A data processing system with increased capacity of devices

Abstract

The application provides a device expansion data processing system, comprising: a sample device ID list, a processor and a memory storing a computer program, when the computer program is executed by the processor, the following steps are realized: obtaining a label type probability list; obtaining an intermediate APP list; obtaining an intermediate knowledge graph; obtaining a target device list; obtaining a first final APP list and a second final APP list; obtaining an intermediate expansion device list; obtaining a target expansion device list; it can be known that, on one hand, the target keyword of the device is obtained by combining the keyword with the type of the device, on the other hand, the keyword of the device and the keyword of the APP are processed, the number of devices of the target expansion device corresponding to the target device can be more accurately obtained, so that the accuracy of device expansion is improved.

Description

Technical Field

[0001] This invention relates to the field of equipment processing, and in particular to a data processing system for expanding equipment capacity. Background Technology

[0002] Existing methods for expanding the user base of devices mostly rely on the semantic features of key keywords corresponding to the devices. First, the key keywords corresponding to the devices are extracted, and then the key keywords are expanded according to semantic similarity. Similar keywords with semantic similarity to the key keywords are obtained as associated keywords for the devices. Based on the associated keywords, associated apps are obtained, thereby expanding the user base of the devices.

[0003] However, the above method also has the following technical problems:

[0004] Existing methods for expanding device reach have two main drawbacks. First, when acquiring related keywords for a device, they can only rely on the semantics of key keywords and cannot combine them with the device type. Second, when expanding device reach based on related keywords, they can only acquire related apps based on the keywords themselves and cannot process the key keywords of the related keywords and apps. This can lead to errors in acquiring related apps, resulting in inaccurate device reach expansion and low precision. Summary of the Invention

[0005] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0006] A data processing system for expanding device capacity includes: a sample device ID list H = {H1, ..., H2} r H s}, processor and memory storing computer programs, wherein, H r ={H r1 H ri H rm}, H ri Given the information of the i-th sample APP corresponding to the r-th sample device ID, where r = 1...s, s is the number of sample devices, and i = 1...m, m is the number of APPs corresponding to the r-th sample device ID, when the computer program is executed by the processor, the following steps are implemented:

[0007] S100, H ri Input into the preset neural network model to obtain H ri The corresponding label type probability list H' ri ={H' 1 ri , ..., H' c ri , ..., H'd ri},H' c ri For H ri The probability value corresponding to the cth label type, c = 1 … d, d is the number of label types.

[0008] S200, according to H' ri , obtain the intermediate APP list B = {B1, …, B c , …, B d} corresponding to H c = {B c1 , …, B cy , …, B cq} corresponding to H cy The first target keyword list corresponding to the yth intermediate APP in the cth label type intermediate APP list corresponding to H, y = 1 … q, q is the number of intermediate APPs in the cth label type intermediate APP list.

[0009] S300, according to B cy , obtain the intermediate knowledge graph G0 corresponding to H.

[0010] S400, obtain the target device list D = {D1, …, D j , …, D n} corresponding to H j = {D j1 , …, D jg , …, D jz} corresponding to H jg The gth target APP information corresponding to the jth target device ID, j = 1 … n, n is the number of target devices, g = 1 … z, z is the number of target APPs corresponding to the jth target device ID.

[0011] S500, according to D jg and the target APP label list corresponding to D jg , obtain the first final APP list C corresponding to D.

[0012] S600, according to C, obtain the intermediate expansion device list corresponding to D.

[0013] S700, when K ≥ K0, the intermediate expansion device list corresponding to D is taken as the target expansion device list corresponding to D, wherein K is the number of intermediate expansion devices in the intermediate expansion device list corresponding to D, K0 is a preset device quantity threshold.

[0014] S800, when K < K0, according to D jg and the intermediate knowledge graph corresponding to H, obtain the second final APP list corresponding to D.

[0015] S900, according to the second final APP list corresponding to D, obtaining the target expansion equipment list corresponding to D.

[0016] The present application has at least the following beneficial effects:

[0017] The present application provides a device expansion data processing system, comprising: a sample device ID list, a processor and a memory storing a computer program, when the computer program is executed by the processor, the following steps are implemented: obtaining a label type probability list; obtaining an intermediate APP list; obtaining an intermediate knowledge graph; obtaining a target device list; obtaining a first final APP list and a second final APP list; obtaining an intermediate expansion equipment list; obtaining a target expansion equipment list; it can be seen that, on the one hand, the present application combines keywords with the type of device to obtain the target keyword of the device, on the other hand, the keywords of the device and the keywords of the APP are processed, so that the number of devices corresponding to the target expansion equipment of the target device can be more accurately obtained, thereby improving the accuracy of device expansion. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0019] Figure 1 The present application provides a device expansion data processing system for executing a computer program. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0022] This embodiment provides a data processing system for expanding device capacity, including: a sample device ID list H = {H1, ..., H...} r H s}, processor and memory storing computer programs, wherein, H r ={H r1 H ri H rm}, H ri For the information of the i-th sample APP corresponding to the r-th sample device ID, r = 1...s, where s is the number of sample devices, i = 1...m, where m is the number of APPs corresponding to the r-th sample device ID, when the computer program is executed by the processor, the following steps are implemented, as follows: Figure 1 As shown:

[0023] S100, H ri Input into the preset neural network model to obtain H ri The corresponding label type probability list H' ri ={H' 1 ri , ..., H' c ri , ..., H' d ri}, H' c ri For H ri The probability value of the c-th label type, where c = 1...d, and d is the number of label types.

[0024] Specifically, APP information includes the APP name, the APP tag corresponding to the APP name, and the APP text corresponding to the APP name. The APP text can be understood as the text describing the APP in the app store.

[0025] Specifically, step S100 also includes the following steps:

[0026] S101, H ri Input into the preset neural network model to obtain H ri The corresponding initial keyword list DY ri ={DY 1 ri ..., DY x ri ..., DY w ri} and DY ri The corresponding first priority list A ri ={A 1 ri , ..., A x ri , ..., A w ri}, A x ri =(A x1 ri , ..., A xc ri , ..., A xd ri ), DY x ri For H ri The corresponding initial keyword is the x-th one, where x = 1...w, and w is H. ri The corresponding initial number of keywords, A xc ri For DY x ri The first priority of the corresponding c-th tag type, wherein, as those skilled in the art know, any neural network model in the prior art that can obtain keywords and the priority and weight of the keywords are within the protection scope of this invention, and will not be elaborated here.

[0027] S103, DY ri Input into the preset dictionary and obtain H ri The corresponding first keyword list DY' ri ={DY' 1 ri ..., DY' e ri ..., DY' f ri}, DY' e ri For H ri The corresponding first keyword is the e-th one, where e = 1...f, and f is H. riThe corresponding first keyword quantity; it can be understood that: the same initial keyword as the word in the preset word library is obtained from the initial keyword list as the first keyword, the same means comparing the word in the word library with the initial keyword, if the word in the word library is the same as the initial keyword, those skilled in the art know that any comparison word method in the prior art belongs to the protection scope of the present application, which will not be repeated here.

[0028] Specifically, the preset word library is a database of words with Chinese semantics pre-set by those skilled in the art according to actual needs.

[0029] S105, according to A ri And DY' ri , get DY' ri The corresponding second priority list A' ri ={A' 1 ri , ……, A' e ri , ……, A' f ri}, A' e ri =(A' e1 ri , ……, A' ec ri , ……, A' ed ri ), A' ec ri For DY' e ri The corresponding second priority of the cth label type; it can be understood that: the first priority of the label type corresponding to the initial keyword identical with the first keyword is obtained as the second priority of the label type corresponding to the first keyword.

[0030] S107, when A' ec ri ≥P0, DY' e ri Insert H ri The corresponding first target keyword list P ri As P c α ri , A' ec ri As P cα ri The corresponding third priority P' cα ri , wherein P ri ={P 1 ri , ……, Pc ri , …, P d ri}, P c ri = (P c1 ri , …, P cα ri , …, P cφ ri ), P cα ri is H ri The first target keyword in the corresponding cth label type, α = 1 … φ, φ is the number of the first target keyword in the cth label type, P0is a preset second priority threshold.

[0031] Specifically, the value range of P0is 0.8-1.

[0032] Preferably, P0is valued at 0.9, preventing the first target keyword from being extracted incorrectly or missed due to the threshold being set too low or too high, and thus leading to the inability to accurately obtain the label type probability.

[0033] S109, according to P' cα ri , obtaining H' c ri , H' c ri satisfies the following conditions:

[0034]

[0035] In the embodiment, by processing the keywords and the priority of the keywords, the label type probability corresponding to the sample APP information is obtained, the sample APP is processed according to the label type probability, which is conducive to constructing the knowledge graph, and the device quantity of the target expansion device corresponding to the target device can be more accurately obtained, thereby improving the accuracy of the device expansion.

[0036] S200, according to H' ri , obtaining the intermediate APP list B = {B1, …, B c , …, B d} corresponding to H c = {B c1 , …, B cy , …, B cq}, B cyLet H be the list of first target keywords corresponding to the y-th intermediate APP in the intermediate APP list of the c-th tag type, where y = 1...q, and q is the number of intermediate APPs in the intermediate APP list of the c-th tag type; this can be understood as classifying the sample APPs according to the probability of the tag type.

[0037] S300, according to B cy Obtain the intermediate knowledge graph G0 corresponding to H.

[0038] Specifically, step S300 also includes the following steps:

[0039] S301. Obtain B according to the preset neural network model. cy The corresponding first intermediate weight list F cy ={F 1 cy , ..., F α cy , ..., F φ cy}, F α cy For B cy The first intermediate weight corresponding to the αth first target keyword.

[0040] S303, according to F α cy , obtain B cy The corresponding intermediate APP's second intermediate weight F' cy , where F' cy The following conditions must be met:

[0041]

[0042] S305, according to F' cy , obtain B cy The corresponding intermediate APP corresponds to the triple X cy , where entity B cy The corresponding intermediate APP name and the list of primary target keywords have an entity relationship of F'. cy .

[0043] S307, According to X cy Obtain the initial knowledge graph set G = {G1, ..., G} corresponding to H. c , ..., G d}, G c Let c be the initial knowledge graph of the c-th tag type. As those skilled in the art know, any method in the prior art that obtains a knowledge graph through triples is within the protection scope of this invention, and will not be elaborated here.

[0044] S309, according to Gc The intermediate knowledge graph G0 corresponding to H is obtained. As those skilled in the art know, any method in the prior art that combines knowledge graphs to obtain a new knowledge graph is within the protection scope of this invention, and will not be elaborated here.

[0045] In this embodiment, apps with the same tag type are grouped into a knowledge graph, and knowledge graphs with different tag types are combined into an intermediate knowledge graph. This facilitates the direct acquisition of the keywords of the app under each tag type and the corresponding weights of the keywords, speeding up the processing and improving processing efficiency.

[0046] S400. Obtain the target device list D = {D1, ..., D2} j , ..., D n}, D j ={D j1 , ..., D jg , ..., D jz}, D jg This is the information for the g-th target app corresponding to the j-th target device ID, where j = 1...n, n is the number of target devices, and g = 1...z, z is the number of target apps corresponding to the j-th target device ID. The target device list can be understood as the list of devices entered by the user.

[0047] S500, according to D jg and D jg Based on the corresponding target app tag, obtain the first final app list C corresponding to D.

[0048] Specifically, the S500 procedure also includes the following steps:

[0049] S501, according to D jg and D jg Get the corresponding target APP tags and D j The corresponding initial APP list U j ={U j1 , ..., U jc , ..., U jd}, U jc =(U 1 jc , ..., U k jc , ..., U p jc ), U k jc D j The corresponding initial APP name in the c-th tag type, k = 1...p, where p is D jThe number of initial APPs in the corresponding cth label type; it can be understood that the target APP name is classified according to the type of the target APP label corresponding to the target APP name, and recombined into the initial APP list corresponding to the target device.

[0050] S502, inputting U k jc into a preset neural network model to obtain U k jc The corresponding second keyword list.

[0051] S503, according to P ri and the second keyword list, obtaining U k jc The corresponding second intermediate keyword list DE k jc ={DE k1 jc , …, DE kλ jc , …, DE kμ jc}, DE kλ jc is U k jc The corresponding λth second intermediate keyword, λ=1…μ, μ is U k jc The corresponding second intermediate keyword quantity; it can be understood that the keywords that exist in both the second keyword list and the first target keyword list are selected as the second intermediate keywords.

[0052] S504, inputting DE k jc into a preset neural network model to obtain DE k jc The corresponding third intermediate weight value list V k jc ={V k1 jc , …, V kλ jc , …, V kμ jc}, V kλ jc is DE kλ jc The corresponding third intermediate weight value.

[0053] S505, according to V kλ jc , obtaining U k jc The corresponding fourth priority Wk jc .

[0054] Specifically, W k jc The following conditions must be met:

[0055]

[0056] S506, according to W k jc From U j Obtain the first final APP list C = {C1, ..., C2} corresponding to D. j , ..., C n}, C j =(C j1 , ..., C jt , ..., C jh ), C jt Let t be the name of the first final APP corresponding to the j-th target device ID corresponding to D, where t = 1...h, and h is the number of first final APPs corresponding to the j-th target device ID.

[0057] Specifically, in C, the fourth priority corresponding to each first final APP is greater than W0, where W0 is a preset fourth priority threshold.

[0058] Specifically, the value of W0 ranges from 0.8 to 1.

[0059] Preferably, W0 is set to 0.9 to prevent errors or omissions in the extraction of the name corresponding to the first final APP due to the threshold being set too low or too high, which would lead to the inability to accurately obtain the list of intermediate extended devices.

[0060] In this embodiment, by processing the weight of the second intermediate keyword, the fourth priority corresponding to the initial APP is obtained. By comparing the fourth priority, the intermediate extended device list can be obtained more accurately, which is beneficial to improving the accuracy of device expansion.

[0061] S600. Based on C, obtain the list of intermediate extended devices corresponding to D.

[0062] Specifically, step S600 also includes the following steps:

[0063] S601. Based on C, obtain a list of preset related values ​​corresponding to C. Those skilled in the art can set the related values ​​according to actual needs.

[0064] S603. Based on the preset related value list corresponding to C, obtain the intermediate extended device list E = {E1, ..., E2} corresponding to D. j , ..., E n}, Ej = {E j1 ,..., E ja ,..., E jb}, E ja is the number of devices corresponding to the a-th intermediate expansion device corresponding to the j-th target device ID, a = 1,..., b, b is the number of intermediate expansion devices corresponding to the j-th target device ID.

[0065] Specifically, the number of devices corresponding to each intermediate expansion device is stored in the system.

[0066] Specifically, each of the preset correlation values corresponding to each intermediate expansion device in E is not less than E0, and E0 is a preset correlation value threshold.

[0067] Specificly, the value range of E0 is 9-12.

[0068] Preferably, the value of E0 is 10, which prevents the threshold from being set too low or too high, causing errors or omissions in the extraction of intermediate expansion devices, and thus failing to accurately expand the number of devices.

[0069] In this embodiment, the keyword of the device is obtained by combining the keyword with the type of the device, and the keyword of the device and the keyword of the APP are processed, so that the number of target expansion devices corresponding to the target device can be more accurately obtained, thereby improving the accuracy of device expansion.

[0070] S700, when K≥K0, the intermediate expansion device list corresponding to D is taken as the target expansion device list corresponding to D, wherein K is the number of intermediate expansion devices in the intermediate expansion device list corresponding to D, and K0 is a preset device number threshold.

[0071] Specifically, K satisfies the following condition:

[0072]

[0073] Specifically, the value of K0 is set by a person skilled in the art according to actual needs.

[0074] S800, when K < K0, according to the intermediate knowledge graph corresponding to D jg and H, a second final APP list corresponding to D is obtained.

[0075] Specifically, according to the intermediate knowledge graph corresponding to D jg and H, a second final APP list corresponding to D is determined, wherein the method for determining the second final APP list corresponding to D according to the intermediate knowledge graph corresponding to D jg and H is consistent with step S500, and will not be described here; it can be understood that the knowledge graph corresponding to D jgAPP names of the same label type are processed to obtain a second final APP list.

[0076] S900, according to the second final APP list corresponding to D, a target expansion device list corresponding to D is obtained.

[0077] Specifically, according to the second final APP list corresponding to D, a target expansion device list corresponding to D is determined, wherein the method for determining the target expansion device list corresponding to D from the second final APP list corresponding to D is consistent with the step S600, and will not be repeated here.

[0078] Compared with the above embodiment, in the embodiment, when the number of devices does not reach the threshold range, the target expansion device list is further selected according to the intermediate knowledge graph, and the number of devices that need to be expanded is more accurately obtained, thereby improving the accuracy of device expansion.

[0079] The application provides a data processing system for device expansion, which comprises a sample device ID list, a processor and a memory storing a computer program, and when the computer program is executed by the processor, the following steps are realized: obtaining a label type probability list; obtaining an intermediate APP list; obtaining an intermediate knowledge graph; obtaining a target device list; obtaining a first final APP list and a second final APP list; obtaining an intermediate expansion device list; and obtaining a target expansion device list.

[0080] Although some specific embodiments of the application have been described in detail above, those skilled in the art should understand that the above examples are only for illustration, and are not intended to limit the scope of the application. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the application. The scope of the application is defined by the appended claims.

Claims

1. A data processing system for scaling up a device, characterized by, The system comprises: a sample device ID list H={H1,..., H r ,..., H s}, a processor and a memory storing a computer program, wherein H r ={H r1 ,..., H ri ,..., H rm}, H ri is the ith sample APP information corresponding to the rth sample device ID, r=1...s, s is the number of sample devices, i=1...m, m is the number of APPs corresponding to the rth sample device ID, and the computer program, when executed by the processor, implements the following steps: S100、H ri is input into a preset neural network model to obtain H ri a corresponding label type probability list H' ri ={H' 1 ri , …, H' c ri , …, H' d ri}, H' c ri is H ri a probability value of a corresponding cth label type, c=1…d, d is the number of label types; S200、according to H' ri , an intermediate APP list B={B1, …, B c , …, B d} corresponding to H is obtained c ={B c1 , …, B cy , …, B cq} corresponding to H cy is a first target keyword list corresponding to the yth intermediate APP in the cth label type intermediate APP list corresponding to H, y=1 … q, q is the number of intermediate APPs in the cth label type intermediate APP list. S300、According to B cy , obtaining the intermediate knowledge graph G0 corresponding to H; In step S300, the following steps are also included: S301, obtaining B cy corresponding first intermediate weight list F cy ={F 1 cy , …, F α cy , …, F φ cy}, F α cy is the first intermediate weight corresponding to the a-th first target keyword pair in B cy ​ S303、According to F α cy , obtaining B cy The second intermediate weight F' of the corresponding intermediate APP cy Wherein F' cy Meet the following conditions: ； S305, according to F' cy , obtaining B cy The corresponding intermediate APP corresponds to the triple X cy , wherein the entity is B cy The corresponding intermediate APP name and the first target keyword list, the relationship between entities is F' cy ; S307、According to X cy , an initial knowledge graph set G corresponding to H is obtained, G={G1, …, G c , …, G d}, G c is the initial knowledge graph of the cth label type; S309、According to G c , obtain the intermediate knowledge graph G0 corresponding to H; S400, acquire a target device list D={D1,..., D j , n D j ={D j1 , jg , jz}D jg is the gth target APP information corresponding to the jth target device ID, j=1...n, n is the number of target devices, g=1...z, z is the number of target APPs corresponding to the jth target device ID; S500、According to D jg and D jg The corresponding target APP tag list is obtained D. The corresponding first final APP list C is obtained. S600, according to C, obtaining a corresponding intermediate expansion device list of D; S700, when K≥K0, taking the corresponding intermediate expansion device list of D as a corresponding target expansion device list of D, wherein K is the number of intermediate expansion devices in the corresponding intermediate expansion device list of D, and K0 is a preset device number threshold; S800、When K < K0, according to D jg and H corresponding to the intermediate knowledge graph, obtain the second final APP list corresponding to D; S900, according to the corresponding second final APP list of D, obtaining the corresponding target expansion device list of D.

2. The data processing system of claim 1, wherein, The APP information includes an APP name, an APP tag corresponding to the APP name, and an APP text corresponding to the APP name.

3. The data processing system of claim 1, wherein, The step S100 further includes the following steps: S101, H ri Input into the preset neural network model to obtain H ri The corresponding initial keyword list DY ri ={DY 1 ri ..., DY x ri ..., DY w ri } and DY ri The corresponding first priority list A ri ={A 1 ri , ..., A x ri , ..., A w ri }, A x ri = (A x1 ri , ..., A xc ri , ..., A xd ri ), DY x ri For H ri The corresponding initial keyword is the x-th one, where x = 1...w, and w is H. ri The corresponding initial number of keywords, A xc ri For DY x ri The first priority of the c-th tag type; S103, DY ri Input into the preset dictionary and obtain H ri The corresponding first keyword list DY' ri ={DY' 1 ri ..., DY' e ri ..., DY' f ri }, DY' e ri For H ri The corresponding first keyword is the e-th one, where e = 1...f, and f is H. ri The corresponding number of primary keywords; S105、according to A ri and DY' ri , obtaining DY' ri corresponding second priority list A' ri ={A' 1 ri ,..., A' e ri ,..., A' f ri}, A' e ri = (A' e1 ri ,..., A' ec ri ,..., A' ed ri ), A' ec ri is DY' e ri corresponding to the cth label type second priority; S107、when A' ec ri ≥ P0, DY' e ri inserted into H ri corresponding to the first target keyword list P ri as P cα ri , A' ec ri as P cα ri corresponding to the third priority P' cα ri , wherein P ri ={P 1 ri , …, P c ri , …, P d ri}, P c ri = (P c1 ri , …, P cα ri , …, P cφ ri ), P cα ri is H ri corresponding to the first target keyword in the cth label type, α = 1 … φ, φ is the number of first target keywords in the cth label type, and P0 is a preset second priority threshold. S109, according to P' cα ri , obtaining H' c ri .

4. The data processing system of claim 1, wherein, The step S500 further includes the following steps: S501、According to D jg and D jg corresponding target APP label, obtain D j corresponding initial APP list U j ={U j1 , …, U jc , …, U jd}, U jc =(U 1 jc , …, U k jc , …, U p jc ), U k jc D j corresponding to the kth initial APP name in the cth label type, k=1 … p, p is the number of D j corresponding to the cth label type; S502、the U k jc The corresponding APP text is input into the preset neural network model to obtain U k jc The corresponding second keyword list; S503、According to P ri and the second keyword list, obtain U k jc The corresponding second intermediate keyword column DE k jc ={DE k1 jc , …, DE kλ jc , …, DE kμ jc}, DE kλ jc for U k jc The corresponding λth second intermediate keyword, λ=1 … μ, μ is U k jc The corresponding number of second intermediate keywords; S504, obtaining DE k jc input into a preset neural network model, to obtain DE k jc corresponding third intermediate weight value list V k jc ={V k1 jc , …, V kλ jc , …, V kμ jc}, V kλ jc DE kλ jc corresponding third intermediate weight value; S505, according to V kλ jc , obtaining U k jc corresponding fourth priority W k jc ; S506, according to W k jc , from U j , the first final APP list C corresponding to D = {C1, …, C j , …, C n}, C j = (C j1 , …, C jt , …, C jh ), C jt is the jth target device ID corresponding to the first final APP name corresponding to D t = 1 … h, h is the first final APP number corresponding to the jth target device ID.

5. The data processing system of claim 1, wherein, The step S600 further includes the following steps: S601, according to C, obtaining a corresponding preset correlation value list of C; S603, according to the preset correlation value list corresponding to C, obtaining the intermediate expansion equipment list E={E1,..., E j ,..., E n} corresponding to D, E j ={E j1 ,..., E ja ,..., E jb}, E ja is the device number corresponding to the a-th intermediate expansion equipment corresponding to the j-th target equipment ID, a=1...b, b is the number of intermediate expansion equipment corresponding to the j-th target equipment ID.

6. The data processing system of claim 1, wherein, In the step S700, K meets the following conditions: 。 7. The data processing system of claim 3, wherein, In step S109, H' c ri meets the following conditions: 。 8. The data processing system of claim 4, wherein, In step S505, W k jc meets the following conditions: 。

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