Equipment operation efficiency evaluation method, intelligent agent and computer readable storage medium

Abstract

The invention discloses an equipment operation efficiency evaluation method, an intelligent agent and a computer readable storage medium, and relates to the technical field of automation equipment.The intelligent agent comprises an equipment end and a cloud end, and the equipment operation efficiency evaluation method comprises the steps that the cloud end receives and stores equipment operation data from the equipment end; and when a preset triggering condition is responded, analyzing the stored equipment operation data to generate a primary analysis result and a secondary analysis result, and generating an equipment operation efficiency evaluation report according to the primary analysis result and / or the secondary analysis result. By adopting the scheme of the invention, data acquisition, analysis and operation efficiency tracking of the equipment in the full life cycle are realized, the analysis result is more accurate, and after the operation efficiency is found to be abnormal, automatic notification and optimization correction can be carried out, so that the effect of predictive maintenance is realized, and the loss of the equipment caused by fault shutdown is avoided.

Description

Technical Field

[0001] This application relates to the field of automation equipment technology, specifically providing a method for evaluating equipment operating efficiency, an intelligent agent, and a computer-readable storage medium. Background Technology

[0002] In existing technologies, the analysis of equipment operating efficiency is usually based on manual statistics and judgment of operating efficiency. Its advantages are simplicity and intuitiveness, but its disadvantages are low efficiency and low accuracy. The judgment criteria are often derived from laboratory data, which may not be updated for a long time and may be out of touch with the production environment, resulting in inaccurate conclusions. In addition, there is the problem that the operating efficiency of the equipment cannot be tracked after it leaves the factory. Summary of the Invention

[0003] To overcome the above-mentioned shortcomings, this application proposes a device operation efficiency evaluation scheme implemented by constructing an intelligent agent, so as to provide a solution or at least partially solve the technical problems of inaccurate device operation efficiency evaluation and inability to track the entire life cycle of the device.

[0004] In a first aspect, this application provides a method for evaluating equipment operating efficiency, applied to an intelligent agent, the intelligent agent including a device and a cloud, the method comprising:

[0005] The cloud receives and saves device operation data from the device; the device operation data includes at least device information and process step information, and the process step information includes process step identifier, process step status and running time.

[0006] When the cloud responds to a preset trigger condition, it analyzes the stored device operation data to generate primary analysis results and secondary analysis results, and generates a device operation efficiency evaluation report based on the primary analysis results and / or the secondary analysis results.

[0007] The first-level analysis results are used to evaluate the time consumption and abnormal status of each device in executing each process step; the second-level analysis results are used to evaluate the time consumption of a single process step and the time consumption deviation of each device in executing each process step.

[0008] In some technical solutions, the cloud includes a data storage module, and the method further includes:

[0009] The device running data is organized into rows according to the receiving time and saved to the main data table in the data storage module; the row data includes the device information, the process step identifier, the process step status, the running time, and the receiving time;

[0010] The primary analysis results and the secondary analysis results are saved to the primary data warehouse and the secondary data warehouse in the data storage module, respectively.

[0011] In some technical solutions, the cloud-based analysis of the device's operational data to generate primary and secondary analysis results includes:

[0012] Select the first data to be processed from the main data table, and based on the first data to be processed, calculate the time consumption and abnormal status of each device in executing each process step. Based on the correspondence between the time consumption and abnormal status and the device and process step, form row data and save it to the first table structure of the first-level data warehouse.

[0013] According to the first time period, the second data to be processed is selected from the first table structure at regular intervals. Based on the second data to be processed, the average time, shortest time and longest time of each process step in the first time period are calculated to obtain the time data of a single process step. Row data is formed according to the time data of the single process step and saved to the second table structure of the secondary data warehouse.

[0014] Based on the first data to be processed and the second data to be processed, the average time of each process step within the second time period is calculated as a standard value. The time deviation of each device in executing each process step is determined according to the difference between the average time and the longest time of each process step within the first time period and the standard value. The time deviation includes the average time deviation and the maximum time deviation. Row data is formed according to the correspondence between the time deviation of each process step and the device and saved in the third table structure of the secondary data warehouse.

[0015] The data in the first table structure is used to characterize the first-level analysis results, and the data in the second and third table structures are used to characterize the second-level analysis results.

[0016] In some technical solutions, the calculation methods for the average time consumption, the shortest time consumption, and the longest time consumption within the first period are as follows:

[0017] The total time and number of executions for each process step within the first time period are statistically analyzed, and the average time is calculated based on the total time and the number of executions.

[0018] The execution time of each process step within the first time period is sorted from smallest to largest. A portion of the time data is selected from the sorted time data and accumulated to calculate the shortest and longest execution times based on the accumulated calculation results and the selected data volume.

[0019] In some technical solutions, the cloud's analysis of the device's operating data to generate primary and secondary analysis results also includes: based on the first data to be processed, calculating the total number of faults that occur when each device executes each process step;

[0020] The step of forming row data based on the time consumption deviation of each process step and the correspondence between the equipment and the data and saving it to the third table structure of the secondary data warehouse includes: forming row data based on the time consumption deviation of each process step, the total number of faults and the correspondence between the equipment and the data and saving it to the third table structure of the secondary data warehouse.

[0021] In some technical solutions, the method further includes a step of identifying abnormal process steps based on the time consumption deviation of each process step and the total number of faults, and the equipment operation efficiency evaluation report also includes information on the identified abnormal process steps.

[0022] In some technical solutions, the preset triggering conditions include receiving a device operation efficiency analysis request initiated by the device and detecting a preset periodic startup request in the cloud;

[0023] When the cloud receives a device operation efficiency evaluation and analysis request initiated by the device, the method further includes: the cloud returning the generated device operation efficiency evaluation report to the device;

[0024] When the cloud detects a pre-set periodic startup request, and the device operation efficiency evaluation report generated by the cloud contains information about abnormal process steps, the method further includes: the cloud sending the information about the abnormal process steps to the device.

[0025] In some technical solutions, the information contained in each row of data in the main data table and the first table structure also includes the processing status of the data, and the first data to be processed and the second data to be processed are both selected row data with an unprocessed processing status; the method also includes the step of updating the processing status of each row of data in the main data table and the first table structure.

[0026] In a second aspect, this application provides an intelligent agent for evaluating device operating efficiency, the intelligent agent comprising a device end and a cloud end;

[0027] The device is configured to upload device operation data to the cloud via an edge gateway during device operation; wherein the device operation data includes at least device information and process step information, and the process step information includes process step identifier, process step status and running time;

[0028] The cloud is configured to: receive device operation data uploaded by the device and save it to the database module; in response to a device operation efficiency analysis request initiated by the device or in response to a preset periodic startup request, analyze the saved device operation data to generate primary and secondary analysis results; and generate a device operation efficiency evaluation report based on the primary and / or secondary analysis results. The primary analysis results are used to evaluate the time consumption and abnormal states of each device executing each process step; the secondary analysis results are used to evaluate the time consumption of a single process step and the time consumption deviation of each device executing each process step.

[0029] In a third aspect, this application provides a computer-readable storage medium storing a plurality of program codes adapted to be loaded and run by a processor to perform the device operating efficiency evaluation method described in any of the above-described technical solutions.

[0030] The equipment operation efficiency evaluation method, intelligent agent, and computer-readable storage medium provided in this application have the following advantages:

[0031] (1) Efficiency analysis is performed based on historical data from all devices, and the results are accurate;

[0032] (2) After the equipment finishes the testing phase and enters the production and use cycle, the operating efficiency of the equipment can also be tracked, and data collection and analysis of the entire equipment life cycle can be carried out.

[0033] (3) After detecting abnormal operating efficiency, it can automatically notify and optimize and correct, thus achieving the effect of predictive maintenance and avoiding losses caused by equipment downtime due to faults. Attached Figure Description

[0034] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Furthermore, similar numbers in the drawings are used to denote similar components, wherein:

[0035] Figure 1 This is a schematic flowchart of the main steps of a device operating efficiency evaluation method according to an embodiment of this application;

[0036] Figure 2 This is a schematic diagram of the implementation steps for generating primary and secondary analysis results provided in an embodiment of this application;

[0037] Figure 3 This is a schematic diagram of the composition architecture of an intelligent agent provided in an embodiment of this application. Detailed Implementation

[0038] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0039] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0040] In existing technologies, after automated equipment is debugged, operational efficiency testing is required. The current mainstream method involves testers writing test cases and then controlling the equipment's operation using a host computer program based on these test cases. This process involves timing individual or overall actions to generate an operational efficiency statistics table. The average value in the table is then compared with a standard to determine whether the operational efficiency is good or bad. The advantage of manual operational efficiency statistics and judgment is its simplicity and intuitiveness; however, it suffers from low efficiency and low accuracy. Judgment standards often originate from laboratory data, which may not be updated regularly and may be out of touch with the production environment, leading to inaccurate conclusions. Another drawback is the inability to track operational efficiency after the equipment leaves the factory.

[0041] This application provides a method and intelligent agent for evaluating equipment operating efficiency, which can automatically record the operating efficiency data of each device throughout its entire life cycle, upload the data to the cloud platform through an edge gateway, collect, analyze and make decisions on all the data in the cloud, and accurately locate and prompt the device when it is found that a certain device or a batch of devices needs to be notified or alarmed, which can provide strong support for judging the future operating status of the device.

[0042] See Figure 1This application provides a method for evaluating equipment operating efficiency, applied to an intelligent agent. The intelligent agent includes a device and a cloud. In this application, the intelligent agent refers to a collective term for a system capable of self-sensing, self-thinking, self-decision-making, and self-execution. It possesses a "sensory system" for collecting information, specifically implemented through configuration of the device; a "brain" capable of thinking and decision-making, including a memory bank, a thinking engine, and learning capabilities; and an action system capable of executing tasks, specifically implemented through configuration of the cloud. For example... Figure 1 The equipment operating efficiency evaluation method shown includes the following steps S11 and S12:

[0043] Step S11: The cloud receives and saves device operation data from the device.

[0044] In this embodiment, the device operation data includes at least device information and process step information, wherein the process step information includes process step identifier, process step status, and running time.

[0045] Step S12: When the cloud responds to the preset trigger condition, it analyzes the saved device operation data to generate a first-level analysis result and a second-level analysis result, and generates a device operation efficiency evaluation report based on the first-level analysis result and / or the second-level analysis result;

[0046] In this embodiment, the first-level analysis results are used to evaluate the time consumption and abnormal status of each device in executing each process step; the second-level analysis results are used to evaluate the time consumption of a single process step and the time consumption deviation of each device in executing each process step.

[0047] The preset triggering conditions can be receiving a device operation efficiency analysis request initiated by the device, or monitoring a preset periodic startup request in the cloud, such as setting the generation of a device operation efficiency evaluation report on a monthly basis in the cloud.

[0048] In one embodiment, when the cloud receives a device operation efficiency evaluation and analysis request initiated by the device, the above step S12 further includes: the cloud returning the generated device operation efficiency evaluation report to the device.

[0049] In one implementation, when the cloud detects a pre-set periodic startup request, and the device operation efficiency evaluation report generated by the cloud contains information about abnormal process steps, step S12 further includes: the cloud sending the information about the abnormal process steps to the device. Correspondingly, upon receiving the information about the abnormal process steps, the device can issue an alarm prompt so that on-site personnel can inspect the relevant components to avoid serious malfunctions. Optionally, the device operation efficiency evaluation report generated by the cloud can also be published on a public WeChat account for authorized users and manufacturer personnel to view.

[0050] Optionally, when the device receives the device operation efficiency evaluation report transmitted back from the cloud, it can also automatically start a correction program to eliminate the abnormality. For example, it can automatically adjust the device operation parameters such as motor torque and acceleration to eliminate the abnormality. It can also observe the effect on a weekly basis after adjustment. If the abnormality is still not eliminated, it can upgrade the alarm and notify the device manufacturer's backend for processing.

[0051] Furthermore, the cloud in this embodiment includes a data storage module, which may consist of one or more databases. Based on this data storage module, the cloud can hierarchically store the device operation data, the primary analysis results, and the secondary analysis results. It is understood that the data stored in the data storage module can adopt one or more data table structures, which can be customized according to actual needs. The implementation method of hierarchical storage of received data, data during analysis, and analysis results in the cloud in this embodiment is as follows:

[0052] The device running data is organized into rows according to the receiving time and saved to the main data table in the data storage module; the row data includes the device information, the process step identifier, the process step status, the running time, and the receiving time;

[0053] The primary analysis results and the secondary analysis results are saved to the primary data warehouse and the secondary data warehouse in the data storage module, respectively.

[0054] The following section uses an automated storage device as an example to explain in detail the device operation efficiency evaluation method and intelligent agent of this application. The automated storage device has a PLC (Programmable Logic Controller) that runs a lower-level program and an industrial control computer that runs a higher-level program. The industrial control computer is connected to an edge gateway that can use an IoT card to transmit messages to a cloud program.

[0055] Based on the above step S12, one specific implementation of the cloud-based analysis of the device operation data to generate primary and secondary analysis results is as follows: Figure 2 The steps shown are S21 to S23:

[0056] Step S21: Select the first data to be processed from the main data table, and based on the first data to be processed, calculate the time consumption and abnormal status of each device in executing each process step. Based on the correspondence between the time consumption and abnormal status and the device and process step, form row data and save it to the first table structure of the first-level data warehouse.

[0057] Step S22: Select second data to be processed from the first table structure according to the first time period, calculate the average time, shortest time and longest time of each process step in the first time period based on the second data to be processed to obtain the time data of a single process step, form row data according to the time data of the single process step and save it to the second table structure of the secondary data warehouse;

[0058] Step S23: Based on the first data to be processed and the second data to be processed, calculate the average time consumption of each process step in the second time period as a standard value. Determine the time consumption deviation of each device in executing each process step according to the difference between the average time consumption and the longest time consumption of each process step in the first time period and the standard value. The time consumption deviation includes the average time consumption deviation and the maximum time consumption deviation. Form row data according to the correspondence between the time consumption deviation of each process step and the device and save it to the third table structure of the secondary data warehouse.

[0059] In this embodiment, the data in the first table structure is used to characterize the first-level analysis results, and the data in the second and third table structures are used to characterize the second-level analysis results. Each row of data in the main data table and the first table structure also includes information about the data processing status; the first unprocessed data and the second unprocessed data are both selected rows whose processing status is "unprocessed." The method further includes the step of updating the processing status of each row of data in the main data table and the first table structure. The first time period and the second time period can be customized; for example, the first time period can be set to 1 day, and the second time period can be set to 1 year.

[0060] In step S22 above, a specific implementation of calculating the average time spent, the shortest time spent, and the longest time spent within the first time period is as follows:

[0061] (1) Calculate the total time and number of executions for each process step within the first time period, and calculate the average time based on the total time and the number of executions;

[0062] For example, if the first time period is one day, then this step specifically involves, based on the time consumption of each process step for each device in the first table structure, taking each process step as the statistical object, calculating the total time consumption of all devices executing a certain process step within a day and the number of rows of data for that process step in the corresponding first table structure. The number of rows of data is the number of times that process step is executed within a day; the average time consumption is obtained by dividing the total time consumption by the number of executions.

[0063] (2) Sort the time consumed by each process step in the first time period in ascending order, and select a portion of the time consumed from the sorted time data for cumulative calculation, so as to calculate the shortest time consumed and the longest time consumed based on the cumulative calculation result and the selected data amount.

[0064] For example, if the number of rows corresponding to the time taken for a certain process step within a day is 100, and these 100 rows are sorted in ascending order of time, the shortest time is obtained by summing the times of the first 10 rows and dividing by 10, and the longest time is obtained by summing the times of the last 10 rows and dividing by 10. It should be understood that the number of data rows selected can be customized when selecting a subset of data.

[0065] It should be understood that the calculation method for the average time spent in the second time period mentioned above can refer to the calculation method for the average time spent in the first time period mentioned above, and will not be repeated here.

[0066] Furthermore, step S23 may also include: based on the first data to be processed, counting the total number of faults that occur when each device executes each process step; correspondingly, the step S23 of forming row data according to the correspondence between the time consumption deviation of each process step and the device and saving it to the third table structure of the secondary data warehouse specifically means: forming row data according to the time consumption deviation of each process step, the correspondence between the total number of faults and the device and saving it to the third table structure of the secondary data warehouse.

[0067] Furthermore, the above step S12 may further include: identifying abnormal process steps based on the time consumption deviation of each process step and the total number of faults. Accordingly, the equipment operation efficiency evaluation report may also include information on the identified abnormal process steps. For example, identifying abnormal process steps based on the time consumption deviation of each process step and the total number of faults may specifically involve: identifying process steps corresponding to equipment with an average time consumption deviation exceeding 20% ​​of the total time consumption, a maximum time consumption deviation exceeding 30% of the total time consumption, and a total number of faults exceeding 5 as abnormal process steps, indicating that the component executing this process step is abnormal or faulty. The information on the abnormal process step may include equipment code, process step ID, component executing the process step, etc.

[0068] For example, the main data table obtained by saving the device operation data uploaded by the automated storage device in this embodiment is shown in Table 1 below:

[0069] Table 1:

[0070]

[0071]

[0072] Among them: Equipment code refers to the equipment's factory serial number; Task type can include: box warehousing, box outbound, pipe warehousing, pipe outbound, fragment sorting, inventory counting, etc.; Process step ID consists of the corresponding ID number of the process step + the execution serial number; Process step status can include: task start execution, execution end, execution failure (or abnormality); Running time refers to the execution duration of the process step; Receiving time refers to the time when the equipment running data is received; Whether to process is used to identify the processing status of each row of data. When the received data is stored in the main data table, the processing status is always unprocessed, that is, whether to process is corresponding to whether to process in the table. It should be understood that a process step refers to which components and what work need to be done to complete a task. In this embodiment, the process steps corresponding to the automated storage device can be divided into: opening the outer door, closing the outer door, extending the inlet / outlet shovel, retracting the inlet / outlet shovel, the buffer room robot arm taking a box from the shovel to the camera, the camera taking a picture, recognizing and reading the code, the buffer room robot arm taking a box from the camera to the internal track, opening the inner door, closing the inner door, extending the internal track to the storage area, retracting the internal track to the buffer room, the storage area robot arm taking a box, the storage area robot arm moving to the front of the shelf, and the storage area robot arm placing a box, etc.

[0073] For example, the structure of the first table stored in the primary data warehouse after preliminary analysis of the data in the aforementioned master data table is shown in Table 2 below:

[0074] Table 2:

[0075]

[0076]

[0077] The data generation process in Table 2 is as follows: The rows in Table 1 that are marked "No" are processed to generate the data in Table 2. The device code, task type, task number, and process step ID are the same as in Table 1. The processing time is calculated by subtracting the end time from the start time of the process step corresponding to the same task number and process step ID, and converting this to seconds. "Fault status" refers to whether the process step corresponding to the same task number and process step ID is in a fault state. After processing a row of data in Table 2, the "No processing" field for that row in Table 1 is updated to "Yes".

[0078] For example, the structures of the second and third tables stored in the secondary data warehouse, obtained through further detailed analysis of the data in Table 2 above, are shown in Tables 3.1 and 3.2 below:

[0079] Table 3.1

[0080]

[0081] Table 3.2

[0082]

[0083]

[0084] The data generation process in Tables 3.1 and 3.2 is as follows: At 00:00 each day, unprocessed data (i.e., data where no processing was required) is retrieved from Table 2, and then processed as follows:

[0085] ① Calculate the average time for each process step of all devices on a daily basis (total time for a process step within a day divided by the number of rows), the shortest time (the top 10% of the time for a process step within a day, sorted from smallest to largest, and the total time divided by the number of rows corresponding to the top 10%), and the longest time (the bottom 10% of the time for a process step within a day, sorted from smallest to largest, and the total time divided by the number of rows corresponding to the bottom 10%), and fill the data into Table 3.1.

[0086] ② Take the average time of a single process step in the past year from 3.2 as the standard value, and then calculate the average time of each process step of a single device on a daily basis. Subtract the standard value from the average time to get the average time deviation. Subtract the standard value from the maximum time to get the maximum time deviation. Thus, the average time deviation and maximum time deviation of a single process step of each device per day are filled into Table 3.2.

[0087] ② Mark the process steps corresponding to the equipment in Table 3.2 whose average time deviation exceeds 20% of the total time, whose foot time deviation exceeds 30% of the total time, and whose total number of failures exceeds 5 as abnormal process steps. The total time refers to the total time of the above-mentioned process steps within one day.

[0088] Those skilled in the art will understand that all or part of the processes in the methods of any of the above embodiments of this application can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0089] Furthermore, this application also provides an intelligent agent for evaluating equipment operating efficiency, such as... Figure 3 As shown, the intelligent agent provided in this application embodiment includes a device and a cloud. Data transmission between the device and the cloud is based on an edge gateway, wherein:

[0090] The device is configured to upload device operation data to the cloud via an edge gateway during device operation; wherein the device operation data includes at least device information and process step information, and the process step information includes process step identifier, process step status and running time.

[0091] The cloud is configured to: receive device operation data uploaded by the device and save it to the database module; in response to a device operation efficiency analysis request initiated by the device or in response to a preset periodic startup request, analyze the saved device operation data to generate primary and secondary analysis results; and generate a device operation efficiency evaluation report based on the primary and / or secondary analysis results. The primary analysis results are used to evaluate the time consumption and abnormal states of each device executing each process step; the secondary analysis results are used to evaluate the time consumption of a single process step and the time consumption deviation of each device executing each process step.

[0092] The intelligent agent provided in this embodiment enables functions such as data uploading, alarm notification, and anomaly self-correction by configuring corresponding programs on the device side, and enables functions such as data reception, data analysis, and action decision-making by configuring corresponding programs in the cloud.

[0093] For example, the device may include a data upload module, an alarm notification module, and an anomaly self-correction module. The data upload module is mainly used to upload device operation data to the cloud via an edge gateway during device operation. Further, the data upload module may be configured with an edge gateway program and a host computer upload program. The edge gateway program provides a host computer call interface. When the interface is called, the device operation data can be transmitted to the cloud server using the MQTT protocol. The host computer upload program is mainly used to break down tasks into process steps that can be executed by individual components, and to call the edge gateway interface and pass corresponding parameters once at the start, end, and exception of each process step. The cloud may include a data receiving module, a data analysis module, and an action decision module. The data receiving module can be further divided into a protocol layer and a data storage layer. The protocol layer uses the MQTT protocol and is mainly used to receive device operation data uploaded by the device. The data storage layer is used to save the received data to a database system, i.e., the data storage module in this embodiment. The alarm notification module is mainly used to issue alarms and prompts after receiving abnormal messages from the cloud; the abnormal self-correction module is mainly used to eliminate abnormalities by starting a correction program after receiving abnormal messages from the cloud.

[0094] The aforementioned intelligent agent is mainly used to execute the aforementioned equipment operation efficiency evaluation method embodiment. The technical principles, technical problems solved, and technical effects of the two are similar. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the intelligent agent can be referred to the content described in the embodiment of the equipment operation efficiency evaluation method, which will not be repeated here.

[0095] Furthermore, this application also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to the present invention, the computer-readable storage medium can be configured to store a program for performing the device operating efficiency evaluation method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described device operating efficiency evaluation method. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0096] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for evaluating equipment operating efficiency, applied to intelligent agents, characterized in that, The intelligent agent includes a device and a cloud, and the method includes: The cloud receives and saves device operation data from the device; the device operation data includes at least device information and process step information, and the process step information includes process step identifier, process step status and running time. When the cloud responds to a preset trigger condition, it analyzes the stored device operation data to generate primary analysis results and secondary analysis results, and generates a device operation efficiency evaluation report based on the primary analysis results and / or the secondary analysis results. The first-level analysis results are used to evaluate the time consumption and abnormal status of each device in executing each process step; the second-level analysis results are used to evaluate the time consumption of a single process step and the time consumption deviation of each device in executing each process step.

2. The method for evaluating equipment operating efficiency according to claim 1, characterized in that, The cloud includes a data storage module, and the method further includes: The device running data is organized into rows according to the receiving time and saved to the main data table in the data storage module; the row data includes the device information, the process step identifier, the process step status, the running time, and the receiving time; The primary analysis results and the secondary analysis results are saved to the primary data warehouse and the secondary data warehouse in the data storage module, respectively.

3. The method for evaluating equipment operating efficiency according to claim 2, characterized in that, The cloud-based analysis of the device's operational data generates primary and secondary analysis results, including: Select the first data to be processed from the main data table, and based on the first data to be processed, calculate the time consumption and abnormal status of each device in executing each process step. Based on the correspondence between the time consumption and abnormal status and the device and process step, form row data and save it to the first table structure of the first-level data warehouse. According to the first time period, the second data to be processed is selected from the first table structure at regular intervals. Based on the second data to be processed, the average time, shortest time and longest time of each process step in the first time period are calculated to obtain the time data of a single process step. Row data is formed according to the time data of the single process step and saved to the second table structure of the secondary data warehouse. Based on the first data to be processed and the second data to be processed, the average time of each process step within the second time period is calculated as a standard value. The time deviation of each device in executing each process step is determined according to the difference between the average time and the longest time of each process step within the first time period and the standard value. The time deviation includes the average time deviation and the maximum time deviation. Row data is formed according to the correspondence between the time deviation of each process step and the device and saved in the third table structure of the secondary data warehouse. The data in the first table structure is used to characterize the first-level analysis results, and the data in the second and third table structures are used to characterize the second-level analysis results.

4. The method for evaluating equipment operating efficiency according to claim 3, characterized in that, The calculation methods for the average time, the shortest time, and the longest time within the first period are as follows: The total time and number of executions for each process step within the first time period are statistically analyzed, and the average time is calculated based on the total time and the number of executions. The execution time of each process step within the first time period is sorted from smallest to largest. A portion of the time data is selected from the sorted time data and accumulated to calculate the shortest and longest execution times based on the accumulated calculation results and the selected data volume.

5. The method for evaluating equipment operating efficiency according to claim 3, characterized in that, The cloud-based analysis of the device operation data to generate primary and secondary analysis results also includes: based on the first data to be processed, calculating the total number of faults that occur when each device executes each process step; The step of forming row data based on the time consumption deviation of each process step and the correspondence between the equipment and the data and saving it to the third table structure of the secondary data warehouse includes: forming row data based on the time consumption deviation of each process step, the total number of faults and the correspondence between the equipment and the data and saving it to the third table structure of the secondary data warehouse.

6. The method for evaluating equipment operating efficiency according to claim 5, characterized in that, The method further includes a step of identifying abnormal process steps based on the time consumption deviation of each process step and the total number of faults, and the equipment operation efficiency evaluation report also includes information on the identified abnormal process steps.

7. The method for evaluating equipment operating efficiency according to claim 3, characterized in that, The preset triggering conditions include receiving a device operation efficiency analysis request initiated by the device and detecting a preset periodic start request in the cloud; When the cloud receives a device operation efficiency evaluation and analysis request initiated by the device, the method further includes: the cloud returning the generated device operation efficiency evaluation report to the device; When the cloud detects a pre-set periodic startup request, and the device operation efficiency evaluation report generated by the cloud contains information about abnormal process steps, the method further includes: the cloud sending the information about the abnormal process steps to the device.

8. The method for evaluating equipment operating efficiency according to claim 3, characterized in that, The information contained in each row of data in the main data table and the first table structure also includes the processing status of the data. The first data to be processed and the second data to be processed are both selected row data with an unprocessed processing status. The method also includes the step of updating the processing status of each row of data in the main data table and the first table structure.

9. An intelligent agent, characterized in that, The intelligent agent is used to evaluate the operating efficiency of the equipment, and the intelligent agent includes the device end and the cloud end. The device is configured to upload device operation data to the cloud via an edge gateway during device operation; wherein the device operation data includes at least device information and process step information, and the process step information includes process step identifier, process step status and running time; The cloud is configured to: receive device operation data uploaded by the device and save it to the database module; in response to a device operation efficiency analysis request initiated by the device or in response to a preset periodic startup request, analyze the saved device operation data to generate primary and secondary analysis results; and generate a device operation efficiency evaluation report based on the primary and / or secondary analysis results. The primary analysis results are used to evaluate the time consumption and abnormal states of each device executing each process step; the secondary analysis results are used to evaluate the time consumption of a single process step and the time consumption deviation of each device executing each process step.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the device operating efficiency evaluation method according to any one of claims 1 to 8.

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