CN117035945A - Electronic information management method based on deep learning and related equipment - Google Patents
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Abstract
The application discloses an electronic information management method based on deep learning and related equipment. The method comprises the following steps: receiving medication order application information of a first user, wherein the medication order application information comprises first address information and first medicine information; inquiring second medicine information included in the medicine order application information of the second user under the condition that a second address included in the medicine order application information of the second user is matched with the first address within preset time; and under the condition that at least one function and use characteristics of the first medicine information and the second medicine information are the same, sending a repeat medicine purchase risk prompt message to the first user before generating a first order associated with the first user. The method can solve the problems that the application rate of electronic information is low and a large number of problems which can be predicted and solved by the electronic information still exist in the application scene of intelligent hardware.
Description
Technical Field
The application relates to the field of electronic information management, in particular to an electronic information management method based on deep learning and related equipment.
Background
The intelligent hardware is a technological concept behind the intelligent mobile phone, and the traditional equipment is modified in a software and hardware combination mode, so that the intelligent mobile phone has an intelligent function. After intellectualization, the hardware has the capability of connection, realizes loading of internet service, forms a typical architecture of 'cloud plus end', and has additional values of big data and the like. Intelligent hardware has extended from wearable devices to the fields of smart televisions, smart homes, smart cars, medical health, smart toys, robots, etc.
At present, many electronic information can be obtained based on intelligent hardware, especially in the intelligent distribution field, the electronic information generated or collected by the distribution end and the user end is important, but the application rate of the electronic information is still low, and a large number of problems which can be predicted and solved by the electronic information still exist in the application scene of the intelligent hardware.
Disclosure of Invention
In the summary, a series of concepts in a simplified form are introduced, which will be further described in detail in the detailed description. The summary of the application is not intended to define the key features and essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
In order to solve the problem that the electronic information application rate is low and a large number of problems which can be predicted and solved by the electronic information still exist in the application scene of intelligent hardware, in a first aspect, the application provides an electronic information management method based on deep learning, which comprises the following steps:
receiving medication order application information of a first user, wherein the medication order application information comprises first address information and first medicine information;
inquiring second medicine information included in the medicine order application information of the second user under the condition that a second address included in the medicine order application information of the second user is matched with the first address within preset time;
and under the condition that at least one function and use characteristics of the first medicine information and the second medicine information are the same, sending a repeat medicine purchase risk prompt message to the first user before generating a first order associated with the first user.
Optionally, the method further comprises:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is the residence attribute, executing the judgment of the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time.
Optionally, the method further comprises:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is public address attribute, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
Optionally, the method further comprises:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and under the condition that the disease information comprises the transmission characteristic, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
Optionally, the method further comprises:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and sending medicine purchase suggestion information to registered users to which a plurality of third addresses matched with the first address belong in the case that the disease information comprises a transmission characteristic.
Optionally, in case the disorder information includes a propagation feature, a propagation feature of the disorder information is acquired, and a plurality of third address area ranges matching the first address are adjusted based on the propagation feature.
Optionally, the repeated purchase risk prompting message includes the second address and/or part of communication information of the second user.
In a second aspect, the present application further provides an electronic information management device based on deep learning, including:
the medication order application system comprises a receiving unit, a first medication order application unit and a second medication order application unit, wherein the receiving unit is used for receiving medication order application information of a first user, and the medication order application information comprises first address information and first medicine information;
the inquiry unit is used for inquiring the second medicine information included in the medicine order application information of the second user under the condition that the second address included in the medicine order application information of the second user is matched with the first address within the preset time;
the generation unit is used for sending a repeat drug purchase risk prompt message to the first user before generating a first order associated with the first user under the condition that at least one function and use characteristics of the first drug information and the second drug information are the same.
In a third aspect, an electronic device, comprising: a memory, a processor and a computer program stored in and executable on the processor for implementing the steps of the deep learning based electronic information management method according to any one of the first aspects as described above when the computer program stored in the memory is executed.
In a fourth aspect, the present application also proposes a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the deep learning based electronic information management method of any of the first aspects.
In summary, the electronic information management method based on deep learning provided by the application is characterized in that medication order application information of a first user is received, wherein the medication order application information comprises first address information and first medicine information; inquiring second medicine information included in the medicine order application information of the second user under the condition that a second address included in the medicine order application information of the second user is matched with the first address within preset time; and under the condition that at least one function and use characteristics of the first medicine information and the second medicine information are the same, sending a repeat medicine purchase risk prompt message to the first user before generating a first order associated with the first user. Therefore, under the condition that the software platform or the dispensing robot receives the application information of the medication order of the first user, whether the family or other acquaintance relation organization consisting of a plurality of people exist or not can be predicted by applying for purchasing the medication possibly with the same efficacy compared with the prior order of the other people at the similar address, and the situation that at least two persons repeatedly purchase the medication for the same person is avoided.
Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the specification. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a schematic flow chart of an electronic information management method based on deep learning according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an electronic information management device based on deep learning according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic information management electronic device based on deep learning according to an embodiment of the present application.
Detailed Description
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments.
In order to solve the problem that the electronic information application rate is low and a large number of problems that can be predicted and solved by the electronic information still exist in an application scene of intelligent hardware, please refer to fig. 1, a schematic flow chart of an electronic information management method based on deep learning provided by an embodiment of the application specifically may include: steps S110, S120, and S130.
S110, medication order application information of a first user is received, wherein the medication order application information comprises first address information and first medicine information.
The first user may be a user currently submitting the application information of the medication order, the first address information may be a receiving address of the user, and the first medicine information may be names and numbers of medicines to be purchased in the user order. In some cases, the user may make a post-purchase or co-city purchase of the drug from a cell phone or a computer device connected to a network.
S120, inquiring second medicine information included in the medicine order application information of the second user under the condition that the second address included in the medicine order application information of the second user is matched with the first address within preset time.
The software platform or the dispensing robot may query the historical order application after receiving the medication order application information of the first user, and indicate that other users (second users) have recently used an address very similar to the receiving address of the first user to perform online medicine purchase when there is a second address included in the medication order application information of the second user that matches the first address within a preset time.
And S130, sending a repeat purchase risk prompt message to the first user before generating a first order associated with the first user under the condition that at least one function and use characteristics of the first medicine information and the second medicine information are the same.
In the case of S120, for example, it may be assisted to confirm whether a different user purchases a drug with a similar efficacy through a similar address by querying the drug information subscribed by the second user, so that it is highly likely that the current order of the first user is repeatedly ordered for the user or for friends, and at this time, a repeat drug purchase risk prompt message may be sent to the first user, so that the situation that multiple persons repeatedly purchase drugs for patients when patients occur in the residences of acquaintances such as families is effectively avoided. In addition, even if patients are more than one, as the purchased medicines have the same efficacy, repeated purchasing of medicines still has the condition that medicines are wasted and even the expiration date is exceeded.
In summary, according to the electronic information management method based on deep learning provided by the embodiment of the application, medication order application information of a first user is received, wherein the medication order application information comprises first address information and first medicine information; inquiring second medicine information included in the medicine order application information of the second user under the condition that a second address included in the medicine order application information of the second user is matched with the first address within preset time; and under the condition that at least one function and use characteristics of the first medicine information and the second medicine information are the same, sending a repeat medicine purchase risk prompt message to the first user before generating a first order associated with the first user. Therefore, under the condition that the software platform or the dispensing robot receives the application information of the medication order of the first user, whether the family or other acquaintance relation organization consisting of a plurality of people exist or not can be predicted by applying for purchasing the medication possibly with the same efficacy compared with the prior order of the other people at the similar address, and the situation that at least two persons repeatedly purchase the medication for the same person is avoided.
According to some embodiments, further comprising:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is the residence attribute, executing the judgment of the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time.
In some cases, although the first address is similar to or the same as the second address, a scene where a family or acquaintance resides may exist only if the address is a residence attribute, and in this scene, mutual or help to purchase medicines may be possible, and in the case that the attribute information of the first address is a residence attribute, the judgment of the matching degree of the first address with the second address included in the application information of the medication order of the second user existing in the preset time is performed, so that the calculation power can be effectively saved and the information analysis and management efficiency can be improved.
In some examples, further comprising:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is public address attribute, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
For example, although the first address is similar to or the same as the second address, only if the address is a residence attribute, there may be a scene where a family or acquaintance resides, and then if the attribute information of the first address is a public address attribute, the situation of purchasing medicines mutually or helped is generally not generated, so that the judgment of the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is avoided, and the calculation force can be effectively saved and the information analysis and management efficiency can be improved.
In some examples, further comprising:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and under the condition that the disease information comprises the transmission characteristic, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
For example, under the condition that the attribute information of the first address is the public address attribute, the situation of purchasing medicines mutually or helped does not generally occur, if the disease information related to the efficacy and use characteristics of the first medicine information can further infer the possibility of multiple persons suffering from diseases under the same address, the risk of repeatedly purchasing medicines for the persons or other persons is further reduced, and then the judgment of the matching degree of the first address and the second address included in the medicine order application information of the second user existing in the preset time is not needed, so that the calculation force can be effectively saved, and the information analysis and management efficiency can be improved.
In some examples, further comprising:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and sending medicine purchase suggestion information to registered users to which a plurality of third addresses matched with the first address belong in the case that the disease information comprises a transmission characteristic.
For example, in the case that the indication of the medicine purchased by the user has a transmission characteristic, the medicine purchase suggestion information is sent to the registered user to which the plurality of third addresses matched with the first address belong, the probability that the user around the first user is transmitted with the illness can be effectively reduced without exposing the privacy of the user, and the medicine purchase suggestion information can be the preventive medicine or the medical protector for preventing the illness from being transmitted.
In some examples, where the disorder information includes a propagation characteristic, a propagation characteristic of the disorder information is obtained, and a plurality of third address region ranges matching the first address are adjusted based on the propagation characteristic.
For example, a plurality of third address area ranges matching the first address may be adjusted based on the propagation capability of the disease, and in the case of the condition information having a strong propagation capability, the plurality of third address area ranges matching the first address may be enlarged.
In some examples, the repeat purchase risk reminder message includes a portion of the communication information of the second address and/or the second user.
By means of the method, the first user can be reminded of avoiding repeated medicine purchase through the second address of the second user prompting the first user to purchase medicine in advance, and the first user can be reminded of avoiding repeated medicine purchase through the information such as the mobile phone tail number of the second user prompting the first user purchasing medicine in advance, so that the acquainted first user can be guaranteed to quickly identify whether acquaintances have purchased the same medicine or not, and the acquainted first user cannot master all information of the second user purchasing the same medicine, and privacy information of the second user is protected.
Referring to fig. 2, an embodiment of an electronic information management device based on deep learning according to an embodiment of the present application may include:
a receiving unit 21 for receiving medication order application information of a first user, the medication order application information including first address information and first medicine information;
a query unit 22, configured to query, when there is already a second address included in the medication order application information of the second user within a preset time and the second address matches the first address, second medicine information included in the medication order application information of the second user;
a generating unit 23, configured to send a repeat purchase risk prompting message to the first user before generating the first order associated with the first user, where at least one of the efficacy use features of the first drug information and the second drug information is the same.
In summary, the electronic information management device based on deep learning provided by the embodiment of the application receives medication order application information of a first user, wherein the medication order application information comprises first address information and first medicine information; inquiring second medicine information included in the medicine order application information of the second user under the condition that a second address included in the medicine order application information of the second user is matched with the first address within preset time; and under the condition that at least one function and use characteristics of the first medicine information and the second medicine information are the same, sending a repeat medicine purchase risk prompt message to the first user before generating a first order associated with the first user. Therefore, under the condition that the software platform or the dispensing robot receives the application information of the medication order of the first user, whether the family or other acquaintance relation organization consisting of a plurality of people exist or not can be predicted by applying for purchasing the medication possibly with the same efficacy compared with the prior order of the other people at the similar address, and the situation that at least two persons repeatedly purchase the medication for the same person is avoided.
As shown in fig. 3, an embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor, where the processor 320 implements any one of the steps of the above-mentioned electronic information management method based on deep learning when executing the computer program 311:
receiving medication order application information of a first user, wherein the medication order application information comprises first address information and first medicine information;
inquiring second medicine information included in the medicine order application information of the second user under the condition that a second address included in the medicine order application information of the second user is matched with the first address within preset time;
and under the condition that at least one function and use characteristics of the first medicine information and the second medicine information are the same, sending a repeat medicine purchase risk prompt message to the first user before generating a first order associated with the first user.
Optionally, the method further comprises:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is the residence attribute, executing the judgment of the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time.
Optionally, the method further comprises:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is public address attribute, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
Optionally, the method further comprises:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and under the condition that the disease information comprises the transmission characteristic, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
Optionally, the method further comprises:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and sending medicine purchase suggestion information to registered users to which a plurality of third addresses matched with the first address belong in the case that the disease information comprises a transmission characteristic.
Optionally, in case the disorder information includes a propagation feature, a propagation feature of the disorder information is acquired, and a plurality of third address area ranges matching the first address are adjusted based on the propagation feature.
Optionally, the repeated purchase risk prompting message includes the second address and/or part of communication information of the second user.
Since the electronic device described in this embodiment is a device for implementing the deep learning-based electronic information management apparatus in this embodiment of the present application, based on the method described in this embodiment of the present application, those skilled in the art can understand the specific implementation of the electronic device in this embodiment and various modifications thereof, so how the electronic device implements the method in this embodiment of the present application will not be described in detail herein, and only those devices for implementing the method in this embodiment of the present application will be within the scope of the application.
In a specific implementation, the computer program 311 may implement any implementation manner of the embodiment corresponding to fig. 1 when executed by a processor:
receiving medication order application information of a first user, wherein the medication order application information comprises first address information and first medicine information;
inquiring second medicine information included in the medicine order application information of the second user under the condition that a second address included in the medicine order application information of the second user is matched with the first address within preset time;
and under the condition that at least one function and use characteristics of the first medicine information and the second medicine information are the same, sending a repeat medicine purchase risk prompt message to the first user before generating a first order associated with the first user.
Optionally, the method further comprises:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is the residence attribute, executing the judgment of the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time.
Optionally, the method further comprises:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is public address attribute, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
Optionally, the method further comprises:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and under the condition that the disease information comprises the transmission characteristic, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
Optionally, the method further comprises:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and sending medicine purchase suggestion information to registered users to which a plurality of third addresses matched with the first address belong in the case that the disease information comprises a transmission characteristic.
Optionally, in case the disorder information includes a propagation feature, a propagation feature of the disorder information is acquired, and a plurality of third address area ranges matching the first address are adjusted based on the propagation feature.
Optionally, the repeated purchase risk prompting message includes the second address and/or part of communication information of the second user.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Embodiments of the present application also provide a computer program product comprising computer software instructions that, when run on a processing device, cause the processing device to perform a flow of deep learning based electronic information management as in the corresponding embodiment of fig. 1.
The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer readable storage media can be any available media that can be stored by a computer or data storage devices such as servers, data centers, etc. that contain an integration of one or more available media. Usable media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid State Disks (SSDs)), among others.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. An electronic information management method based on deep learning is characterized by comprising the following steps:
receiving medication order application information of a first user, wherein the medication order application information comprises first address information and first medicine information;
inquiring second medicine information included in the medicine order application information of the second user under the condition that a second address included in the medicine order application information of the second user is matched with the first address within preset time;
and under the condition that at least one function and use characteristics of the first medicine information and the second medicine information are the same, sending a repeat medicine purchase risk prompt message to the first user before generating a first order associated with the first user.
2. The method as recited in claim 1, further comprising:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is the residence attribute, executing the judgment of the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time.
3. The method as recited in claim 1, further comprising:
acquiring attribute information of the first address;
and under the condition that the attribute information of the first address is public address attribute, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
4. The method as recited in claim 1, further comprising:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and under the condition that the disease information comprises the transmission characteristic, judging the matching degree of the first address and the second address included in the application information of the medication order of the second user existing in the preset time is not executed.
5. A method as recited in claim 3, further comprising:
acquiring attribute information of the first address;
acquiring condition information related to efficacy use characteristics of the first medicine information under the condition that the attribute information of the first address is public address attribute;
and sending medicine purchase suggestion information to registered users to which a plurality of third addresses matched with the first address belong in the case that the disease information comprises a transmission characteristic.
6. The method of claim 5, wherein, where the disorder information includes a propagation characteristic, a propagation characteristic of disorder information is obtained, and a plurality of third address region ranges matching the first address are adjusted based on the propagation characteristic.
7. The method of claim 1, wherein the repeat purchase risk reminder message includes part of the communication information of the second address and/or the second user.
8. An electronic information management apparatus based on deep learning, comprising:
the medication order application system comprises a receiving unit, a first medication order application unit and a second medication order application unit, wherein the receiving unit is used for receiving medication order application information of a first user, and the medication order application information comprises first address information and first medicine information;
the inquiry unit is used for inquiring the second medicine information included in the medicine order application information of the second user under the condition that the second address included in the medicine order application information of the second user is matched with the first address within the preset time;
the generation unit is used for sending a repeat drug purchase risk prompt message to the first user before generating a first order associated with the first user under the condition that at least one function and use characteristics of the first drug information and the second drug information are the same.
9. An electronic device, comprising: memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor is adapted to implement the steps of the deep learning based electronic information management method according to any of claims 1-7 when executing the computer program stored in the memory.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the deep learning-based electronic information management method of any one of claims 1-7.
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