CN112069323B - Recommendation method based on industrial knowledge graph - Google Patents
Recommendation method based on industrial knowledge graph Download PDFInfo
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- CN112069323B CN112069323B CN202010774714.3A CN202010774714A CN112069323B CN 112069323 B CN112069323 B CN 112069323B CN 202010774714 A CN202010774714 A CN 202010774714A CN 112069323 B CN112069323 B CN 112069323B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3329—Natural language query formulation or dialogue systems
Abstract
The invention relates to a recommendation method based on an industrial knowledge graph, which comprises the following steps: acquiring an industrial knowledge graph, wherein the industrial knowledge graph comprises points and edges, each point corresponds to one industrial entity, and if a relation exists between two industrial entities, one edge exists between the points corresponding to the two industrial entities; determining keywords; calculating the point association degree between the key words and each point in the industrial knowledge graph and the side association degree between the key words and each point in the industrial knowledge graph; and determining the recommended object according to the point association degree and the side association degree. The method provided by the invention can be recommended based on the relation between industrial data.
Description
Technical Field
The invention relates to the technical field of information recommendation, in particular to a recommendation method based on an industrial knowledge graph.
Background
In the industrial field, large amounts of data are generated during the operation or production of equipment. For example, the timing signals or analog signals generated by the various sensors on the device during its operation, parameters of the device itself (e.g., hardware address of the device, address of memory). How to make recommendations based on relationships between industrial data is a focus of attention.
Disclosure of Invention
First, the technical problem to be solved
In order to solve the problems, the invention provides a recommendation method based on an industrial knowledge graph.
(II) technical scheme
In order to achieve the above purpose, the main technical scheme adopted by the invention comprises the following steps:
a recommendation method based on an industrial knowledge graph, the method comprising:
s101, acquiring an industrial knowledge graph, wherein the industrial knowledge graph comprises points and edges, each point corresponds to one industrial entity, and if a relation exists between two industrial entities, one edge exists between the points corresponding to the two industrial entities;
s102, determining keywords;
S103, calculating the point association degree between the key words and each point in the industrial knowledge graph and the side association degree between the key words and each point in the industrial knowledge graph;
s104, determining a recommended object according to the point association degree and the side association degree.
Optionally, calculating the point association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
S201, acquiring description information of each industrial entity;
S202, determining the point association degree of the point corresponding to the industrial entity where the description information of the keyword is not included as 0;
S203, determining the point association degree of the point corresponding to the industrial entity where the description information comprising the keyword is located as N1/N;
Wherein N1 is the number of words with the same description information as the keywords, and N is the total number of words of the keywords.
Optionally, calculating the point association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
s211, acquiring description information of each industrial entity;
s212, determining the point association degree of the point corresponding to the industrial entity where the description information of the keyword is not included as 0;
s213, determining the point association degree of the point corresponding to the industrial entity where the description information comprising the keyword is located as X, D, N1/N;
Wherein N1 is the number of words with the same description information as the keywords, N is the total number of words of the keywords, X is the grade of the industrial entity, and D is the degree of the point corresponding to the industrial entity.
Optionally, calculating the point association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
s221, acquiring description information of each industrial entity;
s222, setting the point association degree of points corresponding to all industrial entities to be 0;
s223, determining a point corresponding to the industrial entity where the description information comprising the keyword is located;
s224, sequentially selecting a point corresponding to the industrial entity where the description information comprising the keyword is located, and determining the communication rate between each point in the industrial knowledge graph and the selected point according to the following formula:
Aij＝Yij*Xi*Di/Dj；
s225, determining the point association degree of the point corresponding to the industrial entity where the description information comprising the keyword is located as the current point association degree +xi, di, N1/N; determining the point association degree of the point corresponding to the industrial entity which does not comprise the description information of the keyword as the sum of the current point association degree and the communication rate of the point association degree and the point corresponding to the industrial entity which comprises the description information of the keyword;
Wherein i is the identification of the selected industrial entity, j is the identification of the point in the industrial knowledge graph, aij is the communication rate between the point j in the industrial knowledge graph and the selected point i, YIj is the number of nodes included between the point j in the industrial knowledge graph and the selected point i, xi is the grade of the industrial entity of the selected point i, di is the degree of the selected point i, and Dj is the degree of the point j in the industrial knowledge graph.
Optionally, calculating the side association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
S301, determining the point association degree of two points connected by the edge;
s302, determining the average value of the point association degrees of the two points as the side association degree of the side.
Optionally, calculating the side association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
s311, determining the point association degree of two points connected by the edge;
S312, the degree of association of the edge=the degree of point association of one point u to which the edge is connected/du+the degree of point association of one point v to which the edge is connected/Dv;
Where Du is the degree of point u and Dv is the degree of point v.
Optionally, calculating the side association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
s321, determining the point association degree of two points connected by the edge;
S322, determining the maximum value max (u), the minimum value min (u) and the average value avg (u) in the point association degrees of all points connected by one point u connected by the edge;
s323, determining the maximum value max (v), the minimum value min (v) and the average value avg (v) in the point association degrees of all points connected by one point v connected by the edge;
s324, the association of the edge= (max (u) -avg (u)) × the point association of a point u connected to the edge/Du × (u) + (max (v) -avg (v)) × the point association of a point v connected to the edge/Dv × (v);
Where Du is the degree of point u and Dv is the degree of point v.
Optionally, the step S104 specifically includes:
s104-1, determining a median value of the point association degree;
s104-2, taking the point with the point association degree larger than the median as a candidate point;
S104-3, determining a maximum connected graph in the subgraph formed by the candidate points;
s104-4, determining a recommended object based on the maximum connected graph.
The step S104-4 specifically comprises the following steps:
s104-4-11, determining the side association degree of each side in the maximum connected graph;
s104-4-12, determining a recommended value of each side=association degree of the side; a, namely the highest grade of the industrial entity corresponding to the two nodes connected by the edge, and Da is the sum of the two node degrees connected by the edge;
S104-4-13, determining a graph consisting of edges with recommended values larger than a preset threshold and connected candidate nodes as a recommended object.
Optionally, the step S104-4 specifically includes:
s104-4-21, determining the side association degree of each side in the maximum connected graph;
S104-4-22, determining a recommended value of each side = a correlation degree of the side =a×Da×a minimum correlation degree of two nodes connected by the side/a maximum correlation degree of two nodes connected by the side; a, namely the highest grade of the industrial entity corresponding to the two nodes connected by the edge, and Da is the sum of the two node degrees connected by the edge;
s104-4-23, determining a graph consisting of edges with recommended values larger than a preset threshold and connected candidate nodes as a recommended object.
(III) beneficial effects
The beneficial effects of the invention are as follows: acquiring an industrial knowledge graph, wherein the industrial knowledge graph comprises points and edges, each point corresponds to one industrial entity, and if a relation exists between two industrial entities, one edge exists between the points corresponding to the two industrial entities; determining keywords; calculating the point association degree between the key words and each point in the industrial knowledge graph and the side association degree between the key words and each point in the industrial knowledge graph; and determining a recommended object according to the point association degree and the side association degree, and further realizing recommendation based on the relation between industrial data.
Drawings
Fig. 1 is a schematic flow chart of a recommendation method based on an industrial knowledge graph according to an embodiment of the application;
Fig. 2 is a schematic structural diagram based on an industrial knowledge graph according to an embodiment of the present application.
Detailed Description
The invention will be better explained by the following detailed description of the embodiments with reference to the drawings.
In the industrial field, large amounts of data are generated during the operation or production of equipment. For example, the timing signals or analog signals generated by the various sensors on the device during its operation, parameters of the device itself (e.g., hardware address of the device, address of memory). How to make recommendations based on relationships between industrial data is a focus of attention.
Based on the above, the invention provides a recommendation method based on an industrial knowledge graph, which is used for acquiring the industrial knowledge graph, wherein the industrial knowledge graph comprises points and edges, each point corresponds to one industrial entity, and if a relation exists between two industrial entities, one edge exists between the points corresponding to the two industrial entities; determining keywords; calculating the point association degree between the key words and each point in the industrial knowledge graph and the side association degree between the key words and each point in the industrial knowledge graph; and determining a recommended object according to the point association degree and the side association degree, and further realizing recommendation based on the relation between industrial data.
Referring to fig. 1, the recommendation method based on the industrial knowledge graph comprises the following implementation processes:
S101, acquiring an industrial knowledge graph.
The industrial knowledge graph comprises points and edges, each point corresponds to one industrial entity, and if a relation exists between two industrial entities, one edge exists between the points corresponding to the two industrial entities.
An industrial knowledge graph as shown in fig. 2.
S102, determining keywords.
The keywords herein may be user entered or may be derived from user actions.
And S103, calculating the point association degree between the key words and each point in the industrial knowledge graph and the side association degree between the key words and each point in the industrial knowledge graph.
1) The calculation schemes of the point association degree between the key words and each point in the industrial knowledge graph can be various, and only a few implementation schemes are provided below.
The first implementation scheme is as follows:
s201, acquiring description information of each industrial entity.
The industrial entity herein describes information including, but not limited to, a profile of the industrial entity.
S202, determining the point association degree of the point corresponding to the industrial entity where the description information which does not include the keyword is located as 0.
S203, determining the point association degree of the point corresponding to the industrial entity where the description information comprising the keyword is located as N1/N.
Wherein N1 is the number of words with the same description information as the keywords, and N is the total number of words of the keywords.
The second implementation scheme is as follows:
S211, acquiring description information of each industrial entity.
S212, determining the point association degree of the point corresponding to the industrial entity where the description information which does not include the keyword is located as 0.
S213, determining the point association degree of the point corresponding to the industrial entity where the description information including the keyword is located as X, D and N1/N.
Wherein N1 is the number of words with the same description information as the keywords, N is the total number of words of the keywords, X is the grade of the industrial entity, and D is the degree of the point corresponding to the industrial entity.
The industrial entity level herein is a predetermined level for describing the importance of an industrial entity, such as an industrial entity level 1, a general industrial entity level 10, etc., which relates to confidential information or a confidential product. The setting is specifically performed according to the situation in practical application, and is not limited here.
The third implementation scheme is as follows:
s221, acquiring description information of each industrial entity.
S222, setting the point association degree of the points corresponding to all the industrial entities to be 0.
S223, determining the point corresponding to the industrial entity where the description information comprising the keywords is located.
S224, sequentially selecting a point corresponding to the industrial entity where the description information comprising the keywords is located, and determining the communication rate between each point in the industrial knowledge graph and the selected point according to the following formula:
Aij＝Yij*Xi*Di/Dj。
Wherein i is the identification of the selected industrial entity, j is the identification of the point in the industrial knowledge graph, aij is the communication rate between the point j in the industrial knowledge graph and the selected point i, YIj is the number of nodes included between the point j in the industrial knowledge graph and the selected point i, xi is the grade of the industrial entity of the selected point i, di is the degree of the selected point i, and Dj is the degree of the point j in the industrial knowledge graph.
S225, determining the point association degree of the point corresponding to the industrial entity where the description information comprising the keyword is located as the current point association degree +Xi Di N1/N.
And determining the point association degree of the point corresponding to the industrial entity where the description information of the keyword is not included as follows: the current point association degree is plus the sum of the connectivity rate of the point association degree and the point corresponding to the industrial entity where the descriptive information including the keyword is located, i.e. N1/n|Di-dj|.
Since the point association degrees of the points corresponding to all the industrial entities are set to 0 in S222, there must be a value for the point association degrees at present, and the value may be the true point association degrees calculated recently or 0.
If the point corresponding to the industrial entity where the description information including the keyword does not include is point a, the point association degree of point a=the point association degree of the current point a + (the connectivity ratio of point a to the first point corresponding to the industrial entity where the description information including the keyword includes is equal to N1/n|di-dj|+the connectivity ratio of point a to the second point corresponding to the industrial entity where the description information including the keyword includes is equal to N1/n|di-dj|+ … … +the connectivity ratio of point a to the last point corresponding to the industrial entity where the description information including the keyword includes).
2) The calculation schemes of the side association degree between the key words and each point in the industrial knowledge graph can be various, and only a few implementation schemes are provided below.
The first implementation scheme is as follows:
S301, determining the point association degree of two points connected by the edge.
S302, determining the average value of the point association degrees of the two points as the side association degree of the side.
The second implementation scheme is as follows:
S311, determining the point association degree of two points connected by the edge.
S312, the degree of association of the edge=the degree of association of the point u to which the edge is connected/du+the degree of association of the point v to which the edge is connected/Dv.
Where Du is the degree of point u and Dv is the degree of point v.
The third implementation scheme is as follows:
s321, determining the point association degree of two points connected by the edge.
S322, determining the maximum value max (u), the minimum value min (u) and the average value avg (u) in the point association degrees of all points connected by one point u connected by the edge.
S323, determining the maximum value max (v), the minimum value min (v) and the average value avg (v) in the point association degrees of all the points connected by one point v connected by the edge.
S324, the association of the edge= (max (u) -avg (u)). The point association of a point u connected to the edge/Du + (max (v) -avg (v)). The point association of a point v connected to the edge/Dv × min (v).
Where Du is the degree of point u and Dv is the degree of point v.
S104, determining a recommended object according to the point association degree and the side association degree.
In particular, the method comprises the steps of,
S104-1, determining the median of the point association degrees.
And S104-2, taking the point with the point association degree larger than the median as the candidate point.
S104-3, determining the maximum connected graph in the subgraph formed by the candidate points.
S104-4, determining a recommended object based on the maximum connected graph.
One implementation of S104-4 is as follows,
S104-4-11, determining the side association degree of each side in the maximum connected graph.
S104-4-12, determining a recommended value of each side=association of the side.
And a, namely the highest grade of the industrial entity corresponding to the two nodes connected by the edge, and Da is the sum of the two node degrees connected by the edge.
S104-4-13, determining a graph consisting of edges with recommended values larger than a preset threshold and connected candidate nodes as a recommended object.
Another implementation of S104-4 is as,
S104-4-21, determining the side association degree of each side in the maximum connected graph.
And S104-4-22, determining a recommended value of each side=the association degree of the side, namely a Da, namely the minimum association degree of two nodes connected by the side/the maximum association degree of two nodes connected by the side.
And a, namely the highest grade of the industrial entity corresponding to the two nodes connected by the edge, and Da is the sum of the two node degrees connected by the edge.
S104-4-23, determining a graph consisting of edges with recommended values larger than a preset threshold and connected candidate nodes as a recommended object.
The beneficial effects are that: acquiring an industrial knowledge graph, wherein the industrial knowledge graph comprises points and edges, each point corresponds to one industrial entity, and if a relation exists between two industrial entities, one edge exists between the points corresponding to the two industrial entities; determining keywords; calculating the point association degree between the key words and each point in the industrial knowledge graph and the side association degree between the key words and each point in the industrial knowledge graph; and determining a recommended object according to the point association degree and the side association degree, and further realizing recommendation based on the relation between industrial data.
It should be understood that the invention is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. The method processes of the present invention are not limited to the specific steps described and shown, but various changes, modifications and additions, or the order between steps may be made by those skilled in the art after appreciating the spirit of the present invention.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. The present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
Finally, it should be noted that: the embodiments described above are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (7)
1. A recommendation method based on an industrial knowledge graph, the method comprising:
s101, acquiring an industrial knowledge graph, wherein the industrial knowledge graph comprises points and edges, each point corresponds to one industrial entity, and if a relation exists between two industrial entities, one edge exists between the points corresponding to the two industrial entities;
s102, determining keywords;
S103, calculating the point association degree between the key words and each point in the industrial knowledge graph and the side association degree between the key words and each point in the industrial knowledge graph;
The step S103 of calculating the point association degree between the keyword and each point in the industrial knowledge graph specifically includes:
S201, acquiring description information of each industrial entity;
S202, determining the point association degree of the point corresponding to the industrial entity where the description information of the keyword is not included as 0;
S203, determining the point association degree of the point corresponding to the industrial entity where the description information comprising the keyword is located as N1/N;
wherein N1 is the number of words with the same description information as the keywords, and N is the total number of words of the keywords;
the side association degree between the key words and each point in the industrial knowledge graph specifically comprises the following steps:
S301, determining the point association degree of two points connected by the edge;
s302, determining the average value of the point association degrees of two points as the side association degree of the side;
S104, determining a recommended object according to the point association degree and the side association degree;
The step S104 specifically includes:
s104-1, determining a median value of the point association degree;
s104-2, taking the point with the point association degree larger than the median as a candidate point;
S104-3, determining a maximum connected graph in the subgraph formed by the candidate points;
s104-4, determining a recommended object based on the maximum connected graph.
2. The method according to claim 1, wherein the calculating the point association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
s211, acquiring description information of each industrial entity;
s212, determining the point association degree of the point corresponding to the industrial entity where the description information of the keyword is not included as 0;
s213, determining the point association degree of the point corresponding to the industrial entity where the description information comprising the keyword is located as X, D, N1/N;
Wherein N1 is the number of words with the same description information as the keywords, N is the total number of words of the keywords, X is the grade of the industrial entity, and D is the degree of the point corresponding to the industrial entity.
3. The method according to claim 1, wherein the calculating the point association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
s221, acquiring description information of each industrial entity;
s222, setting the point association degree of points corresponding to all industrial entities to be 0;
s223, determining a point corresponding to the industrial entity where the description information comprising the keyword is located;
s224, sequentially selecting a point corresponding to the industrial entity where the description information comprising the keyword is located, and determining the communication rate between each point in the industrial knowledge graph and the selected point according to the following formula:
Aij＝Yij*Xi*Di/Dj；
S225, determining the point association degree of the point corresponding to the industrial entity where the description information comprising the keyword is located as the current point association degree +xi, di, N1/N; determining the point association degree of the point corresponding to the industrial entity which does not comprise the description information of the keyword as the current point association degree+the communication rate of the point association degree and the point corresponding to the industrial entity which comprises the description information of the keyword
* The sum of N1/n|di-dj|;
Wherein i is the identification of the selected industrial entity, j is the identification of the point in the industrial knowledge graph, aij is the communication rate between the point j in the industrial knowledge graph and the selected point i, YIj is the number of nodes included between the point j in the industrial knowledge graph and the selected point i, xi is the grade of the industrial entity of the selected point i, di is the degree of the selected point i, and Dj is the degree of the point j in the industrial knowledge graph.
4. The method according to claim 1, wherein the calculating of the side association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
s311, determining the point association degree of two points connected by the edge;
S312, the degree of association of the edge=the degree of point association of one point u to which the edge is connected/du+the degree of point association of one point v to which the edge is connected/Dv;
Where Du is the degree of point u and Dv is the degree of point v.
5. The method according to claim 1, wherein the calculating of the side association degree between the keyword and each point in the industrial knowledge graph in S103 specifically includes:
s321, determining the point association degree of two points connected by the edge;
S322, determining the maximum value max (u), the minimum value min (u) and the average value avg (u) in the point association degrees of all points connected by one point u connected by the edge;
s323, determining the maximum value max (v), the minimum value min (v) and the average value avg (v) in the point association degrees of all points connected by one point v connected by the edge;
s324, the association of the edge= (max (u) -avg (u)) × the point association of a point u connected to the edge/Du × (u) + (max (v) -avg (v)) × the point association of a point v connected to the edge/Dv × (v);
Where Du is the degree of point u and Dv is the degree of point v.
6. The method according to claim 1, wherein S104-4 specifically comprises:
s104-4-11, determining the side association degree of each side in the maximum connected graph;
s104-4-12, determining a recommended value of each side=association degree of the side; a, namely the highest grade of the industrial entity corresponding to the two nodes connected by the edge, and Da is the sum of the two node degrees connected by the edge;
S104-4-13, determining a graph consisting of edges with recommended values larger than a preset threshold and connected candidate nodes as a recommended object.
7. The method according to claim 1, wherein S104-4 specifically comprises:
s104-4-21, determining the side association degree of each side in the maximum connected graph;
S104-4-22, determining a recommended value of each side = a correlation degree of the side =a×Da×a minimum correlation degree of two nodes connected by the side/a maximum correlation degree of two nodes connected by the side; a, namely the highest grade of the industrial entity corresponding to the two nodes connected by the edge, and Da is the sum of the two node degrees connected by the edge;
s104-4-23, determining a graph consisting of edges with recommended values larger than a preset threshold and connected candidate nodes as a recommended object.
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