CN113449019B - Industrial resource comprehensive digital platform building method - Google Patents
Industrial resource comprehensive digital platform building method Download PDFInfo
- Publication number
- CN113449019B CN113449019B CN202111000519.6A CN202111000519A CN113449019B CN 113449019 B CN113449019 B CN 113449019B CN 202111000519 A CN202111000519 A CN 202111000519A CN 113449019 B CN113449019 B CN 113449019B
- Authority
- CN
- China
- Prior art keywords
- data
- value
- statistical
- resource data
- resource
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
Abstract
The invention discloses a method for building an industrial resource comprehensive digital platform, which comprises the steps of firstly carrying out proportional unification on innovative resource data of each enterprise, then primarily building a two-dimensional platform, and bringing in a node data module after obtaining a statistical central value and a statistical extension value of a three-dimensional platform, thereby completing the building, clearly displaying the innovation capability of each enterprise, comparing the innovation capability with other enterprise data, providing the comparison condition between enterprises while clearly displaying the self capability, synchronously giving the innovation capability optimization direction and degree of the enterprises, reducing and compressing data processing when calling platform data, reducing the data capacity, improving the transmission speed, and intensively solving the problems that no clear display platform exists for creating new resources of enterprises in each industry at present and no good description is available for the innovation capability of the enterprises per se.
Description
Technical Field
The invention relates to the technical field of industrial resource data processing, in particular to a method for building an industrial resource comprehensive digital platform.
Background
In recent years, with the development of economy in China, the industrial structure begins to change, innovation becomes the first power of leading development, the development of catching innovation is catching development, and the innovation of pursuing innovation is pursuing the future. Although the innovation achieves remarkable achievement in the industrialization of the innovation achievements in China since the innovation is opened, the problem that the transfer mechanism of the innovation achievements is not perfect exists. As a road leader of innovation development, governments must effectively control and understand innovation resources of enterprises in each industry, so that the technological development current situation of each industry is clarified, technological innovation development routes are completely and scientifically planned, and a technological innovation development system is perfected.
At present, the display of enterprise innovation resources in each industry is only realized by centralized collection and recording of big data, for example, the patent numbers are as follows: 201410616636.9-an enterprise innovation resource management and analysis method based on big data, which essentially collects and records data through big data set, collects, stores, collects, manages and classifies the data according to corresponding set program, so as to analyze, and there is no visual and clear display platform in the analysis process; the prior art is also supplemented with relevant technologies related to display platforms, such as the following patent numbers: 201410212869.2-innovative resource information integration service platform, but on one hand, the platform is not further deepened to enterprise innovative data, and the display platform fails to display the innovative capability of each enterprise through specific data collection and calculation, and on the other hand, a data comparison sub-platform between enterprises is not introduced, so that the innovative display platform is not sufficient, the innovative capability portrait of the enterprise is not well described, and the accurate decision is not made for the formulation of each industrial innovative development route.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The invention is provided in view of the problems of the prior enterprise innovative resource display.
Therefore, the technical problem solved by the invention is as follows: the problem that an existing innovation resource integration platform is not further deepened to enterprise innovation data, a display platform cannot display innovation capacity of each enterprise through specific data set recording calculation, a data comparison sub-platform between the enterprises is not introduced, the innovation display platform is not sufficient, and innovation capacity portrayal of the enterprises is not well described is solved.
In order to solve the technical problems, the invention provides the following technical scheme: a method for building an industrial resource comprehensive digital platform comprises the steps of counting and selecting various innovation resource data of all enterprises in the industry by big data, and carrying out proportional unification on various innovation resource data of all the enterprises according to an connotation factor pi; counting each scaled innovation resource data of each enterprise by adopting a Bayesian statistical model to form an original database of each innovation resource data of each enterprise, and recording the statistical model as a preliminary digital platform; respectively establishing data nodes of each innovative resource data of each enterprise in the digital platform by using data in the original database, and constructing respective data modules by using the respective data nodes, wherein the method specifically comprises the step of taking a spherical plane diffusion two-dimensional statistical mode as a statistical mode of a preliminary digital platform to carry out statistics on the data of the original database; sequentially bringing data in the original database into groups to form respective data nodes; connecting the respective data nodes by adopting data lines, defining the formed two-dimensional data space display modules as respective data modules, and recording the central points of the respective two-dimensional data space display modules as representative points of the respective data modules; when the spherical plane diffusion two-dimensional statistical mode is used for statistics, the central sphere value is 0, and the radius statistical diffusion value is 1; determining a statistical center value a and a statistical extension value b of the digital platform through respective data modules, and specifically constructing a spherical plane diffusion three-dimensional statistical platform according to a spherical plane diffusion two-dimensional statistical mode, namely performing three-dimensional module statistics by setting a central sphere point value to be 0 and a radius statistical diffusion value to be 1; the area value of each data module is taken as the statistical solid value of the three-dimensional statistical system and is included in the statistics of the three-dimensional modules, the statistical central value a and the statistical expansion value b of the digital platform are determined according to the following formula,
loading a node value:
loading node positions:
wherein S isNRepresenting the area value of the Nth data module, pi being an connotation factor, alpha being first resource data, beta being second resource data, gamma being third resource data, delta being fourth resource data, and epsilon being fifth resource data; and incorporating each loading node value and the corresponding loading node position into the three-dimensional module statistics to form a three-dimensional digital platform.
As a preferred scheme of the industrial resource comprehensive digital platform building method, the industrial resource comprehensive digital platform building method comprises the following steps: the method specifically comprises the steps of determining the connotation factor pi according to the relevancy, wherein the relevancy is defined as alpha + beta + gamma, gamma-epsilon, gamma-delta and delta-epsilon, alpha is first resource data, beta is second resource data, gamma is third resource data, delta is fourth resource data, and epsilon is fifth resource data; respectively carrying out proportional unification on each innovative resource data of each enterprise according to the connotation factor pi; wherein the formula of the connotation factor pi is determined according to the relevance,
and respectively carrying out proportional unification on each innovative resource data of each enterprise according to the connotation factor pi, namely correspondingly unifying each innovative resource data of each enterprise into first resource data alpha pi (%), second resource data beta pi (%), third resource data gamma pi (%), fourth resource data delta pi (%) and fifth resource data epsilon pi (%).
As a preferred scheme of the industrial resource comprehensive digital platform building method, the industrial resource comprehensive digital platform building method comprises the following steps: the statistics is carried out in a spherical plane diffusion two-dimensional statistical mode, when data lines are adopted to connect respective data nodes to form respective data modules, the following conditions are met,
wherein, sigma represents the data diffusion degree, N is the statistical number of the data modules, S is the area of the data modules, and SNRepresenting the area value of the nth data block.
As a preferred scheme of the industrial resource comprehensive digital platform building method, the industrial resource comprehensive digital platform building method comprises the following steps: after obtaining the respective loaded node values and the loaded node positions, mutually determining a central difference value xi according to the respective loaded node values; determining the center offset degree theta according to the positions of the respective loading nodes; determining mutual comparison data modules according to the mutual determined center difference xi and the center offset theta, and incorporating the mutual comparison data modules into the three-dimensional module statistics for hidden display;
where ξ = ρ2-ρ1，θ=（τ2-τ1）/（τ2+τ1）；
Wherein ρ is a loading node value of the same type of innovation resource data, and τ is a loading node position of the same type of innovation resource data.
As a preferred scheme of the industrial resource comprehensive digital platform building method, the industrial resource comprehensive digital platform building method comprises the following steps: the digital platform also comprises an optimization recommendation mechanism, and the establishment method comprises the steps of comparing any two groups of innovation resource data, obtaining the difference value of each type of innovation resource data loaded into the node value, namely determining the category of the innovation resource data with the largest extension direction of the difference value according to the central difference value xi; obtaining the position distance of the position offset of the loading node of the innovative resource data type with the largest difference value along the direction of the index, namely determining the offset position distance according to the center offset degree theta; defining the category of the innovative resource data with the largest difference extension direction as the optimization direction, and determining the optimization degree according to the position distance.
As a preferred scheme of the industrial resource comprehensive digital platform building method, the industrial resource comprehensive digital platform building method comprises the following steps: the digital platform also comprises a calling mechanism which comprises a reduction module and a calling module, wherein the reduction module is used for carrying out reduction processing on a data module of the digital platform; the compression module is used for compressing the data module of the digital platform; and the transmission module is used for transmitting the compressed data module.
The invention has the beneficial effects that: the invention provides a method for building an industrial resource comprehensive digital platform, which comprises the steps of firstly carrying out proportional unification on innovative resource data of each enterprise, then preliminarily building a two-dimensional platform, and bringing in a node data module after obtaining a statistical central value and a statistical extension value of a three-dimensional platform, thereby completing the building, clearly displaying the innovation capability of each enterprise, comparing the innovation capability with other enterprise data, clearly providing the comparison condition between enterprises while simultaneously providing the comparison condition between the capabilities, synchronously providing the innovation capability optimization direction and degree of the enterprises, reducing and compressing data processing when calling platform data, reducing the data capacity, improving the transmission speed, and intensively solving the problems that no clear display platform exists for creating new resources of the enterprises in each industry at present and no good description is available for the innovation capability of the enterprises per se.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a method flow chart of the construction method provided by the invention.
Fig. 2 is a flowchart of a method for establishing data nodes and constructing respective data modules according to the present invention.
Fig. 3 is a schematic diagram of a two-dimensional statistical method of spherical planar diffusion according to the present invention.
Fig. 4 is a schematic block diagram of a call mechanism provided in the present invention.
FIG. 5 is a flowchart of a method for comparing enterprise innovation capabilities provided by the present invention.
FIG. 6 is a flowchart of a method for optimizing direction and degree of an enterprise according to the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
At present, enterprise innovation resources in each industry are only collected and recorded in a centralized mode through big data, a clear display platform does not exist, innovation capability images of the enterprises are not well described, and accurate decisions are not made for making innovation development routes of each industry.
Therefore, referring to fig. 1, the present invention provides a method for building an industrial resource integrated digital platform, including the following steps:
s1: the big data statistics selects each innovative resource data of all enterprises in the industry, and each innovative resource data of each enterprise is proportionally unified according to the connotation factor pi;
in consideration of inconsistent meaning directivities represented by each innovative resource data of an enterprise, if platform data are directly brought in, the problems of data disorder and unclear meaning can occur if the platform data are directly and widely counted, and direct data operation cannot be performed.
Further, the step of respectively carrying out proportional unification on each innovative resource data of each enterprise according to the connotation factor pi specifically comprises the following steps:
s1.1: determining an connotation factor pi according to the relevance, wherein the relevance is defined as alpha + beta + gamma, gamma epsilon, gamma delta and delta epsilon, wherein alpha is first resource data, beta is second resource data, gamma is third resource data, delta is fourth resource data, and epsilon is fifth resource data;
it should be noted that, for each enterprise, the innovative resource data:
the first resource data α = number of items to be detected by the environmental protection class/total number of items to be detected by the environmental protection class, wherein the total number of items to be detected by the environmental protection class refers to a GB standardized file, and this data is a calculable determination value;
second resource data beta = number of items passed by the high and new declaration/total number of items passed by the high and new declaration, wherein the total number of items passed by the high and new declaration includes the high enterprise declaration, the state-level small and medium enterprise declaration and the like, and specifically refer to a GB high and new declaration standardized file, and the data is a calculable determined value;
third resource data gamma = number of transitions of the enterprise high and new technology project/total number of the enterprise high and new technology project, wherein the total number of the enterprise high and new technology project is several categories related to the type of the high enterprise file specified by the country, and the data is a calculable determined value;
fourth resource data δ = the number of talents/total number of employees of the enterprise that reach the department, and the data is a calculable determined value;
fifth resource data epsilon = enterprise research and development investment fund number/enterprise operation investment total fund number, and the data is a calculable determined value;
s1.2: respectively carrying out proportional unification on each innovation resource data of each enterprise according to the connotation factor pi;
wherein, the formula of the content factor pi is determined according to the relevance:
and respectively carrying out proportional unification on each innovative resource data of each enterprise according to the connotation factor pi, namely correspondingly unifying each innovative resource data of each enterprise into first resource data alpha pi (%), second resource data beta pi (%), third resource data gamma pi (%), fourth resource data delta pi (%) and fifth resource data epsilon pi (%).
It should be additionally noted that the correlation analysis method is based on the similarity or difference degree of the development trend between the factors as the measuring factorA method of correlating. When the relevance analysis is performed, data processing without dimensioning is generally performed; the degree of correlation is substantially the degree of difference in geometry between curves. Therefore, the magnitude of the difference between the curves can be used as a measure of the degree of correlation. The above-mentioned occupation condition is also referred to as a resolution factor. The degree of association between the factors is mainly described by the magnitude order of the degree of association. The association degrees of m sub-sequences to the same mother sequence are arranged according to the size sequence to form an association sequence, which is marked as { x }, and reflects the 'good and bad' relationship of each sub-sequence to the mother sequence. If r0i>r0jThen called { xiFor the same mother sequence { x }0Is superior to { x }jIs marked as { x }i}>{xj} 。
S2: counting each scaled innovation resource data of each enterprise by adopting a Bayesian statistical model to form an original database of each innovation resource data of each enterprise, and recording the statistical model as a preliminary digital platform;
a Bayesian statistical model is utilized to perform broad data statistics first, and an original database is formed.
It should be noted that the bayesian statistical model is a statistical model in the prior art, and redundant description is not repeated here.
S3: respectively establishing data nodes of each innovative resource data of each enterprise in the digital platform by using data in the original database, and constructing respective data modules by using the respective data nodes;
referring to fig. 2, firstly, two-dimensional statistics is performed on data, and a manner of converting two-dimensional statistics into three-dimensional statistics is synchronously calculated, which specifically includes the following steps:
s3.1: taking a spherical plane diffusion two-dimensional statistical mode as a statistical mode of a preliminary digital platform to carry out statistics on original database data;
s3.2: sequentially bringing data in the original database into groups to form respective data nodes;
s3.3: connecting the respective data nodes by using data lines, defining the formed two-dimensional data space display modules as the respective data modules, and recording the central points of the respective two-dimensional data space display modules as representative points of the respective data modules, such as a point P in fig. 3;
as shown in fig. 3, when the two-dimensional statistical method of spherical plane diffusion is used, the central sphere value is 0, and the radius statistical diffusion value is 1.
S4: determining a statistical central value a and a statistical extended value b of the digital platform through respective data modules;
further, the method specifically comprises the following steps:
s4.1: constructing a spherical plane diffusion three-dimensional statistical platform according to a spherical plane diffusion two-dimensional statistical mode, namely performing three-dimensional module statistics by setting a central sphere value to be 0 and a radius statistical diffusion value to be 1;
s4.2: taking the area value of each data module as a statistical solid value of a three-dimensional statistical system, namely the area value = volume value, and incorporating the area value = volume value into the statistics of the three-dimensional module, and determining a statistical center value a and a statistical expansion value b of the digital platform according to the following formula:
wherein a is a statistical central value, b is a statistical extension value, N is a statistical number of data modules, S is an area of the data modules, and S isNRepresenting the area value of the nth data block.
Furthermore, considering that when the statistics is performed in a spherical plane diffusion two-dimensional statistical manner, since the spherical statistics is circular, the area of the data module varies countless, that is, the shape and area of the data module are not fixed, when the data module is formed by connecting the respective data nodes with the data lines, the following conditions need to be satisfied:
wherein, sigma represents the data diffusion degree, N is the statistical number of the data modules, S is the area of the data modules, and SNRepresenting the Nth data modulusArea value of the block.
S5: acquiring a loading node value and a loading node position by combining the statistical center value a, the statistical expansion value b and the scaled innovative resource data;
further, the method specifically comprises the following steps: respectively acquiring a three-dimensional platform loading node value and a loading node position according to the following formula:
loading a node value:
loading node positions:
wherein S isNThe area value of the Nth data module is represented, pi is an connotation factor, alpha is first resource data, beta is second resource data, gamma is third resource data, delta is fourth resource data, and epsilon is fifth resource data.
Namely, each group of innovation resource data of each enterprise corresponds to a loading node value and a loading node position of the three-dimensional digital platform.
S6: and incorporating each loading node value and the corresponding loading node position into the three-dimensional module statistics to form a three-dimensional digital platform.
Further, referring to fig. 5, after obtaining the respective load node value and load node position, the method further includes the following steps:
s1: mutually determining a central difference value xi according to the respective loaded node values;
s2: determining the center offset degree theta according to the positions of the respective loading nodes;
s3: determining mutual comparison data modules according to the mutual determined center difference xi and the center offset theta, and incorporating the mutual comparison data modules into the three-dimensional module statistics for hidden display;
where ξ = ρ2-ρ1，θ=（τ2-τ1）/（τ2+τ1）；
Wherein ρ is a loading node value of the same type of innovation resource data, and τ is a loading node position of the same type of innovation resource data.
Further, referring to fig. 6, the digital platform further includes an optimization recommendation mechanism, and the establishment method includes:
s1: comparing any two groups of innovative resource data to obtain a difference value of each type of innovative resource data loaded into a node value, namely determining the type of the innovative resource data with the largest difference value along the direction according to the central difference value xi;
s2: obtaining the position distance of the position offset of the loading node of the innovative resource data type with the largest difference value along the direction of the index, namely determining the offset position distance according to the center offset degree theta;
s3: defining the category of the innovative resource data with the largest difference extension direction as the optimization direction, and determining the optimization degree according to the position distance.
Additionally, referring to fig. 4, the digital platform further includes a call mechanism, which includes the following modules:
a reduction module 100, configured to perform reduction processing on a data module of the digital platform;
the compression module 200 is used for compressing the data module of the digital platform;
and a transmission module 300 for transmitting the compressed data module.
It should be noted that the reduction processing is to select by using the MCU cpu, for example, selecting the enterprise a and the enterprise B, checking the innovation capabilities of the enterprise a and the enterprise B, and the included data packets include the enterprise innovation data a, the enterprise innovation data B, the innovation capability comparison between the enterprise a and the enterprise B, the optimization direction and optimization degree of the enterprise a, and the optimization direction and optimization degree of the enterprise B, and select the data packet to be looked up by using the MCU processor, to reduce the data packet that does not need to be looked up, thereby increasing the transmission speed.
The invention provides a method for building an industrial resource comprehensive digital platform, which comprises the steps of firstly carrying out proportional unification on innovative resource data of each enterprise, then preliminarily building a two-dimensional platform, and bringing in a node data module after obtaining a statistical central value and a statistical extension value of a three-dimensional platform, thereby completing the building, clearly displaying the innovation capability of each enterprise, comparing the innovation capability with other enterprise data, clearly providing the comparison condition between enterprises while simultaneously providing the comparison condition between the capabilities, synchronously providing the innovation capability optimization direction and degree of the enterprises, reducing and compressing data processing when calling platform data, reducing the data capacity, improving the transmission speed, and intensively solving the problems that no clear display platform exists for creating new resources of the enterprises in each industry at present and no good description is available for the innovation capability of the enterprises per se.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (6)
1. A method for building an industrial resource comprehensive digital platform is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
the big data statistics selects each innovative resource data of all enterprises in the industry, and each innovative resource data of each enterprise is proportionally unified according to the connotation factor pi;
counting each scaled innovation resource data of each enterprise by adopting a Bayesian statistical model to form an original database of each innovation resource data of each enterprise, and recording the statistical model as a preliminary digital platform;
respectively establishing data nodes of each innovative resource data of each enterprise in the digital platform by using data in the original database, and constructing respective data modules by using the respective data nodes, wherein the method specifically comprises the step of taking a spherical plane diffusion two-dimensional statistical mode as a statistical mode of a preliminary digital platform to carry out statistics on the data of the original database; sequentially bringing data in the original database into groups to form respective data nodes; connecting the respective data nodes by adopting data lines, defining the formed two-dimensional data space display modules as respective data modules, and recording the central points of the respective two-dimensional data space display modules as representative points of the respective data modules; when the spherical plane diffusion two-dimensional statistical mode is used for statistics, the central sphere value is 0, and the radius statistical diffusion value is 1;
determining a statistical center value a and a statistical extension value b of the digital platform through respective data modules, and specifically constructing a spherical plane diffusion three-dimensional statistical platform according to a spherical plane diffusion two-dimensional statistical mode, namely performing three-dimensional module statistics by setting a central sphere point value to be 0 and a radius statistical diffusion value to be 1; the area value of each data module is taken as the statistical solid value of the three-dimensional statistical system and is included in the statistics of the three-dimensional modules, the statistical central value a and the statistical expansion value b of the digital platform are determined according to the following formula,
wherein a is a statistical central value, b is a statistical extension value, N is a statistical number of data modules, S is an area of the data modules, and S isNRepresenting an area value of the Nth data module;
combining the statistical center value a, the statistical spread value b and the scaled innovative resource data to obtain a loaded node value and a loaded node position, wherein the loaded node value and the loaded node position are obtained according to the following formulas,
loading a node value:
loading node positions:
wherein S isNRepresenting the Nth data moduleThe area value of (a), pi is an inclusion factor, alpha is first resource data, beta is second resource data, gamma is third resource data, delta is fourth resource data, and epsilon is fifth resource data;
and incorporating each loading node value and the corresponding loading node position into the three-dimensional module statistics to form a three-dimensional digital platform.
2. The industrial resource comprehensive digital platform building method according to claim 1, characterized in that: the method specifically comprises the steps of respectively carrying out scaling unification on each innovative resource data of each enterprise according to the connotation factor pi,
determining the connotation factor pi according to the relevance, wherein the relevance is defined as alpha + beta + gamma, epsilon, gamma, delta and delta, epsilon, alpha is first resource data, beta is second resource data, gamma is third resource data, delta is fourth resource data, and epsilon is fifth resource data;
respectively carrying out proportional unification on each innovative resource data of each enterprise according to the connotation factor pi;
wherein the formula of the connotation factor pi is determined according to the relevance,
；
and respectively carrying out proportional unification on each innovative resource data of each enterprise according to the connotation factor pi, namely correspondingly unifying each innovative resource data of each enterprise into first resource data alpha pi (%), second resource data beta pi (%), third resource data gamma pi (%), fourth resource data delta pi (%) and fifth resource data epsilon pi (%).
3. The industrial resource comprehensive digital platform building method according to claim 2, characterized in that: the statistics is carried out in a spherical plane diffusion two-dimensional statistical mode, when data lines are adopted to connect respective data nodes to form respective data modules, the following conditions are met,
wherein, sigma represents the data diffusion degree, N is the statistical number of the data modules, S is the area of the data modules, and SNRepresenting the area value of the nth data block.
4. The industrial resource comprehensive digital platform building method according to claim 3, characterized in that: the method further comprises the steps of obtaining the respective load node value and the load node position,
mutually determining a central difference value xi according to the respective loaded node values;
determining the center offset degree theta according to the positions of the respective loading nodes;
determining mutual comparison data modules according to the mutual determined center difference xi and the center offset theta, and incorporating the mutual comparison data modules into the three-dimensional module statistics for hidden display;
where ξ = ρ2-ρ1，θ=（τ2-τ1）/（τ2+τ1）；
Wherein ρ is a loading node value of the same type of innovation resource data, and τ is a loading node position of the same type of innovation resource data.
5. The industrial resource comprehensive digital platform building method according to claim 4, characterized in that: the digital platform also comprises an optimization recommendation mechanism which is established by the following steps,
comparing any two groups of innovative resource data to obtain a difference value of each type of innovative resource data loaded into a node value, namely determining the type of the innovative resource data with the largest difference value along the direction according to the central difference value xi;
obtaining the position distance of the position offset of the loading node of the innovative resource data type with the largest difference value along the direction of the index, namely determining the offset position distance according to the center offset degree theta;
defining the category of the innovative resource data with the largest difference extension direction as the optimization direction, and determining the optimization degree according to the position distance.
6. The industrial resource comprehensive digital platform building method according to claim 5, characterized in that: the digitizing platform also includes a call mechanism, including the following modules,
a reduction module (100) for performing a reduction process on a data module of the digitizing platform;
the compression module (200) is used for compressing the data module of the digital platform;
and the transmission module (300) is used for transmitting the compressed data module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111000519.6A CN113449019B (en) | 2021-08-30 | 2021-08-30 | Industrial resource comprehensive digital platform building method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111000519.6A CN113449019B (en) | 2021-08-30 | 2021-08-30 | Industrial resource comprehensive digital platform building method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113449019A CN113449019A (en) | 2021-09-28 |
| CN113449019B true CN113449019B (en) | 2021-11-26 |
Family
ID=77818941
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111000519.6A Active CN113449019B (en) | 2021-08-30 | 2021-08-30 | Industrial resource comprehensive digital platform building method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113449019B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116245260B (en) * | 2023-05-12 | 2023-08-08 | 国网吉林省电力有限公司信息通信公司 | Optimization method for deploying 5G base station based on substation resources |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046455A (en) * | 2015-09-08 | 2015-11-11 | 张战 | Enterprise modeling method and enterprise modeling device |
| CN111784485A (en) * | 2020-05-29 | 2020-10-16 | 浙江保融科技有限公司 | Method for realizing digital self-portrait dynamic portrayal of enterprise financial and resource resources in interactive mode |
| CN111985937A (en) * | 2020-08-13 | 2020-11-24 | 西安科技大学 | Method, system, storage medium and computer equipment for evaluating value information of transaction traders |
| CN112232909A (en) * | 2020-10-13 | 2021-01-15 | 汉唐信通(北京)科技有限公司 | Business opportunity mining method based on enterprise portrait |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11195057B2 (en) * | 2014-03-18 | 2021-12-07 | Z Advanced Computing, Inc. | System and method for extremely efficient image and pattern recognition and artificial intelligence platform |
| CN106096903A (en) * | 2016-05-28 | 2016-11-09 | 张维秀 | A kind of standardization effort management platform |
| US20180308024A1 (en) * | 2017-04-25 | 2018-10-25 | Steve Kilner | Systems and methods for data-driven process visualization |
-
2021
- 2021-08-30 CN CN202111000519.6A patent/CN113449019B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046455A (en) * | 2015-09-08 | 2015-11-11 | 张战 | Enterprise modeling method and enterprise modeling device |
| CN111784485A (en) * | 2020-05-29 | 2020-10-16 | 浙江保融科技有限公司 | Method for realizing digital self-portrait dynamic portrayal of enterprise financial and resource resources in interactive mode |
| CN111985937A (en) * | 2020-08-13 | 2020-11-24 | 西安科技大学 | Method, system, storage medium and computer equipment for evaluating value information of transaction traders |
| CN112232909A (en) * | 2020-10-13 | 2021-01-15 | 汉唐信通(北京)科技有限公司 | Business opportunity mining method based on enterprise portrait |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113449019A (en) | 2021-09-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111401743B (en) | Dynamic traffic influence evaluation method for urban road construction period | |
| CN113449019B (en) | Industrial resource comprehensive digital platform building method | |
| CN106777093A (en) | Skyline inquiry systems based on space time series data stream application | |
| WO2021042748A1 (en) | Estimation method of bearing operation condition stability employing probability distribution, and device | |
| CN115080275B (en) | Twin service assembly based on real-time data model and method thereof | |
| CN112906865A (en) | Neural network architecture searching method and device, electronic equipment and storage medium | |
| US8269764B2 (en) | Three dimensional spatial engine in a relational database management system | |
| CN116167620A (en) | Multi-dimensional risk dynamic evaluation method and system for large-scale vehicle exhibition fire disaster | |
| Zhiling et al. | Location: A feature for service selection in the era of big data | |
| CN113468163B (en) | Multisource heterogeneous public security big data intelligent docking engine system | |
| CN112529292A (en) | Traffic network line optimization method based on improved Floyd algorithm | |
| CN112926991B (en) | Method and system for grading severity level of cash-out group | |
| CN113609126B (en) | Integrated storage management method and system for multi-source space-time data | |
| WO2021227636A1 (en) | Microservice processing method and apparatus, storage medium, and electronic device | |
| CN114022045A (en) | Regional economic development level determining method and device and terminal equipment | |
| CN113516065A (en) | Data weight measuring and calculating method and device based on block chain, server and storage medium | |
| CN113128837A (en) | Big data analysis system of rail transit power supply system | |
| CN114257527B (en) | Network bearing capacity estimation method | |
| CN112434958A (en) | Panic group emergency evacuation analysis method based on big data | |
| Huynh et al. | Recommender systems based on resonance relationship of criteria with Choquet operation | |
| CN109933579A (en) | A kind of part k nearest neighbor missing values interpolation system and method | |
| CN116069595B (en) | Operation and maintenance monitoring method based on log | |
| CN113159409B (en) | National city air quality prediction method based on group perception map neural network | |
| CN116467610B (en) | Data topology analysis method, device, equipment and storage medium based on 5G message | |
| CN112184051A (en) | Employee work investigation method based on social network analysis technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |