CN116048023B - Fine energy management and control method, system, internet of things cloud management and control server and storage medium thereof - Google Patents
Fine energy management and control method, system, internet of things cloud management and control server and storage medium thereof Download PDFInfo
- Publication number
- CN116048023B CN116048023B CN202310050867.7A CN202310050867A CN116048023B CN 116048023 B CN116048023 B CN 116048023B CN 202310050867 A CN202310050867 A CN 202310050867A CN 116048023 B CN116048023 B CN 116048023B
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- management
- energy
- production
- control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000003860 storage Methods 0.000 title claims abstract description 17
- 238000005265 energy consumption Methods 0.000 claims abstract description 211
- 238000004519 manufacturing process Methods 0.000 claims abstract description 150
- 238000007689 inspection Methods 0.000 claims abstract description 28
- 238000012544 monitoring process Methods 0.000 claims abstract description 18
- 238000005056 compaction Methods 0.000 claims abstract description 8
- 238000001514 detection method Methods 0.000 claims abstract description 8
- 238000012806 monitoring device Methods 0.000 claims description 31
- 238000004458 analytical method Methods 0.000 claims description 27
- 230000008859 change Effects 0.000 claims description 11
- 239000013598 vector Substances 0.000 claims description 11
- 238000004891 communication Methods 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000011217 control strategy Methods 0.000 claims description 3
- 230000006872 improvement Effects 0.000 abstract description 7
- 230000009467 reduction Effects 0.000 abstract description 3
- 230000001105 regulatory effect Effects 0.000 abstract description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 abstract description 2
- 229910052799 carbon Inorganic materials 0.000 abstract description 2
- 230000000875 corresponding effect Effects 0.000 description 50
- 238000013528 artificial neural network Methods 0.000 description 4
- 230000001276 controlling effect Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000012797 qualification Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000002035 prolonged effect Effects 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 238000003466 welding Methods 0.000 description 2
- 238000003723 Smelting Methods 0.000 description 1
- 238000012896 Statistical algorithm Methods 0.000 description 1
- 244000269722 Thea sinensis Species 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 235000013361 beverage Nutrition 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000005477 standard model Effects 0.000 description 1
- 235000019505 tobacco product Nutrition 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 235000014101 wine Nutrition 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by quality surveillance of production
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32368—Quality control
Abstract
The invention discloses a refined energy management and control method, a system, an internet of things cloud management and control server and a storage medium thereof, wherein the method is to acquire a historical energy consumption spot inspection data set; splitting the historical energy consumption spot inspection data set into a plurality of time scale management subsets under a plurality of use/application types, determining corresponding energy consumption standards, and determining energy use prediction under a production plan based on the energy consumption standards and the production plan; obtaining a corresponding predicted energy consumption curve by using the energy source use prediction, and obtaining a corresponding real-time energy consumption curve by using the obtained real-time energy consumption point detection data; obtaining a difference group of the pre-compaction differences; determining a corresponding management and control recommendation strategy based on the difference group; implementing the control recommendation strategy to reduce the difference between the actual energy consumption curve and the target energy consumption curve; after the administration of the regulatory recommendation policies, the energy consumption criteria corresponding to the administration subset are synchronously revised. The energy management and control method comprises the steps of constructing an energy management and control mode of combining distributed monitoring equipment with an internet of things cloud management and control server, building an energy management and control model which is not influenced by yield and can be divided in minute precision, automatically giving management and control recommendation strategies, finding energy loss points through the model to give energy management and control/improvement suggestions, solving the problem that accurate metering and accurate management and control cannot be achieved in traditional energy management and control, and enabling an energy demand curve and an energy supply curve to be fitted to each other in a high degree in the production process through continuous repeated management and control/improvement, so that the energy saving and carbon reduction aim is achieved.
Description
Technical Field
The invention relates to the field of energy management and control, in particular to a refined energy management and control method, a system, an internet of things cloud management and control server and a storage medium thereof.
Background
At present, china is in the stage of deep industrialization development, the energy demand of economic and social development is continuously increased, the total energy consumption of industrial production is continuously increased in the urban total energy consumption ratio, the industrial high-quality development is promoted, the call for building energy-saving green factories is increasingly strong, and particularly for high-energy-consumption manufacturing factories, the energy cost is relatively high, so that the energy management and control level is improved to realize energy saving, cost reduction and synergy, and the self-product competitiveness is improved.
The energy metering and energy management and control are important bases for energy saving work effect, the traditional energy data metering and management and control system in the market only stays at the data acquisition, simple statistical algorithm and empirical operation degree, the production energy can be simply analyzed, the analyzed data is difficult to scientifically and effectively provide decision and analysis guidance for management energy saving improvement in the production process, and the energy saving effect cannot be substantially evaluated. In addition, the mode of post-statistics energy metering and controlling can not provide instant energy use state, can not timely and effectively analyze, and leads to energy consumption waste,
in addition, the traditional energy consumption management cannot accurately define the requirement standard, the calculated energy consumption standard error range is larger, the support cannot be accurately provided for production and marketing, when factory production and marketing adjustment is carried out, in addition, the outdoor environment and other working conditions are changed, the workshop cannot accurately predict the energy consumption, the energy pre-compaction difference is large, and the energy consumption target shape is the same as the energy KPI which cannot be substantially controlled by an energy department.
In production practice, at least the classification of fixed and variable amounts of energy consumption is difficult to define; under the conditions of output, beat, attendance days, production continuity and the like and the conditions of prediction occurrence change, the influence value of each part cannot be analyzed; the problems that a single energy consumption analysis according to month is difficult to find a difference point and the like are needed to be solved.
Disclosure of Invention
In view of the above, the invention provides a refined energy management and control method, a system, an internet of things cloud management and control server and a storage medium thereof. So as to solve the defects of the prior energy management and control system.
The invention aims at realizing the following technical scheme: the invention provides a refined energy management and control method, which is characterized by comprising the following steps:
acquiring historical energy consumption spot check data sets of one or more remote distributed monitoring devices associated with a production chain by at least one workshop of a plurality of manufactured products in a production and manufacturing process;
splitting the historical energy consumption spot inspection data set into a management subset of a plurality of time scales under a plurality of use/application types, and determining a corresponding energy consumption standard according to the management subset, wherein the management subset is measured by the remote distributed monitoring equipment and is controlled by energy sources;
determining an energy use prediction under a production plan based on the energy consumption standard in combination with the production plan;
obtaining a corresponding predicted energy consumption curve by using the energy source use prediction, and obtaining a corresponding real-time energy consumption curve by using real-time energy consumption point detection data obtained by one or more remote distributed monitoring devices;
obtaining a difference group of pre-actual differences of management subsets under a plurality of time scales under a plurality of different use/usage types of each monitoring device according to the expected energy consumption curve and the corresponding real-time energy consumption curve;
determining a corresponding management and control recommendation strategy based on the difference group;
implementing the control recommendation strategy to reduce the difference between the actual energy consumption curve and the target energy consumption curve;
after the administration of the regulatory recommendation policies, the energy consumption criteria corresponding to the administration subset are synchronously revised.
Preferably, the remote distributed monitoring device comprises an energy monitoring sensor, a remote communication device, a storage device and a controller, wherein the remote distributed monitoring device is arranged at an input end of one or more production execution devices associated with at least one workshop in a production and manufacturing process and is used for storing the historical energy consumption spot inspection data set of the production execution devices and collecting real-time energy consumption spot inspection data, and the remote communication device uploads the data to a management and control cloud server through a convergence gateway; the control cloud server issues the control recommendation strategy to the controller, and the controller is used for controlling the operation parameters of the corresponding production execution equipment according to the control recommendation strategy.
Preferably, the production schedule includes at least a predetermined production quantity and production schedule.
Preferably, the splitting the historical energy consumption spot inspection data set into a plurality of management subsets of time scales under a plurality of usage/usage types, and determining the corresponding energy consumption standard according to the management subsets comprises:
step one: correlating the historical energy consumption spot inspection data set with a manufactured product in the production and manufacturing process to obtain a historical energy consumption data correlation group corresponding to the manufactured product;
step two: carrying out data cleaning on the energy consumption data association group;
step three: splitting the historical energy consumption data association group into a management subset of a plurality of time scales under a plurality of use/application types, wherein the management subset comprises at least one of production day energy consumption, attendance non-production day energy consumption, weekend energy consumption and holiday energy consumption fields under each use/application type, and dividing the production day energy consumption into at least production period fixed consumption, change period change consumption and non-production period consumption subfields under each use/application type;
step four: and determining corresponding energy consumption standards by the subfields comprising the daily energy consumption fields in the management subset through energy consumption prediction, and determining corresponding energy consumption standards by the management subset of other daily energy consumption not produced through historical means.
Preferably, said determining a prediction of energy usage under said production plan based on said energy consumption criteria in combination with a production plan comprises determining said production schedule of one or more articles of manufacture to be produced according to a production plan split into a combination of a minute plan, an hour plan, a day plan, a week plan and a month plan, and determining a prediction of energy consumption under a corresponding plan according to said energy consumption criteria and said combination.
Preferably, the pre-compaction difference at least comprises at least one of a curve maximum value, a curve minimum value, a curve point change rate and a curve integration area.
Preferably, the determining a corresponding regulatory recommendation policy based on the difference group includes: based on the difference groups of the pre-actual differences of the management subsets under a plurality of time scales under a plurality of different use/usage types of each monitoring device, establishing pre-actual fingerprint information vectors associated with each time scale, matching analysis sentence segments in a preset analysis library by utilizing the fingerprint information vectors, and matching management and control recommendation strategies corresponding to a preset expert library based on the analysis sentence segments.
Another aspect of the present invention provides a refined energy management and control system, comprising
The system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module acquires a historical energy consumption spot inspection data set of one or more remote distributed monitoring devices which are associated with a production chain by at least one workshop in the production and manufacturing process of a plurality of manufactured products;
and the energy consumption standard module is used for splitting the historical energy consumption spot inspection data set into a management subset of a plurality of time scales under a plurality of use/application types, and determining a corresponding energy consumption standard according to the management subset, wherein the management subset is measured by the remote distributed monitoring equipment and is controlled by energy. The method comprises the steps of carrying out a first treatment on the surface of the
The energy prediction module is used for determining energy use prediction under the production plan based on the energy consumption standard and in combination with the production plan;
the energy consumption curve module is used for obtaining a corresponding predicted energy consumption curve by using the energy source use prediction, and obtaining a corresponding real-time energy consumption curve by using real-time energy consumption point detection data obtained by one or more remote distributed monitoring devices;
the pre-real difference module is used for acquiring a difference group of pre-real differences of the management subset under a plurality of time scales under a plurality of different use/usage scales of each monitoring device according to the expected energy consumption curve and the corresponding real-time energy consumption curve;
the control strategy module is used for determining a corresponding control recommendation strategy based on the difference group;
the control implementation module is used for implementing the control recommendation strategy to reduce the difference between the actual energy consumption curve and the target energy consumption curve;
and the revising module synchronously revises the energy consumption standard corresponding to the management subset after the management recommendation strategy is implemented.
The invention also provides an internet of things cloud management and control server which is characterized by at least comprising an internet of things management platform and a rule engine platform, wherein the internet of things management platform and the rule engine platform are interactively coupled and realize energy management and control through an intelligent AI algorithm, the rule engine platform comprises an analysis library and an expert library, the internet of things cloud management and control server at least further comprises a storage device and a processor, and the processor executes instructions stored in the storage device to execute the method according to any one of claims 1-7.
The invention also provides a computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements any of the embodiments as described above.
According to the method, firstly, the tradition of analyzing the energy consumption of a single unit in the traditional train enterprise monthly is broken through, a set of energy classification time-division standard model which is not influenced by the yield is established, and a workshop can acquire the energy standard and the actual energy difference under the actual yield of the production day in near real time and discover the loss as soon as possible; secondly, in the same industry, energy consumption data minute-scale prediction is established first, a plurality of time periods such as fixing and changing under a plurality of use/application types are accurately divided, and high precision of monthly energy consumption prediction of less than +/-3% is realized; thirdly, the analysis reason can be automatically determined based on the pre-real difference, and the management and control recommendation strategy is automatically determined through a preset analysis library and an expert library, so that an equipment management and control system combining with a remote site is constructed, and guidance is effectively provided for energy saving improvement; fourth, an Internet of things cloud control server framework which at least comprises an Internet of things management platform and a rule engine platform which are interactively coupled and realize energy control through an intelligent AI algorithm is constructed, and support is provided for scientific energy control through the Internet of things intelligent control framework.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for controlling refined energy according to an embodiment of the invention.
FIG. 2 is a flow chart of a recommendation policy management and control method according to an embodiment of the invention
FIG. 3 is a schematic diagram of a refined energy management and control system according to an embodiment of the invention
Detailed Description
The following description of the technical solutions in the embodiments of the present invention will be clear and complete, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, the "plurality" generally includes at least two, but does not exclude the case of at least one.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such product or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a commodity or system comprising such elements.
The embodiment of the invention provides a refined energy management and control method, referring to fig. 1, the present embodiment is particularly suitable for a production mode of a semi-automatic or fully automatic production line, which is convenient for monitoring, metering and deciding of a remote distributed monitoring unit, the method can be executed by a refined energy management and control system, and the system can be realized by adopting a software and/or hardware mode, the method comprises the following steps:
step S100, acquiring historical energy consumption spot inspection data sets of one or more remote distributed monitoring devices associated with a production chain by a plurality of manufactured products through at least one workshop in the production and manufacturing process;
in the specific embodiment of the invention, the manufactured product can be automobiles, foods, wine, beverages, refined tea, tobacco products, medical equipment, transportation equipment, electric machinery and equipment, electronic products, printed matters, nonferrous metal smelting and the like.
Preferably, the manufacture is particularly suitable for the automobile manufacturing industry, for example, the production and manufacturing process of automobile manufacturing is carried out through a production chain of a stamping workshop, a welding workshop, a coating workshop and a final assembly workshop, wherein the workshop can comprise a plurality of production lines, and each production line comprises a plurality of remote distributed monitoring devices associated with production.
In a specific embodiment of the present invention, the remote distributed monitoring device includes an energy monitoring sensor, a remote communication device, a storage device and a controller, where the remote distributed monitoring device is disposed at an input end of one or more production executing devices associated with at least one workshop in a production and manufacturing process, and is used to store the historical energy consumption spot inspection data set of the production executing device and collect real-time energy consumption spot inspection data, and the remote communication device uploads the data to a management and control cloud server through a convergence gateway; and the control cloud server transmits the control recommendation strategy to the controller according to the uploading data, and the controller is used for controlling the operation parameters of the corresponding production execution equipment according to the control recommendation strategy.
Preferably, the remote distributed monitoring device may further include a data preprocessing device, where a historical energy consumption point data set and real-time energy consumption point detection data may be pre-formatted according to a user-defined field before the acquisition process, and the field may be expanded, where in general, in the prior art, the field monitored by the monitoring unit is only time and energy consumption, and according to the subunit, a feature monitoring field such as start-stop record and fault code may be added according to a user requirement, which is beneficial to a subsequent management and control decision.
The remote distributed monitoring device may be used to monitor an electric device, a water supply device, a heating device, a functional device, a gas supply device, etc., and the application range thereof may be set according to the actual production, which is not further limited in the present invention.
It can be understood that the above three-level remote monitoring architecture adopting the distributed monitoring unit-convergence gateway-management and control cloud server can be used in combination by adopting a plurality of existing communication modes such as 2G/3G/4G/5G, WIFI, ZIGBEE, bluetooth and the like in order to avoid the influence of interference and faults on a single communication mode.
Step S110, splitting the historical energy consumption spot inspection data set into a management subset of a plurality of time scales under a plurality of use/application types, and determining a corresponding energy consumption standard according to the management subset, wherein the management subset is measured by the remote distributed monitoring equipment and is controlled by energy sources;
it can be appreciated that in general, a single energy consumption management KPI in a factory appears to be measurable, but is a KPI converted from a nominal value of power consumption, and is not hooked precisely with specific equipment, functional use and time difference, so that precise management and control measures cannot be implemented.
In another embodiment of the present invention, the process of splitting the historical energy consumption spot inspection dataset into a plurality of time scale management subsets under a plurality of usage/usage types, and determining the corresponding energy consumption standard according to the management subsets may refer to fig. two, which is a control recommendation policy flowchart provided by the present invention, and the method steps include:
step 200: correlating the historical energy consumption spot inspection data set with a manufactured product in the production and manufacturing process to obtain a historical energy consumption data correlation group corresponding to the manufactured product;
in order to accurately determine the standard energy consumption of the specific product, the historical energy consumption data of the specific product is correlated, namely, the energy consumption data of the production equipment corresponding to each workshop in the whole production period of the product is obtained, and further, the historical energy consumption data can be the production energy consumption data corresponding to each workshop on the production line corresponding to each workshop according to the whole production period of the product.
The association group format can be { production period, product type and number, workshop number, production line number, product type, spot inspection operation equipment serial number, spot inspection corresponding production equipment code, spot inspection equipment start-stop record, spot inspection equipment fault code, … }, and the specific field type can be adjusted according to the control requirement.
For example, the number of the cells to be processed,
{1 month 1 day 13 point 10 min to 1 month 1 day 18 point 15 min, 69771X car, stamping shop, production line No. two, production equipments No. 4, 7, 9 and 10, corresponding energy consumption of 4, 7, 9 and 10, no fault record, … }
…
{1 month 1 day 20 point 10 min to 1 month 2 day 04 point 30 min, 69771X vehicle, welding shop, production line number one, production equipment No. 14,19,22 and 47, corresponding energy consumption, no fault record, … }, 14,19,22 and 47
…, etc.;
the whole production period data can be determined to be related to the group according to the production time sequence.
Step 210: carrying out data cleaning on the energy consumption data association group;
it can be appreciated that data cleansing is a process of rechecking and checking data, aiming at deleting repeated information in the association group, correcting obvious errors, and maintaining data consistency so as to improve the quality of the data.
Step 220: splitting the historical energy consumption data association group into a management subset of a plurality of time scales under a plurality of use/application types, wherein the management subset comprises at least one of production day energy consumption, attendance non-production day energy consumption, weekend energy consumption and holiday energy consumption fields under each use/application type, and dividing the production day energy consumption into at least production period fixed consumption, change period change consumption and non-production period consumption subfields under each use/application type;
it should be noted that, the above-mentioned association group is divided into time subsets of multiple time scales under multiple uses/applications, and the division is to divide the time subsets into energy daily reports according to different production characteristics, and in general, the predictions of the energy consumption of the non-production energy, the energy consumption of the weekend and the energy consumption of the holiday are relatively accurate, and in the production days, since the energy consumption includes the variable consumption, when the production requirements and the external environment change rapidly, the energy consumption index of the workshop is difficult to predict directly, so that the division of the fixed and variable time periods under multiple use/application types is introduced, which is beneficial to fine energy prediction.
Step 230: and determining corresponding energy consumption standards by the subfields comprising the daily energy consumption fields in the management subset through energy consumption prediction, and determining corresponding energy consumption standards by the management subset of other daily energy consumption not produced through historical means.
It can be understood that the above-mentioned energy consumption prediction mode may be selected by any nonlinear regression prediction model in the prior art, and the implementation of the energy consumption prediction mode only needs to learn association group data under different time and different conditions as historical data to obtain energy consumption standards under specific time and specific conditions, and specific model implementation modes may be, for example, a BP neural network, a dynamic window sliding neural network, a convolutional neural network, an LSTM neural network, a radial base neural network, and the like, and details of the model in the prior art are not described again.
In addition, the usage of the non-production daily energy usage of the specific manufactured product is stable on the date, the corresponding energy consumption standard can be obtained by taking the average value of the historical data corresponding to the association group, and the average value calculation range is preferably the association group which is closest to a certain preset time period with the nearest predicted time.
Step S120, determining energy use prediction under the production plan based on the energy consumption standard and in combination with the production plan;
in a specific embodiment of the present invention, the production schedule includes at least a predetermined production quantity and production schedule.
In a specific embodiment of the present invention, the determining the energy forecast under the production plan based on the energy consumption standard in combination with the production plan includes determining the production schedule of one or more manufactured products to be produced according to the production plan, the production plan is divided into a combination of a minute plan, an hour plan, a day plan, a week plan and a month plan, and determining the energy consumption use forecast under the corresponding plan according to the energy consumption standard and the combination.
Preferably, when determining the energy consumption prediction, the condition that the primary qualification rate, the utilization rate, the repair rate and the like affect the energy consumption can be further considered, the energy consumption prediction is adjusted, the adjustment range can be set according to the historical experience of an actual production workshop or calculated by adopting any model, the invention is not limited to the condition, and the energy consumption prediction is implemented by adopting the prior art.
In the above embodiment, the production plan is divided into a plurality of scale combinations, and the production plan in a certain period is brought into the energy consumption standard, so that the energy prediction under a plurality of time scales after the output can be calculated.
The method breaks through the tradition of analyzing the energy consumption of a single unit in the traditional month, and can establish a set of energy refined prediction mode which is not influenced by the yield.
Step S130, obtaining a corresponding predicted energy consumption curve by using the energy use prediction, and obtaining a corresponding real-time energy consumption curve by using real-time energy consumption point detection data obtained by one or more remote distributed monitoring devices;
step S140, obtaining a difference group of pre-real differences of management subsets under a plurality of time scales under a plurality of different use/usage types of each monitoring device according to the expected energy consumption curve and the corresponding real-time energy consumption curve;
optionally, the pre-actual difference at least includes at least one of a curve maximum value, a curve minimum value, a curve point change rate, and a curve integral area, and it is understood that the index of the pre-actual difference reflects the energy consumption difference degree.
It is worth additionally stated that, by acquiring the energy consumption curve, the energy consumption difference can be visually seen in a visual manner, meanwhile, quantitative difference analysis can be performed through curve characteristics, and then, a difference group of pre-real differences under a plurality of different time scales is determined, wherein the difference group reflects the difference condition of each monitoring device under a preset time scale, for example:
{ (monitoring device 1, degree of energy consumption difference of first time scale under first use/usage type 1, degree of energy consumption difference of second time scale under second use/usage type 2, …, attribute field), (monitoring device 2,..degree of energy consumption difference 2, …, attribute field), … }.
Step S150, determining a corresponding management and control recommendation strategy based on the difference group;
in a specific embodiment of the present invention, based on a difference group of pre-implemented differences under different time scales under a plurality of usage/usage types of each monitoring device, pre-implemented fingerprint information vectors associated with each time scale are established, and analysis sentence segments in a preset analysis library are matched by using the fingerprint information vectors, and management and control recommendation strategies corresponding to a preset expert library are matched based on the analysis sentence segments.
It can be understood that the pre-compaction difference under different use/usage types can be reflected under different time scales, and the energy consumption standard of the pre-compaction difference under different production processes and production time periods is determined according to the foregoing, so that the predicted energy consumption under the corresponding time scales is determined, and therefore, when the predicted energy consumption deviates, the situation of energy waste or equipment abnormality can be generated, therefore, the corresponding information vector is obtained according to the difference group of the pre-compaction difference, and is matched with the analysis vector pre-stored in the pre-set analysis library on the management and control platform, and then the analysis sentence segment is matched from the analysis library, wherein the analysis vector is constructed for pre-offline simulation analysis or is extracted from historical data.
The above analysis vector is matched with the information vector, that is, the similarity between the two is measured, and the method can be implemented by any existing method in the prior art, which need not be further described herein.
The analysis library sentence segment is matched with a management and control recommended instruction corresponding to a preset expert library, and can be acquired through a database bar segment association mode in a management and control platform, so that further description is not needed.
The analysis library results can be shown as follows:
nodes a, …, start-stop codes, PRE workshops, energy consumption is electric energy, the difference ratio is 10%, and the possible reasons are: the production equipment is replaced, and the single production time is prolonged;
node B, …, fault code, BIW shop, energy consumption is electric energy, the differential ratio is 1%, the possible reasons are: production equipment failure;
the energy consumption of the nodes C, … and PA workshops is electric energy, the difference proportion is-21%, and the possible reasons are as follows: the qualification rate is 100 percent, and the production speed is improved.
The management and control recommended strategy given by the expert database can be shown as follows:
nodes a, …, start-stop codes, PRE workshops, energy consumption is electric energy, the difference ratio is 10%, and the possible reasons are: the production equipment is replaced, and the single production time is prolonged; the management and control recommended strategy is as follows: and (5) adjusting a production line.
Node B, …, fault code, BIW shop, energy consumption is electric energy, the differential ratio is 1%, the possible reasons are: production equipment faults, and management and control recommended strategies are as follows: after equipment maintenance or replacement, the production is finished according to the original 45 beats, the production is finished 1 hour in advance, and the energy consumption is saved and shortened.
The energy consumption of the nodes C, … and PA workshops is electric energy, the difference proportion is-10%, and the possible reasons are as follows: the primary qualification rate is improved, the production speed is improved, and the management and control recommended strategy is as follows: reducing energy supply ration, adjusting and implementing part of weekend work, and reserving energy consumption adjustment allowance.
Step S160, implementing the control recommendation strategy to reduce the difference between the actual energy consumption curve and the target energy consumption curve;
the embodiment has smaller time scale analysis capability, can predict the energy consumption data in minute scale, so as to determine the reasons of the difference points of targets and actual energy consumption of each time scale under each use/application, discover the loss early, and automatically or semi-automatically regulate and adjust according to expert recommendation strategies, thereby ensuring the accuracy of the overall pre-actual differences of the whole month, quarter and year.
Step S170, after the management recommendation strategy is implemented, the energy consumption standard corresponding to the management subset is synchronously revised.
It can be appreciated that the above embodiment is a process of dynamically adjusting the iterative control, which combines an automatic management and control recommendation strategy, searches for energy loss points through a model to give an energy management and control/improvement suggestion, and can make the energy demand curve highly fit with the energy supply curve in the production process through continuous iterative management and control/improvement, so as to achieve the goal of energy conservation and carbon reduction.
Referring to fig. 3, a schematic diagram of a refined energy management system according to an embodiment of the present invention may be used in energy management in the automotive industry, where the device may be implemented in software and/or hardware.
As shown in fig. 3, the system includes an acquisition module 300, an energy consumption standard module 301, an energy source prediction module 302, an energy consumption curve module 303, a pre-compaction difference module 304, a management policy module 305, a management enforcement module 306, and a revision module 307.
The system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module acquires a historical energy consumption spot inspection data set of one or more remote distributed monitoring devices which are associated with a production chain by at least one workshop in the production and manufacturing process of a plurality of manufactured products;
and the energy consumption standard module is used for splitting the historical energy consumption spot inspection data set into a management subset of a plurality of time scales under a plurality of use/application types, and determining a corresponding energy consumption standard according to the management subset, wherein the management subset is measured by the remote distributed monitoring equipment and is controlled by energy. The method comprises the steps of carrying out a first treatment on the surface of the
The energy prediction module is used for determining energy use prediction under the production plan based on the energy consumption standard and in combination with the production plan;
the energy consumption curve module is used for obtaining a corresponding predicted energy consumption curve by using the energy source use prediction, and obtaining a corresponding real-time energy consumption curve by using real-time energy consumption point detection data obtained by one or more remote distributed monitoring devices;
the pre-real difference module is used for acquiring a difference group of pre-real differences of the management subset under a plurality of time scales under a plurality of different use/usage scales of each monitoring device according to the expected energy consumption curve and the corresponding real-time energy consumption curve;
the control strategy module is used for determining a corresponding control recommendation strategy based on the difference group;
the control implementation module is used for implementing the control recommendation strategy to reduce the difference between the actual energy consumption curve and the target energy consumption curve;
and the revising module synchronously revises the energy consumption standard corresponding to the management subset after the management recommendation strategy is implemented.
Example IV
The invention also provides an Internet of things cloud management and control server which is characterized by at least comprising an Internet of things management platform and a rule engine platform, wherein the Internet of things management platform and the rule engine platform are interactively coupled and realize energy management and control through an intelligent AI algorithm, the rule engine platform comprises an analysis library and an expert library, the Internet of things cloud management and control server at least further comprises a storage device and a processor, and the processor executes instructions stored in the storage device to execute any one of the methods.
It is to be understood that the intelligent AI algorithm may be implemented by any AI algorithm known in the art, and the present invention is not further limited in this regard.
Example five
The present invention provides a computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements any of the method embodiments described above.
The foregoing has described in detail the embodiments of the present disclosure, so as to not obscure the technical idea of the present disclosure, and those skilled in the art will be able to implement the technical scheme of the disclosure based on the description of the embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (5)
1. A refined energy management and control method is characterized by comprising the following steps of
Acquiring historical energy consumption spot check data sets of one or more remote distributed monitoring devices associated with a production chain by at least one workshop of a plurality of manufactured products in a production and manufacturing process;
splitting the historical energy consumption spot inspection data set into a management subset of a plurality of time scales under a plurality of use/application types, and determining a corresponding energy consumption standard according to the management subset, wherein the management subset is measured by the remote distributed monitoring equipment and is controlled by energy sources;
determining an energy use prediction under a production plan based on the energy consumption standard in combination with the production plan;
obtaining a corresponding predicted energy consumption curve by using the energy source use prediction, and obtaining a corresponding real-time energy consumption curve by using real-time energy consumption point detection data obtained by one or more remote distributed monitoring devices;
obtaining a difference group of pre-actual differences of management subsets under a plurality of time scales under a plurality of different use/usage types of each monitoring device according to the expected energy consumption curve and the corresponding real-time energy consumption curve;
determining a corresponding management and control recommendation strategy based on the difference group;
implementing the control recommendation strategy to reduce the difference between the actual energy consumption curve and the target energy consumption curve;
after the management and control recommendation strategy is implemented, synchronously revising the energy consumption standard corresponding to the management subset;
the remote distributed monitoring equipment comprises an energy monitoring sensor, a remote communication device, a storage device and a controller, wherein the remote distributed monitoring equipment is arranged at the input end of one or more production execution devices associated with at least one workshop in the production and manufacturing process and is used for storing the historical energy consumption spot inspection data set of the production execution devices and collecting real-time energy consumption spot inspection data, and the remote communication device uploads the data to a management and control cloud server through a convergence gateway; the control cloud server issues the control recommendation strategy to the controller, and the controller is used for controlling the operation parameters of the corresponding production execution equipment according to the control recommendation strategy;
wherein the production plan includes at least a predetermined production quantity and production schedule;
the splitting the historical energy consumption spot inspection data set into a plurality of management subsets of time scales under a plurality of use/usage types, and determining the corresponding energy consumption standard according to the management subsets comprises:
step one: correlating the historical energy consumption spot inspection data set with a manufactured product in the production and manufacturing process to obtain a historical energy consumption data correlation group corresponding to the manufactured product;
step two: carrying out data cleaning on the energy consumption data association group;
step three: splitting the historical energy consumption data association group into a management subset of a plurality of time scales under a plurality of use/application types, wherein the management subset comprises at least one of production day energy consumption, attendance non-production day energy consumption, weekend energy consumption and holiday energy consumption fields under each use/application type, and dividing the production day energy consumption into at least production period fixed consumption, change period change consumption and non-production period consumption subfields under each use/application type;
step four: determining corresponding energy consumption standards by energy consumption prediction from the subfields comprising the daily energy consumption fields in the management subset, and determining corresponding energy consumption standards by the management subset of other daily energy consumption not produced by the production day through a historical average;
wherein said determining a prediction of energy usage under said production plan based on said energy consumption criteria in combination with a production plan comprises determining said production schedule of one or more articles of manufacture to be produced according to a production plan split into a combination of a minute plan, an hour plan, a day plan, a week plan and a month plan, determining a prediction of energy consumption under a corresponding plan according to said energy consumption criteria and said combination;
the determining a corresponding management recommendation policy based on the difference group includes: based on the difference groups of the pre-actual differences of the management subsets under a plurality of time scales under a plurality of different use/usage types of each monitoring device, establishing pre-actual fingerprint information vectors associated with each time scale, matching analysis sentence segments in a preset analysis library by utilizing the fingerprint information vectors, and matching management and control recommendation strategies corresponding to a preset expert library based on the analysis sentence segments.
2. The refined energy management and control method according to claim 1, characterized in that: the pre-compaction difference at least comprises at least one of a curve maximum value, a curve minimum value, a curve point change rate and a curve integral area.
3. A refined energy management system for implementing the method of any of claims 1-2, comprising
The system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module acquires a historical energy consumption spot inspection data set of one or more remote distributed monitoring devices which are associated with a production chain by at least one workshop in the production and manufacturing process of a plurality of manufactured products;
the energy consumption standard module is used for splitting the historical energy consumption spot inspection data set into a management subset of a plurality of time scales under a plurality of use/application types, and determining a corresponding energy consumption standard according to the management subset, wherein the management subset is measured by the remote distributed monitoring equipment and is controlled by energy sources;
the energy prediction module is used for determining energy use prediction under the production plan based on the energy consumption standard and in combination with the production plan;
the energy consumption curve module is used for obtaining a corresponding predicted energy consumption curve by using the energy source use prediction, and obtaining a corresponding real-time energy consumption curve by using real-time energy consumption point detection data obtained by one or more remote distributed monitoring devices;
the pre-real difference module is used for acquiring a difference group of pre-real differences of the management subset under a plurality of time scales under a plurality of different use/usage scales of each monitoring device according to the expected energy consumption curve and the corresponding real-time energy consumption curve;
the control strategy module is used for determining a corresponding control recommendation strategy based on the difference group;
the control implementation module is used for implementing the control recommendation strategy to reduce the difference between the actual energy consumption curve and the target energy consumption curve;
and the revising module synchronously revises the energy consumption standard corresponding to the management subset after the management recommendation strategy is implemented.
4. An internet of things cloud management and control server, comprising at least an internet of things management platform and a rule engine platform, wherein the internet of things management platform and the rule engine platform are interactively coupled and implement energy management and control through an intelligent AI algorithm, the rule engine platform comprises an analysis library and an expert library, the internet of things cloud management and control server further comprises at least a storage device and a processor, and the processor executes instructions stored in the storage device to perform the method of any of claims 1-2.
5. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310050867.7A CN116048023B (en) | 2023-02-02 | 2023-02-02 | Fine energy management and control method, system, internet of things cloud management and control server and storage medium thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310050867.7A CN116048023B (en) | 2023-02-02 | 2023-02-02 | Fine energy management and control method, system, internet of things cloud management and control server and storage medium thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116048023A CN116048023A (en) | 2023-05-02 |
| CN116048023B true CN116048023B (en) | 2024-01-26 |
Family
ID=86120023
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310050867.7A Active CN116048023B (en) | 2023-02-02 | 2023-02-02 | Fine energy management and control method, system, internet of things cloud management and control server and storage medium thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116048023B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117075522B (en) * | 2023-09-27 | 2024-03-29 | 金麒麟新能源股份有限公司 | Remote centralized operation control method and system for energy system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104992298A (en) * | 2015-07-16 | 2015-10-21 | 南京朗坤软件有限公司 | Energy management system |
| CN105259847A (en) * | 2015-10-15 | 2016-01-20 | 深圳市万嘉明科技发展有限公司 | Energy management control method and system based on energy consumption historical data analysis |
| CN109709912A (en) * | 2018-12-20 | 2019-05-03 | 广西程天电子科技有限公司 | Energy management control method and system based on Internet of Things |
| KR102262045B1 (en) * | 2020-11-02 | 2021-06-08 | 박덕근 | Cloud server for managing predictive maintenance algorithm applied to manufacturing site and smart factory management system including the same |
| CN114169570A (en) * | 2021-11-04 | 2022-03-11 | 合肥中能电力科技有限公司 | Smart energy management platform based on Internet of things and cloud computing technology |
| CN114462761A (en) * | 2021-12-17 | 2022-05-10 | 华电电力科学研究院有限公司 | Distributed energy intelligent management and control system based on Internet of things |
| CN115099479A (en) * | 2022-06-17 | 2022-09-23 | 广州云智易物联网有限公司 | Control method, device and storage medium for energy consumption budget of building energy equipment |
-
2023
- 2023-02-02 CN CN202310050867.7A patent/CN116048023B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104992298A (en) * | 2015-07-16 | 2015-10-21 | 南京朗坤软件有限公司 | Energy management system |
| CN105259847A (en) * | 2015-10-15 | 2016-01-20 | 深圳市万嘉明科技发展有限公司 | Energy management control method and system based on energy consumption historical data analysis |
| CN109709912A (en) * | 2018-12-20 | 2019-05-03 | 广西程天电子科技有限公司 | Energy management control method and system based on Internet of Things |
| KR102262045B1 (en) * | 2020-11-02 | 2021-06-08 | 박덕근 | Cloud server for managing predictive maintenance algorithm applied to manufacturing site and smart factory management system including the same |
| CN114169570A (en) * | 2021-11-04 | 2022-03-11 | 合肥中能电力科技有限公司 | Smart energy management platform based on Internet of things and cloud computing technology |
| CN114462761A (en) * | 2021-12-17 | 2022-05-10 | 华电电力科学研究院有限公司 | Distributed energy intelligent management and control system based on Internet of things |
| CN115099479A (en) * | 2022-06-17 | 2022-09-23 | 广州云智易物联网有限公司 | Control method, device and storage medium for energy consumption budget of building energy equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116048023A (en) | 2023-05-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113835344B (en) | Control optimization method of equipment, display platform, cloud server and storage medium | |
| US10678193B2 (en) | Computer-implemented method and system for automatically monitoring and determining the status of entire process sections in a process unit | |
| Brandenburger et al. | Big data solution for quality monitoring and improvement on flat steel production | |
| CN107533560A (en) | For inferring the data scrubbing system and method for material composition | |
| CN116048023B (en) | Fine energy management and control method, system, internet of things cloud management and control server and storage medium thereof | |
| CN112052992A (en) | Building engineering project progress prediction system and method based on deep learning | |
| CN110544180A (en) | Building energy-saving system based on energy consumption prediction and analysis diagnosis | |
| CN110135636B (en) | Method, device and system for acquiring workshop operation state prediction information | |
| Ungermann et al. | Data analytics for manufacturing systems–a data-driven approach for process optimization | |
| CN111950577A (en) | Point inspection method and device | |
| Gavrysh et al. | The methodological approach to monitoring of the economic and functional state of innovation-oriented machinery engineering enterprises at the modern technological modes | |
| US7155367B1 (en) | Method for evaluating relative efficiency of equipment | |
| Azadeh et al. | A unique fuzzy multivariate modeling approach for performance optimization of maintenance workshops with cognitive factors | |
| CN111142488B (en) | Process industrial optimization method based on working condition comparison driving | |
| CN112348259A (en) | Method for predicting purchase price of power equipment | |
| Kim et al. | Data-analytics-based factory operation strategies for die-casting quality enhancement | |
| Navidi et al. | Multi responses optimization through game theory approach | |
| CN116341740A (en) | Urban fuel gas time-by-time load multi-step prediction method and system based on proportional splitting | |
| TW202347062A (en) | Method for waiting time prediction in semiconductor factory | |
| CN114418177B (en) | New product material distribution prediction method based on digital twin workshops for generating countermeasure network | |
| CN115438832A (en) | Gas load prediction method and system during holidays | |
| RU2295590C1 (en) | Method of the statistical control over the quality of the electrode products | |
| CN112785142B (en) | Intelligent work party early warning method and system for sewage treatment equipment of Internet of things | |
| CN114764741A (en) | Method and system for predicting running wind power of wind driven generator | |
| KR20140147456A (en) | Method for predicting daily water demand in water distribution network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |