CN113421174A - Intellectual property value evaluation reference method and system based on big data - Google Patents
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Abstract
The invention discloses an intellectual property value evaluation reference method and system based on big data, wherein the method comprises the following steps: performing feature matching according to the basic information of the first intellectual property and the intellectual property feature database to obtain first intellectual property grade features; classifying the first intellectual property right to obtain first type information; analyzing the content of the first intellectual property right to obtain first analyzed content information; inputting the first intellectual property grade characteristic, the first type information and the first analysis content information into a first intellectual property value evaluation model to obtain a first intellectual property value evaluation result; and if the first intellectual property value evaluation result is within a preset reasonableness threshold value, sending the first intellectual property value evaluation result to a first user. The method solves the technical problem that the rationality of the evaluation result is influenced by uncertain factors due to lack of market transaction information in intellectual property evaluation in the prior art.
Description
Technical Field
The invention relates to the field of value evaluation, in particular to an intellectual property value evaluation reference method and system based on big data.
Background
Intellectual property rights are intellectual creations such as inventions, designs, literature and art works, and signs, names, images used in commerce, which can be considered to be intellectual property rights possessed by a person or an organization. With the increasing amount of intellectual property trade items, the trade market of intellectual property is more and more active, and the accuracy research on the evaluation of intellectual property value is more and more emphasized and deepened.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
in the prior art, the intellectual property assessment has the technical problem that due to the fact that uncertain factors exist in the market transaction information lack, the rationality of assessment results is affected.
Disclosure of Invention
The embodiment of the application provides the intellectual property value evaluation reference method and system based on the big data, solves the technical problem that the rationality of the value evaluation result is influenced due to the fact that uncertain factors exist due to the lack of market transaction information in the intellectual property evaluation in the prior art, and achieves the purposes that the intellectual property information is collected in a big data mode, the influence of the uncertain factors on the evaluation result is reduced, so that the value evaluation result of the intellectual property is more reasonable and accurate, and the technical effect of the reference value is better achieved.
In view of the above, the present invention has been developed to provide a solution to, or at least partially solve, the above problems.
In a first aspect, an embodiment of the present application provides a big data-based intellectual property value evaluation reference method, where the method includes: constructing an intellectual property characteristic database through a big data platform; performing feature matching according to the basic information of the first intellectual property and the intellectual property feature database to obtain first intellectual property grade features; classifying the first intellectual property right according to a preset classification rule to obtain first type information; obtaining a first analysis instruction, wherein the first analysis instruction is used for analyzing the content of the first intellectual property right to obtain first analysis content information; inputting the first intellectual property grade characteristic, the first type information and the first analysis content information into a first intellectual property value evaluation model to obtain a first intellectual property value evaluation result; if the first intellectual property value evaluation result is within a preset reasonableness threshold value, obtaining a first sending instruction; and sending the first intellectual property value evaluation result to a first user according to the first sending instruction.
In another aspect, the present application further provides a big data-based intellectual property value evaluation reference system, where the system includes: the system comprises a first construction unit, a second construction unit and a third construction unit, wherein the first construction unit is used for constructing an intellectual property characteristic database through a big data platform; the first acquisition unit is used for carrying out feature matching according to the basic information of the first intellectual property and the intellectual property feature database to acquire a first intellectual property grade feature; a second obtaining unit, configured to classify the first intellectual property right according to a predetermined classification rule, and obtain first type information; a third obtaining unit, configured to obtain a first analysis instruction, where the first analysis instruction is configured to analyze content of the first intellectual property right to obtain first analysis content information; a fourth obtaining unit, configured to input the first intellectual property ranking feature, the first type information, and the first analysis content information into a first intellectual property value evaluation model, and obtain a first intellectual property value evaluation result; a fifth obtaining unit, configured to obtain a first sending instruction if the first intellectual property value evaluation result is within a predetermined reasonableness threshold; and the first sending unit is used for sending the first intellectual property value evaluation result to a first user according to the first sending instruction.
In a third aspect, an embodiment of the present invention provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the method for controlling output data includes any one of the steps described above.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for controlling output data according to any one of the above.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the intellectual property characteristic database is constructed by a big data platform; performing feature matching according to the basic information of the first intellectual property and the intellectual property feature database to obtain first intellectual property grade features; classifying the first intellectual property right according to a preset classification rule to obtain first type information; obtaining a first analysis instruction, wherein the first analysis instruction is used for analyzing the content of the first intellectual property right to obtain first analysis content information; inputting the first intellectual property grade characteristic, the first type information and the first analysis content information into a first intellectual property value evaluation model to obtain a first intellectual property value evaluation result; if the first intellectual property value evaluation result is within a preset reasonableness threshold value, obtaining a first sending instruction; and sending the first intellectual property value evaluation result to a first user according to the first sending instruction. And then the intellectual property information is acquired in a big data mode, and the influence of uncertain factors on the evaluation result is reduced, so that the value evaluation result of the intellectual property is more reasonable and accurate, and the technical effect of reference value is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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Fig. 1 is a schematic flow chart of a reference method for intellectual property value evaluation based on big data according to an embodiment of the present application;
fig. 2 is a schematic flow chart illustrating the process of obtaining a first intellectual property ranking characteristic in an intellectual property value evaluation reference method based on big data according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating a first classification result obtained in an intellectual property value evaluation reference method based on big data according to an embodiment of the present application;
fig. 4 is a schematic flow chart illustrating adjustment of a first intellectual property value evaluation result in an intellectual property value evaluation reference method based on big data according to an embodiment of the present application;
fig. 5 is a schematic flow chart illustrating a second intellectual property value evaluation result obtained in an intellectual property value evaluation reference method based on big data according to an embodiment of the present application;
fig. 6 is a schematic flow chart illustrating a process of obtaining a second intellectual property value evaluation model in an intellectual property value evaluation reference method based on big data according to an embodiment of the present application;
fig. 7 is a schematic flow chart illustrating a first intellectual property value evaluation result obtained in an intellectual property value evaluation reference method based on big data according to an embodiment of the present application;
FIG. 8 is a schematic structural diagram of an intellectual property value evaluation reference system based on big data according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device for executing a method of controlling output data according to an embodiment of the present application.
Description of reference numerals: a first constructing unit 11, a first obtaining unit 12, a second obtaining unit 13, a third obtaining unit 14, a fourth obtaining unit 15, a fifth obtaining unit 16, a first sending unit 17, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150 and a user interface 1160.
Detailed Description
In the description of the embodiments of the present invention, it should be apparent to those skilled in the art that the embodiments of the present invention can be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Thus, embodiments of the invention may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the invention may also be embodied in the form of a computer program product in one or more computer-readable storage media having computer program code embodied in the medium.
The computer-readable storage media described above may take any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a flash memory, an optical fiber, a compact disc read-only memory, an optical storage device, a magnetic storage device, or any combination thereof. In embodiments of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device, or apparatus.
Summary of the application
The method, the device and the electronic equipment are described through the flow chart and/or the block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose 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/acts specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner. Thus, the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The embodiments of the present invention will be described below with reference to the drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a method for intellectual property value evaluation reference based on big data, where the method includes:
step S100: constructing an intellectual property characteristic database through a big data platform;
specifically, the intellectual property characteristic database is constructed through a big data platform, and the big data platform is a platform with the purposes of storage, operation and display and is a massive, high-growth-rate and diversified information asset with stronger decision-making power, insight discovery power and flow optimization capability. The intellectual property feature database is a feature database comprising various intellectual property rights, such as intellectual property names, contents, technical fields, right ranges and the like, and various intellectual creations such as inventions, appearance designs, literature and artistic works, marks, names and images used in businesses can be regarded as intellectual property rights owned by a certain person or organization.
Step S200: performing feature matching according to the basic information of the first intellectual property and the intellectual property feature database to obtain first intellectual property grade features;
as shown in fig. 2, further, in which the performing feature matching according to the basic information of the first intellectual property and the intellectual property feature database to obtain the first intellectual property ranking feature, step S200 in this embodiment of the present application further includes:
step S210: constructing an intellectual property grade characteristic coordinate system according to an intellectual property characteristic database, wherein the intellectual property grade characteristic coordinate system is a multi-dimensional coordinate system;
step S220: performing regional labeling classification on the intellectual property characteristic grade coordinate system to obtain a first label classification result;
step S230: inputting the basic information of the first intellectual property into the intellectual property grade characteristic coordinate system to obtain an intellectual property grade characteristic vector;
step S240: mapping and matching are carried out according to the first label classification result and the intellectual property right grade characteristic vector to obtain a first classification result;
step S250: and determining a first intellectual property grade characteristic according to the first classification result.
Specifically, an intellectual property grade characteristic coordinate system is established, the intellectual property grade characteristic is quality grade information of the intellectual property, the higher the quality grade is, the higher the value of the intellectual property is, and the intellectual property grade characteristic coordinate system is a multidimensional coordinate system. And performing regional labeling classification on the intellectual property ranking feature coordinate system, wherein different regions correspond to different label classification results, for example, different regions correspond to different intellectual property ranking features. Inputting the basic information of the first intellectual property, such as intellectual property field, intellectual property type and the like, into the intellectual property level feature coordinate system to obtain corresponding intellectual property level feature vectors, performing mapping matching on the first label classification result according to the intellectual property level feature vectors to obtain a matched intellectual property level classification result, and determining corresponding intellectual property level features according to the first classification result. The method for vector mapping by constructing the intellectual property grade characteristic coordinate system is achieved, so that the intellectual property grade classification result is more accurate, and the technical effect that the value evaluation result of the subsequent intellectual property is more accurate is ensured.
Step S300: classifying the first intellectual property right according to a preset classification rule to obtain first type information;
step S400: obtaining a first analysis instruction, wherein the first analysis instruction is used for analyzing the content of the first intellectual property right to obtain first analysis content information;
specifically, the predetermined classification rule is a presentation form of intellectual property rights, and the first intellectual property right is classified according to the predetermined classification rule to obtain type information of the intellectual property right, such as patents, trademarks, literary works, art works and the like. And analyzing the content of the first intellectual property right according to the first analysis instruction to obtain first analysis content information, wherein the first analysis content information comprises the quality of the written content, the innovation of the content, the characteristics of main content and the like of the intellectual property right, the content is different, and the value of the intellectual property right is also different.
Step S500: inputting the first intellectual property grade characteristic, the first type information and the first analysis content information into a first intellectual property value evaluation model to obtain a first intellectual property value evaluation result;
as shown in fig. 7, further, in which the first intellectual property ranking characteristic, the first type information and the first analysis content information are input into a first intellectual property value evaluation model to obtain a first intellectual property value evaluation result, step S500 of this embodiment of the present application further includes:
step S510: taking the first intellectual property grade characteristic as first input information;
step S520: obtaining second input information, wherein the second input information comprises the first type information and the first analysis content information;
step S530: and inputting the first input information and the second input information into a first intellectual property value evaluation model, and obtaining a first output result of the first intellectual property value evaluation model, wherein the first output result comprises a first intellectual property value evaluation result.
Specifically, the first intellectual property value evaluation result is a comprehensive application value of the intellectual property, including an investment value, a market value, and the like of the intellectual property. The first intellectual property value evaluation model is a Neural network model, namely a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely interconnecting a large number of simple processing units (called neurons), reflects many basic characteristics of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (ANN), is a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And training a large amount of training data, taking the first intellectual property grade characteristic as first input information, taking the first type information and the first analysis content information as second input information, inputting the first input information and the second input information into a neural network model, and outputting a first intellectual property value evaluation result.
More specifically, the training process is essentially a supervised learning process, each group of supervised data includes the first input information, the second input information and identification information for identifying a first intellectual property value evaluation result, the first input information and the second input information are input into a neural network model, the neural network model performs continuous self-correction and adjustment according to the identification information for identifying the first intellectual property value evaluation result, and the group of supervised learning is ended until the obtained first output result is consistent with the identification information, and then the next group of data supervised learning is performed; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through supervised learning of the neural network model, the neural network model can process the input information more accurately, the output information of the first intellectual property value evaluation result is more reasonable and accurate, the intellectual property value is evaluated through model training, and the technical effect of more accurate value evaluation result is achieved by combining multiple factors.
Step S600: if the first intellectual property value evaluation result is within a preset reasonableness threshold value, obtaining a first sending instruction;
step S700: and sending the first intellectual property value evaluation result to a first user according to the first sending instruction.
Specifically, the predetermined reasonableness threshold may be determined by the average market value and innovation promotion of the intellectual property type, and if the first intellectual property value evaluation result is within the predetermined reasonableness threshold, it indicates that the intellectual property value evaluation is reasonable. And sending the first intellectual property value evaluation result to a first user for value reference according to the first sending instruction, wherein the first user is a user for evaluating the value of the intellectual property.
As shown in fig. 3, further, in which the mapping and matching are performed according to the first label classification result and the intellectual property rank feature vector to obtain a first classification result, step S240 in this embodiment of the present application further includes:
step S241: performing distance calculation on the intellectual property grade characteristic vector to obtain an Euclidean distance data set;
step S242: acquiring an intellectual property grade feature classification data set according to the Euclidean distance data set, wherein the intellectual property grade feature classification data set is the shortest k distances in the Euclidean distance data set;
step S243: and mapping and matching are carried out according to the intellectual property grade feature classification data set and the first label classification result to obtain a first classification result.
Specifically, the euclidean distance dataset is an euclidean metric distance dataset, that is, a linear distance between two points in a coordinate system, and the distance between the intellectual property rank feature vectors is calculated to obtain the euclidean distance dataset between the vectors and other intellectual property rank feature vectors. The intellectual property grade feature classification data set is the shortest k distances in the Euclidean distance data set, and the k value is a part of the Euclidean distance data set and can be set by self. And carrying out mapping matching according to the intellectual property grade feature classification data set and the label classification result to obtain a classification result corresponding to the vector. The technical effects of classifying and determining the intellectual property grade characteristics by a classification method for calculating the vector distance and ensuring the rationality and accuracy of the subsequent intellectual property value evaluation are achieved.
As shown in fig. 4, further, the embodiment of the present application further includes:
step S810: obtaining a reference intellectual property set according to the intellectual property feature database, wherein the reference intellectual property set is an intellectual property set with a predetermined association degree with the first intellectual property;
step S820: obtaining a first value influence degree according to the number of the reference intellectual property sets;
step S830: and adjusting the first intellectual property value evaluation result according to the first value influence degree.
Specifically, the predetermined association degree is a predetermined association degree of intellectual property rights, such as an association degree of intellectual property rights contents, a domain, and the like, and the reference intellectual property right set is an intellectual property right set having a predetermined association degree with the first intellectual property right. And determining a first value influence degree according to the number of the reference intellectual property sets, and adjusting the first intellectual property value evaluation result according to the first value influence degree. The more the number of the reference intellectual property sets, the higher the value influence degree, because the more the related similar intellectual property sets, the higher the value influence of the intellectual property sets, resulting in the lower the value of the intellectual property sets themselves. The value evaluation result is adjusted by combining the associated intellectual property influence factors, so that the intellectual property value evaluation result is more accurate and has the technical effect of more reference value.
As shown in fig. 5, further, the embodiment of the present application further includes:
step S910: obtaining a first application influence factor according to the protection period of the first intellectual property right and the application range of the first intellectual property right;
step S920: performing incremental learning on the first intellectual property value evaluation model according to the first application influence factor to obtain a second intellectual property value evaluation model;
step S930: and obtaining a second intellectual property value evaluation result of the first intellectual property according to the second intellectual property value evaluation model.
Specifically, the protection period of the first intellectual property right is protection time for the intellectual property right within a specified period, and different types of intellectual property rights and different protection periods are provided, for example, the protection period of a trademark right in the intellectual property right is ten years, the period of an invention patent right is twenty years, the period of a utility model patent right and an appearance design patent right is ten years, and the protection period of a copyright is generally fifty years. The application range of the first intellectual property right is the protection region range of the intellectual property right, the protection right is only effective in the specified application range, and the larger the application protection range is, the higher the value of the intellectual property right is. The first application influence factors comprise the protection period of the first intellectual property and the application range of the first intellectual property, and the first intellectual property value evaluation model is subjected to incremental learning according to the first application influence factors to obtain a second intellectual property value evaluation model, wherein the second intellectual property value evaluation model is a model subjected to incremental learning. And re-evaluating according to the second intellectual property value evaluation model to obtain a second intellectual property value evaluation result of the first intellectual property, so that the technical effects of performing incremental learning on the delay features and improving the reference performance of the evaluation result are achieved.
As shown in fig. 6, further, in the step S920 of the present application, the incrementally learning the first intellectual property value evaluation model according to the first application influencing factor to obtain a second intellectual property value evaluation model, further includes:
step S921: inputting the first application influence factor into the first intellectual property value evaluation model to obtain a first prediction value evaluation result;
step S922: obtaining first loss data by performing data loss analysis on the first predicted value evaluation result;
step S923: and inputting the first loss data into the first intellectual property value evaluation model for training to obtain the second intellectual property value evaluation model.
Specifically, the first predicted value evaluation result is a corresponding predicted evaluation result obtained by evaluating the value of the first intellectual property value evaluation model based on the first application influence factor, and since the first intellectual property value evaluation is obtained by performing data training based on the first intellectual property level feature, the first type information and the first analysis content information, the first loss data is obtained by introducing a loss function to analyze data loss, wherein the first loss data is related data intellectual loss data representing the first intellectual property value evaluation model to the first application influence factor, and then the incremental learning of the first intellectual property value evaluation model is completed based on the first loss data, and since the first intellectual property value evaluation model is obtained by connecting a plurality of neurons with one another to form a neural network, therefore, the second intellectual property value evaluation model reserves the basic functions of the first intellectual property value evaluation model through the training of loss data, and maintains the performance of continuous updating of the model, so that the updating performance of the value evaluation is improved, and the technical effect of ensuring the accuracy of the value evaluation result is achieved.
To sum up, the intellectual property value evaluation reference method and system based on big data provided by the embodiment of the application have the following technical effects:
the intellectual property characteristic database is constructed by a big data platform; performing feature matching according to the basic information of the first intellectual property and the intellectual property feature database to obtain first intellectual property grade features; classifying the first intellectual property right according to a preset classification rule to obtain first type information; obtaining a first analysis instruction, wherein the first analysis instruction is used for analyzing the content of the first intellectual property right to obtain first analysis content information; inputting the first intellectual property grade characteristic, the first type information and the first analysis content information into a first intellectual property value evaluation model to obtain a first intellectual property value evaluation result; if the first intellectual property value evaluation result is within a preset reasonableness threshold value, obtaining a first sending instruction; and sending the first intellectual property value evaluation result to a first user according to the first sending instruction. And then the intellectual property information is acquired in a big data mode, and the influence of uncertain factors on the evaluation result is reduced, so that the value evaluation result of the intellectual property is more reasonable and accurate, and the technical effect of reference value is achieved.
Example two
Based on the same inventive concept as the intellectual property value evaluation reference method based on big data in the foregoing embodiment, the present invention further provides an intellectual property value evaluation reference system based on big data, as shown in fig. 8, the system includes:
the first building unit 11 is used for building an intellectual property feature database through a big data platform;
a first obtaining unit 12, where the first obtaining unit 12 is configured to perform feature matching according to the basic information of the first intellectual property and the intellectual property feature database to obtain a first intellectual property ranking feature;
a second obtaining unit 13, where the second obtaining unit 13 is configured to classify the first intellectual property right according to a predetermined classification rule to obtain first type information;
a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain a first analysis instruction, where the first analysis instruction is configured to analyze the content of the first intellectual property right to obtain first analysis content information;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to input the first intellectual property ranking feature, the first type information, and the first analysis content information into a first intellectual property value evaluation model, and obtain a first intellectual property value evaluation result;
a fifth obtaining unit 16, wherein the fifth obtaining unit 16 is configured to obtain a first sending instruction if the first intellectual property value evaluation result is within a predetermined reasonableness threshold;
a first sending unit 17, where the first sending unit 17 is configured to send the first intellectual property value evaluation result to a first user according to the first sending instruction.
Further, the system further comprises:
the second construction unit is used for constructing an intellectual property grade characteristic coordinate system according to the intellectual property characteristic database, and the intellectual property grade characteristic coordinate system is a multi-dimensional coordinate system;
a sixth obtaining unit, configured to perform area labeling classification on the intellectual property feature level coordinate system to obtain a first label classification result;
a seventh obtaining unit, configured to input the basic information of the first intellectual property right into the intellectual property right level feature coordinate system, and obtain an intellectual property right level feature vector;
an eighth obtaining unit, configured to perform mapping matching according to the first label classification result and the intellectual property rank feature vector to obtain a first classification result;
a first determining unit for determining a first intellectual property level feature according to the first classification result.
Further, the system further comprises:
a ninth obtaining unit, configured to perform distance calculation on the intellectual property rank feature vector to obtain an euclidean distance data set;
a tenth obtaining unit, configured to obtain, according to the euclidean distance data set, an intellectual property rank feature classification data set, where the intellectual property rank feature classification data set is the shortest k distances in the euclidean distance data set;
an eleventh obtaining unit, configured to perform mapping matching according to the intellectual property level feature classification data set and the first label classification result, so as to obtain a first classification result.
Further, the system further comprises:
a twelfth obtaining unit configured to obtain a reference intellectual property set from the intellectual property feature database, the reference intellectual property set being a set of intellectual property having a predetermined degree of association with the first intellectual property;
a thirteenth obtaining unit, configured to obtain a first value influence degree according to the number of the reference intellectual property sets;
and the first adjusting unit is used for adjusting the first intellectual property value evaluation result according to the first value influence degree.
Further, the system further comprises:
a fourteenth obtaining unit, configured to obtain a first application influencing factor according to the protection period of the first intellectual property right and the application range of the first intellectual property right;
a fifteenth obtaining unit, configured to perform incremental learning on the first intellectual property value evaluation model according to the first application influence factor to obtain a second intellectual property value evaluation model;
a sixteenth obtaining unit, configured to obtain a second intellectual property value evaluation result of the first intellectual property according to the second intellectual property value evaluation model.
Further, the system further comprises:
a seventeenth obtaining unit, configured to input the first application influence factor into the first intellectual property value evaluation model, and obtain a first predicted value evaluation result;
an eighteenth obtaining unit configured to obtain first loss data by performing data loss analysis on the first predicted value evaluation result;
a nineteenth obtaining unit, configured to input the first loss data into the first intellectual property value evaluation model for training, and obtain the second intellectual property value evaluation model.
Further, the system further comprises:
a first input unit for taking the first intellectual property grade feature as first input information;
a twentieth obtaining unit configured to obtain second input information including the first type information and the first analysis content information;
a twenty-first obtaining unit configured to input the first input information and the second input information into a first intellectual property value evaluation model, and obtain a first output result of the first intellectual property value evaluation model, where the first output result includes the first intellectual property value evaluation result.
Various changes and specific examples of the intellectual property value evaluation reference method based on big data in the first embodiment of fig. 1 are also applicable to the intellectual property value evaluation reference system based on big data in the present embodiment, and through the foregoing detailed description of the intellectual property value evaluation reference method based on big data, those skilled in the art can clearly know the implementation method of the intellectual property value evaluation reference system based on big data in the present embodiment, so for the brevity of the description, detailed description is omitted here.
In addition, an embodiment of the present invention further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the processes of the method for controlling output data are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Exemplary electronic device
Specifically, referring to fig. 9, an embodiment of the present invention further provides an electronic device, which includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program, when executed by the processor 1120, implementing the various processes of the method embodiments of controlling output data described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In embodiments of the invention in which a bus architecture (represented by bus 1110) is used, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, the embodiments of the present invention will not be further described.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in embodiments of the invention, the memory 1150 may further include memory located remotely with respect to the processor 1120, which may be coupled to a server via a network. One or more portions of the above-described network may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and a combination of two or more of the above. For example, the cellular telephone network and the wireless network may be a global system for mobile communications, code division multiple access, global microwave interconnect access, general packet radio service, wideband code division multiple access, long term evolution, LTE frequency division duplex, LTE time division duplex, long term evolution-advanced, universal mobile communications, enhanced mobile broadband, mass machine type communications, ultra-reliable low latency communications, etc.
It is to be understood that the memory 1150 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in the embodiments of the invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the present invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various system programs such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media player, browser, used to realize various application services. A program implementing a method of an embodiment of the invention may be included in application program 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements each process of the above method for controlling output data, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The above description is only a specific implementation of the embodiments of the present invention, but the scope of the embodiments of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present invention, and all such changes or substitutions should be covered by the scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An intellectual property value evaluation reference method based on big data, wherein the method comprises the following steps:
constructing an intellectual property characteristic database through a big data platform;
performing feature matching according to the basic information of the first intellectual property and the intellectual property feature database to obtain first intellectual property grade features;
classifying the first intellectual property right according to a preset classification rule to obtain first type information;
obtaining a first analysis instruction, wherein the first analysis instruction is used for analyzing the content of the first intellectual property right to obtain first analysis content information;
inputting the first intellectual property grade characteristic, the first type information and the first analysis content information into a first intellectual property value evaluation model to obtain a first intellectual property value evaluation result;
if the first intellectual property value evaluation result is within a preset reasonableness threshold value, obtaining a first sending instruction;
and sending the first intellectual property value evaluation result to a first user according to the first sending instruction.
2. The method of claim 1, wherein said performing feature matching based on the essential information of the first intellectual property and the intellectual property feature database to obtain the first intellectual property level feature comprises:
constructing an intellectual property grade characteristic coordinate system according to an intellectual property characteristic database, wherein the intellectual property grade characteristic coordinate system is a multi-dimensional coordinate system;
performing regional labeling classification on the intellectual property characteristic grade coordinate system to obtain a first label classification result;
inputting the basic information of the first intellectual property into the intellectual property grade characteristic coordinate system to obtain an intellectual property grade characteristic vector;
mapping and matching are carried out according to the first label classification result and the intellectual property right grade characteristic vector to obtain a first classification result;
and determining a first intellectual property grade characteristic according to the first classification result.
3. The method of claim 2, wherein the performing mapping matching according to the first label classification result and the intellectual property rank feature vector to obtain a first classification result comprises:
performing distance calculation on the intellectual property grade characteristic vector to obtain an Euclidean distance data set;
acquiring an intellectual property grade feature classification data set according to the Euclidean distance data set, wherein the intellectual property grade feature classification data set is the shortest k distances in the Euclidean distance data set;
and mapping and matching are carried out according to the intellectual property grade feature classification data set and the first label classification result to obtain a first classification result.
4. The method of claim 1, wherein the method comprises:
obtaining a reference intellectual property set according to the intellectual property feature database, wherein the reference intellectual property set is an intellectual property set with a predetermined association degree with the first intellectual property;
obtaining a first value influence degree according to the number of the reference intellectual property sets;
and adjusting the first intellectual property value evaluation result according to the first value influence degree.
5. The method of claim 1, wherein the method comprises:
obtaining a first application influence factor according to the protection period of the first intellectual property right and the application range of the first intellectual property right;
performing incremental learning on the first intellectual property value evaluation model according to the first application influence factor to obtain a second intellectual property value evaluation model;
and obtaining a second intellectual property value evaluation result of the first intellectual property according to the second intellectual property value evaluation model.
6. The method of claim 1, wherein the incrementally learning the first intellectual property value assessment model based on the first application influencing factor to obtain a second intellectual property value assessment model comprises:
inputting the first application influence factor into the first intellectual property value evaluation model to obtain a first prediction value evaluation result;
obtaining first loss data by performing data loss analysis on the first predicted value evaluation result;
and inputting the first loss data into the first intellectual property value evaluation model for training to obtain the second intellectual property value evaluation model.
7. The method of claim 1, wherein said inputting the first intellectual property rating characteristic, the first type information and the first analyzed content information into a first intellectual property value evaluation model to obtain a first intellectual property value evaluation result comprises:
taking the first intellectual property grade characteristic as first input information;
obtaining second input information, wherein the second input information comprises the first type information and the first analysis content information;
and inputting the first input information and the second input information into a first intellectual property value evaluation model, and obtaining a first output result of the first intellectual property value evaluation model, wherein the first output result comprises a first intellectual property value evaluation result.
8. An intellectual property value evaluation reference system based on big data, wherein the system comprises:
the system comprises a first construction unit, a second construction unit and a third construction unit, wherein the first construction unit is used for constructing an intellectual property characteristic database through a big data platform;
the first acquisition unit is used for carrying out feature matching according to the basic information of the first intellectual property and the intellectual property feature database to acquire a first intellectual property grade feature;
a second obtaining unit, configured to classify the first intellectual property right according to a predetermined classification rule, and obtain first type information;
a third obtaining unit, configured to obtain a first analysis instruction, where the first analysis instruction is configured to analyze content of the first intellectual property right to obtain first analysis content information;
a fourth obtaining unit, configured to input the first intellectual property ranking feature, the first type information, and the first analysis content information into a first intellectual property value evaluation model, and obtain a first intellectual property value evaluation result;
a fifth obtaining unit, configured to obtain a first sending instruction if the first intellectual property value evaluation result is within a predetermined reasonableness threshold;
and the first sending unit is used for sending the first intellectual property value evaluation result to a first user according to the first sending instruction.
9. An intellectual property value assessment reference electronic device based on big data, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected via the bus, characterized in that the computer program realizes the steps in the method of outputting data according to any of claims 1-7 when executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of outputting data according to any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113988666A (en) * | 2021-11-01 | 2022-01-28 | 常州天晟紫金自动化设备有限公司 | Intelligent quantitative packaging method and system for organic silicon rubber compound |
| CN115170353A (en) * | 2022-07-12 | 2022-10-11 | 朗动信息咨询（上海）有限公司 | Intellectual property achievement transformation analysis and evaluation system based on big data processing |
-
2021
- 2021-07-29 CN CN202110865640.9A patent/CN113421174A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113988666A (en) * | 2021-11-01 | 2022-01-28 | 常州天晟紫金自动化设备有限公司 | Intelligent quantitative packaging method and system for organic silicon rubber compound |
| CN113988666B (en) * | 2021-11-01 | 2022-08-09 | 常州天晟紫金自动化设备有限公司 | Intelligent quantitative packaging method and system for organic silicon rubber compound |
| CN115170353A (en) * | 2022-07-12 | 2022-10-11 | 朗动信息咨询（上海）有限公司 | Intellectual property achievement transformation analysis and evaluation system based on big data processing |
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