CN112561388A - Information processing method, device and equipment based on Internet of things - Google Patents
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Abstract
The invention discloses an information processing method, device and equipment based on the Internet of things. In the method, firstly, the automatic production information and the production index information of the automatic production information collected by an information processing terminal are obtained, secondly, determining that the production index information is not matched with the preset index information, determining production index adjustment information corresponding to the production index information based on a pre-configured production index adjustment strategy, adjusting the information processing terminal, after the adjustment, the step of obtaining the automatic production information and the production index information of the automatic production information collected by the information processing terminal is returned to be executed until the step of obtaining the automatic production information and the production index information of the automatic production information are matched with the preset index information, so that the automatic production information can be accurately obtained, meanwhile, errors can be avoided when the production information is processed subsequently, and then according to a preset automatic processing strategy, the automatic production information is automatically processed, so that the accuracy of an automatic processing result can be ensured.
Description
Technical Field
The disclosure relates to the technical field of information processing of the internet of things, and in particular relates to an information processing method, device and equipment based on the internet of things.
Background
With the rapid development of the internet of things technology and the information automation processing technology, nowadays, in order to reduce the labor cost in the operation process and provide the working efficiency, a large number of enterprises or factories use the automation processing system to process the information, but the automation processing system is relatively fixed when processing the information, errors may occur in the processing of the production information in the processing process, and thus the processed result is inaccurate.
Disclosure of Invention
In order to solve the technical problems in the related art, the present disclosure provides an information processing method, apparatus and device based on the internet of things.
The invention provides an information processing method based on the Internet of things, which comprises the following steps:
acquiring automatic production information acquired by an information processing terminal and production index information of the automatic production information;
if the production index information is not matched with preset index information, determining production index adjusting information corresponding to the production index information based on a preset production index adjusting strategy, wherein the preset index information is a preset production index meeting the requirement of automatic processing;
adjusting the information processing terminal based on the production index adjustment information, and returning to execute the steps of acquiring the automatic production information acquired by the information processing terminal and the production index information of the automatic production information until the production index information is matched with the preset index information;
and carrying out automatic processing on the automatic production information by using a preset automatic processing strategy to obtain an automatic processing result.
Optionally, the configuration process of the production index adjustment policy includes:
acquiring initial reference production information and preset safe production parameters; wherein, different production index information is set in each reference production information;
aiming at any reference production information, carrying out simulation adjustment based on each piece of production index adjustment information under different pieces of production index information in a preset production index adjustment strategy to obtain updated reference production information;
automatically processing the updated reference production information by using a preset automatic processing strategy to obtain automatic processing parameters;
calculating a loss parameter according to a variation parameter between the automated processing parameter and the safety production parameter, wherein the loss parameter is related to the variation parameter;
updating the production index adjustment strategy based on the loss parameters, returning to execute each piece of production index adjustment information based on different pieces of production index information in the production index adjustment strategy, and performing simulation adjustment to obtain updated reference production information; until aiming at the reference production information, the automatic processing parameters are matched with first preset parameters and/or the accumulated result of the loss parameters of multiple iterations is matched with second preset parameters, and the production index adjusting strategy is determined to complete one round of configuration;
aiming at different reference production information, carrying out multi-round configuration on the production index adjustment strategy until the production index adjustment strategy meets set adjustment conditions, and determining that the production index adjustment strategy completes configuration; wherein the set adjustment condition is determined based on the timeliness of the production indicator adjustment.
Optionally, the production indicator adjustment information is discrete; the production index adjusting strategy comprises adjusting weights of all production index adjusting information under different production index information; updating the production indicator adjustment strategy based on the loss parameter includes: based on the loss parameters, correcting the adjustment weight of each piece of production index adjustment information under different pieces of production index information in the production index adjustment strategy;
the determining production index adjustment information corresponding to the production index information based on the pre-configured production index adjustment strategy comprises: and determining the production index adjusting information with the highest adjusting weight corresponding to the production index information based on a pre-configured production index adjusting strategy.
Optionally, the production indicator adjustment information is continuous; the production index adjustment strategy is a production index adjustment strategy network model based on deep learning; updating the production indicator adjustment strategy based on the loss parameter includes: iteratively updating the parameters of the production index adjustment strategy network model based on the loss parameters;
the determining production index adjustment information corresponding to the production index information based on the pre-configured production index adjustment strategy comprises: and inputting the production index information into a pre-configured production index adjustment strategy network model to obtain production index adjustment information corresponding to the production index information.
Optionally, the performing automated processing on the automated production information by using a preset automated processing policy to obtain an automated processing result includes: and inputting the automatic production information into a pre-configured automatic processing model based on deep learning to obtain an automatic processing result.
Optionally, the acquiring of the automated production information acquired by the information processing terminal and the production index information of the automated production information includes:
acquiring automatic production operation information acquired by an information processing terminal;
performing action analysis on each operation action information in the automatic production operation information to obtain automatic production information;
and performing index extraction on the automatic production information based on a preset index extraction mode to obtain production index information.
Optionally, the method further comprises:
taking the automatic processing result as service data to be analyzed;
and determining a service data analysis instruction based on the service data to be analyzed, issuing the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
The invention also provides an information processing device based on the Internet of things, which comprises:
the index information acquisition module is used for acquiring automatic production information acquired by the information processing terminal and production index information of the automatic production information;
the adjustment information determining module is used for determining production index adjustment information corresponding to the production index information based on a pre-configured production index adjustment strategy if the production index information is not matched with preset index information, wherein the preset index information is a preset production index meeting the requirement of automatic processing;
the processing terminal adjusting module is used for adjusting the information processing terminal based on the production index adjusting information and returning to execute the steps of acquiring the automatic production information acquired by the information processing terminal and the production index information of the automatic production information until the production index information is matched with the preset index information;
and the production information processing module is used for carrying out automatic processing on the automatic production information by utilizing a preset automatic processing strategy to obtain an automatic processing result.
The invention also provides an information processing device comprising a processor and a memory which are communicated with each other, wherein the processor is used for calling the computer program from the memory and realizing the method of any one of the above items by running the computer program.
The invention also provides a computer-readable storage medium having stored thereon a computer program which, when run, implements the method of any of the above.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
The invention provides an information processing method, device and equipment based on the Internet of things, which comprises the steps of firstly obtaining automatic production information and production index information of the automatic production information collected by an information processing terminal, secondly judging whether the production index information is matched with preset index information, if not, determining the production index adjustment information corresponding to the production index information based on a preset production index adjustment strategy, further adjusting the information processing terminal, and after adjustment, returning to the step of obtaining the automatic production information and the production index information of the automatic production information collected by the information processing terminal until the production index information is matched with the preset index information, so that the automatic production information can be accurately obtained, errors in subsequent production information processing can be avoided, and then according to the preset automatic processing strategy, the automatic production information is automatically processed to obtain an automatic processing result, so that the accuracy of the automatic processing result can be ensured.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart of an information processing method based on the internet of things according to an embodiment of the present invention.
Fig. 2 is a block diagram of an information processing apparatus based on the internet of things according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a hardware structure of an information processing apparatus according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, the present invention provides a flowchart of an information processing method based on the internet of things, and when the method is implemented, the following contents described in steps S110 to S140 are specifically executed.
Step S110, acquiring automatic production information acquired by an information processing terminal and production index information of the automatic production information.
Step S120, if the production index information is not matched with preset index information, determining production index adjustment information corresponding to the production index information based on a pre-configured production index adjustment strategy. In this embodiment, the preset index information is a preset production index meeting the requirement of the automated processing.
Step S130, based on the production index adjustment information, adjusting the information processing terminal, and returning to execute the steps of acquiring the automatic production information acquired by the information processing terminal and the production index information of the automatic production information until the production index information is matched with the preset index information.
And step S140, carrying out automatic processing on the automatic production information by using a preset automatic processing strategy to obtain an automatic processing result.
The following advantageous effects can be achieved when the method described in the above steps S110 to S140 is performed: firstly, acquiring automatic production information and production index information of the automatic production information acquired by an information processing terminal, secondly, judging whether the production index information is matched with preset index information or not, if not, determining the production index adjustment information corresponding to the production index information based on a preset production index adjustment strategy, further adjusting the information processing terminal, after adjusting, returning to the step of acquiring the automatic production information and the production index information of the automatic production information acquired by the information processing terminal until the production index information is matched with the preset index information, thus ensuring that the automatic production information is accurately acquired, simultaneously avoiding errors occurring in subsequent processing of the production information, and then automatically processing the automatic production information according to the preset automatic processing strategy to obtain an automatic processing result, thus, the accuracy of the automated processing result can be ensured.
Optionally, the configuration process of the production index adjustment policy includes:
acquiring initial reference production information and preset safe production parameters; wherein, different production index information is set in each reference production information;
aiming at any reference production information, carrying out simulation adjustment based on each piece of production index adjustment information under different pieces of production index information in a preset production index adjustment strategy to obtain updated reference production information;
automatically processing the updated reference production information by using a preset automatic processing strategy to obtain automatic processing parameters;
calculating a loss parameter according to a variation parameter between the automated processing parameter and the safety production parameter, wherein the loss parameter is related to the variation parameter;
updating the production index adjustment strategy based on the loss parameters, returning to execute each piece of production index adjustment information based on different pieces of production index information in the production index adjustment strategy, and performing simulation adjustment to obtain updated reference production information; until aiming at the reference production information, the automatic processing parameters are matched with first preset parameters and/or the accumulated result of the loss parameters of multiple iterations is matched with second preset parameters, and the production index adjusting strategy is determined to complete one round of configuration;
aiming at different reference production information, carrying out multi-round configuration on the production index adjustment strategy until the production index adjustment strategy meets set adjustment conditions, and determining that the production index adjustment strategy completes configuration; wherein the set adjustment condition is determined based on the timeliness of the production indicator adjustment.
Optionally, the production indicator adjustment information is discrete; the production index adjusting strategy comprises adjusting weights of all production index adjusting information under different production index information; updating the production indicator adjustment strategy based on the loss parameter includes: based on the loss parameters, correcting the adjustment weight of each piece of production index adjustment information under different pieces of production index information in the production index adjustment strategy;
the determining production index adjustment information corresponding to the production index information based on the pre-configured production index adjustment strategy comprises: and determining the production index adjusting information with the highest adjusting weight corresponding to the production index information based on a pre-configured production index adjusting strategy.
Optionally, the production indicator adjustment information is continuous; the production index adjustment strategy is a production index adjustment strategy network model based on deep learning; updating the production indicator adjustment strategy based on the loss parameter includes: iteratively updating the parameters of the production index adjustment strategy network model based on the loss parameters;
the determining production index adjustment information corresponding to the production index information based on the pre-configured production index adjustment strategy comprises: and inputting the production index information into a pre-configured production index adjustment strategy network model to obtain production index adjustment information corresponding to the production index information.
Optionally, the performing automated processing on the automated production information by using a preset automated processing policy to obtain an automated processing result includes: and inputting the automatic production information into a pre-configured automatic processing model based on deep learning to obtain an automatic processing result.
Optionally, the acquiring of the automated production information acquired by the information processing terminal and the production index information of the automated production information includes:
acquiring automatic production operation information acquired by an information processing terminal;
performing action analysis on each operation action information in the automatic production operation information to obtain automatic production information;
and performing index extraction on the automatic production information based on a preset index extraction mode to obtain production index information.
Based on the above description, the present invention may further include step S150: taking the automatic processing result as service data to be analyzed; and determining a service data analysis instruction based on the service data to be analyzed, issuing the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
Further, the step S150 takes the automatic processing result as the business data to be analyzed; determining a service data analysis instruction based on the service data to be analyzed, issuing the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
Step S151, acquiring a service index record track set corresponding to each service index analysis time node of the service data to be analyzed in the first service analysis time period.
In this embodiment, the first service analysis time period includes at least two service index analysis time nodes, and the service index record track set corresponding to each service index analysis time node includes an index record track of target service data marked or received in the corresponding service index analysis time node by the service attribute marking module in the service data to be analyzed.
Step S152, determining index record track similarity among service index record track sets corresponding to each service index analysis time node in the first service analysis time period; and determining the service running state data of the service data to be analyzed in the first service analysis time period according to the index record track similarity between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period.
Step S153, determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the service operation state data; and sending the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
It can be understood that, by executing the above steps S151 to S153, first, a service index recording track set corresponding to each service index analysis time node of the service data to be analyzed in the first service analysis time period is obtained, then, an index recording track similarity between the service index recording track sets corresponding to each service index analysis time node in the first service analysis time period is determined, service running state data of the service data to be analyzed in the first service analysis time period is determined, and finally, a service data analysis instruction of the service data to be analyzed in the first service analysis time period is determined according to the service running state data and is issued to a service analysis terminal corresponding to the service data to be analyzed, and the service data analysis instruction is updated in real time.
Therefore, by analyzing the service index record track set of different service index analysis time nodes corresponding to the service data to be analyzed, the service data analysis instruction corresponding to the service data to be analyzed can be timely and accurately generated, so that the service analysis terminal corresponding to the service data to be analyzed can realize real-time monitoring and analysis of the service data to be analyzed based on the service data analysis instruction, the service data is prevented from being mechanically analyzed, further relevant important analysis angles or analysis contents of the service data to be analyzed are prevented from being omitted in the actual analysis process, in this way, deep analysis and mining of the service data to be analyzed can be ensured, and the data value of the back of the service data to be analyzed can be obtained as much as possible. In addition, by updating the service data analysis instruction in real time, the real-time update data condition of the service analysis terminal can be taken into account, so that the response adjustment of the newly added service data index or the emergency of other services is realized based on the Internet of things equipment, and the integrity and the orderliness of the service data to be analyzed can be ensured.
In a specific implementation, the acquiring of the service index record track set corresponding to each service index analysis time node of the service data to be analyzed in the first service analysis time period described in step S151 may include the contents described in step S1511 and step S1512 below.
Step S1511, acquiring an index recording track of the target service data marked within a set time period step after the first service index analysis time node starts by the service attribute marking module in the service data to be analyzed, and determining a set of service index recording tracks corresponding to the first service index analysis time node according to the index recording track of the target service data marked within the set time period step after the first service index analysis time node starts by the service attribute marking module in the service data to be analyzed, where the first service index analysis time node is any service index analysis time node in the first service analysis time period.
Step S1512, in a case that the service attribute marking module in the service data to be analyzed does not mark the target service data within a set time interval step after a second service index analysis time node starts, determining a set of service index recording tracks corresponding to the second service index analysis time node according to an index recording track of the target service data received by the service attribute marking module in the service data to be analyzed, where the second service index analysis time node is any service index analysis time node other than the first service index analysis time node within the first service analysis time interval.
Further, in addition to step S1511 and step S1512, the contents described in the following step S1513 and step S1514 may be included.
Step S1513, the service attribute marking module in the service data to be analyzed does not mark the target service data within the set time period step length after the third service index analysis time node starts, and the service index record track set corresponding to the service index analysis time node of the first set accumulated quantity which is continuous before the third service index analysis time node is determined according to the index record track of the target service data received by the service attribute marking module, a target service data marking instruction is sent to the service attribute marking module, so that the service attribute marking module marks target service data in response to the target service data marking instruction, the third service index analysis time node is any service index analysis time node except the first service index analysis time node and the second service index analysis time node in the first service analysis time period.
Step S1514, acquiring an index recording track of the target service data marked by the service attribute marking module in response to the target service data marking instruction, and determining a service index recording track set corresponding to the third service index analysis time node according to the index recording track of the target service data marked by the service attribute marking module in response to the target service data marking instruction.
By the design, the target service data can be marked in the set time period step corresponding to different service index analysis time nodes based on the service attribute marking module by executing the steps S1511 to S1514, so that the integrity of the service index record track set is ensured, and the target service data index record track is prevented from being missed in some time periods.
Optionally, the determining of the index record track similarity between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period in step S152 may be implemented by any one of the following two implementation manners.
In the first embodiment, a reference index recording track subset is determined from a service index recording track set corresponding to each service index analysis time node in a first service analysis time period; and respectively determining index recording track similarity between each service index recording track set except the reference index recording track subset in the service index recording track set corresponding to each service index analysis time node in the first service analysis time period and the reference index recording track subset.
In a second implementation manner, index record track similarities between service index record track sets corresponding to every two adjacent service index analysis time nodes in the first service analysis time period are respectively determined.
Therefore, the similarity of the index recording tracks can be accurately calculated in the service analysis time period.
In an actual application process, the service index record track set corresponding to each service index analysis time node in the first service analysis time period includes a dynamic service index record track set and a static service index record track set, and the service operation state data includes first service operation state data determined according to index record track similarity corresponding to the dynamic service index record track set of each service index analysis time node specified in the first service analysis time period, and second service operation state data determined according to index record track similarity corresponding to the static service index record track set of each service index analysis time node specified in the first service analysis time period.
On this basis, the determining, according to the service operation state data, a service data analysis instruction of the service data to be analyzed in the first service analysis time period in step S153 includes: and determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the first service operation state data and the second service operation state data. By the design, different service operation state data can be taken into account by the service data analysis instruction, so that the service data to be analyzed in the service analysis terminal can be comprehensively analyzed.
Further, determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period according to the first service operation state data and the second service operation state data, and further including the contents described in the following steps (1) to (3).
And (1) determining a service data analysis instruction of the service data to be analyzed in the first service analysis time period as a continuity analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service running state data is not larger than a preset first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service running state data is not larger than a preset second target abnormity coefficient.
And (2) determining that a service data analysis instruction of the service data to be analyzed in the first service analysis time period is an intermittent analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service running state data is not larger than the first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service running state data is smaller than the second target abnormity coefficient.
And (3) determining that a service data analysis instruction of the service data to be analyzed in the first service analysis time period is a delayed analysis instruction under the condition that a service state abnormity coefficient corresponding to the first service running state data is smaller than the first target abnormity coefficient and a service state abnormity coefficient corresponding to the second service running state data is smaller than the second target abnormity coefficient.
Thus, the service data analysis instructions corresponding to the service state abnormal coefficients of the different service operation state data under different conditions can be determined by executing the contents described in the steps (1) to (3), and when the service data to be analyzed is analyzed based on the different service data analysis instructions, the relevant important analysis angles of the service data to be analyzed or the analysis contents can be prevented from being omitted in the actual analysis process
In a possible example, the determining, according to the similarity of the index record tracks between the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period, the service operation state data of the service data to be analyzed in the first service analysis time period, which is described in step S152, may be implemented by any one of the following two implementation manners.
In the first embodiment, from the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period, at least one target dynamic service index record track set of which the identification weight of the mark corresponding to the target service data is lower than a first preset identification weight and at least one target static service index record track set of which the identification weight of the mark corresponding to the target service data is lower than a second preset identification weight are determined; and determining the first service running state data according to the index recording track similarity corresponding to the at least one target dynamic service index recording track set, and determining the second service running state data according to the index recording track similarity corresponding to the at least one target static service index recording track set.
In a second implementation manner, an integrity coefficient of similarity of each index recording track is determined according to track characteristic distribution of the service index recording track included in a service index recording track set corresponding to each service index analysis time node in the first service analysis time period; and determining the service operation state data of the service data to be analyzed in the first service analysis time period according to the index record track similarity among the service index record track sets corresponding to the service index analysis time nodes in the first service analysis time period and the integrity coefficient of the index record track similarity.
In this way, the service operation state data is determined by any one of the above two implementation manners, and the identification weight level or the integrity coefficient level of the mark corresponding to the target service data can be considered, so that the service operation state data can be determined flexibly and accurately.
In specific implementation, when the service data analysis instruction is issued and updated in real time, the matching with the instruction format of each service data in the service analysis terminal needs to be considered, so that the service analysis terminal is prevented from being unable to receive or being unable to execute the corresponding service data analysis instruction. Further, the service class information at the time of real-time update is also considered. In order to achieve the above object, the step S153 of issuing the service data analysis instruction to the service analysis terminal corresponding to the service data to be analyzed and updating the service data analysis instruction in real time may be further implemented by the contents described in the following steps S1531 to S1535.
Step S1531, determining an instruction format sequence in the service data analysis instruction, and generating a first format feature set corresponding to the instruction format sequence, where the instruction format sequence is a sequence obtained by analyzing, by an Internet of things device, the service data analysis instruction by using a preset format analysis model, and a format text corresponding to the sequence is unchanged; and acquiring a terminal configuration parameter list of the service analysis terminal, and calculating the matching description weight between the instruction format sequence and the terminal configuration parameter list according to the first format feature set.
Step S1532, if the matching description weight between the instruction format sequence and the terminal configuration parameter list is smaller than a preset matching threshold, matching an instruction receiving log corresponding to the service analysis terminal with the first format feature set to obtain a second format feature set; converting the instruction receiving log into an instruction list, taking the instruction list as a first object to be processed, taking the service data analysis instruction as a second object to be processed, and performing instruction matching to obtain a first matching result; screening the first matching result according to the second format feature set to obtain a second matching result with a confidence weight higher than that of the first matching result; and determining the second matching result and first instruction operation information of the instruction list, and matching operation logic information corresponding to the first instruction operation information with the instruction receiving log to obtain instruction operation indication information.
Step S1533, if the matching description weight between the instruction format sequence and the terminal configuration parameter list is greater than or equal to the matching threshold, determining the first matching result and the second instruction operation information of the instruction list, and matching the operation logic information in the second instruction operation information with the instruction receiving log to obtain instruction operation indication information.
Step S1534, based on the instruction operation instruction information, operating the service data analysis instruction to obtain a target instruction, and sending the target instruction to the service analysis terminal; after the target instruction is sent to the service analysis terminal, extracting multi-dimensional information of target service data in a target area based on generation time information corresponding to the service data analysis instruction to obtain a first multi-dimensional information set and a second multi-dimensional information set corresponding to the target service data; the first multidimensional information set is used for representing a feature set corresponding to service value information corresponding to the target service data, and the second multidimensional information set is used for representing a feature set corresponding to a newly added service data index corresponding to the target service data.
Step S1535, after acquiring the first multi-dimensional information set and the second multi-dimensional information set, acquiring a first service change data set of the first multi-dimensional information set and a second service change data set of the second multi-dimensional information set, where the first multi-dimensional information set includes first service category information, and the second multi-dimensional information set includes second service category information; acquiring each group of data nodes in the first service change data set and each group of data nodes in the second service change data set to obtain service change node distribution; determining a relevance index between any two groups of data nodes in the service change node distribution to obtain an initial relevance index queue; adjusting the relevance indexes smaller than the set relevance indexes in the initial relevance index queue to be set relevance indexes to obtain a current relevance index queue; performing update frequency identification on the current relevance index queue to obtain a real-time service demand identification result, wherein the real-time service demand identification result is used for indicating that the first service category information and the second service category information are the same service category information or different service category information; and updating the service data analysis command in real time based on the real-time service demand identification result, and returning to execute the step of issuing the service data analysis command to the service analysis terminal corresponding to the service data to be analyzed.
It should be noted that after the real-time update, when the service data analysis command is returned to the service analysis terminal corresponding to the service data to be analyzed, the service data analysis command is different.
It can be understood that, by executing the contents described in the above steps S1531 to S1535, when the service data analysis command is issued and updated in real time, the matching between the service data analysis command and the command format of each service data in the service analysis terminal of the service analysis terminal is considered, so as to avoid that the service analysis terminal cannot receive or cannot execute the corresponding service data analysis command. In addition, the service type information during real-time updating is also considered, so that accurate and real-time updating of the service data analysis instruction is ensured.
On the basis of the above, please refer to fig. 2, the invention further provides a block diagram of an information processing apparatus 200 based on the internet of things, which may include the following functional modules.
The index information obtaining module 210 is configured to obtain the automated production information collected by the information processing terminal and the production index information of the automated production information.
The adjustment information determining module 220 is configured to determine, based on a pre-configured production index adjustment policy, production index adjustment information corresponding to the production index information if the production index information does not match preset index information, where the preset index information is a preset production index meeting an automation processing requirement.
And a processing terminal adjusting module 230, configured to adjust the information processing terminal based on the production index adjusting information, and return to perform the steps of obtaining the automated production information acquired by the information processing terminal and the production index information of the automated production information until the production index information matches the preset index information.
And the production information processing module 240 is configured to perform automatic processing on the automatic production information by using a preset automatic processing strategy to obtain an automatic processing result.
Further, the apparatus may further include an analysis instruction sending module 250, configured to use the automation processing result as service data to be analyzed; and determining a service data analysis instruction based on the service data to be analyzed, issuing the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
On the basis of the above, please refer to fig. 3 in combination, there is provided an information processing apparatus 110, which includes a processor 111, and a memory 112 and a bus 113 connected to the processor 111; wherein, the processor 111 and the memory 112 complete the communication with each other through the bus 113; the processor 111 is used to call program instructions in the memory 112 to perform the above-described method.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. An information processing method based on the Internet of things is characterized by comprising the following steps:
acquiring automatic production information acquired by an information processing terminal and production index information of the automatic production information;
if the production index information is not matched with preset index information, determining production index adjusting information corresponding to the production index information based on a preset production index adjusting strategy, wherein the preset index information is a preset production index meeting the requirement of automatic processing;
adjusting the information processing terminal based on the production index adjustment information, and returning to execute the steps of acquiring the automatic production information acquired by the information processing terminal and the production index information of the automatic production information until the production index information is matched with the preset index information;
and carrying out automatic processing on the automatic production information by using a preset automatic processing strategy to obtain an automatic processing result.
2. The method of claim 1, wherein the configuration process of the production index adjustment strategy comprises:
acquiring initial reference production information and preset safe production parameters; wherein, different production index information is set in each reference production information;
aiming at any reference production information, carrying out simulation adjustment based on each piece of production index adjustment information under different pieces of production index information in a preset production index adjustment strategy to obtain updated reference production information;
automatically processing the updated reference production information by using a preset automatic processing strategy to obtain automatic processing parameters;
calculating a loss parameter according to a variation parameter between the automated processing parameter and the safety production parameter, wherein the loss parameter is related to the variation parameter;
updating the production index adjustment strategy based on the loss parameters, returning to execute each piece of production index adjustment information based on different pieces of production index information in the production index adjustment strategy, and performing simulation adjustment to obtain updated reference production information; until aiming at the reference production information, the automatic processing parameters are matched with first preset parameters and/or the accumulated result of the loss parameters of multiple iterations is matched with second preset parameters, and the production index adjusting strategy is determined to complete one round of configuration;
aiming at different reference production information, carrying out multi-round configuration on the production index adjustment strategy until the production index adjustment strategy meets set adjustment conditions, and determining that the production index adjustment strategy completes configuration; wherein the set adjustment condition is determined based on the timeliness of the production indicator adjustment.
3. The method of claim 2, wherein the production index adjustment information is discrete; the production index adjusting strategy comprises adjusting weights of all production index adjusting information under different production index information; updating the production indicator adjustment strategy based on the loss parameter includes: based on the loss parameters, correcting the adjustment weight of each piece of production index adjustment information under different pieces of production index information in the production index adjustment strategy;
the determining production index adjustment information corresponding to the production index information based on the pre-configured production index adjustment strategy comprises: and determining the production index adjusting information with the highest adjusting weight corresponding to the production index information based on a pre-configured production index adjusting strategy.
4. The method of claim 2, wherein the production index adjustment information is continuous; the production index adjustment strategy is a production index adjustment strategy network model based on deep learning; updating the production indicator adjustment strategy based on the loss parameter includes: iteratively updating the parameters of the production index adjustment strategy network model based on the loss parameters;
the determining production index adjustment information corresponding to the production index information based on the pre-configured production index adjustment strategy comprises: and inputting the production index information into a pre-configured production index adjustment strategy network model to obtain production index adjustment information corresponding to the production index information.
5. The method of claim 1, wherein the automated processing of the automated production information using a preset automated processing strategy to obtain an automated processing result comprises: and inputting the automatic production information into a pre-configured automatic processing model based on deep learning to obtain an automatic processing result.
6. The method according to claim 1, wherein the acquiring of the automated production information and the production index information of the automated production information collected by the information processing terminal comprises:
acquiring automatic production operation information acquired by an information processing terminal;
performing action analysis on each operation action information in the automatic production operation information to obtain automatic production information;
and performing index extraction on the automatic production information based on a preset index extraction mode to obtain production index information.
7. The method of claim 1, further comprising:
taking the automatic processing result as service data to be analyzed;
and determining a service data analysis instruction based on the service data to be analyzed, issuing the service data analysis instruction to a service analysis terminal corresponding to the service data to be analyzed, and updating the service data analysis instruction in real time.
8. An information processing apparatus based on the internet of things, the apparatus comprising:
the index information acquisition module is used for acquiring automatic production information acquired by the information processing terminal and production index information of the automatic production information;
the adjustment information determining module is used for determining production index adjustment information corresponding to the production index information based on a pre-configured production index adjustment strategy if the production index information is not matched with preset index information, wherein the preset index information is a preset production index meeting the requirement of automatic processing;
the processing terminal adjusting module is used for adjusting the information processing terminal based on the production index adjusting information and returning to execute the steps of acquiring the automatic production information acquired by the information processing terminal and the production index information of the automatic production information until the production index information is matched with the preset index information;
and the production information processing module is used for carrying out automatic processing on the automatic production information by utilizing a preset automatic processing strategy to obtain an automatic processing result.
9. An information processing apparatus comprising a processor and a memory in communication with each other, the processor being configured to retrieve a computer program from the memory and to implement the method of any one of claims 1 to 7 by executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any of claims 1-7.
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| CN202011555064.XA CN112561388A (en) | 2020-12-24 | 2020-12-24 | Information processing method, device and equipment based on Internet of things |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657747A (en) * | 2021-08-12 | 2021-11-16 | 中国安全生产科学研究院 | Enterprise safety production standardization level intelligent evaluation system |
| CN117930742A (en) * | 2024-03-21 | 2024-04-26 | 山西坚科控制技术有限公司 | Automatic control system based on PLC |
-
2020
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657747A (en) * | 2021-08-12 | 2021-11-16 | 中国安全生产科学研究院 | Enterprise safety production standardization level intelligent evaluation system |
| CN113657747B (en) * | 2021-08-12 | 2023-06-16 | 中国安全生产科学研究院 | Intelligent assessment system for enterprise safety production standardization level |
| CN117930742A (en) * | 2024-03-21 | 2024-04-26 | 山西坚科控制技术有限公司 | Automatic control system based on PLC |
| CN117930742B (en) * | 2024-03-21 | 2024-05-31 | 山西坚科控制技术有限公司 | Automatic control system based on PLC |
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Application publication date: 20210326 |