CN108932261B - Method and device for updating business data processing information table of knowledge base - Google Patents

Abstract

According to the present invention, there is provided a method for updating a business data processing information table of a knowledge base of an intelligent factory, comprising: acquiring correction quantity of time sensitivity factor corresponding to processing rule of service data in service data processing information table; reading a historical time sensitivity factor of the processing rule; updating the time sensitivity factor of the processing rule using the correction amount of the acquired time sensitivity factor and the read historical time sensitivity factor; reading the credibility of the processing rule from the knowledge base; calculating a ranking parameter of the processing rule based on the updated time sensitivity factor and the read confidence level; and updating the processing rule and the ordering of the service data corresponding to the processing rule in the service data processing information table according to the calculated ordering parameter.

Description

Method and device for updating business data processing information table of knowledge base

technical field

The invention relates to a method and device for updating a business data processing information table of a knowledge base of an intelligent factory, which can use the credibility and time sensitivity factors of the business data processing rules to correspond to the processing rules Sort and update the business data of the smart factory, thereby updating the business data processing information table of the knowledge base of the smart factory.

Background technique

Knowledge Base, also known as intelligent database or artificial intelligence database. The knowledge base is a structured, easy-to-operate, easy-to-use, comprehensively organized knowledge cluster in knowledge engineering. It is aimed at the needs of solving problems in a certain (or some) fields, and uses a certain (or several) knowledge representation methods in the computer memory. A collection of interconnected pieces of knowledge stored, organized, managed, and used in . These pieces of knowledge include domain-related theoretical knowledge, factual data, and heuristic knowledge obtained from expert experience, such as definitions, theorems, algorithms, and common sense knowledge related to a certain domain.

As shown in Figure 1, in the smart factory, the devices connected to the smart gateway include not only the traditional PLC (programmable logic controller) and sensors connected to it via the industrial gateway, but also some new types of devices directly connected to it. Sensors, cameras, etc. New devices bring a variety of business data, such as video, audio and voice data, temperature, humidity, rotational speed, and so on. In addition, with the development of customized production, business problems and business rules are bound to become more and more complex. In order to adapt to this situation, the knowledge base can be applied in the factory (private cloud) and in the public cloud outside the factory to increase flexibility.

Chinese patent publication CN103177092A proposes a scheme for updating knowledge base data. As shown in Figure 4, according to this solution, in step S110, the feedback information of the user on the data information is obtained; in step S120, the credibility, feedback times and feedback information corresponding to the data information are read; The credibility level corresponding to the data information, the number of feedbacks and the feedback information are used to update the credibility level. Through this solution, the accuracy of data access can be effectively improved. The method for updating the credibility of the knowledge base is B=(b*k*cl+c*c2)/(k*c1+c2) (where b and k are respectively the corresponding credibility of the read data information and feedback times, c is the user's feedback information on the data information, c1 and c2 are weights, and B is the updated credibility). In addition, the data information may be reordered according to the degree of credibility. When accessing the data information in the knowledge base, the data information can be queried in descending order of reliability. When the matching data information is found for the first time, the data information is the most reliable data information among all the matching data information.

In the above solutions of the prior art, the data information in the knowledge base is only sorted according to the degree of reliability. However, there is usually such a situation in the production of smart factories: although the reliability is not high, it must be dealt with in time if it occurs. For example, some time-critical tasks, such as alarm processing, need to respond as quickly as possible. Alarm information may be classified according to urgency, such as first-level alarm, second-level alarm, third-level alarm, etc. The corresponding response time of each level of alarm is different. Therefore, there may be a risk that the time response will not be timely in the existing sorting method only according to the degree of credibility.

Contents of the invention

The present invention has been proposed in order to overcome the above-mentioned drawbacks of the prior art. Therefore, one of the purposes of the present invention is to propose a method and device for updating the business data processing information table of the knowledge base of the smart factory, which can utilize the credibility and time sensitivity factors of the business data processing rules to process The rule and the business data corresponding to the processing rule are sorted and updated, so as to update the business data processing information table of the knowledge base of the smart factory.

According to the present invention, a method for updating the business data processing information table of the knowledge base of the smart factory is proposed, including: obtaining the correction amount of the time sensitivity factor corresponding to the processing rule of the business data in the business data processing information table ; Read the historical time sensitivity factor of the processing rule; update the time sensitivity factor of the processing rule by using the obtained correction amount of the time sensitivity factor and the read historical time sensitivity factor; from the knowledge base Read the credibility of the processing rule; calculate the sorting parameter of the processing rule based on the updated time sensitivity factor and the read credibility; and according to the calculated sorting parameter, sort the business data The processing rule in the processing information table and the sorting of the business data corresponding to the processing rule are updated.

Preferably, the business data processing information table includes: business data, business data processing rules, credibility of the processing rules, and time sensitivity factors of the processing rules.

Preferably, the smaller the time sensitivity factor, the higher the ranking of the business data processing rule and the business data corresponding to the processing rule; the larger the time sensitivity factor, the higher the ranking of the business data processing rule. The lower the ranking of the business data corresponding to the processing rule.

Preferably, the sorting parameters of the processing rules are related to the industry coefficient of the industry in which the smart factory is located.

Preferably, the business data is processed by the top-ranked processing rules for the business data obtained by querying the business data processing information table.

Preferably, the correction amount of the time sensitivity factor is obtained from a device including a sensor and a camera.

In addition, according to the present invention, a device for updating the business data processing information table of the knowledge base of the smart factory is also proposed, including: acquiring the time sensitivity factor corresponding to the processing rule of the business data in the business data processing information table The unit of the correction amount; read the unit of the historical time sensitivity factor of the processing rule; use the obtained correction amount of the time sensitivity factor and the read historical time sensitivity factor to update the time sensitivity of the processing rule a unit of factor; a unit of reading the confidence level of the processing rule from the knowledge base; a unit of calculating the ranking parameter of the processing rule based on the updated time sensitivity factor and the read confidence level; and A unit for updating the sorting of the processing rule and the business data corresponding to the processing rule in the business data processing information table according to the calculated sorting parameter.

Invention effect

According to the present invention, the credibility and time sensitivity factor of the processing rules of business data can be used to sort and update the processing rules and the business data corresponding to the processing rules, so as to process information of the business data in the knowledge base of the smart factory The table is updated. Thus, when processing business processing, the business data can be processed with such sorted first processing rules, so the business data can be processed in consideration of time urgency and the like.

Description of drawings

The above objects and advantages of the present invention will become clearer by referring to the detailed description of the accompanying drawings, in which:

FIG. 1 is a schematic diagram for explaining device configuration and knowledge base configuration in a smart factory.

Fig. 2 is a flowchart showing a method for processing business data in a knowledge base by a server according to the present invention.

Fig. 3 is a flowchart showing a method for updating a business data processing information table in a knowledge base according to the present invention.

Fig. 4 is a flowchart showing a method for updating the credibility of data information in the prior art.

Detailed ways

Preferred embodiments of the present invention will be described below with reference to the accompanying drawings. In the drawings, the same elements will be denoted by the same reference symbols or numerals. Also, in the following description of the present invention, detailed descriptions of known functions and configurations will be omitted to avoid making the subject matter of the present invention unclear.

According to the present invention, in the business data processing information table in the knowledge base of the smart factory, a response time field may be added for business data and its processing rules, and the unit may be, for example, milliseconds. This response time can be specified as the time sensitivity factor T. The more sensitive a piece of data information is to time, the smaller T is and T>0. The value of T can indicate the level of time urgency of this piece of data information.

Table 1 below shows an example of a business data processing information table in the knowledge base of a smart factory, where a time sensitivity factor T is added.

Table 1

As shown in Table 1, a reliability B and a time sensitivity factor T are set for business data and its processing rules (processing methods specified according to the characteristics of business data).

Regarding the value of the time sensitivity factor T, respective initial values can be set in advance for different business data processing rules, and then can be updated according to the correction value of the T value received from devices such as sensors and cameras.

The update value T _new of the T value can be calculated according to the following formula 1.

T _new = T _{. ld} +x formula 1

Here, T _old is the time sensitivity factor of the currently read data information, and x is the correction amount of the time sensitivity factor of the business data processing rules received from devices such as sensors and cameras.

The sorting parameter F for business data can be calculated according to formula 2 such as the following

F＝B+C/T Formula 2

Here, C is the industry coefficient of the industry in which the smart factory is located. Here, the industry coefficient can be determined according to the percentage of time-critical tasks in the manufacturing industry to all tasks. C>0 and is a constant; B is credibility; T is The aforementioned time sensitivity factor.

According to the present invention, the time sensitivity factor T is added as the sorting basis. Here, the larger the sorting parameter F is, the higher the sorting of the business data and its processing rules is. According to formula 2, when the reliability B is determined, the larger the time sensitivity factor T is, that is, the less sensitive to time (less urgent), the smaller the sorting parameter F is, that is, the business data and its processing rules On the contrary, the smaller the time sensitivity factor T is, that is, the more sensitive to time (more urgent), the larger the sorting parameter F is, that is, the higher the sorting of business data and its processing rules.

Fig. 2 is a flowchart showing a method for processing business data in a knowledge base by a server according to the present invention.

As shown in FIG. 2, in step 201, the server receives service data (Di) uploaded by a device (such as a sensor, a camera).

In step 203, the server determines the processing rule (Ri) for the uploaded business data according to the business data processing information table in the knowledge base. The meaning of a processing rule is: if the business data meets a certain condition, a certain processing is performed. A processing rule is a rule for a certain business data. Here, it should be pointed out that, for the same business data with different processing rules, the processing rule determined according to the present invention is the processing rule with the highest ranking for the business data in the business data processing information table. Later, it will be specifically described with reference to examples.

Finally, in step 205, the server processes the uploaded business data (Di) according to the determined business rules.

Fig. 3 is a flowchart showing a method for updating a business data processing information table in a knowledge base according to the present invention.

In step 301, the server obtains the correction amount (x) of the time sensitivity factor corresponding to the processing rule of the service data from the device (eg, sensor, camera).

In step 303, the server reads the historical time sensitivity factor (T _old ) of the processing rule

In step 305, the server can update the time sensitivity of the processing rule in the business data processing information table by using the correction amount obtained in step 301 and the historical time sensitivity factor read in step 303, such as the previous formula 1 degree factor (updated to T _new ).

In step 307, the server reads the credibility (Bi) of the processing rule from the business data processing information table in the knowledge base.

In step 309 , based on the time sensitivity factor updated in step 305 and the reliability read in step 307 , the sorting parameter F for the processing rule is calculated according to the aforementioned formula 2.

In step 311 , based on the sorting parameter F calculated in step 309 , the processing rule of the business data processing information table and the sorting of the corresponding business data are updated in descending order of the sorting parameter F.

According to the above-mentioned embodiment, the ranking of business data and its processing rules can be updated according to the time urgency of the processing rules combined with its credibility, which is convenient for business data, from the business data processing information table to the consideration of the top ranking Time-critical processing rules, and use the processing rules to process business data.

Next, the first embodiment and the second embodiment of updating the sorting of the business data and the processing rules thereof in the business data processing information table will be described as examples.

first embodiment

Assume that during the production of parts in a smart factory, the camera monitors the movement status of parts on a certain section of the conveyor belt in real time.

For this scenario, the initial business data processing information table in the knowledge base of the smart factory is shown in Table 2 below.

Table 2

As shown in Table 2, the business data includes the following fact 1, fact 2 and fact 3, etc.

Fact 1: In 100 frames of images, the motion trajectory is an approximate straight line, and the offset value is less than or equal to 0.5cm.

Fact 2: In 100 frames of images, the motion trajectory is an approximate straight line, and the offset value is greater than 0.5cm and less than 1cm.

Fact 3: In 100 frames of images, the motion track is a curve.

The processing rules for business data include the following processing rule 1, processing rule 2, and processing rule 3, etc.

Processing rule 1: If the trajectory is a curve, issue a secondary alarm.

Processing rule 2: If the trajectory is an approximate straight line and the deviation value is greater than 0.5cm and less than 1cm, a first-level alarm will be issued.

Processing rule 3: If the trajectory is an approximate straight line and the offset value is less than or equal to 0.5cm, a normal notification will be issued.

Level 2 alerts require a faster response than Level 1 alerts.

The reliability and time sensitivity factors for processing rule 1 are: 0.9, 10, respectively

The reliability and time sensitivity factors for processing rule 2 are: 0.89, 40, respectively.

The reliability and time sensitivity factors for processing rule 3 are: 0.9, 100, respectively.

Here, the time sensitivity factors of 10, 40, and 100 represent that the service data must be responded within 10 milliseconds, 40 milliseconds, and 100 milliseconds. As shown in Table 2, the business data and their processing rules in the business data processing information table are sorted according to the sorting parameter F from large to small.

Next, how to update the sorting of the service data in the service data processing information table according to the present invention will be described.

As explained above, the aforementioned formula 1 can be used to update the time sensitivity factor, and the aforementioned formula 2 can be used to calculate the sorting parameter F for service data. Here, for the convenience of calculation, the value of the industry coefficient C in Formula 2 can be set to 1.

When deciding to update the time sensitivity factor T of processing rule 2, the server will receive the correction value x=-30 of the time sensitivity factor from devices such as sensors or cameras.

T _new =40−30=10 for the processing rule 2 can be calculated according to the foregoing formula 1, and then the sorting parameter F=B+1/T=0.99 for the processing rule 2 can be calculated. Next, according to the size of the sorting parameter F, the business data in it are re-sorted, and an updated business data processing information table is generated, as shown in Table 3.

table 3

As shown in Table 3 above, if the data information is only sorted according to the degree of credibility, the business data with serial number 1001 will be located after the business data with serial number 2000 in the data table, and the time spent querying the business data with serial number 1001 will be longer than querying The time spent on the service data of sequence number 2000.

Therefore, according to this embodiment, after increasing the time sensitivity factor, the time spent inquiring about the alarm information (sequence number 1001) with a reliability of 0.89 will be less than the time spent inquiring about the normal notification (serial number 2000) with a reliability of 0.9. It is beneficial for the knowledge base system to process specific time-sensitive information more quickly.

According to this embodiment, after updating the business data processing information table of the knowledge base as above, when accessing the data information (business data and its processing rules) in the knowledge base, it can be sorted according to the value of the parameter F from large to small Query data information sequentially. When the matching data information is found for the first time, the data information is the data information that can take both reliability and time response into account among all the matching data information.

According to this embodiment, increasing the time sensitivity factor enables a timely response to time-critical tasks in a smart factory. And with the continuous feedback correction of the time sensitivity factor, the timely response rate of the entire system can be improved.

second embodiment

Assume that during the production of parts in a smart factory, the camera monitors the movement status of parts on a certain section of the conveyor belt in real time.

For this scenario, the initial business data processing information table in the knowledge base of the smart factory is shown in Table 4 below.

Table 4

As shown in Table 4, the business data includes the following fact 1, fact 2 and fact 3, etc.

Fact 1: In 100 frames of images, the motion trajectory is an approximate straight line, and the offset value is less than or equal to 0.5cm.

Fact 2: In 100 frames of images, the motion trajectory is an approximate straight line, and the offset value is greater than 0.5cm and less than 1cm.

Fact 3: In 100 frames of images, the motion track is a curve.

The processing rules for business data include the following processing rule 1, processing rule 2, processing rule 3, and processing rule 4, etc.

Processing rule 1: If the trajectory is a curve, a three-level alarm is issued.

Processing rule 2: If the trajectory is an approximate straight line and the deviation value is greater than 0.5cm and less than 1cm, a first-level alarm will be issued.

Processing rule 3: If the trajectory is an approximate straight line and the deviation value is greater than 0.5cm and less than 1cm, a secondary alarm will be issued.

Processing rule 4: If the trajectory is an approximate straight line and the offset value is less than or equal to 0.5cm, a normal notification will be issued.

Level 2 alerts require a faster response than Level 1 alerts, and Level 3 alerts require a faster response than Level 2 alerts.

The reliability and time sensitivity factors for processing rule 1 are: 0.9, 10, respectively

The reliability and time sensitivity factors for processing rule 2 are: 0.89, 40, respectively.

The reliability and time sensitivity factors for processing rule 3 are: 0.85, 40, respectively.

The reliability and time sensitivity factors for processing rule 4 are: 0.8, 100, respectively.

Here, the time sensitivity factors of 10, 40, and 100 represent that the service data must be responded within 10 milliseconds, 40 milliseconds, and 100 milliseconds. As shown in Table 4, the business data and its processing rules in the business data processing information table are sorted according to the sorting parameter F from large to small.

Next, how to update the sorting of the service data in the service data processing information table according to the present invention will be described.

As explained above, the aforementioned formula 1 can be used to update the time sensitivity factor, and the aforementioned formula 2 can be used to calculate the sorting parameter F for service data. Here, for the convenience of calculation, the value of the industry coefficient C in Formula 2 can be set to 1.

When deciding to update the time sensitivity factor T of processing rule 3, the server will receive a correction value x=-30 of the time sensitivity factor from devices such as sensors or cameras.

T _new =40−30=10 for the processing rule 3 can be calculated according to the aforementioned formula 1, and then the sorting parameter F=B+1/T=0.95 for the processing rule 2 can be calculated. Next, according to the size of the sorting parameter F, the business data in it are re-sorted, and an updated business data processing information table is generated, as shown in Table 5.

table 5

As shown in Table 5, if the time sensitivity factor T is only modified for rule 3 for different rules 2 and 3 with the same fact 2, the sequence numbers for fact 2 and rule 3 become 1010. That is, after reordering, the ordering changes to be before fact 2 and rule 2. Thus, when querying the business data processing information table using Fact 2, the record with the sort number 1010 will be obtained. Then, Fact 2 can be processed by using the processing rule 3 which is sorted earlier than the rule 2 which is sorted later.

Therefore, according to the second embodiment, for the same business data with different processing rules, it is beneficial for the knowledge base system to process business data more quickly using specific time-sensitive processing rules.

In addition, it should be pointed out that the technology of the present disclosure can be implemented in the form of hardware and/or software (including firmware, microcode, etc.). Additionally, the technology of the present disclosure may take the form of a computer program product on a computer-readable medium having instructions stored thereon for use by or in connection with an instruction execution system (e.g., one or more processors) . In the context of the present disclosure, a computer-readable medium is any medium that can contain, store, convey, propagate or transport instructions. For example, a computer readable medium may include, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Specific examples of computer-readable media include: magnetic storage devices, such as magnetic tape or hard disk (HDD); optical storage devices, such as compact discs (CD-ROM); memory, such as random access memory (RAM) or flash memory; and/or wired / wireless communication link.

Several specific examples have been listed above to illustrate the present invention in detail. These individual examples are only used to illustrate the principle of the present invention and its implementation method, rather than to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention Various modifications and improvements can also be made by those skilled in the art. Accordingly, the invention should not be limited by the above-described embodiments, but by the appended claims and their equivalents.

Claims (6)

1. A method of updating a business data processing information table of a knowledge base of an intelligent plant, comprising:

acquiring correction quantity of time sensitivity factor corresponding to processing rule of service data in service data processing information table;

reading a historical time sensitivity factor of the processing rule;

updating the time sensitivity factor of the processing rule using the correction amount of the acquired time sensitivity factor and the read historical time sensitivity factor;

reading the credibility of the processing rule from the knowledge base;

calculating a ranking parameter of the processing rule based on the updated time sensitivity factor and the read confidence level; and

updating the processing rule and the ordering of the business data corresponding to the processing rule in the business data processing information table according to the calculated ordering parameter,

under the condition that the credibility is determined, the smaller the time sensitivity factor is, the earlier the processing rule of the service data and the sequence of the service data corresponding to the processing rule are; the larger the time sensitivity factor is, the later the processing rule of the service data and the sequence of the service data corresponding to the processing rule are.

2. The method of claim 1, wherein,

the service data processing information table includes: business data, processing rules for the business data, trustworthiness of the processing rules, and time sensitivity factors for the processing rules.

3. The method of claim 1, wherein,

the ordering parameter of the processing rule is related to an industry coefficient of an industry in which the intelligent factory is located.

4. The method of claim 2, wherein,

the service data is processed by a processing rule for the service data, which is obtained by querying a service data processing information table and is ranked forward.

5. The method of claim 1, wherein,

the correction of the time sensitivity factor is obtained from a device comprising a sensor and a camera.

6. An apparatus for updating a business data processing information table of a knowledge base of an intelligent plant, comprising:

a unit that acquires a correction amount of the time sensitivity factor corresponding to a processing rule of the service data in the service data processing information table;

a unit for reading a historical time sensitivity factor of the processing rule;

a unit that updates the time sensitivity factor of the processing rule using the correction amount of the acquired time sensitivity factor and the read history time sensitivity factor;

a unit for reading the credibility of the processing rule from the knowledge base;

a unit that calculates an ordering parameter of the processing rule based on the updated time sensitivity factor and the read confidence level; and

a unit for updating the processing rule and the sequence of the service data corresponding to the processing rule in the service data processing information table according to the calculated sequence parameter,

under the condition that the credibility is determined, the smaller the time sensitivity factor is, the earlier the processing rule of the service data and the sequence of the service data corresponding to the processing rule are; the larger the time sensitivity factor is, the later the processing rule of the service data and the sequence of the service data corresponding to the processing rule are.