of the embodiment of the present disclosure, relevant technical means may be used to determine the dumb resource features of the dumb resource image.

[0077] In one embodiment, all dummy resource features that match the dummy resource image can be determined based on the label dictionary to obtain the dummy resource features of the dummy resource image, wherein the label dictionary contains all dummy resource features involved in the channel rule model. When determining all dummy resource features that match the dummy resource image, it can be determined whether each dummy resource feature in the label dictionary matches the dummy resource image, and all dummy resource features that match the dummy resource image can be determined. For the matching of dummy resource features in the label dictionary, it can prevent the vector dimension from being too large and consuming a lot of computing resources. In one embodiment, see Figure 4 , based on the label dictionary, determine all dumb resource features that match the dumb resource image, including:

[0078] S401, obtaining image features of a dummy resource image.

[0079] In step S401 of the disclosed embodiment, the ORB (Oriented Fast and Rotated Brief, which quickly creates feature vectors for key points in an image) feature extraction technology can be used to obtain image features of the dummy resource image. It should be understood that other methods can also be used to obtain image features of the dummy resource image.

[0080] S402: Determine all dummy resource features that match the dummy resource image according to the image features of the dummy resource image and the respective dummy resource features of the tag dictionary.

[0081] There is a correspondence between the image features of the dummy resource image and the various dummy resource features of the label dictionary. For example, the image features of the dummy resource image include the image features of stainless steel and the image features of optical cross-linking boxes. Then all dummy resource features that match the dummy resource image include the dummy resource feature "stainless steel" and the dummy resource feature "optical cross-linking box".

[0082] Exemplarily, a dummy resource sample image and a dummy resource feature label of the corresponding dummy resource sample image (a label of the dummy resource feature in the label dictionary) may be obtained, and a neural network model may be trained to obtain a matching model. The neural network model may be a convolutional neural network model, a residual neural network model, etc. The matching model uses the input dummy resource image to determine all dummy resource features that match the dummy resource image.

[0083] S104, based on the channel rule model and the dumb resource features of the dumb resource image, determine the distribution channel of the dumb resource image, and distribute the dumb resource image according to the distribution channel, wherein the distribution channel is an interface channel of the target dumb resource capability module, and the target dumb resource capability module is a dumb resource capability module whose dumb resource feature combination matches the dumb resource features of the dumb resource image.

[0084] In step S104 of the embodiment of the present disclosure, the dummy resource features of the dummy resource image may be matched with the dummy resource feature combination in the channel rule model to determine which dummy resource capability modules have the ability to process the dummy resource image, determine the distribution channel of the dummy resource image, and distribute the dummy resource image according to the distribution channel.

[0085] It should be understood that the distribution channel of the dummy resource image may be one or more channels, and therefore, the determined distribution channel of the dummy resource image may be a channel list.

[0086] In the disclosed embodiment, the dummy resource image can be distributed to the capability interface of the corresponding dummy resource capability module according to the distribution channel to realize the automatic call of the external capability cluster. Since the on-site construction personnel may call the same capability interface multiple times in a short period of time, the rule model and label dictionary can be temporarily stored locally after initialization to speed up the system preloading rate.

[0087] In one embodiment, see Figure 5 , based on the channel rule model and the dumb resource features of the dumb resource image, determine the distribution channel of the dumb resource image, including:

[0088] S501 , determining an interface channel of a dumb resource capability module supporting a dumb resource image according to a dumb resource feature combination supported by each dumb resource capability module indicated by a channel rule model and dumb resource features of a dumb resource image.

[0089] In step S501 of the disclosed embodiment, if the dummy resource features of the dummy resource image match a dummy resource feature combination of the channel rule model, then the interface channel of the dummy resource capability module corresponding to the dummy resource feature combination is the channel of the dummy resource capability module supporting the dummy resource image.

[0090] S502: Determine a distribution channel of the dummy resource image according to an interface channel of a dummy resource capability module supporting the dummy resource image.

[0091] In step S502 of the embodiment of the present disclosure, the interface channel of the dummy resource capability module supporting the dummy resource image is used as the distribution channel of the dummy resource image, or the interface channel of the dummy resource capability module supporting the dummy resource image can be manually intervened and selected as the distribution channel of the dummy resource image.

[0092] For example, the dummy resource feature combination supported by each dummy resource capability module includes multiple OR gate chains, each OR gate chain includes multiple OR operation combinations of AND gate chains, and each AND gate chain includes several AND operation combinations of dummy resource features. During matching, the dummy resource image can be distributed to the interface channels of multiple dummy resource capability modules, and the matching results of each interface channel do not affect each other; see Figure 3 For the convenience of description, the AND gate chain containing the dummy resource features in the OR gate chain is recorded as a primitive, and the dummy resource features in the AND gate chain are recorded as elements. The channel rule model consists of multiple OR gate chains. Among the primitives on the OR gate chain, as long as the dummy resource features of the dummy resource image meet any primitive, the dummy resource image is considered to match the corresponding channel, and the interface channel is added to the distribution list of the corresponding dummy resource image as the distribution channel of the dummy resource image. Whether the dumb resource feature of the dumb resource image satisfies the primitive, the primitive is composed of multiple elements connected by an AND gate chain. If all elements of the AND gate chain are included in the dumb resource feature of the dumb resource image, it is considered that the dumb resource feature of the dumb resource image satisfies the primitive on the OR gate chain, and the dumb resource image matches the corresponding interface channel; otherwise, the dumb resource feature of the dumb resource image does not satisfy the primitive, and the next primitive can be judged; it can be known that if the element contains a negative mark, then when judging whether the element is included in the dumb resource feature of the dumb resource image, the result is negated. For example, the element is stainless steel, which contains a negative mark, and the dumb resource feature of the dumb resource image is stainless steel. Since the element contains a negative mark, the dumb resource feature of the dumb resource image does not satisfy the primitive at this time; the matching logic of the dumb resource image and the interface channel is looped until the last channel matching is completed, and the system outputs a list of channels that successfully match the image.

[0093] See also Figure 6 The exemplary embodiment of the present disclosure further provides a dummy resource image distribution device, comprising:

[0094] A first acquisition module 601 is used to acquire a dummy resource image to be distributed;

[0095] A second acquisition module 602 is used to acquire a channel rule model, where the channel rule model is used to indicate a combination of dummy resource features supported by each dummy resource capability module;

[0096] A determination module 603, used to determine the dumb resource features of the dumb resource image;

[0097] The image distribution module 604 is used to determine the distribution channel of the dumb resource image based on the channel rule model and the dumb resource features of the dumb resource image, and distribute the dumb resource image according to the distribution channel, wherein the distribution channel is the interface channel of the target dumb resource capability module, and the target dumb resource capability module is a dumb resource capability module whose dumb resource feature combination matches the dumb resource features of the dumb resource image.

[0098] In one embodiment, the channel rule model uses a rule expression based on dummy resource features to indicate dummy resource feature combinations supported by each dummy resource capability module.

[0099] In one embodiment, the dummy resource feature combination supported by each dummy resource capability module includes multiple OR gate chains, each OR gate chain includes an OR operation combination of multiple AND gate chains, and each AND gate chain includes an AND operation combination of several dummy resource features, wherein if all dummy resource features of any AND gate chain in the dummy resource feature combination are included in the dummy resource features of the dummy resource image, then the dummy resource feature combination matches the dummy resource features of the dummy resource image.

[0100] In one embodiment, the dummy resource image distribution device further includes:

[0101] The generation module is used to generate a channel rule model based on the description of the dummy resource information that can be processed by each dummy resource capability module.

[0102] In one embodiment, the generation module is used to generate a channel rule model based on the description of dummy resource information that can be processed by each dummy resource capability module, specifically for:

[0103] Based on the description of the dummy resource information that each dummy resource capability module can process, determine the dummy resource features that each dummy resource capability module can process, and construct the dummy resource feature combinations supported by each dummy resource capability module;

[0104] A channel rule model is generated according to the combination of dumb resource features supported by each dumb resource capability module.

[0105] In one embodiment, the determination module 603 is used to determine the dummy resource features of the dummy resource image, specifically to:

[0106] Based on the label dictionary, all dummy resource features matching the dummy resource image are determined to obtain dummy resource features of the dummy resource image, wherein the label dictionary contains all dummy resource features involved in the channel rule model.

[0107] The determination module 603 is used to determine all dummy resource features matching the dummy resource image based on the label dictionary, specifically to:

[0108] Get image features of dummy resource images;

[0109] According to the image features of the dummy resource image and the respective dummy resource features of the label dictionary, all dummy resource features matching the dummy resource image are determined.

[0110] In one embodiment, the image distribution module 604 is used to determine the distribution channel of the dummy resource image based on the channel rule model and the dummy resource features of the dummy resource image, specifically to:

[0111] Determine an interface channel of a dumb resource capability module supporting the dumb resource image according to a dumb resource feature combination supported by each dumb resource capability module indicated by the channel rule model and a dumb resource feature of the dumb resource image;

[0112] A distribution channel of the dummy resource image is determined according to an interface channel of a dummy resource capability module that supports the dummy resource image.

[0113] The exemplary embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to cause the electronic device to perform the method according to the embodiment of the present disclosure when executed by the at least one processor.

[0114] Exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method according to an embodiment of the present disclosure.

[0115] refer to Figure 7 The exemplary embodiment of the present disclosure further provides a computer program product 700, including a computer program 701, wherein when the computer program is executed by a processor of a computer, it is used to enable the computer to perform the method according to the embodiment of the present disclosure.

[0116] refer to Figure 8, a block diagram of an electronic device 800 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0117] The electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0118] A plurality of components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 may be any type of device capable of inputting information to the electronic device 800, and the input unit 806 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 807 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 808 may include, but is not limited to, a disk, an optical disk. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0119] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above. For example, in some embodiments, the method of the embodiment of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. In some embodiments, the computing unit 801 may be configured to perform the method of the embodiment of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0120] In the above, a method, apparatus, device, storage medium and program product for distributing dumb resource images according to an embodiment of the present disclosure are described with reference to the accompanying drawings. By acquiring a channel rule model and a label dictionary, all dumb resource features matching the dumb resource image are determined based on the label dictionary. Based on each of the channel rule models and the dumb resource features matching the dumb resource image, the distribution channel of the dumb resource image is determined, and the dumb resource image is distributed according to the distribution channel. The distribution channel of the dumb resource image is automatically determined, thereby achieving a technical effect of improving the distribution efficiency of the dumb resource image.

[0121] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0122] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0123] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0124] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0125] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings indicated by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0126] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles indicated herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0127] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for distributing dumb resource images, characterized in that: include: Get the dummy resource image to be distributed; Acquire a channel rule model, where the channel rule model is used to indicate a combination of dummy resource features supported by each dummy resource capability module; Determining a dumb resource feature of the dumb resource image; Based on the channel rule model and the dumb resource features of the dumb resource image, a distribution channel of the dumb resource image is determined, and the dumb resource image is distributed according to the distribution channel, wherein the distribution channel is an interface channel of a target dumb resource capability module, and the target dumb resource capability module is a dumb resource capability module whose dumb resource feature combination matches the dumb resource features of the dumb resource image.

2. The method according to claim 1, characterized in that The channel rule model uses a rule expression based on dummy resource features to indicate dummy resource feature combinations supported by each dummy resource capability module.

3. The method according to claim 1 or 2, characterized in that: The dummy resource feature combination supported by each of the dummy resource capability modules includes multiple OR gate chains, each of the OR gate chains includes an OR operation combination of multiple AND gate chains, and each of the AND gate chains includes an AND operation combination of several dummy resource features, wherein if all the dummy resource features of any AND gate chain in the dummy resource feature combination are included in the dummy resource features of the dummy resource image, then the dummy resource feature combination matches the dummy resource features of the dummy resource image.

4. The method according to claim 1, characterized in that The method further comprises: A channel rule model is generated based on the description of dummy resource information that can be processed by each of the dummy resource capability modules.

5. The method according to claim 4, characterized in that Based on the description of the dummy resource information that can be processed by each of the dummy resource capability modules, a channel rule model is generated, including: Based on the description of the dummy resource information that each of the dummy resource capability modules can process, determining the dummy resource features that each of the dummy resource capability modules can process, and constructing a combination of dummy resource features supported by each of the dummy resource capability modules; The channel rule model is generated according to the combination of dummy resource features supported by each of the dummy resource capability modules.

6. The method according to claim 1, characterized in that The determining of the dummy resource feature of the dummy resource image includes: Based on a label dictionary, all dummy resource features matching the dummy resource image are determined to obtain dummy resource features of the dummy resource image, wherein the label dictionary contains all dummy resource features involved in the channel rule model.

7. The method according to claim 6, characterized in that The determining, based on the label dictionary, all dummy resource features matching the dummy resource image comprises: Acquire image features of the dummy resource image; According to the image features of the dummy resource image and the respective dummy resource features of the tag dictionary, all dummy resource features matching the dummy resource image are determined.

8. The method according to claim 1, characterized in that Determining a distribution channel of the dummy resource image based on the channel rule model and the dummy resource features of the dummy resource image includes: Determine an interface channel of the dumb resource capability module supporting the dumb resource image according to the dumb resource feature combinations supported by each of the dumb resource capability modules indicated by the channel rule model and the dumb resource features of the dumb resource image; A distribution channel of the dummy resource image is determined according to an interface channel of the dummy resource capability module supporting the dummy resource image.

9. A dumb resource image distribution device, characterized in that: include: A first acquisition module, used for acquiring a dummy resource image to be distributed; A second acquisition module is used to acquire a channel rule model, where the channel rule model is used to indicate a combination of dummy resource features supported by each dummy resource capability module; A determination module, used to determine the dumb resource features of the dumb resource image; An image distribution module is used to determine the distribution channel of the dumb resource image based on the channel rule model and the dumb resource features of the dumb resource image, and distribute the dumb resource image according to the distribution channel, wherein the distribution channel is an interface channel of a target dumb resource capability module, and the target dumb resource capability module is a dumb resource capability module whose dumb resource feature combination matches the dumb resource features of the dumb resource image.

10. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of any method described in claims 1 to 8 are implemented.

12. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of any method described in claims 1 to 8 are implemented.

Images