CN115660312A

CN115660312A - Parameter adjusting method and device, electronic equipment and storage medium

Info

Publication number: CN115660312A
Application number: CN202211086006.6A
Authority: CN
Inventors: 蒋冠莹; 卢勇
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-09-06
Filing date: 2022-09-06
Publication date: 2023-01-31
Anticipated expiration: 2042-09-06
Also published as: CN115660312B

Abstract

The disclosure provides a parameter adjusting method and device, electronic equipment and a storage medium, relates to the field of artificial intelligence such as Internet of things, deep learning and big data processing, and is suitable for various process type process scenes. The method comprises the following steps: acquiring order output data; determining an unfinished order according to order yield data, and determining an optimal parameter value for a parameter to be adjusted corresponding to the unfinished order, wherein the parameter to be adjusted is an energy consumption related parameter; and adjusting the parameters to be adjusted based on the optimal parameter values. By applying the scheme disclosed by the invention, the energy-saving effect can be improved, the implementation cost can be reduced, and the like.

Description

Parameter adjusting method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for adjusting parameters in the fields of internet of things, deep learning, and big data processing, an electronic device, and a storage medium.

Background

In the production of the manufacturing industry, for example, the flow type processes such as long car dyeing machine tables in the printing and dyeing industry are often faced with the problem of high energy consumption, thereby bringing great cost pressure to enterprises.

Disclosure of Invention

The disclosure provides a parameter adjusting method, a parameter adjusting device, an electronic device and a storage medium.

A parameter adjustment method, comprising:

acquiring order output data;

determining an optimal parameter value aiming at a parameter to be adjusted corresponding to the order which is not finished in response to the determination that the order which is not finished exists according to the order yield data, wherein the parameter to be adjusted is an energy consumption related parameter;

and adjusting the parameters to be adjusted based on the optimal parameter values.

A parameter adjustment apparatus comprising: the device comprises a first acquisition module, a second acquisition module and an adjustment module;

the first acquisition module is used for acquiring order yield data;

the second obtaining module is configured to determine, in response to determining that an unfinished order exists according to the order yield data, an optimal parameter value for a parameter to be adjusted corresponding to the unfinished order, where the parameter to be adjusted is an energy consumption related parameter;

and the adjusting module is used for adjusting the parameter to be adjusted based on the optimal parameter value.

An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as described above.

A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method as described above.

A computer program product comprising computer programs/instructions which, when executed by a processor, implement a method as described above.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a flow chart of an embodiment of a parameter adjustment method according to the present disclosure;

FIG. 2 is a flowchart illustrating the processing of an unfinished order according to the present disclosure;

FIG. 3 is a schematic view of a processing flow performed by the present disclosure based on the order statistics recalculation flag table and the order comprehensive statistics table;

FIG. 4 is a schematic diagram illustrating a data flow of a parameter adjustment method according to the present disclosure;

FIG. 5 is a schematic diagram illustrating a first embodiment 500 of a parameter adjustment apparatus according to the present disclosure;

fig. 6 is a schematic structural diagram illustrating a second embodiment 600 of a parameter adjustment apparatus according to the present disclosure;

FIG. 7 illustrates a schematic block diagram of an electronic device 700 that may be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In addition, it should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

Fig. 1 is a flowchart of an embodiment of a parameter adjustment method according to the present disclosure. As shown in fig. 1, the following detailed implementation is included.

In step 101, order production data is acquired.

In step 102, in response to determining that an unfinished order exists according to the order yield data, an optimal parameter value is determined for a parameter to be adjusted corresponding to the unfinished order, where the parameter to be adjusted is an energy consumption related parameter.

In step 103, the parameter to be adjusted is adjusted based on the optimal parameter value.

By adopting the scheme of the method embodiment, the parameter to be adjusted can be adjusted according to the determined optimal parameter value, and the parameter to be adjusted is the energy consumption related parameter, so that the energy-saving effect is improved, the implementation cost is reduced, and the energy-saving optimization, cost reduction and efficiency improvement and the like are realized.

Preferably, the scheme of the method embodiment can be applied to a long car dyeing machine in the printing and dyeing industry.

The long car dyeing machine generally comprises 11 links/devices, the whole machine is divided into a front car and a rear car, the lengths of the front car and the rear car are respectively 190 meters and 410 meters, the total length of the long car dyeing machine is 600 meters, the length of cloth needing to pass through is mainly the length of the cloth, namely, the circumference of a drying cylinder and a water tank roller and the like are included, each link/device relates to different device parameters, energy consumption parameters and environmental indexes, and the following description is respectively given.

The energy consumption parameters of the front vehicle part may include: water consumption of a front vehicle: instantaneous flow, real-time average unit consumption value; front vehicle power consumption: instantaneous flow, real-time average unit consumption value; the opening value of a steam valve of the front vehicle; front vehicle steam consumption: instantaneous flow, pressure, temperature, real-time average unit consumption value.

The environmental indicators of the front vehicle part may include: real-time outdoor temperature, real-time outdoor humidity, special holiday marks, scheduling and scheduling information, standing time between front and rear vehicles, machine idling time and front vehicle length (190 meters).

The equipment parameters of the front vehicle part may include: a) The ingot car: an infrared inductor: judging the cropping and the cloth arrangement; the speed of the front vehicle is as follows: since the front car is mainly dyed, the speed of the car is usually a constant value of 45 m/min; b) Dyeing machine: the batching system: preparing ingredients and proportioning; c) Two infrared prebakers: case temperature, centrifugal wind speed, circulating fan and air duct temperature; d) Three pre-drying air boxes: a circulating air frequency value, an exhaust air frequency value, a tension value, a pre-drying temperature control actual value and a pre-drying temperature control set value; e) Two drying cylinders of the drying room: drying room temperature, drying room humidity; f) The ingot car: an infrared inductor: and monitoring cloth arranging and falling.

The energy consumption parameters of the rear vehicle part may include: water consumption of a rear vehicle: instantaneous flow, real-time average unit consumption value; electric consumption of the rear vehicle: instantaneous flow, real-time average unit consumption value; steam consumption of the rear vehicle: instantaneous flow, pressure, temperature, real-time average unit consumption value; opening value of a steam valve of the rear vehicle; and (4) real-time average unit consumption value of the steam of the whole vehicle.

The environmental indicators of the rear vehicle part may include: the environmental temperature and the environmental humidity between the No. 10 rinsing bath and the drying cylinder; the ambient temperature and the ambient humidity outside the No. 10 rinsing bath; the speed of the rear vehicle can be very different between different specifications, and is usually 60-150 m/min; the rear car is long (410 m).

The equipment parameters of the rear vehicle part may include: a) Two open rinsing baths: washing temperature, calculated amount of hydrogen peroxide and soaping amount; b) Two closed rinsing baths: actual value and set value of each soaping temperature; padder pressure, tension frame pressure; c) Eight steam water tanks: actual soaping temperature values and set values of all machines; chemical reaction of the water tank: a lower pH value, a pH temperature, an upper pH value and an actual pH value; padder pressure, tension frame pressure; d) A steam drying cylinder: the actual value, the set value and the early warning critical value of the moisture content of the cloth cover; evaluating the hand-touch dryness of a technician; e) The ingot car: an infrared inductor: and detecting cloth swinging and falling.

Correspondingly, the parameters to be adjusted can comprise the speed of the front vehicle, the speed of the rear vehicle, the opening of a steam valve of the rear vehicle and the like, and specifically comprises which parameter/parameters can be determined according to actual needs.

In actual practice, order yield data (orders) may be obtained. In one embodiment of the present disclosure, the order yield data may be acquired periodically, and the specific duration of the period may be determined according to actual needs, and if the duration is shorter, then the order yield data is acquired in real time. By periodically acquiring, the timeliness of the processing can be ensured, such as the latest optimal parameter value can be ensured to be recommended in time.

The order production data may include an order identifier (e.g., an unfinished order identifier and an order identifier finished in the last cycle), as well as a start time, an end time, a color name, a specification, a material, team information, etc. of the order.

The order is the scheduling made according to the order made by the client. Taking a long car dyeing machine as an example, a manufacturer can make a certain production beat according to the color, the cloth, the quantity and the like corresponding to an order and by matching with the machine and a team, so as to finish production.

If the unfinished order is determined to exist according to the order yield data, the optimal parameter value can be determined according to the parameter to be adjusted corresponding to the unfinished order.

For example, if the ending time of an order in the order production data is determined to be empty, then the order may be determined to be an unfinished order, and vice versa, then the order may be determined to be a finished order.

In an embodiment of the present disclosure, the determining of the optimal parameter value may include one or any combination of the following: 1) Determining an optimal parameter value based on the service specification and the security policy; 2) Determining an optimal parameter value based on the historical working condition track; 3) And determining an optimal parameter value based on a strategy model obtained by pre-training.

Preferably, the three manners may be respectively adopted to obtain the optimal parameter value, but in practical application, for any manner, the optimal parameter value may be obtained, or the optimal parameter value may not be obtained, for example, for a manner based on a historical operating condition trajectory, if a sufficient historical operating condition trajectory is not obtained, for example, if less than 5, the corresponding optimal parameter value may not be obtained.

For a long car dyeing machine, two potential safety hazards are usually involved, firstly, steam is used, when pressure or temperature is too high, tube explosion is easy to occur, safety accidents and economic losses are caused, secondly, acid-base reaction occurs in a water washing tank of a rear car, water temperature and environment temperature influence chemical reaction results, and in addition, equipment parameters such as car speed and the like are strictly controlled according to a production manual.

Accordingly, in the method 1), the optimal parameter value may be determined based on the service specification and the security policy, for example, the optimal parameter value may be determined based on a preset rule, or the preset optimal parameter value may be directly used.

Mode 2) is a white box model mode based on quantiles, and in one embodiment of the present disclosure, historical operating condition tracks corresponding to unfinished orders may be obtained, and historical operating condition tracks meeting the following requirements may be screened out from the obtained historical operating condition tracks: and the preset index is located in a preset quantile range, the screened historical working condition track can be used as an expert track, and then the optimal parameter value can be determined according to the expert track.

For a factory with incomplete informatization and automation, cloth with the same specification is produced under the operation of different workers, the actual energy consumption may be greatly different, but the parameter value may not be greatly different, and based on the difference, a white box model based on quantiles can be adopted for constraint. Specifically, the historical operating condition tracks corresponding to the unfinished order can be obtained, for example, the historical operating condition tracks of the same category as the unfinished order and the historical operating condition tracks of the similar category as the unfinished order can be included, the obtained historical operating condition tracks can be collected, abnormal tracks in the historical operating condition tracks can be eliminated, and further the historical operating condition tracks which are located in a preset quantile range, for example, the 20% -25% quantile range can be selected as expert tracks according to preset indexes, such as steam consumption in unit time, steam consumption per ten thousand meters, water consumption per ten thousand meters or electricity consumption per ten thousand meters. The abnormal trajectory may be caused by a process table issuing error, a machine error, a manual error, and the like.

The historical operating condition track refers to an actual production track generated historically, for example, a track formed by operating condition data from the start time to the end time of an order for a certain order.

The working condition data refers to the data of the condition change inside and around the production environment. Taking a long car dyeing machine as an example, the working condition data may include production and reporting data, product specification data, equipment parameters, energy consumption information, and the like.

Through the processing, the selected expert track is more stable and has more reference value, and the accuracy of the optimal parameter value obtained based on the expert track is improved.

And determining the optimal parameter value according to the selected expert track. For example, for any parameter to be adjusted, the current progress of the unfinished order can be determined (for example, 20 minutes is performed), the mean value or the median of the parameter values of the parameter at the same progress time in each expert trajectory can be obtained, and the obtained mean value or median is used as the optimal parameter value of the parameter.

In the method 3), the optimal parameter values may be determined based on a strategy model obtained by pre-training, that is, the method 3) may be a black-box mode method based on deep reinforcement Learning, and the strategy model may be an autonomous optimization model based on a generation-oriented adaptive Learning (GAIL) framework/framework.

In practical application, which of the above manners is specifically adopted may be determined according to actual needs, and preferably, the above three manners may be simultaneously adopted, so as to provide more optimal parameter values for selection.

Accordingly, in one embodiment of the present disclosure, in response to determining that the number of the optimal parameter values is greater than one group, a group of optimal parameter values selected from the optimal parameter values may be obtained as target parameter values, and then the parameter to be adjusted may be adjusted according to the target parameter values.

For example, the number of the optimal parameter values is three groups, which are respectively the optimal parameter values obtained according to the above mode 1), mode 2) and mode 3), and then a group of optimal parameter values can be selected from the optimal parameter values as target parameter values, either by a manual selection mode or an automatic selection mode.

For example, the three sets of optimal parameter values and the currently used parameter value may be displayed to the user, and the user may select the best optimal parameter value as the target parameter value by data comparison, for example, by comparing the three sets of optimal parameter values with the currently used parameter value respectively. If the automatic selection method is adopted, the optimal parameter value corresponding to the method with the highest priority may be selected according to different priorities set for the three methods in advance, for example, the priority of the method 3) may be considered to be the highest, and accordingly, the optimal parameter value corresponding to the method may be used as the target parameter value.

Through the processing, the target parameter value can be simply and conveniently determined, and then the parameters to be adjusted can be adjusted according to the target parameter value, for example, if the number of the parameters to be adjusted is 3, the parameters to be adjusted can be adjusted respectively according to the target parameter value corresponding to each parameter to be adjusted.

In an embodiment of the present disclosure, for the parameter to be adjusted, a parameter value set by a user may be further used as a target parameter value, or a parameter value selected from historical parameter values corresponding to an unfinished order may be used as a target parameter value.

For example, if the user considers that the determined optimal parameter values are not appropriate, the user may set the parameter values autonomously as target parameter values, or may obtain historical parameter values of orders of the same or similar categories as the unfinished order, and may select one of the historical parameter values as the target parameter value.

That is, the target parameter value can be obtained in various ways, and is not limited to a certain way, so that the method is very flexible and convenient in implementation, and can meet different use requirements of users.

In an embodiment of the present disclosure, a monitoring threshold may also be determined according to the target parameter value, and an actual parameter value of the parameter to be adjusted may be monitored, and an alarm may be given in response to determining that the actual parameter value exceeds the monitoring threshold according to the monitoring result.

For example, if the target parameter value of a certain parameter to be adjusted is 1.2, the corresponding monitoring threshold value may be 1.0-1.4, and the actual parameter value of the parameter to be adjusted may be monitored, and if the target parameter value exceeds the range of 1.0-1.4, an alarm may be given.

In practical application, a large deviation between an actual parameter value and a target parameter value may occur due to various reasons, accordingly, an alarm may be given, for example, related personnel may be prompted by a mail or a short message, and an optimization suggestion may be given, so that the related personnel may view the reason of the alarm in time and solve a possible problem.

In addition, various information related in the process, such as the optimal parameter value, the target parameter value, the alarm information and the like, can be recorded in the corresponding intermediate data table for subsequent analysis and check.

With the above description in mind, fig. 2 is a flowchart illustrating the processing procedure of the unfinished order according to the present disclosure. For each unfinished order, it may be processed in this manner separately. As shown in fig. 2, the following detailed implementation is included.

In step 201, three sets of optimal parameter values are determined for the unfinished order.

For example, a set of optimal parameter values may be determined based on the traffic profile and the security policy, a set of optimal parameter values may be determined based on historical operating condition trajectories, and a set of optimal parameter values may be determined based on a policy model obtained through pre-training.

In step 202, a set of optimal parameter values selected from the three sets of optimal parameter values is obtained as target parameter values, and the parameters to be adjusted are adjusted according to the target parameter values.

In practical application, the parameter values set by the user can be used as target parameter values, or the parameter values selected from historical parameter values corresponding to an unfinished order can be used as target parameter values.

In addition, the three groups of optimal parameter values can be written into an optimal working condition recommendation table (opt _ recommendation) and comprise order identifications, three groups of corresponding optimal parameter values and the like, correspondingly, the three groups of optimal parameter values recorded in the optimal working condition recommendation table can be displayed to a user through a model tuning page for the user to select, and after the user selects a target parameter value, the target parameter value can be recorded into a model execution recording table (adv _ models _ logs), wherein the information comprises the selection time, the corresponding order identification and the like.

In step 203, a monitoring threshold is determined according to the target parameter value.

For example, information such as an upper limit and a lower limit may be obtained from the alarm look-up table (alarms), for example, the upper limit and the lower limit are respectively 0.2, and if a target parameter value of a certain parameter to be adjusted is 1.2, the determined monitoring threshold may be 1.0 to 1.4 accordingly. The alarm comparison table can also comprise information such as treatment suggestions exceeding the upper limit and treatment suggestions exceeding the lower limit, and optimization suggestions can be given according to the information.

In step 204, the actual parameter value of the parameter to be adjusted is monitored, and if it is determined according to the monitoring result that the actual parameter value exceeds the monitoring threshold, an alarm is given.

And, the alarm information can be recorded into an alarm record table (alarm _ logs), wherein the alarm information can comprise information such as alarm time, measuring point and description.

In addition, a model version maintenance table (adv _ models) may be established, in which parameter values, generation times, and the like corresponding to different orders may be recorded, so as to query historical parameter values and the like based on the model version maintenance table.

As previously described, order production data may be acquired periodically. In one embodiment of the present disclosure, in response to determining from the order yield data that there is an order that has ended, the order that has ended in a last cycle, statistical processing may be performed on the order that has ended, and the statistical processing may include: and acquiring a comprehensive statistical result of the finished order, and recording the comprehensive statistical result into an order comprehensive statistical table (order _ stats).

For example, if the ending time of an order is not empty, the order may be determined to be an ended order. For the finished order, whether the order is a valid order may be further determined, for example, whether the order is a valid order may be determined based on a preset validity determination rule, if the order is an invalid order, the order may not be processed, and if the order is a valid order, the order may be statistically processed, that is, a comprehensive statistical result of the finished order may be obtained, and the comprehensive statistical result is recorded in the order comprehensive statistical table.

The order comprehensive statistical table may record comprehensive statistical results of each order, such as order identification, start time, end time, corresponding card number, class, gram weight, color name, color depth, time consumption, unit time output, production efficiency, front vehicle energy consumption, rear vehicle energy consumption, whole vehicle energy consumption, idle running energy consumption, standing energy consumption, front vehicle energy consumption, rear vehicle energy consumption, whole vehicle energy consumption, and the like.

For any finished order, how to obtain the comprehensive statistical result is not limited, for example, the device parameter (device _ para) information and the energy consumption information can be obtained while obtaining the order yield data. Wherein the device parameter information may include: the method comprises the following steps of front vehicle air exhaust information (such as air exhaust frequency and pre-drying air frequency), front vehicle pressure information (such as tension), front vehicle pre-drying temperature control information (such as a pre-drying temperature actual value and a pre-drying temperature set value), front vehicle bottoming parameter information (such as front vehicle speed), front vehicle temperature and humidity information (such as drying room temperature and drying room humidity), rear vehicle soaping information (such as a washing tank temperature actual value and a washing tank temperature set value), rear vehicle cloth surface moisture content information (such as a cloth surface moisture content actual value, a set value and an alarm value) and rear vehicle hydrogen peroxide calculated amount information. The energy consumption information can comprise the pressure, temperature, instantaneous flow value and the like of the steam consumption of the hydropower station. And determining a comprehensive statistical result of the finished order by combining the equipment parameter information, the energy consumption information and the like acquired in the past.

In an embodiment of the present disclosure, an order statistics recalculation flag table (recalculation _ errors) may be further maintained, and in response to determining that the unfinished order is not recorded in the order statistics recalculation flag table, the unfinished order may be recorded in the order statistics recalculation flag table, and the recalculation flag of the unfinished order may be set to a first value, and accordingly, in response to determining that any order in the order statistics recalculation flag table is finished and the statistical processing is finished, the recalculation flag of the order may be set to a second value, preferably, the first value may be 0 and the second value may be 1, and in addition, in response to determining that the predetermined time is reached and determining that the order which is finished but has not finished the statistical processing exists in the order statistics recalculation flag table, the statistical processing may be performed on the order.

Through the processing, the problem that the comprehensive statistical result of the order is missed in the order comprehensive statistical table due to the fact that the statistical result is not finished due to some reason after the order is finished can be prevented.

In an embodiment of the present disclosure, the query request may also be obtained, and the query result is determined and returned according to the order comprehensive statistical table and/or another statistical table generated based on the order comprehensive statistical table.

Through the processing, various query operations of the user can be supported, so that the user can conveniently acquire the information, the information utilization rate is improved, and the like.

With the above introduction, fig. 3 is a schematic processing flow chart of the present disclosure performed based on the order statistics recalculation flag table and the order comprehensive statistics table. As shown in fig. 3, the following detailed implementation is included.

In step 301, any unfinished order is recorded in the order statistics recalculation flag table, and the recalculation flag of the unfinished order is set to 0.

In step 302, in response to determining that any order in the order statistics recalc flag table has ended and the statistical process has completed, setting the recalc flag for that order to 1, the statistical process comprising: and acquiring a comprehensive statistical result of the order, and recording the comprehensive statistical result into an order comprehensive statistical table.

In step 303, in response to determining that the predetermined time has been reached and that there is an order that has ended but for which the recalculation flag is 0 in the order statistics recalculation flag table, the statistical processing is performed on the order.

A timed service may be used, such as at zero point of each day, to determine whether there are orders in the order statistics recalculation flag table that have ended but have not completed the statistical processing, and if so, the statistical processing may be performed on those orders.

In step 304, a day integrated statistics table (day _ stats) and a history integrated statistics table (clock _ stats) are generated based on the order integrated statistics table.

The daily comprehensive statistical table can be generated at the zero point of each day, and the historical comprehensive statistical table can be updated at the zero point of each day. The comprehensive daily statistics table can comprise: date, equipment number, class number, order quantity, average production duration, total energy consumption, yield, average unit consumption, production efficiency, average unit consumption ring ratio, production efficiency ring ratio, production energy consumption, standing energy consumption, idling energy consumption and the like. The historical comprehensive statistical table can comprise: updating date, equipment number, class number, historical order count, historical average unit consumption of the front vehicle, historical average unit consumption of the rear vehicle, historical average unit consumption of the whole vehicle and the like.

The generation, the updating and the like of each table can adopt a multithreading non-blocking task processing mode to reduce write library conflict and the like.

In step 305, a query result corresponding to the obtained query request is generated and returned based on the order comprehensive statistical table and/or the daily degree comprehensive statistical table and/or the history comprehensive statistical table.

The query may be a fuzzy query, and may support a query of one or more conditions, and if no condition is specified, the full amount of data may be returned, and the one or more conditions may include categories, color names, grammars, and the like, such as various historical information queries, daily information queries, and the like may be supported.

The above mentioned comprehensive statistical table of orders, comprehensive statistical table of daily degree, comprehensive statistical table of categories, maintenance table of model version, execution record table of model, alarm comparison table, alarm record table, recommendation table of optimal working condition and recalculation marking table of orders are all intermediate data tables, used for storing various intermediate results, and can be used for supporting the above mentioned inquiry and various data monitoring.

Fig. 4 is a schematic data flow diagram of the parameter adjustment method according to the present disclosure. As shown in fig. 4, the real-time data (water, electricity, steam, and other sensor data) of the secondary metering device can be collected through the intelligent gateway, and the real-time working condition data can be acquired through a Manufacturing Execution System (MES), so that the required order output data, equipment parameter information, energy consumption information, and the like can be obtained. The obtained information (i.e., data) may be forwarded by an internet of things platform (IoT-platform) and then may be doubly written into a Database, where the Database may be an online analytical processing (OLAP Doris) Database, an online transaction relational Database management system (OLTP MySQL) Database, a Time Series Database (TSDB, time Series Database), or the like. In addition, data processing may be performed in the manner described in this disclosure based on the information obtained.

In addition, in practical application, the scheme of the present disclosure may adopt a cloud deployment mode, and may also adopt a deployment mode combining cloud sides, and the specific mode is not limited.

It is noted that while for simplicity of explanation, the foregoing method embodiments are described as a series of acts, those skilled in the art will appreciate that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required for the disclosure. In addition, for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions in other embodiments.

In a word, by adopting the scheme of the embodiment of the method, the energy-saving effect can be improved, the implementation cost can be reduced, and the like.

The above is a description of embodiments of the method, and the embodiments of the apparatus are described below to further illustrate the aspects of the disclosure.

Fig. 5 is a schematic structural diagram illustrating a first embodiment 500 of a parameter adjusting apparatus according to the present disclosure. As shown in fig. 5, includes: a first obtaining module 501, a second obtaining module 502, and an adjusting module 503.

A first obtaining module 501, configured to obtain order yield data.

A second obtaining module 502, configured to determine, in response to determining that an unfinished order exists according to the order yield data, an optimal parameter value for a parameter to be adjusted corresponding to the unfinished order, where the parameter to be adjusted is an energy consumption related parameter.

An adjusting module 503, configured to adjust a parameter to be adjusted based on the optimal parameter value.

By adopting the scheme of the embodiment of the device, the parameter to be adjusted can be adjusted according to the determined optimal parameter value, and the parameter to be adjusted is the energy consumption related parameter, so that the energy-saving effect is improved, the implementation cost is reduced, and the energy-saving optimization, cost reduction and efficiency improvement and the like are realized.

Preferably, the scheme of the device embodiment can be applied to a long car dyeing machine in the printing and dyeing industry. Correspondingly, the parameters to be adjusted can comprise the speed of the front vehicle, the speed of the rear vehicle, the opening of a steam valve of the rear vehicle and the like, and specifically comprises which parameter/parameters can be determined according to actual needs.

The first obtaining module 501 may obtain the order yield data, and if it is determined that the unfinished order exists according to the order yield data, the second obtaining module 502 may determine an optimal parameter value for a parameter to be adjusted corresponding to the unfinished order.

For the method 2), the second obtaining module 502 may obtain the historical operating condition tracks corresponding to the unfinished order, and screen out the historical operating condition tracks meeting the following requirements from the historical operating condition tracks: and (4) the preset index is located in a preset quantile range, the screened historical working condition track is used as an expert track, and an optimal parameter value is determined according to the expert track.

In addition, in an embodiment of the disclosure, in response to determining that the number of the optimal parameter values is greater than one, the adjusting module 503 may obtain a group of optimal parameter values selected from the optimal parameter values as target parameter values, and then may adjust the parameter to be adjusted according to the target parameter values. Either a manual or automatic selection may be used.

In an embodiment of the present disclosure, for the parameter to be adjusted, the adjusting module 503 may further use a parameter value set by the user as a target parameter value, or may use a parameter value selected from historical parameter values corresponding to an unfinished order as a target parameter value.

In an embodiment of the disclosure, the adjusting module 503 may further determine a monitoring threshold according to the target parameter value, monitor an actual parameter value of the parameter to be adjusted, and alarm in response to determining that the actual parameter value exceeds the monitoring threshold according to the monitoring result.

Fig. 6 is a schematic structural diagram illustrating a second embodiment 600 of a parameter adjusting apparatus according to the present disclosure. As shown in fig. 6, includes: a first obtaining module 501, a second obtaining module 502, an adjusting module 503, and a counting module 504.

The first obtaining module 501 may obtain the order yield data periodically, and accordingly, the statistical module 504 may perform statistical processing on the finished order in response to determining that the finished order exists according to the order yield data, where the finished order is an order finished in the latest period, where the statistical processing includes: and acquiring a comprehensive statistical result of the finished order, and recording the comprehensive statistical result into an order comprehensive statistical table.

In addition, in an embodiment of the disclosure, the statistics module 504 may record the unfinished order into the order statistics recalculation flag table in response to determining that the unfinished order is not recorded into the order statistics recalculation flag table, may set the recalculation flag of the unfinished order to a first value, may set the recalculation flag of any order in the order statistics recalculation flag table to a second value in response to determining that the statistical processing has been completed and any order in the order statistics recalculation flag table has been finished, and may perform the statistical processing on the order in response to determining that a predetermined time is reached and determining that an order that has been finished but has not been completed exists in the order statistics recalculation flag table.

Accordingly, the statistical module 504 may further obtain the query request, determine the query result according to the order comprehensive statistical table and/or other statistical tables generated based on the order comprehensive statistical table, and return the query result.

The specific work flow of the embodiments of the apparatus shown in fig. 5 and 6 can be referred to the related description of the foregoing method embodiments.

In a word, by adopting the scheme of the embodiment of the device disclosed by the invention, the energy-saving effect can be improved, the implementation cost can be reduced, and the like.

The scheme can be applied to the field of artificial intelligence, and particularly relates to the fields of Internet of things, deep learning, big data processing and the like. Artificial intelligence is a subject for studying a computer to simulate some thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning and the like) of a human, and has a hardware technology and a software technology, the artificial intelligence hardware technology generally comprises technologies such as a sensor, a special artificial intelligence chip, cloud computing, distributed storage, big data processing and the like, and the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge graph technology and the like.

The order in the embodiment of the present disclosure is not specific to a specific user, and does not reflect personal information of a specific user. In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 7 illustrates a schematic block diagram of an electronic device 700 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the device 700 comprises a computing unit 701, which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

Computing unit 701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 701 performs the various methods and processes described above, such as the methods described in this disclosure. For example, in some embodiments, the methods described in this disclosure may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM 702 and/or communications unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the methods described in the present disclosure may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured by any other suitable means (e.g., by means of firmware) to perform the methods described in the present disclosure.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A parameter adjustment method, comprising:

acquiring order output data;

determining an unfinished order according to the order yield data, and determining an optimal parameter value for a parameter to be adjusted corresponding to the unfinished order, wherein the parameter to be adjusted is an energy consumption related parameter;

2. The method of claim 1, wherein the determining optimal parameter values comprises one or any combination of the following:

determining the optimal parameter value based on the service specification and the security policy;

determining the optimal parameter value based on the historical working condition track;

and determining the optimal parameter value based on a strategy model obtained by pre-training.

3. The method of claim 2, wherein the determining the optimal parameter value based on historical operating condition trajectories comprises:

acquiring the historical working condition track corresponding to the unfinished order;

screening out the historical working condition tracks meeting the following requirements from the historical working condition tracks: the predetermined index is within a predetermined quantile range;

and taking the screened historical working condition track as an expert track, and determining the optimal parameter value according to the expert track.

4. The method of claim 2, wherein the adjusting the parameter to be adjusted based on the optimal parameter value comprises:

and in response to the fact that the number of the optimal parameter values is larger than one group, acquiring a group of optimal parameter values selected from the optimal parameter values as target parameter values, and adjusting the parameters to be adjusted according to the target parameter values.

5. The method of claim 4, further comprising:

taking the parameter value autonomously set by the user as the target parameter value;

or, taking a parameter value selected from historical parameter values corresponding to the unfinished order as the target parameter value.

6. The method of claim 4 or 5, further comprising:

determining a monitoring threshold value according to the target parameter value;

monitoring the actual parameter value of the parameter to be adjusted;

and alarming in response to determining that the actual parameter value exceeds the monitoring threshold value according to the monitoring result.

7. The method according to claim 1 to 5,

wherein the obtaining order production data comprises: periodically acquiring the order yield data;

the method further comprises the following steps: in response to determining that there is an order that has ended according to the order yield data, the order that has ended being an order that ended in a latest period, performing statistical processing on the order that has ended, the statistical processing including: and acquiring a comprehensive statistical result of the finished order, and recording the comprehensive statistical result into an order comprehensive statistical table.

8. The method of claim 7, further comprising:

in response to determining that the unfinished order is not recorded in an order statistics recalculation flag table, recording the unfinished order in the order statistics recalculation flag table, and setting a recalculation flag of the unfinished order to be a first value;

in response to determining that any order in the order statistics recalculation flag table has ended and the statistical processing has completed, setting the recalculation flag of the order to a second value;

performing the statistical processing on the order in response to determining that a predetermined time is reached and determining that there is an order in the order statistics recalculation flag table that has ended but has not completed the statistical processing.

9. The method of claim 7, further comprising:

and acquiring a query request, determining a query result according to the order comprehensive statistical table and/or other statistical tables generated based on the order comprehensive statistical table, and returning.

10. A parameter adjustment apparatus comprising: the device comprises a first acquisition module, a second acquisition module and an adjustment module;

the first acquisition module is used for acquiring order yield data;

11. The apparatus of claim 10, wherein the optimal parameter value comprises one or any combination of:

the optimal parameter value is determined based on the service specification and the security policy;

12. The apparatus of claim 11, wherein,

the second obtaining module obtains the historical working condition tracks corresponding to the unfinished order, and the historical working condition tracks meeting the following requirements are screened out from the historical working condition tracks: and (4) the preset index is located in a preset quantile range, the screened historical working condition track is used as an expert track, and the optimal parameter value is determined according to the expert track.

13. The apparatus of claim 11, wherein,

and the adjusting module responds to the fact that the number of the optimal parameter values is larger than one group, obtains a group of optimal parameter values selected from the optimal parameter values as target parameter values, and adjusts the parameters to be adjusted according to the target parameter values.

14. The apparatus of claim 13, wherein,

the adjusting module is further configured to use a parameter value autonomously set by a user as the target parameter value, or use a parameter value selected from historical parameter values corresponding to the unfinished order as the target parameter value.

15. The apparatus of claim 13 or 14,

the adjusting module is further used for determining a monitoring threshold value according to the target parameter value, monitoring the actual parameter value of the parameter to be adjusted, responding to the fact that the actual parameter value exceeds the monitoring threshold value according to the monitoring result, and giving an alarm.

16. The apparatus according to any one of claims 10 to 14,

the first obtaining module periodically obtains the order yield data;

the device further comprises: a statistical module, configured to perform statistical processing on a finished order in response to determining that a finished order exists according to the order yield data, where the finished order is an order finished in a latest period, and the statistical processing includes: and acquiring a comprehensive statistical result of the finished order, and recording the comprehensive statistical result into an order comprehensive statistical table.

17. The apparatus of claim 16, wherein,

the statistical module is further configured to, in response to determining that the unfinished order is not recorded in an order statistics recalculation flag table, record the unfinished order in the order statistics recalculation flag table, and set a recalculation flag of the unfinished order to a first value, in response to determining that any order in the order statistics recalculation flag table is finished and the statistical processing is completed, set the recalculation flag of the order to a second value, and in response to determining that a predetermined time is reached and determining that an order which is finished but the statistical processing is not completed exists in the order statistics recalculation flag table, perform the statistical processing on the order.

18. The apparatus of claim 16, wherein,

the statistical module is further used for obtaining the query request, determining a query result according to the order comprehensive statistical table and/or other statistical tables generated based on the order comprehensive statistical table, and returning the query result.

19. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-9.

21. A computer program product comprising a computer program/instructions which, when executed by a processor, implement the method of any one of claims 1-9.