Information processing method and device, equipment, storage medium and program product

Abstract

The invention discloses an information processing method and device, equipment, a storage medium and a program product, and relates to the technical field of information processing.The information processing method comprises the steps that multiple pieces of user information are obtained; for any two pieces of user information, based on the sub-information, calculating a sub-information matching degree of each preset type; for any two pieces of user information, taking the sum of the target weights as information similarity; the target weight is a weight of a preset type corresponding to the sub-information matching degree greater than a preset threshold value; if the information similarity is greater than a preset similarity, determining that any two pieces of user information belong to the same user; and combining any two pieces of user information. According to the method, more complete user information can be obtained, so that an accurate analysis result can be obtained when data analysis is performed by using the user information subsequently.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to information processing methods, devices, equipment, storage media and program products. Background Art

[0002] At present, when users seek medical treatment in different medical institutions, different medical institutions adopt different information collection standards and processes. As a result, the information of the same user may be different and missing in different systems, resulting in low accuracy of the analysis results when the user information is subsequently used for data analysis. Summary of the invention

[0003] The main purpose of this application is to provide an information processing method, device, equipment, storage medium and program product, aiming to solve the technical problem that the information of the same patient may be different and missing in different systems.

[0004] To achieve the above objectives, the present application proposes an information processing method, comprising:

[0005] Acquire multiple user information; wherein the user information includes at least one sub-information, the type of the sub-information is one of multiple preset types, and the multiple preset types include gender information, age information, name information, mobile phone number information, ID number information and address information;

[0006] For any two pieces of user information, based on the sub-information, calculate the sub-information matching degree of each preset type;

[0007] For any two pieces of user information, the sum of the target weights is used as the information similarity; the target weight is the weight of the preset type corresponding to the sub-information matching degree greater than the preset threshold;

[0008] If the information similarity is greater than the preset similarity, it is determined that any two pieces of user information belong to the same user;

[0009] Merge any two user information.

[0010] In one embodiment, for any two pieces of user information, based on the sub-information, calculating the sub-information matching degree of each preset type includes:

[0011] The matching degree calculation module of the similarity recognition model is used to calculate the matching degree of each preset type of sub-information based on the sub-information for any two pieces of user information;

[0012] For any two user information, the sum of the target weights is used as the information similarity, including:

[0013] The similarity calculation module of the similarity recognition model is used to execute the sum of the target weights as the information similarity for any two user information.

[0014] In one embodiment, before the similarity calculation module of the similarity recognition model performs the calculation for any two user information and takes the sum of the target weights as the information similarity, the method further includes:

[0015] Obtain a training data set; wherein the user information training sample of the training data set includes at least one sub-information, and each user information training sample is marked with the percentage of each sub-information being empty and the percentage of each sub-information being recognizable;

[0016] In the process of training the similarity recognition model using the training data set, a gradient algorithm is used to determine the contribution of each preset type to the output data of the similarity recognition model;

[0017] The contribution degree is used to determine the weight of each preset type.

[0018] In one embodiment, for any two pieces of user information, the step of calculating the matching degree of each preset type of sub-information based on the sub-information includes:

[0019] Determine, from any two pieces of user information, target sub-information belonging to a target preset type in the preset type set;

[0020] For any two pieces of user information, based on the target sub-information, the matching degree of each target preset type of sub-information is calculated.

[0021] In one embodiment, after determining the weight of each preset type by using the contribution, the method further includes:

[0022] Based on the weight of each preset type, all preset types are processed in descending order to obtain a preset type sequence;

[0023] A preset number of preset types before sorting in the preset type sequence are taken as target preset types to obtain a preset sub-information set.

[0024] In one embodiment, after merging any two user information, the process further includes:

[0025] Output the merged user information.

[0026] In addition, to achieve the above-mentioned purpose, the present application also proposes an information processing device, the information processing device comprising:

[0027] An acquisition module, used to acquire multiple user information; wherein the user information includes at least one sub-information, the type of the sub-information is one of multiple preset types, and the multiple preset types include gender information, age information, name information, mobile phone number information, ID number information and address information;

[0028] A first calculation module, for calculating, for any two pieces of user information, a matching degree of each of the preset types of sub-information based on the sub-information;

[0029] The second calculation module is used to use the sum of target weights as information similarity for any two pieces of user information; the target weight is a weight of a preset type corresponding to a sub-information matching degree greater than a preset threshold;

[0030] A determination module, configured to determine that any two pieces of user information belong to the same user if the information similarity is greater than a preset similarity;

[0031] The merging module is used to merge any two user information.

[0032] In addition, to achieve the above objectives, the present application also proposes an information processing device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the information processing method as described above.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the information processing method as described above are implemented.

[0034] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the information processing method as described above are implemented.

[0035] One or more technical solutions proposed in this application have at least the following technical effects:

[0036] After obtaining multiple user information, the present application calculates the sub-information matching degree of each preset type for the sub-information in the user information, and accumulates the corresponding target weight when the sub-information matching degree is greater than the preset threshold, and uses the sum of the target weights as the information similarity. When the information similarity is greater than the preset similarity, it is determined that any two user information belong to the same user, and any two user information are merged. That is, the present application can determine multiple user information belonging to the same user from multiple user information by calculating the information similarity, and merge multiple user information belonging to the same user, which can make the user information more complete, and then when the user information is used for data analysis later, accurate analysis results can be obtained.

[0037] In addition, compared to the related art of directly converting user information into feature vectors and taking the similarity of any two feature vectors as information similarity, the present application calculates the matching degree of sub-information of each preset type and then takes the sum of the weights of the preset types as information similarity. This can avoid merging user information of different users, such as merging two users with the same name and the same gender, so that more accurate user information can be obtained after merging the user information. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A flowchart of the first embodiment of the information processing method of this application is provided;

[0041] Figure 2 This is a schematic diagram of the module structure of the information processing device according to an embodiment of the present application;

[0042] Figure 3 Schematic diagram of the device structure of the hardware operating environment involved in the information processing method in the embodiment of the present application.

[0043] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0044] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0045] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0046] The main solution of the embodiment of the present application is to obtain multiple user information; wherein the user information includes at least one sub-information, the type of the sub-information is one of multiple preset types, and the multiple preset types include gender information, age information, name information, mobile phone number information, ID number information and address information; for any two user information, based on the sub-information, calculate the sub-information matching degree of each preset type; for any two user information, take the sum of the target weights as the information similarity; the target weight is the weight of the preset type corresponding to the sub-information matching degree greater than the preset threshold; if the information similarity is greater than the preset similarity, it is determined that any two of the user information belong to the same user; and merge any two user information.

[0047] Because in the relevant technology, different medical institutions adopt different information collection standards and processes when users seek medical treatment in different medical institutions, the user information of the same user may have differences and omissions in different systems, resulting in low accuracy of the analysis results when the user information is subsequently used for data analysis.

[0048] The present application provides a solution. After obtaining multiple user information, the sub-information matching degree of each preset type is calculated for the sub-information in the user information, and the corresponding target weight is accumulated when the sub-information matching degree is greater than the preset threshold, and the sum of the target weights is used as the information similarity. When the information similarity is greater than the preset similarity, it is determined that any two user information belong to the same user, and any two user information are merged. That is, the present application can determine multiple user information belonging to the same user from multiple user information by calculating the information similarity, and merge multiple user information belonging to the same user, which can make the user information more complete, and then when the user information is used for data analysis later, accurate analysis results can be obtained.

[0049] In addition, compared to the related art of directly converting user information into feature vectors and taking the similarity of any two feature vectors as information similarity, the present application calculates the matching degree of each preset type of sub-information and then takes the sum of the weights of the preset type of sub-information as information similarity. This can avoid merging user information of different users, such as merging two users with the same name and the same gender, so that more accurate user information can be obtained after merging the user information.

[0050] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an information processing device, etc. The following takes an information processing device as an example to illustrate this embodiment and the following embodiments.

[0051] Based on this, the present application embodiment provides an information processing method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the information processing method of this application.

[0052] In this embodiment, the information processing method includes steps S10 to S50:

[0053] Step S10, obtaining information of multiple users.

[0054] Among them, the user information includes at least one sub-information, and the type of the sub-information is one of multiple preset types, and the multiple preset types include gender information, age information, name information, mobile phone number information, ID number information and address information.

[0055] It should be noted that, in addition to the aforementioned preset types of sub-information, the preset types of sub-information may also be date of birth information, nationality information, work unit information, ethnicity information, occupation information, educational background information, and email information, etc., and this embodiment does not limit this.

[0056] It is understandable that when a user visits different medical institutions, the user information stored in different institutions is incomplete because different medical institutions have different information collection standards. In order to obtain more complete user information, the user information in different institutions can be integrated to obtain complete user information.

[0057] Specifically, the staff can log in to different institutional systems through information processing equipment to read multiple user information from the user information database in each institutional system.

[0058] Step S20: for any two pieces of user information, based on the sub-information, calculate the matching degree of each preset type of sub-information.

[0059] Specifically, for the sub-information of any two user information, for each type of gender information, age information, name information, mobile phone number information, ID number information and address information, the sub-information matching degree of each type is calculated, that is, the sub-information matching degree of any two gender information, the sub-information matching degree of any two age information, the sub-information matching degree of any two name information, the sub-information matching degree of any two mobile phone number information, the sub-information matching degree of any two ID number information, and the sub-information matching degree of any two address information are calculated.

[0060] When calculating the matching degree of each preset type of sub-information, a string similarity algorithm may be used, such as edit distance, Jaccard coefficient, cosine similarity, etc., which is not limited in this embodiment. Alternatively, further, as another optional implementation, any two user information may be input into a similarity recognition model to calculate the matching degree of each preset type of sub-information through a matching degree calculation module of the similarity recognition model, that is, the matching degree calculation module of the similarity recognition model is used to perform the calculation of the matching degree of each preset type of sub-information based on the sub-information for any two user information.

[0061] Step S30: for any two pieces of user information, the sum of the target weights is used as the information similarity; the target weight is the weight of a preset type corresponding to the sub-information matching degree greater than a preset threshold.

[0062] Specifically, for any two user information, if the string similarity algorithm is used to calculate the sub-information matching degree of each preset type, after obtaining the sub-information matching degree of each preset type, for each preset type, the preset type weight corresponding to the sub-information matching degree greater than the preset threshold can be used as the target weight, and the sum of the target weights can be used as the information similarity. Or, as another optional implementation, if the matching degree of each preset type of sub-information is calculated using the matching degree calculation module of the similarity recognition model, after obtaining the sub-information matching degree of each preset type, the similarity recognition model can be used to perform the sum of the target weights for any two user information as the information similarity, that is, the adaptability of step S30 is changed to: the similarity recognition model is used to perform the sum of the target weights for any two user information as the information similarity.

[0063] It should be noted that user information may include multiple sub-information, and for different user information, the preset sub-information of the user information may be the same. For example, the name information of user information A is Zhang Wei, and the gender information is male. The name information of user information B is Zhang Wei, and the gender information is also male. If the information similarity is determined only by the matching degree of each preset sub-information type, the user information of the same user information will be merged together. In order to avoid the above situation, this embodiment further determines the information similarity by the matching weight of the preset sub-information type. When determining the weight of each preset type, in order to accurately determine the weight of each preset type, further, as an optional implementation method, before step S30, it also includes:

[0064] Step S27, obtaining a training data set.

[0065] The user information training samples of the training data set include at least one sub-information, and each of the user information training samples is marked with the percentage of each sub-information being empty and the percentage of each sub-information being recognizable.

[0066] Step S28, in the process of training the similarity recognition model using the training data set, a gradient algorithm is used to determine the contribution of each preset type to the output data of the similarity recognition model.

[0067] Step S29: using the contribution degree, determine the weight of each preset type.

[0068] In this embodiment, user information of multiple users can be stored locally or in the cloud of the information processing device. When step S27 is executed, user information of multiple users can be directly called from the local or cloud of the information processing device, and sub-information in the multiple user information can be used as user training samples.

[0069] Specifically, after obtaining the training data set, the similarity recognition model can be trained using the training data set, and in the process of training the similarity recognition model using the training data set, the contribution of each preset type to the output data of the similarity recognition model can be determined using a gradient algorithm. For each preset type, after determining its contribution to the output data of the similarity recognition model, the weight of each preset type can be determined according to the contribution.

[0070] In this embodiment, in the process of training the similarity recognition model using the training data set, the gradient algorithm is used to determine the contribution of each of the preset types to the output data of the similarity recognition model, and the weight of each preset type is further determined based on the contribution. That is, the weight of each preset type is determined by determining the influence of the preset type on the similarity recognition model. A higher weight can be assigned to a unique preset type, such as assigning a higher weight to sub-information whose preset type is a document number type, thereby avoiding the situation where user information of users with the same name is merged together.

[0071] Step S40: If the information similarity is greater than a preset similarity, it is determined that any two pieces of user information belong to the same user.

[0072] Step S50: merge any two user information.

[0073] Specifically, for any two pieces of user information, after obtaining the information similarity of the two pieces of user information, when the information similarity is greater than a preset similarity, it is determined that the two pieces of user information belong to the same user, and the two pieces of user information can be merged to obtain merged user information.

[0074] It is understandable that when merging any two user information, the sub-information of the same preset type that appears multiple times can be deduplicated to avoid the sub-information of the same preset type appearing multiple times in the merged user information, thereby making the merged user information more concise. After obtaining the merged user information, as an optional implementation, the merged user information can be output to show the complete user information to relevant personnel.

[0075] Furthermore, in order to obtain more complete user information, after merging any two pieces of user information to obtain merged user information, the aforementioned operation can be repeated for the merged user information to calculate the information similarity between the merged user information and the remaining user information until all user information for the same user is traversed.

[0076] It should be noted that, for any two user information, when calculating the matching degree of each preset type of sub-information, since the user information includes more preset types of sub-information in addition to the aforementioned preset types of sub-information, such as but not limited to marital status, height information, weight information, etc., the sub-information of preset types such as marital status, height information, weight information, etc. may change over time. Therefore, when using such preset types to determine the information similarity of any two user information, not only the number of calculations will increase, but also the accuracy of the information similarity will be affected. Therefore, in order to reduce the amount of calculation while reducing the impact of the variable preset types of sub-information on the information similarity, further, as an optional implementation, step S20 specifically includes:

[0077] Step S201: determining target sub-information corresponding to a target preset type in a preset type set from any two pieces of user information.

[0078] Step S202: for any two pieces of user information, based on the target sub-information, calculate the matching degree of each target preset type of sub-information.

[0079] In this embodiment, the preset type set may include multiple target preset types, the target preset type is a preset type with a preset number before sorting in the preset type sequence, and the preset type sequence is an arrangement order of the preset types obtained by descending all preset types based on the weight of each preset type. That is, after step S29, it also includes: descending all preset types based on the weight of each preset type to obtain a preset type sequence; taking the preset type with a preset number before sorting in the preset type sequence as the target preset type to obtain a preset sub-information set.

[0080] Specifically, after obtaining the user information, for any two pieces of user information, the target sub-information corresponding to the target preset type in the preset type set can be determined from the any two pieces of user information. After determining the target sub-information in any two pieces of target user information, the above method can be used to calculate the sub-information matching degree of each target preset type.

[0081] In this embodiment, target sub-information corresponding to the target preset type in the preset type set is determined from any two pieces of user information, and based on the target sub-information, the sub-information matching degree of each target preset type is calculated. That is, when the information similarity is calculated using the sub-information matching degree, the selected preset type of information has better stability and is not easy to change with the passage of time. While reducing the amount of calculation, it can also avoid the influence of the variable sub-information of the preset type on the information similarity, thereby obtaining a more accurate information similarity.

[0082] In this embodiment, after obtaining multiple user information, the sub-information matching degree of each preset type is calculated for the sub-information in the user information, and the corresponding target weight is accumulated when the sub-information matching degree is greater than the preset threshold, and the sum of the target weights is used as the information similarity. When the information similarity is greater than the preset similarity, it is determined that any two user information belong to the same user, and any two user information are merged. That is, this embodiment can determine multiple user information belonging to the same user from multiple user information by calculating the information similarity, and merge multiple user information belonging to the same user, which can make the user information more complete, and then when the user information is used for data analysis later, accurate analysis results can be obtained.

[0083] In addition, compared with the related art of directly converting user information into feature vectors and taking the similarity of any two feature vectors as information similarity, this embodiment calculates the matching degree of sub-information of each preset type and then takes the sum of the weights of the preset types as information similarity. This can avoid merging user information of different users, such as merging two users with the same name and the same gender, so that more accurate user information can be obtained after merging the user information.

[0084] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the information processing method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0085] This application also provides an information processing device, please refer to Figure 2 , the information processing device comprises:

[0086] An acquisition module, used to acquire multiple user information; wherein the user information includes at least one sub-information, the type of the sub-information is one of multiple preset types, and the multiple preset types include gender information, age information, name information, mobile phone number information, ID number information and address information;

[0087] A first calculation module, for calculating, for any two pieces of user information, a matching degree of each of the preset types of sub-information based on the sub-information;

[0088] The second calculation module is used to use the sum of target weights as information similarity for any two pieces of user information; the target weight is a weight of a preset type corresponding to a sub-information matching degree greater than a preset threshold;

[0089] A determination module, configured to determine that any two pieces of user information belong to the same user if the information similarity is greater than a preset similarity;

[0090] The merging module is used to merge any two user information.

[0091] The information processing device provided by the present application adopts the information processing method in the above embodiment, which can solve the technical problem that the information of the same patient may be different and omitted in different systems. Compared with the related art, the beneficial effects of the information processing device provided by the present application are the same as the beneficial effects of the information processing method provided by the above embodiment, and the other technical features in the information processing device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0092] The present application provides an information processing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the information processing method in the above-mentioned embodiment one.

[0093] Reference below Figure 3 , which shows a schematic diagram of the structure of an information processing device suitable for implementing an embodiment of the present application. The information processing device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3The information processing device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0094] like Figure 3 As shown, the information processing device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the information processing device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the information processing device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an information processing device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0095] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0096] The information processing device provided by the present application adopts the information processing method in the above embodiment, which can solve the technical problem that the information of the same patient may be different and omitted in different systems. Compared with the related art, the beneficial effects of the information processing device provided by the present application are the same as the beneficial effects of the information processing method provided by the above embodiment, and the other technical features in the information processing device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0097] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0098] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0099] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the information processing method in the above-mentioned embodiment.

[0100] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0101] The computer-readable storage medium may be included in the information processing device; or may exist independently without being installed in the information processing device.

[0102] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the information processing device, the information processing device: obtains multiple user information; wherein the user information includes at least one sub-information, and the type of the sub-information is one of multiple preset types, and the multiple preset types include gender information, age information, name information, mobile phone number information, ID number information and address information; for any two user information, based on the sub-information, calculate the sub-information matching degree of each preset type; for any two user information, take the sum of the target weights as the information similarity; the target weight is the weight of the preset type corresponding to the sub-information matching degree greater than the preset threshold; if the information similarity is greater than the preset similarity, it is determined that any two of the user information belong to the same user; and merge any two user information.

[0103] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user information computer, partially on the user information computer, as a separate software package, partially on the user information computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user information computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0106] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned information processing method, and can solve the technical problem that the information of the same patient may be different and omitted in different systems. Compared with the related art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the information processing method provided in the above-mentioned embodiment, and will not be repeated here.

[0107] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned information processing method when executed by a processor.

[0108] The computer program product provided by this application can solve the technical problem that the information of the same patient may be different and missing in different systems. Compared with the related art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the information processing method provided by the above embodiment, which will not be repeated here.

[0109] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An information processing method, characterized in that: The method comprises: Acquire multiple user information; wherein the user information includes at least one sub-information, and the type of the sub-information is one of multiple preset types, and the multiple preset types include gender information, age information, name information, mobile phone number information, ID number information and address information; For any two pieces of user information, based on the sub-information, calculating the matching degree of each of the preset types of sub-information; For any two pieces of user information, the sum of the target weights is used as the information similarity; the target weight is the weight of the preset type corresponding to the sub-information matching degree greater than the preset threshold; If the information similarity is greater than a preset similarity, it is determined that the any two pieces of user information belong to the same user; Merge any two pieces of user information.

2. The method according to claim 1, characterized in that The calculating, for any two pieces of user information, a matching degree of each preset type of sub-information based on the sub-information includes: Executing, for any two pieces of the user information, calculating the matching degree of each of the preset types of sub-information based on the sub-information by a matching degree calculation module of the similarity recognition model; For any two pieces of user information, taking the sum of the target weights as the information similarity includes: The similarity calculation module of the similarity recognition model is used to perform the above steps of taking the sum of the target weights as the information similarity for any two pieces of user information.

3. The method according to claim 2, characterized in that Before the similarity calculation module of the similarity recognition model performs the step of taking the sum of the target weights as the information similarity for any two pieces of user information, the method further comprises: Acquire a training data set; wherein the user information training samples of the training data set include at least one sub-information, and each of the user information training samples is marked with the percentage of each sub-information being empty and the percentage of each sub-information being recognizable; In the process of training the similarity recognition model using the training data set, a gradient algorithm is used to determine the contribution of each of the preset types to the output data of the similarity recognition model; The contribution degree is used to determine the weight of each preset type.

4. The method according to claim 3, characterized in that The step of calculating the matching degree of each preset type of sub-information based on the sub-information for any two pieces of user information comprises: Determine, from any two pieces of user information, target sub-information belonging to a target preset type in the preset type set; For any two pieces of user information, based on the target sub-information, the matching degree of the sub-information of the preset type of each target is calculated.

5. The method according to claim 4, characterized in that After determining the weight of each of the preset types by using the contribution, the method further includes: Based on the weight of each of the preset types, all the preset types are processed in descending order to obtain a preset type sequence; A preset number of the preset types before sorting in the preset type sequence are taken as target preset types to obtain a preset sub-information set.

6. The method according to claim 1, characterized in that After the step of merging any two pieces of user information to obtain a user information graph, the method further includes: Output the merged user information.

7. An information processing device, characterized in that: The device comprises: An acquisition module, used to acquire multiple user information; wherein the user information includes at least one sub-information, and the type of the sub-information is one of multiple preset types, and the multiple preset types include gender information, age information, name information, mobile phone number information, ID number information and address information; A first calculation module, configured to calculate, for any two pieces of user information, a matching degree of each of the preset types of sub-information based on the sub-information; A second calculation module is used to use the sum of target weights as information similarity for any two pieces of user information; the target weight is a weight of a preset type corresponding to the sub-information matching degree greater than a preset threshold; A determination module, configured to determine that any two pieces of user information belong to the same user if the information similarity is greater than a preset similarity; The merging module is used to merge any two pieces of user information.

8. An information processing device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the information processing method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the information processing method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the information processing method according to any one of claims 1 to 6 are implemented.

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