CN111241483B

CN111241483B - Resource value evaluation processing method based on cloud platform and related products

Info

Publication number: CN111241483B
Application number: CN202010028612.7A
Authority: CN
Inventors: 苑文佳
Original assignee: Shenzhen United Assets And Equity Exchange Co ltd
Current assignee: Shenzhen United Assets And Equity Exchange Co ltd
Priority date: 2019-01-11
Filing date: 2020-01-11
Publication date: 2020-10-30
Anticipated expiration: 2040-01-11
Also published as: CN111241483A

Abstract

The embodiment of the application discloses an evaluation processing method based on a cloud platform and a related product. The evaluation processing method based on the cloud platform comprises the following steps: the cloud platform receives a resource value evaluation request from a Client 1; the cloud platform obtains a resource identifier and a resource type identifier carried in the value evaluation request by analyzing the resource value evaluation request; and the cloud platform sends a resource value evaluation response Res1 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

Description

Resource value evaluation processing method based on cloud platform and related products

Technical Field

The application relates to the technical field of networks and computers, in particular to a resource value evaluation processing method based on a cloud platform and a related product.

Background

Currently, there is an important technical requirement for resource (physical resource or non-physical resource) value assessment. Due to the fact that the resource types are all five-door, different types of resources need to use different value evaluation models; even if the resources are the same kind, under many conditions, different enterprises can use different value evaluation models to evaluate the values, which greatly affects the wide application of resource sharing and resource exchange, and further reduces the resource utilization efficiency.

Disclosure of Invention

The embodiment of the application provides an evaluation processing method based on a cloud platform and a related product.

In a first aspect, an embodiment of the present application provides an evaluation processing method based on a cloud platform, including:

the cloud platform receives a resource value evaluation request from a Client 1;

the cloud platform obtains a resource identifier and a resource type identifier carried in the value evaluation request by analyzing the resource value evaluation request;

the cloud platform searches a value evaluation model matched with the resource type identifier in a local standard model library;

the cloud platform determines K1 dimensional parameters required by the value evaluation model M1 for value evaluation in the case that the value evaluation model M1 matching the resource type identification is searched from the local standard model library;

the cloud platform sends a dimensional parameter value obtaining request get-rq1 to the Client1, where the dimensional parameter value obtaining request get-rq1 carries a dimensional parameter requirement page P1 to be filled, where the dimensional parameter requirement page P1 to be filled includes K1 value-taking filling areas, the K1 value-taking filling areas are used to respectively fill standardized values of the resource R1 represented by the resource identifier at the K1 dimensional parameters, and the K1 is an integer greater than 1;

the cloud platform receives a dimensional parameter value acquisition response get-res1 which is sent by the Client terminal 1 and used for responding to the dimensional parameter value acquisition request get-rq1, wherein the dimensional parameter value acquisition response get-res1 carries a filled dimensional parameter demand page P1, and K1 filling value areas of the filled dimensional parameter demand page P1 fill standardized values of the K1 dimensional parameters; the standardized values of the K1 dimensional parameters are obtained by carrying out standardized numerical processing on the original values of the K1 dimensional parameters filled in by the user through the Client 1;

the cloud platform extracts the standardized values of the resource R1 in the K1 dimensional parameters from the filled-in dimensional parameter demand page P1; importing the standardized values of the K1 dimensional parameters into the value evaluation model M1 to obtain a value evaluation report of the resource R1 output by the value evaluation model M1;

and the cloud platform sends a resource value evaluation response Res1 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

Optionally, the resource value evaluation request further carries a user account, where before the cloud platform searches for the value evaluation model matching the resource type identifier in the standard model library, the method further includes:

the cloud platform extracts the user account carried in the resource value evaluation request, and searches a signing record matched with the user account in a signing user database;

if the contract record CR1 matching the user account is searched in the contract user database, and the user account is marked as a valid account in the contract record CR1, the cloud platform performs a search for a value assessment model matching the resource type identifier in a standard model library.

Optionally, the method further includes:

if the contract record CR1 matched with the user account is not searched in the contract user database, the cloud platform sends a resource value evaluation response Res2 used for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries a prompt that the account is not registered;

if the contract record CR1 matched with the user account is searched in the contract user database, but the user account is marked as an invalid account in the contract record CR1, the cloud platform sends a resource value evaluation response Res3 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries a prompt that the account is invalid.

Optionally, the method further includes:

under the condition that any value evaluation model matched with the resource type identifier is not searched in the local standard model library by the cloud platform, the cloud platform sends a model acquisition request to a shared standard model library, wherein the model acquisition request carries the resource type identifier;

if a model acquisition response which is sent by the shared standard model library and used for responding to the model acquisition request is received, and the model acquisition response carries a value evaluation model M2 which is searched from the shared standard model library and matched with the resource type identifier, K2 dimension parameters required by the value evaluation model M2 for value evaluation are determined;

the cloud platform sends a dimensional parameter value obtaining request get-rq2 to the Client1, where the dimensional parameter value obtaining request get-R2 carries a dimensional parameter requirement page P2 to be filled, where the dimensional parameter requirement page P2 to be filled includes K2 value-taking filling areas, the K2 value-taking filling areas are used to respectively fill standardized values of the resource R1 represented by the resource identifier at the K2 dimensional parameters, and the K2 is an integer greater than 1;

the cloud platform receives a dimensional parameter value acquisition response get-res2 which is sent by the Client terminal 1 and used for responding to the dimensional parameter value acquisition request get-rq2, wherein the dimensional parameter value acquisition response get-res2 carries a filled dimensional parameter demand page P2, and K2 filling value areas of the filled dimensional parameter demand page P2 fill standardized values of the K2 dimensional parameters; the standardized values of the K2 dimensional parameters are obtained by carrying out standardized numerical processing on the original values of the K2 dimensional parameters filled in by the user through the Client 1;

the cloud platform extracts the standardized values of the resource R1 in the K2 dimensional parameters from the filled-in dimensional parameter demand page P2; importing the standardized values of the K2 dimensional parameters into the value evaluation model M2 to obtain a value evaluation report of the resource R1 output by the value evaluation model M2;

and the cloud platform sends a resource value evaluation response Res4 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

Optionally, before the cloud platform sends the model obtaining request to the shared standard model library, the method further includes: determining that the importance rating of the user account flagged in the subscription record CR1 is above a preset rating threshold T1.

In a second aspect, an embodiment of the present application provides a cloud platform, including:

a processor and a memory coupled to each other;

the processor is used for receiving a resource value evaluation request from the Client 1;

analyzing the resource value evaluation request to obtain a resource identifier and a resource type identifier carried in the value evaluation request;

searching a value evaluation model matched with the resource type identifier in a local standard model library;

in the case that a value evaluation model M1 matching the resource type identification is searched from the local standard model library, K1 dimensional parameters required by the value evaluation model M1 for value evaluation are determined;

sending a dimension parameter value obtaining request get-rq1 to the Client1, where the dimension parameter value obtaining request get-rq1 carries a dimension parameter requirement page P1 to be filled, where the dimension parameter requirement page P1 to be filled includes K1 value-taking filling areas, the K1 value-taking filling areas are used to respectively fill standardized values of the resource R1 represented by the resource identifier at the K1 dimension parameters, and the K1 is an integer greater than 1;

receiving a dimensional parameter value acquisition response get-res1 sent by the Client1 and used for responding to the dimensional parameter value acquisition request get-rq1, where the dimensional parameter value acquisition response get-res1 carries a filled-in dimensional parameter demand page P1, and K1 value filling areas of the filled-in dimensional parameter demand page P1 fill in the normalization of the K1 dimensional parameters; the standardized values of the K1 dimensional parameters are obtained by carrying out standardized numerical processing on the original values of the K1 dimensional parameters filled in by the user through the Client 1;

extracting standardized values of the resource R1 in the K1 dimension parameters from the filled-in dimension parameter requirement page P1; importing the standardized values of the K1 dimensional parameters into the value evaluation model M1 to obtain a value evaluation report of the resource R1 output by the value evaluation model M1;

and sending a resource value evaluation response Res1 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

Optionally, the resource value evaluation request further carries a user account, where the processor is further configured to, before searching for a value evaluation model matching the resource type identifier in a standard model library, extract the user account carried in the resource value evaluation request, and search for a subscription record matching the user account in a subscription user database; if the contract record CR1 matching the user account is searched in the contract user database, and the user account is marked as a valid account in the contract record CR1, the cloud platform performs a search for a value assessment model matching the resource type identifier in a standard model library.

Optionally, the processor is further configured to, if a subscription record CR1 matching the user account is not searched in the subscriber database, send, to the Client1, a resource value assessment response Res2 for responding to the resource value assessment request, where the resource value assessment response carries a prompt that an account is not registered;

Optionally, the processor is further configured to, when no value evaluation model matching the resource type identifier is searched from the local standard model library, send a model acquisition request to a shared standard model library by the cloud platform, where the model acquisition request carries the resource type identifier;

sending a dimension parameter value obtaining request get-rq2 to the Client1, where the dimension parameter value obtaining request get-rq2 carries a dimension parameter requirement page P2 to be filled, where the dimension parameter requirement page P2 to be filled includes K2 value-taking filling areas, the K2 value-taking filling areas are used to respectively fill standardized values of the resource R1 represented by the resource identifier at the K2 dimension parameters, and the K2 is an integer greater than 1;

receiving a dimensional parameter value acquisition response get-res2 sent by the Client1 and used for responding to the dimensional parameter value acquisition request get-rq2, where the dimensional parameter value acquisition response get-res2 carries a filled-in dimensional parameter demand page P2, and K2 value filling areas of the filled-in dimensional parameter demand page P2 fill in the normalization of the K2 dimensional parameters; the standardized values of the K2 dimensional parameters are obtained by carrying out standardized numerical processing on the original values of the K2 dimensional parameters filled in by the user through the Client 1;

extracting standardized values of the resource R1 in the K2 dimension parameters from the filled-in dimension parameter requirement page P2; importing the standardized values of the K2 dimensional parameters into the value evaluation model M2 to obtain a value evaluation report of the resource R1 output by the value evaluation model M2;

and sending a resource value evaluation response Res4 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

Optionally, the processor is further configured to determine that the importance level of the user account marked in the subscription record CR1 is higher than a preset level threshold T1 before sending the model acquisition request to the shared standard model library.

In a third aspect, embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, where the computer program is executed by hardware (for example, a processor, and the like) to implement part or all of steps of any one of the methods performed by the cloud platform in the embodiments of the present application.

In a fourth aspect, embodiments of the present application provide a computer program product comprising instructions that, when run on a cloud platform, cause the cloud platform to perform some or all of the steps of the method of the above aspects.

Drawings

Some drawings to which embodiments of the present application relate will be described below.

Fig. 1 is a schematic structural diagram of a communication system according to an embodiment of the present application;

FIG. 2 is a schematic flow chart diagram of a value assessment method provided in an embodiment of the present application;

FIG. 3a is a schematic flow chart of a method for evaluating patent value according to an embodiment of the present application;

FIG. 3b is a schematic diagram of a patent value evaluation dimension parameter input interface provided by an embodiment of the present application;

FIG. 4a is a schematic flow chart illustrating a method for evaluating a value of an electronic device according to an embodiment of the present application;

FIG. 4b is a schematic diagram of an electronic device value evaluation dimension parameter input interface according to an embodiment of the present disclosure;

fig. 5a is a schematic flow chart of a building value evaluation method provided in an embodiment of the present application;

FIG. 5b is a schematic diagram of a building value evaluation dimension parameter input interface according to an embodiment of the present disclosure;

fig. 6 is an architecture schematic diagram of a cloud platform provided in an embodiment of the present application.

Detailed Description

The embodiments of the present application will be described below with reference to the drawings.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a communication system according to an embodiment of the present disclosure, where the communication system may include a cloud platform 110 and a client 120 interconnected through a network. The technical solution of the embodiment of the present application may be implemented based on the communication system with the architecture illustrated in fig. 1 by way of example or a modified architecture thereof.

Referring to fig. 2, fig. 2 is a schematic flow chart of a value evaluation method provided in an embodiment of the present application, which may include, but is not limited to, the following steps:

210. the cloud platform receives a resource value evaluation request from a Client 1;

220. the cloud platform obtains a resource identifier and a resource type identifier carried in the value evaluation request by analyzing the resource value evaluation request;

230. the cloud platform searches a value evaluation model matched with the resource type identifier in a local standard model library;

240. the cloud platform determines K1 dimensional parameters required by the value evaluation model M1 for value evaluation in the case that the value evaluation model M1 matching the resource type identification is searched from the local standard model library;

250. the cloud platform sends a dimensional parameter value obtaining request get-rq1 to the Client1, where the dimensional parameter value obtaining request get-rq1 carries a dimensional parameter requirement page P1 to be filled, where the dimensional parameter requirement page P1 to be filled includes K1 value-taking filling areas, the K1 value-taking filling areas are used to respectively fill standardized values of the resource R1 represented by the resource identifier at the K1 dimensional parameters, and the K1 is an integer greater than 1;

260. the cloud platform receives a dimensional parameter value acquisition response get-res1 which is sent by the Client terminal 1 and used for responding to the dimensional parameter value acquisition request get-rq1, wherein the dimensional parameter value acquisition response get-res1 carries a filled dimensional parameter demand page P1, and K1 filling value areas of the filled dimensional parameter demand page P1 fill standardized values of the K1 dimensional parameters; the standardized values of the K1 dimensional parameters are obtained by carrying out standardized numerical processing on the original values of the K1 dimensional parameters filled in by the user through the Client 1;

270. the cloud platform extracts the standardized values of the resource R1 in the K1 dimensional parameters from the filled-in dimensional parameter demand page P1; importing the standardized values of the K1 dimensional parameters into the value evaluation model M1 to obtain a value evaluation report of the resource R1 output by the value evaluation model M1;

280. and the cloud platform sends a resource value evaluation response Res1 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

Optionally, the method further includes:

the cloud platform sends a dimensional parameter value obtaining request get-rq2 to the Client1, where the dimensional parameter value obtaining request get-rq2 carries a dimensional parameter requirement page P2 to be filled, where the dimensional parameter requirement page P2 to be filled includes K2 value-taking filling areas, the K2 value-taking filling areas are used to respectively fill standardized values of the resource R1 represented by the resource identifier at the K2 dimensional parameters, and the K2 is an integer greater than 1;

In addition, before the cloud platform sends the model acquisition request to the shared standard model library, if it is determined that the importance level of the user account marked in the subscription record CR1 is not higher than the preset level threshold T1, the cloud platform sends friend assistance requests to Client1, the better assistance requests carry three friend accounts having friend relationship with the user account marked in the subscription record CR1, wherein the importance levels of the three friend accounts are higher than a preset level threshold T1, when receiving the friend assistance response sent by the Client1, the friend assistance response carries prestored assistance codes corresponding to at least 2 friend accounts in the three friend accounts, then it is determined that the Client1 obtains effective assistance from friends, the cloud platform performs the step of sending a model acquisition request to the shared standard model library.

In addition, before the resource value evaluation request is sent out, the Client1 may perform authentication on the user to improve security, for example, the Client1 may pop up a fingerprint collection prompt box to prompt the user to input a fingerprint image; the Client1 can acquire a fingerprint image of a user, compare the fingerprint image with a pre-stored fingerprint image template, send a resource value evaluation request to the cloud platform if the comparison is successful, pop up a fingerprint comparison failure if the comparison is failed, and request to re-input a prompt of the fingerprint image.

Optionally, the above specific implementation of the Client1 acquiring the fingerprint image may include: acquiring F1 normal bottom layer data values through F1 normal sensing electrodes of a fingerprint identification module, and acquiring F2 abnormal bottom layer data values through F2 abnormal sensing electrodes of the fingerprint identification module, wherein a sensing electrode array of the fingerprint module comprises the F1 normal sensing electrodes and the F2 abnormal sensing electrodes, and the F1 and the F2 are positive integers;

the Client1 determines F2 normal bottom layer data corresponding to the F2 abnormal sensing electrodes according to the F1 normal bottom layer data values; the Client1 generates the fingerprint image according to the F1 normal underlying data values and the F2 normal underlying data values.

In addition, in the above example process, the specific implementation manner of determining, by the Client1, the F2 normal bottom layer data corresponding to the F2 abnormal sensing electrodes according to the F1 normal bottom layer data values is as follows:

the Client1 determines the average of the F1 normal underlying data values; and the Client1 changes all the F2 abnormal bottom layer data values into the average value to obtain F2 normal bottom layer data corresponding to the F2 abnormal induction electrodes.

In addition, in the above example process, the specific implementation manner of determining, by the Client1, the F2 normal bottom layer data corresponding to the F2 abnormal sensing electrodes according to the F1 normal bottom layer data values is as follows: the Client1 acquires coordinate values of each abnormal induction electrode in F2 abnormal induction electrodes; the Client1 performs mean processing on each abnormal sensing electrode according to the coordinate value of each abnormal sensing electrode and the F1 bottom layer data values to obtain F2 normal bottom layer data corresponding to the F2 abnormal sensing electrodes;

the averaging process includes: determining x sensing electrodes of which the distance from the coordinate value of the abnormal sensing electrode processed by the current mean value is smaller than a preset distance in the F1 normal sensing electrodes, calculating the mean value of x bottom layer data values corresponding to the x sensing electrodes, and determining that the mean value is the reference bottom layer data value of the abnormal sensing electrode processed by the current mean value, wherein x is a positive integer.

The underlying data values may include capacitance values and/or voltage values, etc. The abnormal induction electrode is an induction electrode with abnormality in the fingerprint acquisition area. The induction electrode of fingerprint identification module can be detected by the little the control unit of fingerprint identification module.

It should be noted that, along with the increase of fingerprint identification module live time, the response electrode of fingerprint identification module has some response electrode to appear unusually, and the bottom data that these unusual response electrode obtained are unusual, can lead to Client1 to appear the noise point with the fingerprint image that the bottom data that normal response electrode and unusual response electrode obtained generated, and then influence the contrast of follow-up fingerprint contrast, therefore, before generating the fingerprint image, can revise these unusual bottom data earlier, with the noise point that reduces the fingerprint image, and then can improve the contrast of fingerprint contrast.

In addition, for a scene in which a fingerprint image is acquired by shooting, a specific implementation of the client ClieAt1 acquiring the fingerprint image may include: shooting through a camera module to obtain a fingerprint image, and acquiring an image area to be corrected in the fingerprint image, wherein the distortion degree parameter of the image area to be corrected is greater than or equal to a preset distortion degree parameter; acquiring a central pixel grid of the image according to a preset size; dividing the image area to be corrected into A pixel grids with the same size as the central pixel grid, wherein A is an integer larger than 2; and correcting the A pixel grids by taking the central pixel grid as a distortion correction reference to obtain a corrected fingerprint image.

Wherein correcting the A pixel meshes with the central pixel mesh as a distortion correction reference comprises: acquiring the transverse distance and the longitudinal distance from the A pixel grids to the central pixel grid; according to the transverse distance and the longitudinal distance from the A pixel grids to the central pixel grid, converting transverse distortion degree parameters and longitudinal distortion degrees corresponding to the A pixel grids; and carrying out distortion correction on the A pixel grids according to the distortion degree parameters corresponding to the A pixel grids.

For example, the acquiring an image region to be corrected in the image comprises: analyzing the fingerprint image to obtain an image area with a distortion degree parameter greater than or equal to a preset distortion degree parameter; displaying the image area with the distortion degree parameter larger than or equal to the preset distortion degree parameter on a display interface of the mobile terminal, and prompting a user to select the image area to be corrected; if a selection instruction input by a user is received, acquiring an image area to be corrected corresponding to the selection instruction to obtain the image area to be corrected.

By way of further example, the acquiring an image region to be corrected in the image comprises: analyzing the image to obtain all image areas of which the distortion degree parameters are greater than or equal to preset distortion degree parameters in the image, wherein all the image areas are image areas to be corrected.

For example, the dividing the image area to be corrected into a pixel grids of the same size as the central pixel grid comprises: and gridding the image area to be corrected according to a preset size to obtain A pixel grids with the same size as the central pixel grid.

In addition, before comparing the fingerprint image with the pre-stored fingerprint image template, the method can also comprise the step of carrying out image precision enhancement processing on the fingerprint image. Comparing the fingerprint image with a pre-stored fingerprint image template comprises: and comparing the fingerprint image subjected to the image precision enhancement processing with a pre-stored fingerprint image template.

The image precision enhancement processing on the acquired fingerprint image may include: acquiring a target image area to be enhanced in a fingerprint image, wherein the target image area comprises a plurality of sub-area images; performing image quality evaluation on each subarea image in the plurality of subarea images to obtain a plurality of image quality evaluation values; selecting at least one target image quality evaluation value smaller than a preset image quality evaluation threshold value from the plurality of image quality evaluation values; acquiring at least one target sub-area image corresponding to the at least one target image quality evaluation value; and performing image precision enhancement processing on the at least one target subregion image to obtain the enhanced target image region. The image quality evaluation index may include, but is not limited to: mean gray, mean square error, entropy, edge preservation, and the like. It is considered that the larger the obtained image quality evaluation value is, the better the image quality is. Alternatively, in the case where the requirement on the accuracy of the image quality evaluation is not high, the evaluation may be performed by using one image quality evaluation index, for example, the image quality evaluation value may be performed on the image to be processed by using entropy, and it may be considered that the larger the entropy, the better the image quality is, and conversely, the smaller the entropy, the worse the image quality is.

Wherein, the image precision enhancement processing is carried out on the at least one target subregion image, and comprises the following steps: dividing a target subregion image i into a plurality of region blocks, wherein the target subregion image i is any one target subregion image in the at least one target subregion image; respectively extracting the features of each of the plurality of region blocks to obtain a plurality of feature point sets, wherein each region block corresponds to one feature point set; determining the feature point distribution density corresponding to each of the plurality of region blocks according to the plurality of feature point sets to obtain a plurality of feature point distribution densities; determining a target image enhancement control coefficient corresponding to each feature point distribution density in the plurality of feature point distribution densities according to a preset mapping relation between the feature point distribution densities and the image enhancement control coefficients to obtain a plurality of target image enhancement control coefficients; respectively carrying out image enhancement processing on each of the plurality of area blocks according to the plurality of target image enhancement control coefficients to obtain an enhanced target subregion image i; and smoothing the target subregion image i to obtain a final target subregion image i.

Each type of image enhancement algorithm can correspond to an image enhancement control coefficient, and the size of the image enhancement control coefficient controls the image enhancement effect. The embodiment of the application can carry out pertinence enhancement according to the characteristics of each region block, so that the details of each region are clearer, in addition, in order to ensure the transition between different regions to be natural, the smooth processing is adopted, the enhancement effect is achieved, the difference enhancement feeling between each region is optimized, and the fingerprint image quality is better.

Taking resource R1 as an example, a possible mechanism for evaluating the value by using the value evaluation model is shown below.

The value evaluation model can evaluate the value of resource R1 by the following steps:

A. preprocessing data;

in the step (a),

(1) the data preprocessing further comprises: data cleaning, namely completing or shifting out missing data, unifying repeated data and converting fields with wrong formats;

(2) extracting data information, extracting key time information, calculating time intervals, carrying out quantitative processing on texts such as an abstract and a specification, and extracting discrete ordered fields and unordered classified field information;

(3) and (4) replanning the data, and endowing the extracted information with new values again so as to achieve the purposes that the number of samples corresponding to each value is sufficient and the whole structure presents a certain distribution structure.

The process is standardized intellectual property data warehousing. First, each patent document is standardized, and useful information of a patent is extracted and planned into a plurality of fields and stored in a database, wherein each patent is a point described by the planned fields. And after the data are put in storage, cleaning the data for the first time, and supplementing or removing missing data.

The text and symbolic information for each patent field in the intellectual property database is then mapped into numerical information. Such as the number of words in the statistical summary, the number of claims, etc. And cleaning the data again, and supplementing and correcting the missing data and the error data.

Furthermore, the quantitative indicators related to the merits of the patents, such as the indicators of whether the patents survive in the renewal cycle, whether the patents are authorized, whether the patents are victory or not, and the like, are found. And screening patent data of a sufficiently large sample.

b. And (5) establishing a model.

In step (b), the modeling further comprises:

(1) eliminating fields irrelevant to the evaluation of the quality of the patent through a patent regulation;

(2) selecting a field for measuring the quality of the patent as a modeling target field, and further planning the target field to achieve the purpose that the distribution structure is more suitable for modeling requirements;

(3) selecting fields with significant correlation with the target fields through statistical significance and checking the significance of the fields;

(4) classifying the modeling data according to the key fields of the classification characteristics distributed to the statistical graph;

(5) through likelihood ratio index, the computer automatically carries out dimensionality reduction treatment before modeling;

(6) optimizing model parameters and completing modeling;

(7) the model is deployed online.

c. Patent evaluation, in step (c), further comprising:

(1) evaluating a rough score for the patent using the obtained model, i.e. in step (b), the model obtained after the model is built;

(2) mapping the rough score to be between 0 and 200 to give an IPV score;

(3) calculating a percentage grade based on the IPV score, and giving a patent rating;

(4) predicting the life span of the patent based on the IPV score, re-planning the IPV score into a plurality of grades, respectively counting the survival rate of the patent of each grade, and further calculating a whole patent life span prediction table;

(5) the system automatically generates a patent online evaluation report, wherein the patent online evaluation report comprises basic attributes, evaluation scores, star ranking and patent life span prediction of patents.

Learning optimization of a value evaluation model by first determining patent survival ability using patent business knowledge and descriptive statisticsAnd (3) fields which are obviously related and have no obvious correlation with each other are subjected to data binning again, so that the data volume of each grade is enough, and the average life span of patents of each grade has obvious correlation. Assuming that all patent evaluators are rational and only pay attention to the merits of patents, the estimated patent life spans only fluctuate around the true life span, and the errors belong to normal distribution, in step (c), based on the accuracy of data, the patent evaluation selects a multiple regression model as an evaluation model, and the parameter optimization selects a common least square method, wherein Y is β X +, Y is a dependent variable matrix, X is an independent variable matrix, β is a coefficient matrix, and is a residual matrix. Parameter calculation, β ═ X' X)^-1(X 'Y), X' is the transpose of the X matrix. Introducing likelihood ratio values to carry out dimension selection afterwards, and defining: LR1 is the likelihood function of the unconstrained equation, T is the number of points,

variance estimation for unconstrained equations; LR2 is a likelihood function of a constraint equation,

for variance estimation of constrained equations, LR1, LR2 obey χ²Distribution, each time trying to eliminate the influence of an independent variable on the original equation, if the influence exceeds x²And (3) a critical value proves that the elimination of the independent variable has overlarge influence on the equation, if two independent variables with strong correlation exist in the equation, when one independent variable is eliminated, the other independent variable does not have great influence on the equation, and the effect of carrying out dimension selection on the original multiple regression model is achieved.

The value evaluation model can be obtained and optimized through the steps. And deploying the evaluation rule into an optimized value evaluation model, and evaluating and scoring all patents. The model can obtain the rough score of the Chinese patent corresponding to the life span. To reflect the details of the score more significantly, the coarse results are mapped onto a larger interval. The positions of the patents in the whole patent data set are calculated in order of fraction size, and the whole patent set is divided into five grades to grade each patent.

And predicting the life of the patent. Chinese patents are adjusted for cost approximately once every three years, so the life span of the patents is predicted to be one period of three years. The patents are firstly divided into more detailed grades, and the viability of all expired or invalid patents in each grade in the current period is taken as a research sample for estimating the viability of the patents in the current life period. And when entering the next renewal period, the method is reused for estimating the survival capacity of the patents, and the like until the limitation of the longest life span of the Chinese patents is reached. By examining the scored patents: the evaluation method proves that the evaluation method can give higher scores to excellent patents with high probability from the aspects of the status of the applied company in the related technical field, the advancement of the applied content and the like.

It is understood that other resource evaluation mechanisms may be analogized.

Specifically, the method for evaluating the value of the patent resources may be as shown in fig. 3a, and the method may include, but is not limited to, the following steps:

11) the cloud platform receives a resource value evaluation request initiated by a Client1 for a patent;

12) the cloud platform analyzes the resource value evaluation request to obtain that the carried resource identifier is a patent number and the resource type identifier is a patent;

the resource identifier may be used to indicate a specific resource, and the resource type identifier may be used to indicate which type of resource the specific resource belongs to. The patent number input by the user can be used for obtaining that the resource to be evaluated by the user is a patent.

13) The cloud platform searches a value evaluation model matched with the patent in a local standard model library;

14) the cloud platform determines K1 dimensional parameters required by a value evaluation model M1 in the case that the value evaluation model M1 matched with a patent is searched from the local standard model library;

15) the cloud platform sends a dimensional parameter value acquisition request get-rq1 to the Client1 in a manner of outputting a graphical interface P1, where the dimensional parameter value acquisition request get-rq1 carries a dimensional parameter requirement page P1 to be filled, where the dimensional parameter requirement page P1 to be filled includes K1 value-taking filling areas, the K1 value-taking filling areas are used for respectively filling standardized values of the patent resources R1 at K1 dimensional parameters, and the K1 is an integer greater than 1;

specifically, the dimension parameter requirement page P1 to be filled in may be as shown in fig. 3b, where the K1 value filling areas may include, but are not limited to, an application date, a patent name, a key field, a patent category, a patent state, a payment state, a technical field, and the like of a patent. The key field may input key technical points of the patent, some keywords with strong correlation, or may input abstract or beneficial effect, etc., which is not limited herein. The patent category may be one of invention, reality, or appearance. The patent status may include one of underwriting, authorized or failed, and may be further subdivided, such as in preliminary examination, in actual examination, authorized, withdrawn, rejected, etc., without limitation.

16) The cloud platform receives a dimensional parameter value acquisition response get-res1 which is sent by the Client terminal 1 and used for responding to the dimensional parameter value acquisition request get-rq1, wherein the dimensional parameter value acquisition response get-res1 carries a filled dimensional parameter demand page P1, and K1 filling value areas of the filled dimensional parameter demand page P1 fill standardized values of the K1 dimensional parameters; the standardized values of the K1 dimensional parameters are obtained by carrying out standardized numerical processing on the original values of the K1 dimensional parameters filled in by the user through the Client 1;

17) the cloud platform extracts standardized values of the patent resource R1 in the K1 dimensional parameters from a filled-in dimensional parameter demand page P1; importing the standardized values of the K1 dimensional parameters into the value evaluation model M1 to obtain a value evaluation report of the patent resource R1 output by the value evaluation model M1;

18) and the cloud platform sends a resource value evaluation response Res1 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

When evaluating the value of an authorized patent, the remaining effective period of the patent can be calculated through the patent application date and the patent category input by the user, the technical field, the technical means and the technical novelty degree of the patent can be roughly known according to the patent name and the key field, and then the value of the patent is evaluated according to a value evaluation model. For example, a patent with a high degree of novelty in general technology has a higher value than a patent with a low degree of novelty, and a patent with a long remaining life is higher than a patent with a short remaining life.

A method for evaluating the value of an electronic device resource may be as shown in fig. 4a, and the method may include, but is not limited to, the following steps:

21) the cloud platform receives a resource value evaluation request initiated by a Client1 aiming at the electronic equipment;

22) the cloud platform analyzes the resource value evaluation request to obtain that the carried resource identifier is a computer and the resource type identifier is electronic equipment;

when a user wants to evaluate the value of a computer, the user can classify the computer into electronic equipment by the resource identifier 'computer'. The electronic device resources include, but are not limited to, computers, mobile phones, server clusters, electronic readers, wearable devices, and the like.

23) The cloud platform searches a value evaluation model matched with the electronic equipment in a local standard model library;

24) the cloud platform determines K1 dimensional parameters required by a value evaluation model M1 for value evaluation under the condition that the value evaluation model M1 matched with the electronic equipment is searched from the local standard model library;

25) the cloud platform sends a dimensional parameter value acquisition request get-rq1 to the Client1 in a manner of outputting a graphical interface P1, where the dimensional parameter value acquisition request get-rq1 carries a dimensional parameter requirement page P1 to be filled, where the dimensional parameter requirement page P1 to be filled includes K1 value-taking filling areas, the K1 value-taking filling areas are used for respectively filling standardized values of the electronic device resource R1 at the K1 dimensional parameters, and the K1 is an integer greater than 1;

specifically, the dimension parameter requirement page P1 to be filled in may be as shown in fig. 4b, where the K1 value filling areas may include, but are not limited to, a device model, a processor (CPU), a computing rate of the device, a purchase date or a production date of the device, a price when the device is purchased, a service life of the device, a memory size, and the like.

26) The cloud platform receives a dimensional parameter value acquisition response get-res1 which is sent by the Client terminal 1 and used for responding to the dimensional parameter value acquisition request get-rq1, wherein the dimensional parameter value acquisition response get-res1 carries a filled dimensional parameter demand page P1, and K1 filling value areas of the filled dimensional parameter demand page P1 fill standardized values of the K1 dimensional parameters; the standardized values of the K1 dimensional parameters are obtained by carrying out standardized numerical processing on the original values of the K1 dimensional parameters filled in by the user through the Client 1;

27) the cloud platform extracts standardized values of the electronic equipment resource R1 in the K1 dimensional parameters from a filled-in dimensional parameter demand page P1; importing the standardized values of the K1 dimensional parameters into the value evaluation model M1 to obtain a value evaluation report of the electronic equipment resource R1 output by the value evaluation model M1;

28) and the cloud platform sends a resource value evaluation response Res1 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

The method comprises the steps of carrying out value evaluation on a computer, calculating the remaining service life of the computer according to the purchase date (or production date) input by a user, the current time and the service life of equipment, calculating the operating efficiency of the computer according to the model number, CPU and calculation rate of the computer input by the user to roughly estimate the loss degree of the computer, using the purchase price of the computer as a reference value, and then estimating the value of the computer according to a value evaluation model. For example, a computer with a long remaining lifetime is more valuable than a computer with a short remaining lifetime, a computer with a high operating efficiency and a low loss is more valuable than a computer with a low operating efficiency and a low loss, and so on.

A method for evaluating the value of a building resource may be as shown in fig. 5a, and the method may include, but is not limited to, the following steps:

31) the cloud platform receives a resource value evaluation request initiated by a Client1 aiming at a building;

32) the cloud platform analyzes the resource value evaluation request to obtain that the resource identifier carried by the cloud platform is a house and the resource type identifier carried by the cloud platform is a building;

when a user wants to evaluate a value for a house, the resource identifier "house" entered by the user can be classified into a building class. Building resources include, but are not limited to, houses, gyms, factories, libraries, museums, and the like.

33) The cloud platform searches a value evaluation model matched with the building in a local standard model library;

34) the cloud platform determines K1 dimensional parameters required by the value evaluation model M1 for value evaluation under the condition that the value evaluation model M1 matched with the building is searched from the local standard model library;

35) the cloud platform sends a dimensional parameter value acquisition request get-rq1 to the Client1 in a manner of outputting a graphical interface P1, where the dimensional parameter value acquisition request get-rq1 carries a dimensional parameter requirement page P1 to be filled, where the dimensional parameter requirement page P1 to be filled includes K1 value-taking filling areas, the K1 value-taking filling areas are used for respectively filling standardized values of the electronic device resource R1 at the K1 dimensional parameters, and the K1 is an integer greater than 1;

specifically, the dimension parameter requirement page P1 to be filled in may be as shown in fig. 5b, where the K1 value filling areas may include, but are not limited to, house attributes, area size, location, purchase date or land use right acquisition date, purchase unit price or total price, service life, affiliated cell, cell greening area, traffic around the cell, and so on. The property of the house may include commercial, civil or industrial use, and may be further subdivided, such as self-building, commercial, apartment, talent accommodation, and the like, without limitation. The seating location may include urban or suburban areas, or may be further subdivided, such as civic rings, bi-rings, tri-rings, and the like, without limitation.

36) The cloud platform receives a dimensional parameter value acquisition response get-res1 which is sent by the Client terminal 1 and used for responding to the dimensional parameter value acquisition request get-rq1, wherein the dimensional parameter value acquisition response get-res1 carries a filled dimensional parameter demand page P1, and K1 filling value areas of the filled dimensional parameter demand page P1 fill standardized values of the K1 dimensional parameters; the standardized values of the K1 dimensional parameters are obtained by carrying out standardized numerical processing on the original values of the K1 dimensional parameters filled in by the user through the Client 1;

37) the cloud platform extracts standardized values of the building resources R1 in the K1 dimensional parameters from a filled-in dimensional parameter demand page P1; importing the standardized values of the K1 dimensional parameters into the value evaluation model M1 to obtain a value evaluation report of the building resource R1 output by the value evaluation model M1;

38) and the cloud platform sends a resource value evaluation response Res1 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

The method comprises the steps of carrying out value evaluation on a house, calculating the remaining service life of the house according to the purchase date or the land use right acquisition date, the current time and the service life input by a user, calculating the overall value of the house according to the house attribute, the area size, the sitting position and the purchase unit price or total price input by the user, and then combining a value evaluation model to estimate the current value of the house. For example, houses with long remaining life span have higher value than houses with short remaining life span, houses with large areas and good locations have higher value than houses with small areas and poor locations, and so on.

Referring to fig. 6, an embodiment of the present application provides a cloud platform 500, which may include: a processor 530 and a memory 510 coupled to each other; for example, the processor 530 and the memory 510 may be coupled by a bus 540.

The Memory 510 may include, but is not limited to, a Random Access Memory (RAM), an Erasable Programmable Read Only Memory (EPROM), a Read-Only Memory (ROM), or a portable Read-Only Memory (CD-ROM), and the like, and the Memory 510 is used for related instructions and data.

The processor 530 may be one or more Central Processing Units (CPUs), and in the case that the processor 530 is one CPU, the CPU may be a single-core CPU or a multi-core CPU.

The processor 530 is configured to read the program code stored in the memory 510, and perform part or all of the steps of the method performed by the cloud platform 500 in the above embodiments of the present application.

A processor and a memory coupled to each other;

extracting standardized values of the resource R1 in the K2 dimension parameters from the filled-in dimension parameter requirement page P2; importing the standardized values of the K2 dimensional parameters into the value evaluation model M2 to obtain a value evaluation report of the resource R1 output by the value evaluation model M2; and sending a resource value evaluation response Res4 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., compact disk), or a semiconductor medium (e.g., solid state disk), among others.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is merely a logical division, and the actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the indirect coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, and may be electrical or in other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage media may include, for example: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

Claims

1. An evaluation processing method based on a cloud platform is characterized by comprising the following steps:

the cloud platform obtains a resource identifier and a resource type identifier carried in the resource value evaluation request by analyzing the resource value evaluation request;

the cloud platform sends a resource value evaluation response Res1 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report;

wherein the content of the first and second substances,

the resource value evaluation request also carries a user account, wherein before the cloud platform searches a value evaluation model matched with the resource type identifier in a standard model library, the method further comprises the following steps:

if the contract record CR1 matched with the user account is searched in the contract user database and the user account is marked as a valid account in the contract record CR1, the cloud platform executes the value evaluation model matched with the resource type identifier in a standard model library;

wherein the content of the first and second substances,

the method further comprises the following steps:

the cloud platform sends a resource value evaluation response Res4 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report;

before the cloud platform sends a model acquisition request to the shared standard model library, the method further comprises the following steps: determining that the importance rating of the user account flagged in the subscription record CR1 is above a preset rating threshold T1;

wherein the content of the first and second substances,

the method further comprises the following steps: before the cloud platform sends a model acquisition request to a shared standard model library, if it is determined that the importance level of the user account marked in the subscription record CR1 is not higher than a preset level threshold T1, the cloud platform sends a friend assistance request to a Client1, the friend assistance request carries three friend accounts having a friend relationship with the user account marked in the subscription record CR1, wherein the importance levels of the three friend accounts are higher than a preset level threshold T1, when receiving the friend assistance response sent by the Client1, the friend assistance response carries prestored assistance codes corresponding to at least 2 friend accounts in the three friend accounts, then it is determined that the Client1 obtains effective assistance from friends, the cloud platform performs the step of sending a model acquisition request to the shared standard model library.

2. The method of claim 1, further comprising:

3. A cloud platform, comprising:

a processor and a memory coupled to each other;

analyzing the resource value evaluation request to obtain a resource identifier and a resource type identifier carried in the resource value evaluation request;

sending a resource value evaluation response Res1 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report;

wherein the content of the first and second substances,

the resource value evaluation request also carries a user account, wherein the processor is further used for extracting the user account carried in the resource value evaluation request and searching a signing record matched with the user account in a signing user database before searching a value evaluation model matched with the resource type identifier in a standard model library; if the contract record CR1 matched with the user account is searched in the contract user database and the user account is marked as a valid account in the contract record CR1, the cloud platform executes the value evaluation model matched with the resource type identifier in a standard model library;

wherein the content of the first and second substances,

the processor is further configured to, when any value evaluation model matching the resource type identifier is not searched from the local standard model library, send a model acquisition request to a shared standard model library, where the model acquisition request carries the resource type identifier;

sending a resource value evaluation response Res4 for responding to the resource value evaluation request to the Client1, wherein the resource value evaluation response carries the value evaluation report;

wherein the processor is further configured to determine that the importance level of the user account marked in the subscription record CR1 is higher than a preset level threshold T1 before sending a model acquisition request to a shared standard model library;

wherein, before sending the model acquisition request to the shared standard model library, if it is determined that the importance level of the user account marked in the subscription record CR1 is not higher than the preset level threshold T1, the processor is further configured to send a friend assistance request to the Client1, the buddy assistance request carries three buddy accounts having a buddy relationship with the user account marked in the subscription record CR1, wherein the importance levels of the three friend accounts are higher than a preset level threshold T1, when receiving the friend assistance response sent by the Client1, the friend assistance response carries prestored assistance codes corresponding to at least 2 friend accounts in the three friend accounts, then it is determined that the Client1 obtained effective assistance from the friend, the step of sending a model acquisition request to the shared standard model library is performed.

4. The cloud platform of claim 3, wherein the processor is further configured to, if a subscription record CR1 matching the user account is not searched in the subscriber database, send a resource value assessment response Res2 to the Client1 for responding to the resource value assessment request, where the resource value assessment response carries a prompt that an account is not registered;