CN115660312B

CN115660312B - Parameter adjustment method, device, electronic equipment and storage medium

Info

Publication number: CN115660312B
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: 2023-12-22
Anticipated expiration: 2042-09-06
Also published as: CN115660312A

Abstract

The disclosure provides a parameter adjustment method, a device, electronic equipment and a storage medium, relates to the artificial intelligence fields of Internet of things, deep learning, big data processing and the like, and is suitable for various flow process scenes. The method may include: acquiring order yield data; determining an optimal parameter value aiming at parameters to be adjusted corresponding to an unfinished order according to the order yield data, wherein the parameters to be adjusted are energy consumption related parameters; and adjusting the parameter to be adjusted based on the optimal parameter value. By applying the scheme disclosed by the disclosure, the energy-saving effect can be improved, the implementation cost can be reduced, and the like.

Description

Parameter adjustment method, device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of artificial intelligence, in particular to a parameter adjustment method, a device, electronic equipment and a storage medium in the fields of Internet of things, deep learning, big data processing and the like.

Background

In the production of the manufacturing industry, such as the process of a long-vehicle dyeing machine in the printing and dyeing industry, the problem of high energy consumption is often faced, so that great cost pressure is brought to enterprises.

Disclosure of Invention

The disclosure provides a parameter adjustment method, a parameter adjustment device, electronic equipment and a storage medium.

A method of parameter adjustment, comprising:

acquiring order yield data;

determining an optimal parameter value aiming at parameters to be adjusted corresponding to the unfinished order according to the order yield data, wherein the parameters to be adjusted are energy consumption related parameters;

and adjusting the parameter to be adjusted based on the optimal parameter value.

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 used for determining an optimal parameter value aiming at a parameter to be adjusted corresponding to the unfinished order in response to determining that the unfinished order exists according to the order yield data, wherein the parameter to be adjusted is an energy consumption related parameter;

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 the method as described above.

A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a 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 description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for 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 flow chart of a process for an unfinished order according to the present disclosure;

FIG. 3 is a schematic illustration of a process flow of the present disclosure based on the order statistics recalculation flag table and the order synthesis statistics table;

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

fig. 5 is a schematic structural diagram of a first embodiment 500 of a parameter adjustment device according to the present disclosure;

FIG. 6 is a schematic diagram of the structure of a second embodiment 600 of the parameter adjustment device according to the present disclosure;

fig. 7 shows 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 in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one 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 present 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 association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are 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 yield data is acquired.

In step 102, in response to determining that there is an unfinished order 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 embodiment of the method, the parameters to be adjusted can be adjusted according to the determined optimal parameter values, and the parameters to be adjusted are energy consumption related parameters, so that the energy saving effect is improved, the realization cost is reduced, namely, energy saving adjustment, cost reduction, synergy and the like are realized.

Preferably, the scheme of the method embodiment can be applied to a long-vehicle dyeing machine in the dyeing industry.

The long-car dyeing machine generally comprises 11 links/devices, the whole of which is divided into a front car and a rear car, the length of which is 190 meters and 410 meters respectively, and the total length of which is 600 meters, and mainly refers to the length of a cloth to be passed through, namely, the circumference of a drying cylinder and the circumference of a water tank cylinder are included, and each link/device can relate to different equipment parameters, energy consumption parameters and environmental indexes, and is described below respectively.

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

Environmental indicators of the front vehicle portion 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 idle time and front vehicle length (190 meters).

The device parameters of the front vehicle section may include: a) Shoe-shaped gold ingot car: infrared sensor: judging cropping and swing cloth; front vehicle speed: since the front vehicle is mainly dyed, the vehicle speed is usually a constant value of 45 m/min; b) Dyeing machine: and the batching system comprises: proportioning the ingredients; c) Two infrared pre-dryers: the temperature of the case, the centrifugal wind speed, the circulating fan and the air duct; d) Three pre-baking boxes: circulating air frequency value, exhaust frequency value, tension value, pre-baking temperature control actual value and pre-baking temperature control set value; e) Two baking cylinders in a baking room: drying room temperature and drying room humidity; f) Shoe-shaped gold ingot car: infrared sensor: monitoring the swing cloth and falling cloth.

The energy consumption parameters of the rear vehicle section may include: water consumption of rear vehicle: instantaneous flow, real-time average unit consumption value; electricity consumption of rear vehicle: instantaneous flow, real-time average unit consumption value; rear vehicle steam consumption: instantaneous flow, pressure, temperature, real-time average unit consumption value; opening value of steam valve of rear vehicle; real-time average unit consumption value of whole car steam.

The environmental indicators of the rear vehicle portion may include: the ambient temperature and the ambient humidity between the No. 10 washing tank and the drying cylinder; ambient temperature and ambient humidity outside the # 10 rinse tank; the speed of the rear vehicle is very large, and the difference between different specifications is usually 60-150 m/min; rear vehicle length (410 meters).

The device parameters of the rear vehicle section may include: a) Two open type washing tanks: washing temperature, calculated amount of hydrogen peroxide and soap washing amount; b) Two closed washing tanks: the actual soaping temperature and the set value of each soap-washing temperature; the pressure of the padder and the pressure of the tension frame; c) Eight steam water tanks: the actual soaping temperature and the set value of each machine; chemical reaction of the water tank: a pH lower limit value, a pH temperature, a pH upper limit value and a pH actual value; the pressure of the padder and the pressure of the tension frame; d) And (3) a steam drying cylinder: the actual value, the set value and the early warning critical value of the moisture content of the cloth cover; the dryness evaluation is carried out by hands of a mechanic; e) Shoe-shaped gold ingot car: infrared sensor: and detecting swing cloth and cropping cloth.

Correspondingly, the parameters to be adjusted can comprise the speed of the front vehicle, the speed of the rear vehicle, the opening degree of the steam valve of the rear vehicle and the like, and the specific parameters/parameters can be determined according to actual needs.

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

Order yield data may include order identifications (e.g., an unfinished order identification, an order identification that ended in the last cycle, etc.), as well as start time, end time, color name, gauge, cloth, team information, etc. of the order.

Wherein, the order refers to the scheduling according to the order of the customer. Taking a long car dyeing machine as an example, manufacturers can formulate a certain production beat according to colors, cloth, quantity and the like corresponding to orders and matching with the machine and a team to finish production.

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

For example, if it is determined that the ending time of an order in the order yield data is empty, then the order may be determined to be an unfinished order, otherwise, the order may be determined to be an ended order.

In one embodiment of the present disclosure, the manner of determining 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 history working condition track; 3) And determining the optimal parameter value based on a strategy model obtained through pre-training.

Preferably, the above three ways may be used to obtain the optimal parameter value, but in practical application, for any way, the optimal parameter value may not be obtained, for example, for a way based on a history track, if enough history tracks, such as less than 5 tracks, may not be obtained.

For the long car dyeing machine, two potential safety hazards are generally involved, namely, steam is used, when the pressure or the temperature is too high, a pipe is easy to burst, so that safety accidents and economic losses are brought, and the rear car washing tank is provided with an acid-base reaction, the water temperature and the environmental temperature influence the chemical reaction result, and in addition, equipment parameters such as the speed of a car and the like are strictly controlled according to a production manual.

Accordingly, in mode 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 a fractional number, in one embodiment of the present disclosure, a history working condition track corresponding to an unfinished order may be obtained, and a history working condition track meeting the following requirements may be screened from the obtained history working condition tracks: the predetermined index is located in a predetermined quantile range, the screened historical working condition track can be used as an expert track, and then an optimal parameter value can be determined according to the expert track.

For factories with incomplete informatization and automation, cloth with the same specification can be produced under the operation of different workers, the actual energy consumption can have great difference, but the difference of parameter values can not be great, and based on the difference, the constraint can be carried out by adopting a white box model based on a fractional number. Specifically, a history working condition track corresponding to the unfinished order can be obtained, for example, the history working condition track can comprise a history working condition track similar to the unfinished order and a history working condition track similar to the unfinished order, the obtained history working condition track can be summarized, abnormal tracks in the history working condition track can be eliminated, and further the history working condition track which is located in a preset quantile range, for example, a 20% -25% quantile range can be selected as an expert track according to preset indexes, for example, steam consumption per unit time, steam consumption per every ten meters, water consumption per every ten meters or electricity consumption per every ten meters, and the like. The abnormal track may be caused by the reasons of error in issuing the process table, machine error, manual error, etc.

The historical working condition track refers to an actual production track generated in history, such as a track formed by working condition data from the starting time to the ending time of an order for a certain order.

The working condition data refers to the condition change data inside and around the production environment. Taking a long-vehicle dyeing machine as an example, the working condition data can comprise production report data, class 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 acquired based on the expert track is improved.

And determining the optimal parameter value according to the selected expert trajectory. The implementation is not limited, for example, for any parameter to be adjusted, the current progress (for example, 20 minutes) of the unfinished order can be determined, the average value or the median of the parameter values of the parameter at the same progress moment in each expert track can be obtained, and the obtained average value or median is used as the optimal parameter value of the parameter.

In mode 3), the optimal parameter values may be determined based on a pre-trained strategy model, i.e., mode 3) may be a black box mode based on deep reinforcement learning, and the strategy model may be an autonomous optimization model based on generating an antagonism imitation learning (GAIL, generative Adversarial Imitation Learning) architecture/framework.

In practical applications, which mode is specifically adopted may depend on practical needs, and preferably, the above three modes may be adopted simultaneously, 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 optimal parameter values is greater than a set, a set of optimal parameter values selected therefrom may be obtained as target parameter values, and the parameter to be adjusted may be adjusted according to the target parameter values.

For example, if the number of the optimal parameter values is three, and the optimal parameter values are obtained according to the above-mentioned modes 1), 2) and 3), then one group of the optimal parameter values may be selected as the target parameter values, and a manual selection mode or an automatic selection mode may be adopted.

For example, three sets of optimal parameter values and the currently used parameter values may be displayed to the user, and the user may select the optimal parameter value considered to be the best as the target parameter value by comparing the data, for example, comparing the three sets of optimal parameter values with the currently used parameter values, respectively. If the automatic selection mode is adopted, the optimal parameter value corresponding to the mode with the highest priority can be selected according to different priorities preset for the three modes, for example, the mode 3) can be considered to have the highest priority, and accordingly, the optimal parameter value corresponding to the mode can be taken as the target parameter value.

Through the above 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 respectively adjusted according to the target parameter values respectively corresponding to each parameter to be adjusted.

In one embodiment of the present disclosure, for the parameter to be adjusted, a parameter value autonomously set by the user may also be used as a target parameter value, or a parameter value selected from historical parameter values corresponding to an never-ending order may be used as a target parameter value.

For example, if the user considers that the determined optimal parameter values are not suitable, the parameter value may be set autonomously as the target parameter value, or a history parameter value of an order of the same class as or similar to the unfinished order may be acquired, and one of the parameter values may be selected as the target parameter value.

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

In one embodiment of the disclosure, a monitoring threshold may also be determined according to a target parameter value, 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 a 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, the actual parameter value of the parameter to be adjusted may be monitored, and if the actual parameter value exceeds the range of 1.0-1.4, an alarm may be given.

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

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 viewing.

In connection with the above description, FIG. 2 is a flow chart of a process for an unfinished order according to the present disclosure. For each outstanding order, processing may be performed 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 an unfinished order.

For example, a set of optimal parameter values may be determined based on the business specifications and the security policies, a set of optimal parameter values may be determined based on the historical operating profile, and a set of optimal parameter values may be determined based on a pre-trained policy model.

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

In practical application, the parameter value set by the user independently may be used as the target parameter value, or the parameter value selected from the historical parameter values corresponding to the never-ending order may be used as the target parameter value.

In addition, three sets of optimal parameter values may be written into an optimal condition recommendation table (opt_recommend), which may include an order identifier and three corresponding sets of optimal parameter values, and accordingly, the three sets of optimal parameter values recorded in the optimal condition recommendation table may be displayed to a user through a model tuning page for selection by the user, and after the user selects a target parameter value, the target parameter value may also be recorded into a model execution record table (adv_models_logs), where information such as a selected time and a corresponding order identifier may be included.

In step 203, a monitoring threshold is determined based on the target parameter value.

For example, the corresponding information such as the upper limit and the lower limit may be obtained from an alarm comparison table (alarm), 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 1.0-1.4 correspondingly. The alarm comparison table can also comprise information such as processing suggestions exceeding the upper limit, processing suggestions exceeding the lower limit and the like, and optimization suggestions can be given when an alarm is 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 that the actual parameter value exceeds the monitoring threshold according to the monitoring result, an alarm is given.

And, alarm information can be recorded in 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, etc. corresponding to different orders, respectively, may be recorded, so that historical parameter values, etc. are queried based on the model version maintenance table.

As previously described, order yield data may be periodically acquired. In one embodiment of the present disclosure, in response to determining from the order yield data that there is an ended order, the ended order being an order ended in a last cycle, the ended order may be statistically processed, which may include: and acquiring the 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, then the order may be determined to be an ended order. For the finished order, whether the order is a valid order can be further determined, for example, whether the order is a valid order can be determined based on a preset valid determination rule, if the order is a valid order, the processing can be omitted, if the order is a valid order, the statistical processing can be performed on the order, namely, the comprehensive statistical result of the finished order is obtained, and the comprehensive statistical result is recorded in an order comprehensive statistical table.

The order comprehensive statistical table can record comprehensive statistical results of each order, for example, the comprehensive statistical results can comprise order identification, starting time, ending time, corresponding card numbers, types, gram weights, color names, color depths, time consumption, unit time yield, production efficiency, front vehicle energy consumption, rear vehicle energy consumption, whole vehicle energy consumption, idle energy consumption, standing energy consumption, front vehicle unit consumption, rear vehicle unit consumption, whole vehicle unit consumption and the like.

For any finished order, how to acquire the comprehensive statistics is not limited, for example, equipment parameter (device_para) information and energy consumption information can be acquired while acquiring the order yield data. Wherein the device parameter information may include: front car exhaust information (such as exhaust frequency and pre-drying frequency), front car pressure information (such as tension), front car pre-drying temperature control information (such as pre-drying temperature actual value and pre-drying temperature set value), front car bottoming parameter information (such as front car speed), front car temperature and humidity information (such as drying room temperature and drying room humidity), rear car soaping information (such as washing tank temperature actual value and washing tank temperature set value), rear car cloth cover moisture content information (such as cloth cover moisture content actual value, set value and alarm value) and rear car hydrogen peroxide calculated amount information. The energy consumption information may include the pressure, temperature, instantaneous flow value, etc. of the water and electricity steam consumption. The comprehensive statistical result of the finished order can be determined by combining the equipment parameter information, the energy consumption information and the like which are acquired in the past.

In one embodiment of the present disclosure, an order statistics recalculation flag table (recompute_units) may also be maintained, for any outstanding order, in response to determining that the outstanding order is not recorded in the order statistics recalculation flag table, the outstanding order may be recorded in the order statistics recalculation flag table, and the recalculation flag for the outstanding order may be set to a first value, and in response to determining that any order in the order statistics recalculation flag table has ended and the statistical processing has been completed, the recalculation flag for the order may be set to a second value, preferably, the first value may be 0, the second value may be 1, and in addition, in response to determining that a predetermined time is reached, and in determining that there is an order in the order statistics recalculation flag table that has ended but has not completed the statistical processing, the statistical processing may be performed on the order.

By the above processing, it is possible to prevent a problem that the statistical result is not completed for some reason after a certain order has ended, thereby causing a missing of the comprehensive statistical result of the order in the order comprehensive statistical table.

In one embodiment of the present disclosure, a query request may also be obtained, and a query result may be determined and returned based on the order synthesis statistics and/or other statistics generated based on the order synthesis statistics.

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

In connection with the above description, fig. 3 is a schematic diagram of a process flow performed by the present disclosure based on the order statistics recalculation flag table and the order synthesis statistics table. As shown in fig. 3, the following detailed implementation is included.

In step 301, for any outstanding order, it is recorded into the order statistics recalculation flag table and the recalculation flag for that outstanding order is set to 0.

In step 302, in response to determining that any order in the order statistics recalculation flag table has ended and the statistics processing has completed, the recalculation flag for that order is set to 1, the statistics processing comprising: and acquiring the 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 in the order statistics recalculation flag table that has ended but that recalculation flag is 0, the statistical processing is performed on the order.

A timing service mode, such as at the zero point of each day, can be adopted to determine whether the order statistics recalculation flag table has the order which is finished but not completed, and if so, the statistical processing can be carried out on the orders.

In step 304, a date_stats (day_stats) and a historical_stats (close_stats) are generated based on the order comprehensive statistics.

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 statistics of the daily degree can comprise: date, equipment number, class number, order quantity, average production time length, total energy consumption, yield, average unit consumption, production efficiency, average unit consumption cycle ratio, production efficiency cycle ratio, production energy consumption, standing energy consumption, idle energy consumption and the like. The history comprehensive statistics table may include: update date, equipment number, class number, historical order count, historical average unit consumption of front vehicles, historical average unit consumption of rear vehicles, historical average unit consumption of whole vehicles and the like.

The generation, updating and the like of each table can adopt a multithreading non-blocking task processing mode so as 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 statistics table and/or the daily comprehensive statistics table and/or the historical comprehensive statistics table.

The query may be a fuzzy query, may support one or more conditions of query, and may return a full amount of data if no conditions are specified, the one or more conditions may include category, color name, grammage, etc., such as may support various historical information queries, daily degree information queries, etc.

The above-mentioned order comprehensive statistics table, daily degree comprehensive statistics table, class comprehensive statistics table, model version maintenance table, model execution record table, alarm comparison table, alarm record table, optimum condition recommendation table and order statistics recalculation mark table are all intermediate data tables, and are 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 diagram of a data flow of the parameter adjustment method according to the present disclosure. As shown in fig. 4, the intelligent gateway can be used to collect real-time data (such as water, electricity and gas metering and other sensor data) of the secondary metering device, and the manufacturing execution system (MES, manufacturing Execution System) can be used to obtain real-time working condition data, so as to obtain required order yield data, equipment parameter information, energy consumption information and the like. The obtained information (i.e. data) can be written into a database after being forwarded through an internet of things platform (IoT-platform), wherein the database can 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 the present disclosure based on the acquired information.

In addition, in practical application, the scheme disclosed by the disclosure can adopt a cloud deployment mode, can also adopt a cloud-edge combined deployment mode, and is not limited in specific mode.

It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of actions described, as some steps may take place in other order or simultaneously in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure. In addition, portions of one embodiment that are not described in detail may be referred to in the description of other embodiments.

In a word, the scheme of the embodiment of the method disclosed by the invention can improve the energy-saving effect, reduce the realization cost and the like, and in addition, the long-vehicle dyeing machine in the printing and dyeing industry is taken as an example, but the scheme of the embodiment of the method disclosed by the invention is not only suitable for the long-vehicle dyeing machine in the printing and dyeing industry, but also suitable for other flow process scenes, such as flow process scenes of textile, papermaking, chemical industry, biological manufacturing, food and the like, and has wide applicability.

The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.

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

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

The second obtaining module 502 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 503 is configured to adjust the parameter to be adjusted based on the optimal parameter value.

By adopting the scheme of the embodiment of the device, the parameters to be adjusted can be adjusted according to the determined optimal parameter values, and the parameters to be adjusted are energy consumption related parameters, so that the energy saving effect is improved, the realization cost is reduced, namely, energy saving adjustment, cost reduction, synergy and the like are realized.

Preferably, the scheme of the device embodiment can be applied to a long-vehicle dyeing machine table in the dyeing industry. Correspondingly, the parameters to be adjusted can comprise the speed of the front vehicle, the speed of the rear vehicle, the opening degree of the steam valve of the rear vehicle and the like, and the specific parameters/parameters can be determined according to actual needs.

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

For mode 2), the second obtaining module 502 may obtain a history working condition track corresponding to the unfinished order, and screen the history working condition track meeting the following requirements from the history working condition tracks: 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.

Additionally, in one embodiment of the present disclosure, the adjustment module 503 may obtain a set of optimal parameter values selected from the set of optimal parameter values as the target parameter values in response to determining that the number of optimal parameter values is greater than the set, and may further adjust the parameter to be adjusted according to the target parameter values. The manual selection mode can be adopted, and the automatic selection mode can also be adopted.

In one embodiment of the present disclosure, for the parameter to be adjusted, the adjustment module 503 may further use a parameter value autonomously set by the user as a target parameter value, or may use a parameter value selected from historical parameter values corresponding to an never-ending order as a target parameter value.

In one embodiment of the present disclosure, the adjustment module 503 may further determine a monitoring threshold according to the target parameter value, may monitor an actual parameter value of the parameter to be adjusted, and may 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 of a second embodiment 600 of the parameter adjustment device according to the present disclosure. As shown in fig. 6, includes: a first acquisition module 501, a second acquisition module 502, an adjustment module 503, and a statistics module 504.

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

Additionally, in one embodiment of the present disclosure, the statistics module 504 may record an outstanding order into the order statistics recalculation flag table in response to determining that the outstanding order is not recorded into the order statistics recalculation flag table, and may set the recalculation flag for the outstanding order to a first value, may set the recalculation flag for any order in the order statistics recalculation flag table to a second value in response to determining that the order has ended and completed the statistics processing, and may perform the statistics processing on the order in response to determining that a predetermined time has been reached and that there is an order in the order statistics recalculation flag table that has ended but has not completed the statistics processing.

Accordingly, the statistics module 504 may also obtain the query request, determine the query result and return based on the order synthesis statistics and/or other statistics generated based on the order synthesis statistics.

The specific workflow of the apparatus embodiment shown in fig. 5 and 6 may be referred to the relevant description of the method embodiments described above.

In a word, the scheme of the embodiment of the disclosure can improve the energy-saving effect, reduce the realization cost and the like, and in addition, the long-vehicle dyeing machine in the printing and dyeing industry is taken as an example, but the scheme of the embodiment of the disclosure is not only suitable for the long-vehicle dyeing machine in the printing and dyeing industry, but also suitable for other flow process scenes, such as flow process scenes of textile, papermaking, chemical industry, biological manufacturing, food and the like, and has wide applicability.

The scheme disclosed by the disclosure 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 the subject of studying certain thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.) that make a computer simulate a person, and has technology at both hardware and software levels, and artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, etc., and artificial intelligence software technologies mainly include computer vision technologies, speech recognition technologies, natural language processing technologies, machine learning/deep learning, big data processing technologies, knowledge graph technologies, etc.

The order in the embodiments of the present disclosure is not specific to a particular user and does not reflect personal information of a particular user. In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.

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

Fig. 7 shows 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 apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the apparatus 700 includes a computing unit 701 that can perform various appropriate 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 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in device 700 are connected to I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, etc.; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, an 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.

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

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On 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, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code 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 code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. 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. The 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 portable 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 pointing device (e.g., a mouse or 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 may 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 input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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 a client and a server. The client and server are typically 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 incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A parameter adjustment method is applied to a long-vehicle dyeing machine in the printing and dyeing industry, and comprises the following steps:

acquiring order yield data;

adjusting the parameter to be adjusted based on the optimal parameter value;

Wherein, the determining the optimal parameter value comprises the following three modes: determining the optimal parameter value based on the service specification and the security policy; determining the optimal parameter value based on a strategy model obtained through pre-training; acquiring a history working condition track similar to the unfinished order and a history working condition track similar to the unfinished order, summarizing the acquired history working condition track as the history working condition track corresponding to the unfinished order, removing an abnormal track in the history working condition track, and screening the history working condition track meeting the following requirements from the rest history working condition tracks: 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; wherein, the determining the optimal parameter value according to the expert trajectory includes: determining the current progress of the unfinished order according to any parameter to be adjusted, respectively acquiring the average value or the median of the parameter values of the parameter to be adjusted at the same progress moment in each expert track, and taking the acquired average value or median as the optimal parameter value of the parameter to be adjusted;

The method further comprises the steps of: in response to determining that the number of the optimal parameter values is greater than one group, selecting a group of optimal parameter values corresponding to a mode with highest priority from the optimal parameter values as target parameter values, and adjusting the parameter to be adjusted according to the target parameter values; and determining a monitoring threshold according to the target parameter value, monitoring the actual parameter value of the parameter to be adjusted, alarming in response to determining that the actual parameter value exceeds the monitoring threshold according to a monitoring result, and giving corresponding optimization suggestions during alarming according to preset processing suggestions exceeding the upper limit of the monitoring threshold and processing suggestions exceeding the lower limit of the monitoring threshold.

2. The method of claim 1, further comprising:

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

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

3. The method according to any one of claim 1 to 2,

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

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

4. A method according to claim 3, further comprising:

in response to determining that the outstanding order is not recorded into an order statistics recalculation flag table, recording the outstanding order into the order statistics recalculation flag table, and setting a recalculation flag of the outstanding order to 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 for the order to a second value;

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

5. A method according to claim 3, 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.

6. A parameter adjusting device is applied to a long-vehicle dyeing machine in the printing and dyeing industry, and comprises: 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 used for determining an optimal parameter value aiming at a parameter to be adjusted corresponding to the unfinished order in response to determining that the unfinished order exists according to the order yield data, wherein the parameter to be adjusted is an energy consumption related parameter; wherein, the determining the optimal parameter value comprises the following three modes: determining the optimal parameter value based on the service specification and the security policy; determining the optimal parameter value based on a strategy model obtained through pre-training; acquiring a history working condition track similar to the unfinished order and a history working condition track similar to the unfinished order, summarizing the acquired history working condition track as the history working condition track corresponding to the unfinished order, removing an abnormal track in the history working condition track, and screening the history working condition track meeting the following requirements from the rest history working condition tracks: 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; wherein, the determining the optimal parameter value according to the expert trajectory includes: determining the current progress of the unfinished order according to any parameter to be adjusted, respectively acquiring the average value or the median of the parameter values of the parameter to be adjusted at the same progress moment in each expert track, and taking the acquired average value or median as the optimal parameter value of the parameter to be adjusted;

The adjusting module is used for adjusting the parameter to be adjusted based on the optimal parameter value;

the adjustment module is further configured to, in response to determining that the number of the optimal parameter values is greater than one, select, from among the optimal parameter values, a group of optimal parameter values corresponding to a mode with a highest priority as a target parameter value, and adjust the parameter to be adjusted according to the target parameter value;

the adjusting module is further configured to determine a monitoring threshold according to the target parameter value, monitor an actual parameter value of the parameter to be adjusted, respond to determining that the actual parameter value exceeds the monitoring threshold according to a monitoring result, alarm, and give a corresponding optimization suggestion when alarming according to a preset processing suggestion exceeding an upper limit of the monitoring threshold and a preset processing suggestion exceeding a lower limit of the monitoring threshold.

7. The apparatus of claim 6, wherein,

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

8. The apparatus according to any one of claim 6 to 7,

the first acquisition module periodically acquires the order yield data;

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

9. The apparatus of claim 8, wherein,

the statistics module is further configured to record the outstanding order into an order statistics recalculation flag table in response to determining that the outstanding order is not recorded into the order statistics recalculation flag table, and set a recalculation flag for the outstanding order to a first value, set the recalculation flag for the order to a second value in response to determining that any order in the order statistics recalculation flag table has ended and the statistics process has completed, and perform the statistics process for the order in response to determining that a predetermined time has arrived and that there is an order in the order statistics recalculation flag table that has ended but has not completed the statistics process.

10. The apparatus of claim 8, wherein,

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

11. 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 1-5.

12. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-5.