CN116187803A - Enterprise innovation capability evaluation system based on big data - Google Patents
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Abstract
The utility model belongs to the technical field of big data application, in particular relates to an enterprise innovation capability evaluation system based on big data, and aims to solve the problems that an existing enterprise innovation capability evaluation system is insufficient in calculation and storage resources and low in evaluation accuracy. The system of the utility model comprises: one or more clients, a first server, a second server; the client is in communication connection with the first server and the second server; the first server is configured to acquire enterprise basic information data and intellectual property work data of all the existing enterprises of different industries, and store the enterprise basic information data and the intellectual property work data in an associated mode and update the enterprise basic information data and the intellectual property work data periodically; a second server configured to request intellectual property work data from the first server and perform preprocessing; the client is configured to acquire a first enterprise and a first industry and calculate innovation ability indexes of enterprises to be subjected to innovation ability evaluation. The utility model improves the storage and calculation capacity of the system, realizes the efficient real-time processing of data and improves the evaluation accuracy.
Description
Technical Field
The utility model belongs to the technical field of big data application, and particularly relates to an enterprise innovation capability evaluation system based on big data.
Background
The innovation is a source power for sustainable development of enterprises, is a gear for promoting economic high-quality development and promoting human progress, and is a new wave of the age formed by current mass entrepreneurs and general innovations along with the progress of the age, and the innovation results are tired and innovative.
The current innovation ability evaluation system generally relates to internal data indexes related to enterprises such as enterprise research personnel, sales and the like, and can evaluate the innovation ability of the enterprise relatively deeply, but the evaluation is used for primarily screening a large number of enterprise sea selection scenes (such as a potential enterprise list defined by a campus recruitment, an enterprise investigation list selected by an investment institution in a certain industry and the like) of excellent enterprises from the whole industry, so that obvious shortfalls exist. There are several general problems:
1) The participation of the enterprise party is needed. The evaluation system is used for deeply evaluating the innovation capability of enterprises, can be performed by matching the enterprises with reporting operation related data, is suitable for the autonomous reporting scene of the enterprises, and is limited by insufficient data in the scene of preliminarily selecting high-quality enterprises from mass enterprises or comprehensively grading and sequencing the whole enterprises (such as a supply chain selector);
2) The pertinence is insufficient. The evaluation systems consider universality, are suitable for all industries, have different characteristics in different industries, have different innovation yields, and have the potential to influence the accuracy of evaluation results if the innovation capacity of enterprises is measured by using an index system with the same weight;
3) The weight setting is biased. The weight of the evaluation index is mostly drawn by adopting experts, the expert value is different, the evaluation system depends on factors such as the number of people and the hierarchy of the participated experts, and the expert's view can also change along with the change of time and reading, in a word, the weight setting artificial factors are more subjective, and the fairness, the rationality and the accuracy of the result are likely to be influenced;
4) Low efficiency and poor real-time performance. The existing evaluation system mostly adopts manual calculation or a traditional centralized processing mode; in the face of massive application scenes, a manual calculation mode is adopted, so that a large amount of manpower is consumed, the efficiency is low, and the instantaneity is poor; the traditional centralized processing mode (namely, a central server performs data storage, calculation and the like) cannot efficiently process the data requests of multiple clients, and the problems of insufficient bandwidth, large network transmission delay, high data transmission cost, limited resources such as storage and the like, low calculation efficiency and the like are faced.
Based on the method, the enterprise innovation capability evaluation system based on big data is provided.
Disclosure of Invention
In order to solve the above problems in the prior art, namely, in order to solve the problem that the existing enterprise innovation ability evaluation system is insufficient in calculation and storage resources through traditional manual or centralized processing modes, and cannot realize efficient real-time processing of data, on the other hand, because of insufficient data volume and strong human subjectivity, the evaluation accuracy is low, and the innovation ability of an enterprise cannot be rapidly screened, the utility model provides an enterprise innovation ability evaluation system based on big data, which comprises: one or more clients, a first server, a second server; the client is in communication connection with the first server and the second server;
the first server is configured to acquire enterprise basic information data and intellectual property work data of all the existing enterprises of different industries, and store the enterprise basic information data and the intellectual property work data in an associated mode and update the enterprise basic information data and the intellectual property work data periodically;
the second server is configured to request intellectual property work data from the first server and perform preprocessing; the system is further configured to update the preprocessed data when the first server is detected to update the data; the preprocessing comprises the steps of calculating weight coefficients of intellectual property works of different types in each industry and calculating the passing rate of the intellectual property works of different types in all industries;
the client is configured to acquire a first enterprise and a first industry; respectively requesting intellectual property work data of the first enterprise from the first server, requesting the passing rate and weight coefficients of different types of intellectual property works corresponding to the first enterprise from the second server, calculating and displaying innovation ability indexes of the first enterprise; the first enterprise is an enterprise to be subjected to innovation capability evaluation; the first industry is an industry where the first enterprise is located.
In some preferred embodiments, the second server is further configured to perform data update based on the last update data of the first server according to a set update time when the time interval of detecting the data update of the first server is less than a set first time interval threshold or the data update is performed multiple times within the first time interval threshold.
In some preferred embodiments, the weighting coefficients for different types of intellectual property works for each industry are calculated by:
based on the intellectual property work data requested by the second server to the first server, counting the number of effective intellectual property works of all the continuous industrial enterprises of each industry; intellectual property works include utility model patents, design patents, software works and work works;
calculating the median of the number of effective intellectual property works in each industry, and converting the median into a quantile;
processing the converted quantiles and calculating the ratio of the quantiles processed by the intellectual property works of different types in each industry, and further constructing an importance comparison matrix based on the ratio;
and calculating the weight coefficients of the intellectual property works of different types in each industry by using an analytic hierarchy process based on the importance comparison matrix.
In some preferred embodiments, the median is converted to a quantile in combination with a set percentage bit by: the median is converted into quantiles by combining the set percentage bits, and the method comprises the following steps: ascending and sorting the median of the number of intellectual property works of the same type in all industries, and taking the sorted queue as a first queue; and taking the ratio of the number of the positions of each industry in the first queue to the total number of all industries as the quantile corresponding to the intellectual property work of the current type of each industry.
In some preferred embodiments, the converted quantiles are processed by:
Q′ ix ＝Q ix +k
wherein Q is ix Representing the untreated quantile, Q' ix The number of quantiles after the processing is represented, and k represents a set constant.
In some preferred embodiments, the importance comparison matrix is:
wherein A is x Representing an importance comparison matrix corresponding to the first industry x, Q' ix 、Q′ ux 、Q′ dx 、Q′ sx 、Q′ wx The number of digits after processing corresponding to the first industrial x patent, the utility model patent, the design patent, the software work and the work are respectively shown.
In some preferred embodiments, the pass rates of different types of intellectual property works for all industries are calculated by: acquiring the total amount of effective intellectual property works and the total amount of ineffective intellectual property works of the same type in all industries, and taking the ratio of the total amount of the effective intellectual property works of the same type in all industries to the first total amount as the passing rate of the intellectual property works of the current type; the first aggregate is a sum of the aggregate of valid intellectual property works and the aggregate of invalid intellectual property works of the same type for all industries.
In some preferred embodiments, the method for calculating the innovation ability index of the first enterprise includes the steps of respectively requesting the intellectual property work data of the first enterprise from the first server, requesting the passing rate and the weight coefficient of different types of intellectual property works corresponding to the first enterprise from the second server, and:
counting a first number of different types of intellectual property works of the first enterprise based on the intellectual property work data of the first enterprise; the first quantity is the quantity for which the intellectual property work is in effect;
matching the first quantity according to a preset intellectual property work sub-term score rule table to obtain sub-term scores of different types of intellectual property works of the first enterprise;
calculating innovation capability indexes of the first enterprise based on the passing rate and weight coefficients of the intellectual property works of the different types corresponding to the first enterprise by combining the item scores and the passing rate of the intellectual property works of the different types of the first enterprise;
the intellectual property work sub-item score rule table is a mapping relation between a preset sub-item score and a preset interval of numerical values of percentage bits corresponding to the first quantity of the different types of intellectual property works;
the method comprises the steps of sequencing the number of effective intellectual property works of the same type of all the existing enterprises in each industry, and taking the sequenced queue as a second queue; and selecting the numerical value of the effective intellectual property works of the current type of enterprises positioned on the set percentage in the second queue as the numerical value of the percentage.
In some preferred embodiments, the matching is performed on the first quantity to obtain the score of the sub-items of the different types of intellectual property works of the first enterprise, and the method comprises the following steps:
wherein S is pxy Score for p-type intellectual property works in first business x and first business y xy Representing the number of P-type valid intellectual property works in a first business x, a first business y, P x1 、P x2 、P x3 、P x4 、P x5 、P x6 、P x7 、P x8 、P x9 And the numerical value corresponding to 9 percentage bits which are set for the p-type intellectual property works in the first industry x is represented.
In some preferred embodiments, the innovation ability index of the first enterprise is calculated by:
wherein the IC y Representing innovation ability index, w, corresponding to first enterprise y x ＝(w ix ，w ux ，w dx ，w sx ，w wx ) T ，w ix 、w ux 、w dx 、w sx 、w wx Respectively representing the weight coefficients corresponding to the utility model patent, the design patent, the software work and the work corresponding to the first industry, T represents the transposition and R i 、R u 、R d Respectively representing the passing rate of the utility model patents, the utility model patents and the design patents of all industries, S ixy 、S uxy 、S dxy 、S sxy 、S wxy The method respectively represents the score of the corresponding items of the utility model patent, the appearance design patent, the software work and the work corresponding to the first industry x.
The utility model has the beneficial effects that:
the utility model improves the storage and calculation capacity of the system, realizes the high-efficiency real-time processing of data, improves the evaluation accuracy, and can rapidly screen the innovation capacity of enterprises.
1) Through the distributed storage and calculation of multiple clients and multiple servers, on one hand, the storage pressure of a centralized processing mode can be reduced, on the other hand, the calculation process of partial data is lowered to the clients, the calculation capacity of the multiple clients is used, the overall calculation capacity of the system is further improved, and the processing efficiency of the data is improved.
2) The utility model adopts big data machine learning technology, can regularly and automatically grab, adjust and update data to adapt to the current situation, and has higher timeliness; based on the collected big data, the data is processed and calculated by an analytic hierarchy process, so that the accuracy of innovation ability index calculation can be improved, the evaluation accuracy is further improved, and the innovation ability of an enterprise can be reflected more objectively and efficiently.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings.
FIG. 1 is a schematic diagram of a framework of a big data based enterprise innovation ability assessment system in accordance with one embodiment of the present utility model;
FIG. 2 is a schematic diagram illustrating a first server according to an embodiment of the present utility model;
FIG. 3 is a schematic diagram of a client framework in accordance with one embodiment of the present utility model;
FIG. 4 is a schematic diagram of a second server framework in accordance with one embodiment of the present utility model;
fig. 5 is a schematic diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present utility model.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present utility model more apparent, the technical solutions of the embodiments of the present utility model will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present utility model, and it is apparent that the described embodiments are some embodiments of the present utility model, but not all embodiments of the present utility model. All other embodiments, which can be made by those skilled in the art based on the embodiments of the utility model without making any inventive effort, are intended to be within the scope of the utility model.
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the utility model and are not limiting of the utility model. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
The first embodiment of the utility model is an enterprise innovation ability evaluation system based on big data, as shown in fig. 1, the system comprises: one or more clients, a first server, a second server; the client is in communication connection with the first server and the second server;
the first server is configured to acquire enterprise basic information data and intellectual property work data of all the existing enterprises of different industries, and store the enterprise basic information data and the intellectual property work data in an associated mode and update the enterprise basic information data and the intellectual property work data periodically;
the second server is configured to request intellectual property work data from the first server and perform preprocessing; the system is further configured to update the preprocessed data when the first server is detected to update the data; the preprocessing comprises the steps of calculating weight coefficients of intellectual property works of different types in each industry and calculating the passing rate of the intellectual property works of different types in all industries;
the client is configured to acquire a first enterprise and a first industry; respectively requesting intellectual property work data of the first enterprise from the first server, requesting the passing rate and weight coefficients of different types of intellectual property works corresponding to the first enterprise from the second server, calculating and displaying innovation ability indexes of the first enterprise; the first enterprise is an enterprise to be subjected to innovation capability evaluation; the first industry is an industry where the first enterprise is located.
In order to more clearly describe the enterprise innovation ability evaluation system based on big data of the present utility model, each module in one embodiment of the system of the present utility model is described in detail below with reference to the accompanying drawings.
The enterprise innovation capability evaluation system based on big data comprises one or more clients, a first server and a second server; the client is in communication connection with the first server and the second server;
the first server is configured to acquire enterprise basic information data and intellectual property work data of all the existing enterprises of different industries, and store the enterprise basic information data and the intellectual property work data in an associated mode and update the enterprise basic information data and the intellectual property work data periodically;
in this embodiment, the first server is a data storage server, including a big data storage module and a big data update module, as shown in fig. 2;
the big data storage module is configured to acquire enterprise basic information data and intellectual property work data of all resident enterprises of different industries, correlate the enterprise basic information data and the intellectual property work data, and store the correlated data; the enterprise basic information comprises names, addresses, registration types, approval establishment institutions, organization codes, license numbers, operating time, operating range, industries, legal representatives and persistence states; intellectual property works, including patent of utility model, design of appearance patent, software work and work.
In the storage, the utility model preferably performs data collection according to industries where enterprises are located, for example, the number of the utility model patents effectively authorized by each resident enterprise in a certain industry x is expressed as follows: i.e x1 ，i x2 ，i x3 ,...wherein i. > x1 Representing the number of patent numbers of the utility model, i, of the effective grant of industry x 1 st resident enterprise x2 ，i x3 ,..
The basic information data and intellectual property work data of enterprises are preferably obtained by a method of butting a data system of the enterprises or using information acquisition equipment to grab big data. In other embodiments, the manner in which the data is obtained and updated may be selected based on the actual selection.
And the big data updating module is configured to update the basic information data of the enterprises and the intellectual property work data which are stored in a correlated mode at regular intervals. In other embodiments, the update may also be based on the time of the data acquisition.
The second server is configured to request intellectual property work data from the first server and perform preprocessing; the system is further configured to update the preprocessed data when the first server is detected to update the data; the preprocessing comprises the steps of calculating weight coefficients of intellectual property works of different types in each industry and calculating the passing rate of the intellectual property works of different types in all industries;
in this embodiment, the second server is a data preprocessing server, specifically, requests intellectual property work data from the first server to perform preprocessing; the preprocessing comprises the steps of constructing an importance comparison matrix, calculating weight coefficients of intellectual property works of different types in each industry based on the importance comparison matrix, and finally calculating the passing rate of the intellectual property works of different types in all industries; and the data updating device is further configured to update the preprocessed data when the first server is detected to update the data. Namely, the second server comprises an industry big data calculation module, an index weight calculation module, an index difficulty coefficient calculation module and a preprocessing data updating module, as shown in fig. 4.
The industry big data calculation module is configured to calculate the median of the number of effective intellectual property works in each industry and convert the median into a quantile; processing the converted quantiles and calculating the ratio of the quantiles processed by the intellectual property works of different types in each industry, and further constructing an importance comparison matrix based on the ratio; the method comprises the following steps:
based on the intellectual property work data requested by the second server to the first server, counting the number of effective intellectual property works of all the continuous industrial enterprises of each industry;
and calculating the median of the number of the effective intellectual property works in each industry, namely sorting the number of the effective intellectual property works in each industry, and taking the number in the middle after sorting as the median. If the number of effective intellectual property works is even, the average of the two numbers in the middle is taken as the median.
Ascending and sorting the median of the number of intellectual property works of the same type in all industries, and taking the sorted queue as a first queue; taking the ratio of the ranking number of the current industry (namely the number of positions in the first queue) to the total number of all industries as the quantile number of the intellectual property works of the type of the current industry; the number of effective intellectual property works of the same type of all the continuous industrial enterprises in each industry is ordered, and the ordered queue is used as a second queue; selecting the value of the effective intellectual property works of the current type of enterprises positioned on the set percentage in the second queue as the value of the percentage; in the present utility model, the set percentage is preferably set to 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%.
For example, the number median of the patents of the utility model which are valid for the respective industries (i.e. valid as described below) are respectively M i1 ，M i2 ，M i3 ，......，M in M is set to i1 ，M i2 ，M i3 ，......，M in Performing ascending sorting, and if n is 100 and the ranking number corresponding to the median of the current industry is 1, then the ranking number of the intellectual property works of the type in the current industry is 1%; then if the A industry has 100 continuous industrial enterprises, the number of the effective utility model patents of the 100 enterprises is ranked, and the number of the effective utility model patents of the enterprises positioned at the 10 th position, namely the number on 10% of the positions, is sequentially obtained, and the number on 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of the positions is sequentially obtained, so that the number of the percentage positions of the effective utility model patents of the A industry can be obtained. In the present utility model, the numerical value of each percentage bit of the patent of the utility model which is effectively granted by industry x is expressed as I x1 ，I x2 ，I x3 ，I x4 ，I x5 ，I x6 ，I x7 ，I x8 ，I x9 。
And processing the converted quantiles and calculating the ratio of the quantiles processed by the intellectual property works of different types in each industry, thereby constructing an importance comparison matrix based on the ratio.
The method for processing the converted quantile comprises the following steps:
Q′ ix ＝Q ix +k (1)
wherein Q is ix Representing the untreated quantile, Q' ix The number of the quantiles after treatment is represented by i, the utility model patent is represented by x, the industry (the industry where the enterprise to be subjected to innovation capability evaluation is located in the utility model) is represented by k, the set constant is represented by k, k is preferably set to be 0.125-1 in the utility model, and the value range of k is calculated by the value range 1-9 of the importance comparison matrix of the chromatographic analysis AHP.
The importance comparison matrix is as follows:
wherein A is x Representing an importance comparison matrix corresponding to the first industry x, Q' ix 、Q′ ux 、Q′ dx 、Q′ sx 、Q′ wx The number of digits after processing corresponding to the first industrial x patent, the utility model patent, the design patent, the software work and the work are respectively shown.
The index weight calculation module is configured to calculate weight coefficients of intellectual property works of different types in each industry by a hierarchical analysis method based on the constructed importance comparison matrix, and the weight coefficients are expressed as w x ＝(w ix ，w ux ，w dx ，w sx ，w wx ) T Wherein w is ix 、w ux 、w dx 、w sx 、w wx The utility model patent, the design patent, the software work and the work corresponding to the first industry x are respectively represented, and T represents the transposition.
The index difficulty coefficient calculating module is configured to calculate the passing rate of intellectual property works of different types in all industries;
the method for calculating the passing rate of the intellectual property works of different types in all industries comprises the following steps:
acquiring the total amount of effective intellectual property works and the total amount of ineffective intellectual property works of the same type in all industries, and taking the ratio of the total amount of the effective intellectual property works of the same type in all industries to the first total amount as the passing rate of the intellectual property works of the current type; the first aggregate is a sum of the aggregate of valid intellectual property works and the aggregate of invalid intellectual property works of the same type for all industries. For example, the total amount of patent of the utility model which is effectively granted by all enterprises is calculated as C 0 Total amount C of invalid (overrule) utility model patent 1 The utility model patent's rate of authority (i.e. passing rate)
Similarly, the utility model patent authorization rate R is obtained by calculating based on a calculation formula of the utility model patent authorization rate u Rate of authority R of design patent d Since the copyright is registered, the pass rate R of the software copyright and the work copyright s 、R w And is designated 1.
The second server is further provided with an update triggering mechanism, namely the preprocessed data updating module is configured to update preprocessed data when the first server is detected to update the data;
in addition, when the time interval of the data updating of the first server is detected to be smaller than a set first time interval threshold or the data updating is carried out for a plurality of times within the first time interval threshold, the second server carries out the data updating based on the last updated data of the first server according to the set updating time. For example, the update time interval threshold set by the second server is 1h, after the last update, the first server is detected to update data or update data for 3 or 4 times within 1h, so as to save computing resources and storage resources, the second server performs data update based on the last updated data (i.e. the latest updated data) of the first server according to the set update time 1h to a corresponding time point.
The client is configured to acquire a first enterprise and a first industry; respectively requesting intellectual property work data of the first enterprise from the first server, requesting the passing rate and weight coefficients of different types of intellectual property works corresponding to the first enterprise from the second server, calculating and displaying innovation ability indexes of the first enterprise; the first enterprise is an enterprise to be subjected to innovation capability evaluation; the first industry is an industry where the first enterprise is located.
In this embodiment, the client includes an input information acquisition module, an innovation ability index calculation module, and a display module, as shown in fig. 3;
the input information acquisition module is configured to acquire an enterprise to be subjected to innovation capability evaluation and an industry where the enterprise is located, wherein the enterprise to be subjected to innovation capability evaluation is taken as a first enterprise, and the industry where the first enterprise is located is taken as a first industry;
the innovation ability index calculating module is configured to respectively request the intellectual property work data of the first enterprise from the first server, request the passing rate from the second server and the weight coefficients of different types of intellectual property works corresponding to the first enterprise, and calculate the innovation ability index of the first enterprise;
in order to further enhance the computing power of the system, the computing of the innovation ability index to be evaluated for innovation ability is put down to each client (in other embodiments, in order to further enhance the computing efficiency, the computing process of the innovation ability index of the client may be distributed to each edge node (or referred to as an edge server), that is, in other embodiments, the client is used for obtaining the enterprise to be evaluated for innovation ability and the industry where the enterprise is located, and sending the obtained enterprise to the edge server, and the edge server is configured to request the intellectual property work data of the first enterprise from the first server, request the passing rate from the second server and the weight coefficient of the different types of intellectual property works corresponding to the first enterprise, and send the obtained innovation ability index back to the client. The method comprises the following steps:
each client side requesting the innovation ability evaluation of the enterprise requests the intellectual property work data of the first enterprise from the first server, requests the passing rate and the weight coefficients of different types of intellectual property works corresponding to the first enterprise from the second server according to the enterprise to be subjected to the innovation ability evaluation and the industry where the enterprise is located, and then calculates the innovation ability index of the first enterprise, wherein the process comprises the following steps:
counting a first number of different types of intellectual property works of the first enterprise based on the intellectual property work data of the first enterprise; the first quantity is the quantity for which the intellectual property work is in effect;
matching the first quantity according to a preset intellectual property work sub-term score rule table to obtain sub-term scores of different types of intellectual property works of the first enterprise;
the intellectual property work sub-item score rule table is a mapping relation between a preset sub-item score and a preset interval of numerical values of percentage sub-bits corresponding to the first quantity of the intellectual property works of different types.
Matching the first quantity to obtain the sub-item scores of the intellectual property works of different types of the first enterprise, wherein the method comprises the following steps:
wherein S is pxy Score for p-type intellectual property works in first business x and first business y xy Representing the number of P-type valid intellectual property works in a first business x, a first business y, P x1 、P x2 、P x3 、P x4 、P x5 、P x6 、P x7 、P x8 、P x9 And a numerical value representing the percentage bit corresponding to the 9 percentage bits set by the p-type intellectual property works in the first industry x.
For example, the number of effective utility model patents corresponding to enterprise y to be subjected to innovation ability evaluation in the industry x of the enterprise to be subjected to innovation ability evaluation is i xy The utility model patent obtains the item score S ixy The calculation process is as follows:
calculating innovation ability indexes of the first enterprise based on the passing rate and weight coefficients of the intellectual property works of the different types corresponding to the first enterprise and combining the sub-item scores of the intellectual property works of the different types of the first enterprise;
wherein the IC y Representing innovation ability index, w, corresponding to first enterprise y x
(w ix ，w ux ，w dx ，w sx ，w wx ) T ，w ix 、w ux 、w dx 、w sx 、w wx Respectively representing the weight coefficients corresponding to the utility model patent, the design patent, the software work and the work corresponding to the first industry, S ixy 、S uxy 、S dxy 、S sxy 、S wxy The method respectively represents the score of the corresponding items of the utility model patent, the appearance design patent, the software work and the work corresponding to the first industry x. Innovative power index lies in interval [0,1 ]]The index may be amplified according to the use condition.
The display module is configured to display the acquired innovation ability index in an interface.
In summary, in view of the value orientation and the different working modes of each industry, the intellectual property outputs of different industries in each category are different, for example, the software industry pays more attention to the software copyright and the utility model patent, and the corresponding outputs are also generally more, and the cultural art industry pays more attention to the work copyright. When the innovation capability of enterprises is evaluated, the characteristics of each industry are fully considered, the full-quantity analysis is firstly carried out on each industry in a big data mode, the value orientations of each industry are transversely compared, the importance of each category of intellectual property of each industry is analyzed, the weight of each category of intellectual property of each industry is calculated, and then the enterprise evaluation in the industry is carried out, so that the innovation capability of the enterprises is obtained. The method can avoid influencing the evaluation result due to inconsistent expert levels, and can reflect the innovation capability of enterprises more objectively and efficiently. And the acquisition difficulty of the intellectual property is fully considered, and the intellectual property with different difficulty is given different weights by calculating the passing rate/authorized rate and the like of different intellectual property.
It should be noted that, in the enterprise innovation ability evaluation system based on big data provided in the foregoing embodiment, only the division of the foregoing functional modules is illustrated, in practical application, the foregoing functional allocation may be performed by different functional modules according to needs, that is, the modules or steps in the foregoing embodiment of the present utility model are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps related to the embodiments of the present utility model are merely for distinguishing the respective modules or steps, and are not to be construed as unduly limiting the present utility model.
Referring now to FIG. 5, there is shown a schematic diagram of a computer system suitable for use in implementing a server of an embodiment of the system of the present application. The server illustrated in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments herein.
As shown in fig. 5, the computer system includes a central processing unit (CPU, central Processing Unit) 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a random access Memory (RAM, random Access Memory) 503. In the RAM503, various programs and data required for the system operation are also stored. The CPU501, ROM502, and RAM503 are connected to each other through a bus 504. An Input/Output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a liquid crystal display (LCD, liquid Crystal Display), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN (local area network ) card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 501. It should be noted that the computer readable medium described in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, 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, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The terms "first," "second," and the like, are used for distinguishing between similar objects and not for describing a particular sequential or chronological order.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus/apparatus.
Thus far, the technical solution of the present utility model has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present utility model is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present utility model, and such modifications and substitutions will fall within the scope of the present utility model.
Claims (10)
1. An enterprise innovation capability evaluation system based on big data is characterized by comprising one or more clients, a first server and a second server; the client is in communication connection with the first server and the second server;
the first server is configured to acquire enterprise basic information data and intellectual property work data of all the existing enterprises of different industries, and store the enterprise basic information data and the intellectual property work data in an associated mode and update the enterprise basic information data and the intellectual property work data periodically;
the second server is configured to request intellectual property work data from the first server and perform preprocessing; the system is further configured to update the preprocessed data when the first server is detected to update the data; the preprocessing comprises the steps of calculating weight coefficients of intellectual property works of different types in each industry and calculating the passing rate of the intellectual property works of different types in all industries;
the client is configured to acquire a first enterprise and a first industry; respectively requesting intellectual property work data of the first enterprise from the first server, requesting the passing rate and weight coefficients of different types of intellectual property works corresponding to the first enterprise from the second server, calculating and displaying innovation ability indexes of the first enterprise; the first enterprise is an enterprise to be subjected to innovation capability evaluation; the first industry is an industry where the first enterprise is located.
2. The big data based enterprise innovation ability assessment system of claim 1, wherein the second server is further configured to perform data updates based on last update data of the first server according to a set update time when a time interval of detecting the first server data updates is less than a set first time interval threshold or a plurality of times of data updates within the first time interval threshold.
3. The big data based enterprise innovation ability evaluation system of claim 1, wherein the method for calculating the weight coefficient of the intellectual property works of different types in each industry is as follows:
based on the intellectual property work data requested by the second server to the first server, counting the number of effective intellectual property works of all the continuous industrial enterprises of each industry; intellectual property works include utility model patents, design patents, software works and work works;
calculating the median of the number of effective intellectual property works in each industry, and converting the median into a quantile;
processing the converted quantiles and calculating the ratio of the quantiles processed by the intellectual property works of different types in each industry, and further constructing an importance comparison matrix based on the ratio;
and calculating the weight coefficients of the intellectual property works of different types in each industry by using an analytic hierarchy process based on the importance comparison matrix.
4. The big data based business innovation ability assessment system of claim 3, wherein the median is converted to a quantile by the method of: ascending and sorting the median of the number of intellectual property works of the same type in all industries, and taking the sorted queue as a first queue; and taking the ratio of the number of the positions of each industry in the first queue to the total number of all industries as the quantile corresponding to the intellectual property work of the current type of each industry.
5. The enterprise innovation ability evaluation system based on big data of claim 3, wherein the method for processing the converted quantiles is as follows:
Q′ ix ＝Q ix +k
wherein Q is ix Representing the untreated quantile, Q' ix The number of quantiles after the processing is represented, and k represents a set constant.
6. The big data based business innovation ability assessment system of claim 5, wherein the importance comparison matrix is:
wherein A is x Representing an importance comparison matrix corresponding to the first industry x, Q' ix 、Q′ ux 、Q′ dx 、Q′ sx 、Q′ wx The number of digits after processing corresponding to the first industrial x patent, the utility model patent, the design patent, the software work and the work are respectively shown.
7. The big data based enterprise innovation ability evaluation system of claim 1, wherein the passing rate of the intellectual property works of different types in all industries is calculated by the following method: acquiring the total amount of effective intellectual property works and the total amount of ineffective intellectual property works of the same type in all industries, and taking the ratio of the total amount of the effective intellectual property works of the same type in all industries to the first total amount as the passing rate of the intellectual property works of the current type; the first aggregate is a sum of the aggregate of valid intellectual property works and the aggregate of invalid intellectual property works of the same type for all industries.
8. The big data based business innovation ability evaluation system of claim 1, wherein the innovation ability index of the first business is calculated by requesting the intellectual property work data of the first business from the first server, requesting the passing rate from the second server, and the weight coefficients of different types of intellectual property works corresponding to the first business, respectively, by:
counting a first number of different types of intellectual property works of the first enterprise based on the intellectual property work data of the first enterprise; the first quantity is the quantity for which the intellectual property work is in effect;
matching the first quantity according to a preset intellectual property work sub-term score rule table to obtain sub-term scores of different types of intellectual property works of the first enterprise;
calculating innovation ability indexes of the first enterprise based on the passing rate and weight coefficients of the intellectual property works of the different types corresponding to the first enterprise and combining the sub-item scores of the intellectual property works of the different types of the first enterprise;
the intellectual property work sub-item score rule table is a mapping relation between a preset sub-item score and a preset interval of numerical values of percentage bits corresponding to the first quantity of the different types of intellectual property works;
the method comprises the steps of sequencing the number of effective intellectual property works of the same type of all the existing enterprises in each industry, and taking the sequenced queue as a second queue; and selecting the numerical value of the effective intellectual property works of the current type of enterprises positioned on the set percentage in the second queue as the numerical value of the percentage.
9. The big data based business innovation ability evaluation system of claim 8, wherein the matching of the first quantity obtains the sub-score of the first business' different types of intellectual property works by:
wherein S is pxy Score for p-type intellectual property works in first business x and first business y xy Representing the number of P-type valid intellectual property works in a first business x, a first business y, P x1 、P x2 、P x3 、P x4 、P x5 、P x6 、P x7 、P x8 、P x9 And the numerical value corresponding to 9 percentage bits which are set for the p-type intellectual property works in the first industry x is represented.
10. The big data based enterprise innovation ability assessment system of claim 8, wherein the innovation ability index of the first enterprise is calculated by:
wherein the IC y Representing innovation ability index, w, corresponding to first enterprise y x ＝(w ix ，w ux ，w dx ，w sx ，w wx ) T ，w ix 、w ux 、w dx 、w sx 、w wx Respectively representing the weight coefficients corresponding to the utility model patent, the design patent, the software work and the work corresponding to the first industry x, T represents the transposition and R i 、R u 、R d Respectively representing the passing rate of the utility model patents, the utility model patents and the design patents of all industries, S ixy 、S uxy 、S dxy 、S sxy 、S wxy The method respectively represents the score of the corresponding items of the utility model patent, the appearance design patent, the software work and the work corresponding to the first industry x.
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| CN118012988B (en) * | 2024-02-29 | 2024-08-02 | 领先未来科技集团有限公司 | Enterprise resource utilization supervision system and method based on cloud computing technology |
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