CN118014486B - Green commodity circulation warehouse optimizing system - Google Patents
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Abstract
The invention belongs to the technical field of warehouse optimization, and particularly discloses a green logistics warehouse optimization system, which comprises: the system comprises a meteorological information importing module, a warehouse information importing module, a green plant adaptation analysis module, an energy-saving optimization confirming module, an information base and an optimization scheme feedback terminal. According to the invention, through carrying out the green plant setting suitability analysis and the green plant setting energy-saving optimization index analysis, an adaptive energy-saving optimization scheme is confirmed according to the green plant setting suitability analysis and the green plant setting energy-saving optimization index analysis, the problem that green ecological setting is not fully considered when energy loss optimization is carried out currently is effectively solved, the current energy loss optimization effect is ensured, the comprehensive optimization analysis and the overall planning of a logistics warehouse are realized, the energy consumption is further remarkably reduced, the negative influence on the environment is reduced, and the maintenance aging of the energy-saving effect is also ensured.
Description
Technical Field
The invention belongs to the technical field of warehouse optimization, and particularly relates to a green logistics warehouse optimization system.
Background
The development of green logistics aims at realizing the overall optimization of links such as logistics transportation, loading, unloading, storage, packaging and the like by means of reducing energy consumption, improving logistics efficiency, reducing logistics cost and the like.
At present, the logistics storage monitors and manages the energy use condition of storage facilities and equipment at the energy consumption reduction level, so that effective energy-saving measures are adopted, such as optimizing a lighting system, improving an air-conditioning refrigerating system, using high-efficiency equipment and the like, so that the energy consumption is reduced, the energy consumption is optimized by not fully considering green ecological setting, and obviously, the optimization effect of the current optimization mode is still deficient to a certain extent, and the following aspects are also deficient: 1. the attention to green energy conservation is insufficient, the current energy conservation transformation possibly only relates to a local area or specific equipment, cannot fully cover the energy conservation optimization requirement of the whole building, belongs to scattered improvement, lacks overall planning and comprehensive analysis, and is difficult to realize the maximized energy conservation effect.
2. The energy-saving effect maintains the degree of difficulty greatly, and the effect of energy-saving transformation to facility and equipment probably weakens gradually along with the lapse of time, is difficult to keep the effect for a long time, needs regularly to carry out equipment replacement and maintenance, and the maintenance cost is higher.
3. The investment return ratio has low guarantee, the current attention to natural energy is low, a certain resource waste exists, the balance between investment and energy conservation is difficult to maintain, the current energy conservation optimization mode has high limitation, and the negative influence on the environment cannot be reduced.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the background art, a green logistics warehouse optimization system is now proposed.
The aim of the invention can be achieved by the following technical scheme: the invention provides a green logistics warehouse optimization system, which comprises: the weather information importing module is used for marking the area where the target logistics warehouse is located as a target area, importing weather information of the target area on each monitoring day, wherein the weather information consists of weather types and weather parameters.
The warehouse information importing module is used for importing the internal environment monitoring data of the target logistics warehouse on each monitoring day, importing the structure related data of the target logistics warehouse, and currently setting an energy-saving optimization scheme and saving power consumption on each monitoring day.
The green plant adaptation analysis module is used for carrying out green plant setting adaptation analysis, outputting a green plant setting adaptation index, and starting the energy-saving optimization confirmation module if the adaptation index is larger than 0.
The energy-saving optimization confirmation module is used for analyzing the green plant setting energy-saving optimization indexAnd confirming the adaptation of the energy-saving optimization scheme according to the energy-saving optimization scheme.
The information base is used for storing the reference impurity removal power consumption of the impurity degree of each water body in unit volume, storing the expected heat insulation temperature, the suitable planting area, the wind resistance level, the light preference type, the humidity demand type and the suitable growth temperature interval of each green planting, and storing the suitable internal temperature interval of the target logistics warehouse.
And the optimization scheme feedback terminal is used for feeding back the adaptive energy-saving optimization scheme to the operation management center of the target logistics warehouse.
Further, the performing green plant setting suitability analysis includes: and extracting proper planting areas of the various kinds of green plants from the information base, and taking the various kinds of green plants with the proper planting areas being roofs as the various analysis green plants.
Analyzing the illumination resource sufficiency of the target area based on the meteorological informationFull of water resource/>And wind disturbance tendency/>。
Extracting the highest temperature and the lowest temperature from the meteorological parameters of each monitoring day, combining to obtain an actual temperature interval, and counting the deviation degree of the growth temperature of each analysis green plant，/>Representing the analysis of green plant number,/>。
Will beAnd matching and comparing the target area with the required illumination resource abundance corresponding to each set light preference type to obtain the matched light preference type of the target area.
And matching the humidity demand type and the wind resistance level of the target area according to the matching mode of the target area.
Taking the wind resistance grade, the light preference type and the humidity demand type as each evaluation resource item, if a certain evaluation resource item of a certain analysis green plant is consistent with a matched evaluation resource item, marking the evaluation resource item as an identical item, and counting the number of the identical items of each analysis green plant。
Statistics of green plant setting adaptation index，/>，/>For the set allowable growth temperature deviation degree,/>And (5) the set reference growth fitness is obtained.
Further, the analyzing the illumination resource fullness of the target area includes: and (3) recording each monitoring diary with the weather type of sunny day as each illumination day, and comparing each illumination day to obtain the interval days among each illumination day.
Constructing illumination interval curve by taking illumination day as abscissa and interval day as ordinate, and performing slopeAnd amplitude/>Extracting and setting an illumination interval error compensation factor/>And confirm the reference illumination interval days based on this, and record as/>。
The illumination intensity and the illumination time length are screened from the meteorological parameters of each illumination day, each illumination day is effectively assessed through an effective illumination assessment rule, the illumination diary with the assessment result being effective is used as the effective illumination day, and then the illumination resource assessment compensation factor is set based on the illumination intensity and the illumination time length of the effective illumination day。
The number of the interval days between the first monitoring day and the last monitoring day is recorded asSimultaneously, the number of illumination days and the number of effective illumination days are respectively recorded as/>And/>。
Counting the sufficiency of illumination resources of a target area，，/>Respectively set reference illumination interval ratio, illumination ratio and effective illumination ratio,/>And respectively evaluating the compensation factors for the set unit illumination resources to correspondingly compensate the sufficiency of the illumination resources.
Further, the analyzing the water resource fullness of the target area includes: each monitoring diary with weather type of rainy day is marked as each drop rain day, and each drop rain day is marked on the electronic calendar.
If a marked drop rain day exists in a certain month, the month is marked as a rainfall month, the number of drops rain day of each rainfall month is counted, the number of drops rain day of each rainfall month is compared with the number of days of each rainfall month, and the ratio is marked as rainfall frequency.
Constructing a rainfall frequency change curve by taking the rainfall month as an abscissa and the rainfall frequency as an ordinate, and positioning the length of the rainfall frequency change curveAnd a curve length/>, above the set rainfall frequency。
Extracting accumulated rainfall from the meteorological parameters, extracting the gradient, the material and the area of the roof from the structure related data, and further evaluating through a green-Amsterm formula and a hydraulics model to obtain rainfall runoff loss of each drop rain day, wherein the rainfall runoff loss is taken as the allowable water storage capacity of each drop rain day and is recorded as，/>Number of rainfall day is shown,/>。
The accumulated rainfall of each drop rain day is recorded asAnd extracting the capacity/>, of the water guide tank from the structure-related data。
Counting the fullness of water resources of a target area，，/>For the set reference rainfall,/>To round down the sign,/>For the number of rainfall days,/>For a set excess tank capacity value.
Further, the analysis of wind disturbance trend of the target area includes extracting maximum wind speed from weather information of each monitoring day, and selecting maximum valueMeanwhile, the maximum wind speed of each monitoring day is subjected to mean value calculation, and the calculation result is recorded as/>。
The maximum wind speed and the set interference wind speed of each monitoring dayComparing, to be greater than or equal to the set disturbance wind speed/>Is recorded as wind speed interference day.
Comparing the wind speed interference days to obtain the number of interval days among the wind speed interference days, further calculating the variance of the interval days, and recording the calculation result asAnd will/>As wind speed disturbance duration/>。
Statistics of wind interference tendencies of target areas，/>，/>For a set reference wind speed difference,/>To set the reference wind speed disturbance duration.
Further, the counting the deviation degree of the growth temperature of each analysis green plant comprises the following steps: the actual temperature interval of each monitoring day and the suitable growth temperature interval of each analysis green plant are marked on the numerical axis respectively.
Extracting the length of the marked area of each monitoring day and marking the length asAnd extracting the length of the overlapping marking area corresponding to each monitoring day and each analysis green plant, and marking as/>，/>Indicates the monitoring day number,/>。
Counting the deviation degree of the growth temperature of each analysis green plant，/>，/>Indicating the number of monitoring days.
Further, the analyzing the green plants to set the energy saving optimization index includes: the starting monitoring day and the stopping monitoring day are combined into a monitoring period.
And positioning a water guide treatment tracking log positioned in a composition monitoring period from the structure related data, and further extracting index values and water storage volumes of each monitoring water body in the corresponding water guide tank during each water guide.
The water impurity degree during each water diversion is estimated and obtained through a water state estimation model, and the reference impurity removal power consumption corresponding to the water impurity degree during each water diversion under unit volume is extracted from an information base and is recorded as，/>The number of the water guiding sequence is indicated,。
The water storage volume in the corresponding water guide tank during each water guide is recorded asStatistics of accumulated filtered power consumption of water body/>，。
Based on the internal environment information of each monitoring day, the temperature-saving power consumption of the roof green plants in each monitoring day is predicted, and the current accumulated temperature-saving power consumption is obtained by summation。
Based on the meteorological information of each monitoring day, the green plant ecological regulation power consumption of each monitoring day is predicted, and the current accumulated predicted green plant ecological regulation power consumption is obtained by summation。
The energy saving and power consumption of the currently set energy saving optimization scheme on each monitoring day is summed and recorded asFurther, the energy-saving optimization index/>, which is set by green planting, is counted，/>，/>And the electric energy is saved for the effective optimization of the set reference.
Further, the predicting the green plant ecological adjustment power consumption of each monitoring day includes: the accumulated rainfall of each monitoring day and the set green plant bearing rainfall are combinedBy contrast, if the accumulated rainfall on a certain monitoring day is greater than/>The cumulative rainfall on this monitoring day is denoted as/>。
Extracting roof height from the structural correlation dataThe area and gradient of the roof corresponding to the target logistics warehouse are respectively recorded as/>And/>And will/>The green plant ecology used as the drainage day is used for regulating the power consumption and is recorded as/>，/>The unit water discharge amount under the set unit height is correspondingly consumed electric energy,The roof area and roof slope of the reference are set.
If the accumulated rainfall on a certain monitoring day is less than or equal toAnd is greater than or equal to the set proper water supplementing amount for green plantsAnd taking 0 as the green plant ecological regulation power consumption of the monitoring day.
If the accumulated rainfall on a certain monitoring day is smaller thanThe cumulative rainfall on this monitoring day is denoted as/>And then willThe green plant ecology used as the water supplementing day is used for regulating the power consumption and is recorded as/>，/>Corresponding consumption of electric energy for unit water supplementing quantity under set unit height,/>。
Further, the predicting the temperature-saving power consumption of the roof green plant in each monitoring day includes: will beAnd taking the analyzed green plants with the maximum growth fitness as target planting green plants as the growth fitness of each analyzed green plant.
Extracting a proper internal temperature interval of a target logistics warehouse from an information base, and extracting the predicted heat insulation temperature of the green plant of the target planting。
Extracting the temperature of each monitoring time point from the internal environment information of each monitoring day, judging the high-temperature regulation and control duration, the low-temperature regulation and control duration, the regulation and control high-temperature value and the regulation and control low-temperature value of each monitoring day according to a temperature regulation judgment rule, and respectively marking as、、/>And/>。
Extracting the volume of the target logistics warehouse from the structure related data, which is recorded asStatistics of roof green plants in each monitoring day to save high-temperature regulation and control power consumption/>，/>，/>And the power consumption is regulated and controlled according to the unit regulation and control time length corresponding to the unit regulation and control temperature value under the set unit regulation and control space volume.
According toThe statistical mode of the system is similar to that of the system for monitoring the roof green planting in each day to save low-temperature regulation and control power consumption/>Will beThe method is used for saving temperature and power consumption of roof green plants in each monitoring day.
Further, the validating adapts a power saving optimization scheme, comprising: if it isGreen plant layout is used as an adaptive energy-saving optimization scheme,/>Optimizing the index for the set reference energy conservation.
If it isAnd taking the currently set energy-saving optimization scheme as an adaptive energy-saving optimization scheme.
Compared with the prior art, the invention has the following beneficial effects: (1) According to the invention, through carrying out the green plant setting suitability analysis and the green plant setting energy-saving optimization index analysis, an adaptive energy-saving optimization scheme is confirmed according to the suitability analysis, the problem that the green ecological setting is not fully considered when the energy loss optimization is carried out currently is effectively solved, the current energy loss optimization effect is ensured, the limitation of the current energy-saving optimization mode is broken, the comprehensive optimization analysis and the overall planning of a logistics warehouse are realized, the energy-saving optimization requirement of the whole building is comprehensively covered, the energy consumption is obviously reduced, the negative influence on the environment is reduced, the energy-saving effect of a logistics warehouse Chu Yunwei is greatly improved, the defect that the maintenance difficulty of the current energy-saving effect is high is overcome, the defects of higher maintenance cost and more complicated maintenance existing in the current energy-saving optimization mode are overcome, and the maintenance ageing and the return on-investment ratio of the energy-saving effect are also ensured on the other level.
(2) According to the method, the illumination resource abundance, the water resource abundance, the wind power interference trend degree and the growth temperature deviation degree of the green plants are analyzed, so that the green plant setting suitability analysis is further carried out, the multi-dimensional suitability assessment of the green plant setting is realized, the feasibility of the warehouse green plant arrangement and the current natural resource condition are comprehensively displayed, the defect of insufficient attention to green energy conservation at present is overcome, the subsequent utilization rate of natural resources is improved, and the assistance is provided for reducing the dependence on traditional energy sources and realizing the aims of energy conservation and emission reduction.
(3) According to the invention, four indexes of impurity removal energy consumption, green planting temperature adjustment energy consumption saving, green planting ecological adjustment energy consumption and current energy consumption saving are used for carrying out green planting setting energy saving optimization index analysis from a corresponding water guiding body of a warehouse roof, so that multi-azimuth detailed assessment of green planting setting energy saving optimization is realized, the influence of green planting setting on energy saving is comprehensively assessed, the energy saving effect of the green planting setting can be more scientifically assessed, the possible one-sided property and misleading property of single index analysis are avoided, and meanwhile, a direction is provided for maintaining the balance between investment and energy saving, and a decision maker is helped to more comprehensively know the energy saving potential of the green planting setting.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present 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 schematic diagram of the connection of the modules of the system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described 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.
Referring to fig. 1, the present invention provides a green logistics warehouse optimization system, which comprises: the system comprises a meteorological information importing module, a warehouse information importing module, a green plant adaptation analysis module, an energy-saving optimization confirming module, an information base and an optimization scheme feedback terminal.
The green plant adaptation analysis module is respectively connected with the meteorological information introduction module, the warehouse information introduction module, the information base and the energy-saving optimization confirmation module, and the energy-saving optimization confirmation module is also respectively connected with the meteorological information introduction module, the warehouse information introduction module, the information base and the optimization scheme feedback terminal.
The weather information importing module is used for marking the area where the target logistics warehouse is located as a target area, importing weather information of the target area on each monitoring day, wherein the weather information consists of weather types and weather parameters.
In particular, the meteorological parameters include, but are not limited to, maximum temperature, minimum temperature, rainfall, illumination intensity, and illumination duration.
The warehouse information importing module is used for importing the internal environment monitoring data of the target logistics warehouse on each monitoring day, importing the structure related data of the target logistics warehouse, and currently setting an energy-saving optimization scheme and saving power consumption on each monitoring day.
In particular, the structure-related data includes, but is not limited to, grade of roof, height, material, area, tank capacity, treatment tracking log, and volume of the target logistics warehouse.
The water guiding treatment tracking log is used for recording information such as index values and water storage volumes of all monitoring water bodies in the corresponding water guiding tank during water guiding, and in a specific embodiment, the monitoring water body indexes comprise but are not limited to suspended matter content and pH value.
The green plant adaptation analysis module is used for carrying out green plant setting adaptation analysis, outputting a green plant setting adaptation index, and starting the energy-saving optimization confirmation module if the adaptation index is larger than 0.
Illustratively, performing a green plant setting suitability analysis includes: a1, extracting proper planting areas of various green plants from an information base, and taking various green plants with proper planting areas as roofs as various analysis green plants.
A2, analyzing the illumination resource sufficiency of the target area based on the meteorological informationFull of water resource/>And wind disturbance tendency/>。
A3, extracting the highest temperature and the lowest temperature from the meteorological parameters of each monitoring day, combining to obtain an actual temperature interval, and counting the deviation degree of the growth temperature of each analysis green plant，/>Representing the analysis of green plant number,/>。
A4, willAnd matching and comparing the target area with the required illumination resource abundance corresponding to each set light preference type to obtain the matched light preference type of the target area.
And A5, matching the humidity demand type and the wind resistance level of the target area according to the matching mode of the target area for matching the happy light type.
A6, taking the wind resistance grade, the light preference type and the humidity demand type as each evaluation resource item, if a certain evaluation resource item of a certain analysis green plant is consistent with a matched evaluation resource item, marking the evaluation resource item as an identical item, and counting the number of the identical items of each analysis green plant。
A7, counting green plant setting adaptation index，/>，/>For the set allowable growth temperature deviation degree,/>And (5) the set reference growth fitness is obtained.
According to the embodiment of the invention, the illumination resource abundance, the water resource abundance, the wind power interference trend and the growth temperature deviation of the green plants are analyzed, so that the green plant setting suitability analysis is further carried out, the multi-dimensional suitability assessment of the green plant setting is realized, the feasibility of the warehouse green plant arrangement and the current natural resource condition are comprehensively displayed, the defect of insufficient attention to green energy conservation in the prior art is overcome, the subsequent utilization of natural resources is promoted, and the assistance is provided for reducing the dependence of traditional energy sources and realizing the aims of energy conservation and emission reduction.
Further, in the step A2, analyzing the illumination resource fullness of the target area, including: and U1, recording each monitoring diary with the weather type of sunny day as each illumination day, and comparing each illumination day to obtain the interval days among each illumination day.
U2, constructing an illumination interval curve by taking illumination days as an abscissa and interval days as an ordinate, and carrying out slope from the illumination interval curveAnd amplitude/>Extracting and setting an illumination interval error compensation factor/>And confirm the reference illumination interval days based on this, and record as/>。
It should be added that the specific setting formula of the illumination interval error compensation factor is as follows:，/> the change rate of the reference illumination interval and the difference of the illumination interval days are respectively set.
It is also to be added that the specific confirmation procedure for confirming the number of days of the reference illumination interval is as follows: screening the maximum interval days and the minimum interval days from the interval days between the illumination days, and respectively marking asAnd/>And obtaining the average illumination interval days/>, through mean value calculation。
Will be、/>、/>And/>Introducing into illumination interval compensation model, outputting reference illumination interval days/>The specific expression formula of the illumination interval compensation model is as follows: /(I)。
In a specific embodiment, the slope of the curve refers to the slope of the curve corresponding to the regression line.
U3, screening out illumination intensity and illumination duration from meteorological parameters of each illumination day, effectively evaluating each illumination day through an effective illumination evaluation rule, and recording the illumination day with an effective evaluation result as the effective illumination day, thereby setting an illumination resource evaluation compensation factor based on the illumination intensity and illumination duration of the effective illumination day。
It should be added that the specific rules of the effective illumination evaluation rule are as follows: and taking the illumination intensity which is larger than or equal to the minimum illumination intensity required by the set plant growth as an assessment condition 1.
And taking the sunshine duration with the illumination time length being more than or equal to one third of the set sunshine duration as an evaluation condition 2, taking the validity as an evaluation result when the evaluation condition 1 and the evaluation condition 2 are simultaneously established, and taking the invalidity as the evaluation result when a certain evaluation condition is not established in the evaluation condition 1 and the evaluation condition 2.
The specific setting process for setting the illumination resource evaluation compensation factor is as follows: respectively carrying out average value calculation on the illumination intensity and the illumination duration of each effective illumination day, and respectively marking the calculation results asAnd/>。
Setting illumination resource evaluation compensation factors，/>，/>Respectively setting the minimum illumination intensity and the sunshine duration of plant growth requirements,/>And (5) exceeding the degree for the set reference illumination resource.
U4, counting the number of the interval days between the first monitoring day and the last monitoring day asSimultaneously, the number of illumination days and the number of effective illumination days are respectively recorded as/>And/>。
U5, counting the sufficiency of illumination resources of the target area，，/>Respectively set reference illumination interval ratio, illumination ratio and effective illumination ratio,/>And respectively evaluating the compensation factors for the set unit illumination resources to correspondingly compensate the sufficiency of the illumination resources.
Further, in the step A2, analyzing the fullness of the water resource of the target area, including: and L1, marking each monitoring diary with weather type of rainy day as each drop rain day, and marking each drop rain day on the electronic calendar.
And L2, if a marked drop rain day exists in a certain month, marking the month as a rainfall month, counting the number of drops rain day of each rainfall month, comparing the number of drops with the number of days of each rainfall month, and marking the ratio as rainfall frequency.
L3, constructing a rainfall frequency change curve by taking rainfall months as an abscissa and rainfall frequencies as an ordinate, and positioning the length of the rainfall frequency change curveAnd a curve length/>, above the set rainfall frequency。
L4, extracting accumulated rainfall from the meteorological parameters, extracting the gradient, the material and the area of the roof from the structure related data, and further evaluating through a Grignard-Amsterm formula and a hydraulics model to obtain rainfall runoff loss of each drop rain day, wherein the rainfall runoff loss is taken as the allowable water storage capacity of each drop rain day and is recorded as，/>Number of rainfall day is shown,/>。
In a specific embodiment, the evaluation of rainfall runoff loss through the green-amsterone formula and the hydraulic model is a relatively mature technical means, and the specific evaluation process is not described herein.
L5, the accumulated rainfall of each drop rain day is recorded asAnd extracting the capacity/>, of the water guide tank from the structure-related data。
L6, counting the fullness of water resources of the target area，，/>For the set reference rainfall,/>To round down the sign,/>For the number of rainfall days,/>For a set excess tank capacity value.
The method is used for analyzing the fullness of the illumination resources and the fullness of the water resources, and is mainly based on the condition of the plants on illumination and water requirements, so that the method for analyzing the fullness of the illumination resources and the fullness of the water resources is not the same analysis mode.
Further, the step A2 of analyzing the wind disturbance trend of the target area includes N1 of extracting maximum wind speed from weather information of each monitoring day and screening the maximum valueMeanwhile, the maximum wind speed of each monitoring day is subjected to mean value calculation, and the calculation result is recorded as/>。
N2, setting the maximum wind speed and the disturbance wind speed of each monitoring dayComparing, to be greater than or equal to the set disturbance wind speed/>Is recorded as wind speed interference day.
N3, comparing the wind speed interference days to obtain the interval days among the wind speed interference days, further calculating the variance of the interval days, and recording the calculation result asAnd will/>As wind speed disturbance duration/>。
N4, counting wind power interference trend degree of target area，/>，For a set reference wind speed difference,/>To set the reference wind speed disturbance duration.
Further, in step A3, the statistics of the deviation of the growth temperature of each analyzed green plant include: a31, marking the actual temperature interval of each monitoring day and the proper growth temperature interval of each analysis green plant on a numerical axis respectively.
A32, extracting the length of the marked area of each monitoring day, and marking asAnd extracting the length of the overlapping marking area corresponding to each monitoring day and each analysis green plant, and marking as/>，/>Indicates the monitoring day number,/>。
A33, counting the deviation degree of the growth temperature of each analysis green plant，/>，/>Indicating the number of monitoring days.
The energy-saving optimization confirmation module is used for analyzing the green plant setting energy-saving optimization indexAnd confirming the adaptation of the energy-saving optimization scheme according to the energy-saving optimization scheme.
Illustratively, analyzing the green plants to set an energy saving optimization index includes: and J1, forming a monitoring period by the starting monitoring day and the stopping monitoring day.
And J2, positioning a water guide treatment tracking log positioned in a composition monitoring period from the structure related data, and further extracting index values and water storage volumes of each monitoring water body in the corresponding water guide tank during each water guide.
J3, evaluating the water impurity degree of each water diversion through a water state evaluation model, extracting reference impurity removal power consumption corresponding to the water impurity degree of each water diversion under unit volume from an information base, and marking as，/>Indicates the water guiding sequence number,/>。
The specific setting process of the water body state evaluation model is as follows: positioning the content of suspended matters and the pH value from the index values of each monitored water body, and marking the content and the pH value asAnd/>。
Will beAnd/>As an input of a water body state evaluation model, taking the impurity degree of the water body as an output, wherein the water body state evaluation model specifically represents the following formula: /(I)，/>Respectively setting the reference suspended matter content and pH value of impurity water assessment,/>To set the pH deviation of the reference,/>Is the impurity degree of the water body.
J4, recording the water storage volume in the corresponding water guide tank during each water guide asStatistics of accumulated water filtering power consumption，/>。
J5, based on the internal environment information of each monitoring day, predicting the temperature-saving power consumption of the roof green plants in each monitoring day, and summing to obtain the current accumulated temperature-saving power consumption。
J6, predicting the green plant ecological adjustment power consumption of each monitoring day based on the meteorological information of each monitoring day, and summing to obtain the current accumulated predicted green plant ecological adjustment power consumption。
J7, summing the power consumption saved in each monitoring day by the currently set energy-saving optimization scheme, and recording asFurther, the energy-saving optimization index/>, which is set by green planting, is counted，/>，/>And the electric energy is saved for the effective optimization of the set reference.
According to the embodiment of the invention, the four indexes of impurity removal energy consumption, green planting temperature adjustment energy consumption, green planting ecological adjustment energy consumption and current energy consumption saving of the corresponding water guide body of the warehouse roof are used for carrying out green planting setting energy-saving optimization index analysis, so that multidirectional detailed assessment of green planting setting energy-saving optimization is realized, the influence of green planting setting on energy saving is comprehensively assessed, the energy-saving effect of the green planting setting can be more scientifically assessed, the possible one-sided performance and misleading performance of single index analysis are avoided, meanwhile, the direction is provided for maintaining the balance between investment and energy saving, and a decision maker is helped to more comprehensively know the energy-saving potential of the green planting setting.
Further, in the step J5, the green plant ecological regulation power consumption of each monitoring day is predicted, which comprises the following steps: j51, accumulating rainfall on each monitoring day and setting green plant bearing rainfallBy contrast, if the accumulated rainfall on a certain monitoring day is greater than/>The cumulative rainfall on this monitoring day is denoted as/>。
J52, extracting roof height from the structure related dataThe area and gradient of the roof corresponding to the target logistics warehouse are respectively recorded as/>And/>And will/>The green plant ecology used as the drainage day is used for regulating the power consumption and is recorded as/>，/>For the corresponding consumption of electric energy of unit drainage under the set unit height,/>The roof area and roof slope of the reference are set.
J53, if the accumulated rainfall on a certain monitoring day is less than or equal toAnd is greater than or equal to the set proper water supplementing amount/>, for green plantingAnd taking 0 as the green plant ecological regulation power consumption of the monitoring day.
J54, if the accumulated rainfall on a certain monitoring day is smaller thanThe cumulative rainfall on this monitoring day is denoted as/>And will/>The green plant ecology used as the water supplementing day is used for regulating the power consumption and is recorded as/>，/>Corresponding consumption of electric energy for unit water supplementing quantity under set unit height,/>。
Further, in step J6, the method for predicting the temperature-saving power consumption of the roof green plant in each monitoring day includes: j61, willAnd taking the analyzed green plants with the maximum growth fitness as target planting green plants as the growth fitness of each analyzed green plant.
J62, extracting a proper internal temperature interval of the target logistics warehouse from the information base, and extracting the predicted heat insulation temperature of the target planting green plant。
J63, extracting the temperature of each monitoring time point from the internal environment information of each monitoring day, judging the high-temperature regulation and control duration, the low-temperature regulation and control duration, the regulation and control high-temperature value and the regulation and control low-temperature value of each monitoring day according to a temperature regulation judgment rule, and respectively marking as、/>、/>And/>。
J64, extracting the volume of the target logistics warehouse from the structure-related data, and recording asStatistics of roof green plants in each monitoring day to save high-temperature regulation and control power consumption/>，/>，/>And the power consumption is regulated and controlled according to the unit regulation and control time length corresponding to the unit regulation and control temperature value under the set unit regulation and control space volume.
J65 according toThe statistical mode of the system is similar to that of the system for monitoring the roof green planting in each day to save low-temperature regulation and control power consumption/>And will/>The method is used for saving temperature and power consumption of roof green plants in each monitoring day.
The specific judging process of the temperature regulation judging rule in the step J63 is as follows: b1, the monitoring time point with the temperature larger than the upper limit value of the proper internal temperature interval of the target logistics warehouse is marked as a high-temperature time point, and the monitoring time point with the temperature larger than the upper limit value of the proper internal temperature interval of the target logistics warehouse is marked as a low-temperature time point.
B2, forming high Wen Shijian sequences by all high-temperature time points, and initializing a variableStoring the number of the current continuous high-temperature time points, traversing the high-temperature time sequence, and if the interval duration between one high-temperature time point and other high-temperature time points is within the preset continuous high-temperature Wen Pingding interval duration, stopping the high-temperature time sequenceAdd 1, otherwise will/>Reset to 0 and record the corresponding cut-off record for the high temperature time point before each reset.
And B3, recording a continuous high-temperature time point corresponding to the cut-off record before the first reset as a target high-temperature time point, positioning an initial high-temperature time point from the high-temperature time sequence, and recording the interval duration between the target high-temperature time point and the initial high-temperature time point as a continuous high-temperature duration of the first reset.
And B4, taking the interval duration between the continuous high-temperature time point of the cut-off record before the second time reset and the continuous high-temperature time point of the corresponding cut-off record before the first time reset as the continuous high-temperature duration of the second time reset.
And B5, taking the interval duration between the cut-off record continuous high-temperature time point before the third resetting and the corresponding cut-off record continuous high-temperature time point before the second resetting as the continuous high-temperature duration of the third resetting, obtaining the continuous high-temperature duration of each resetting at one time according to the rule, and summing to obtain the high-temperature regulation duration.
And B6, locating each high-temperature time point which is positioned before the continuous high-temperature time point of the corresponding cut-off record before the first reset from the time sequence as each first high-temperature time point.
And B7, carrying out difference between the temperature of each first high-temperature time point and the upper limit value of the proper internal temperature interval corresponding to the target logistics warehouse, carrying out mean value calculation on the difference value, and recording the calculation result as a first reset regulation temperature value.
And B8, locating each high-temperature time point which is positioned after the continuous high-temperature time point of the corresponding cut-off record before the first reset and is positioned before the continuous high-temperature time point of the corresponding cut-off record before the second reset from the time sequence, and obtaining a second reset regulation temperature value according to calculation of the first reset regulation temperature value as each second high-temperature time point.
And B9, sequentially calculating according to the rule to obtain a reset regulation temperature value, calculating through a mean value to obtain a regulation high temperature value, and judging the regulation high temperature value and the regulation low temperature value in the same way according to the judgment mode of the high temperature regulation duration and the regulation high temperature value.
Still another exemplary embodiment of the present invention provides a method for validating an adapted power saving optimization scheme, comprising: if it isGreen plant layout is used as an adaptive energy-saving optimization scheme,/>Optimizing the index for the set reference energy conservation.
If it isAnd taking the currently set energy-saving optimization scheme as an adaptive energy-saving optimization scheme.
The information base is used for storing the reference impurity removal power consumption of the impurity degree of each water body in unit volume, storing the expected heat insulation temperature, the suitable planting area, the wind resistance level, the light preference type, the humidity demand type and the suitable growth temperature interval of each green planting, and storing the suitable internal temperature interval of the target logistics warehouse.
The optimization scheme feedback terminal is used for feeding the adaptive energy-saving optimization scheme back to the operation management center of the target logistics warehouse.
According to the embodiment of the invention, through carrying out green plant setting suitability analysis and green plant setting energy-saving optimization index analysis, an adaptive energy-saving optimization scheme is confirmed according to the suitability analysis, the problem that green ecological setting is not fully considered when energy loss optimization is carried out currently is effectively solved, the current energy loss optimization effect is ensured, the limitation of the current energy-saving optimization mode is broken, comprehensive optimization analysis and integral planning of a logistics warehouse are realized, the energy-saving optimization requirement of the whole building is comprehensively covered, the energy consumption is obviously reduced, meanwhile, the negative influence on the environment is reduced, the energy-saving effect of a logistics warehouse Chu Yunwei is greatly improved, the defect that the current energy-saving effect is high in maintenance difficulty is overcome, the defects of higher maintenance cost and more complicated maintenance in the current energy-saving optimization mode are overcome, and the maintenance ageing and investment return ratio of the energy-saving effect are also ensured on the other level.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (8)
1. A green commodity circulation warehouse optimizing system, its characterized in that: the system comprises:
the weather information importing module is used for marking the area where the target logistics warehouse is located as a target area, importing weather information of the target area on each monitoring day, wherein the weather information consists of weather types and weather parameters;
The warehouse information importing module is used for importing the internal environment monitoring data of the target logistics warehouse on each monitoring day, importing the structure related data of the target logistics warehouse, and currently setting an energy-saving optimization scheme and saving power consumption on each monitoring day;
The green plant adaptation analysis module is used for carrying out green plant setting adaptation analysis, outputting a green plant setting adaptation index, and starting the energy-saving optimization confirmation module if the adaptation index is greater than 0;
the energy-saving optimization confirmation module is used for analyzing the green plant setting energy-saving optimization index Confirming an adaptive energy-saving optimization scheme according to the method;
the information base is used for storing the reference impurity removal power consumption of the impurity degree of each water body in unit volume, storing the expected heat insulation temperature, the suitable planting area, the wind resistance level, the light preference type, the humidity demand type and the suitable growth temperature interval of each green planting, and storing the suitable internal temperature interval of the target logistics warehouse;
the optimization scheme feedback terminal is used for feeding back the adaptive energy-saving optimization scheme to an operation management center of the target logistics warehouse;
The green plant setting suitability analysis comprises the following steps:
Extracting proper planting areas of various green plants from the information base, and taking various green plants with proper planting areas as roofs as various analysis green plants;
analyzing the illumination resource sufficiency of the target area based on the meteorological information Full of water resource/>And wind disturbance tendency/>；
Extracting the highest temperature and the lowest temperature from the meteorological parameters of each monitoring day, combining to obtain an actual temperature interval, and counting the deviation degree of the growth temperature of each analysis green plant，/>Representing the analysis of green plant number,/>；
Will beMatching and comparing the required illumination resource abundance corresponding to each set light preference type to obtain a matched light preference type of the target area;
Matching humidity demand types and wind resistance grades of the matched target areas in the same way according to the matching mode of the matched light preference types of the target areas;
Taking the wind resistance grade, the light preference type and the humidity demand type as each evaluation resource item, if a certain evaluation resource item of a certain analysis green plant is consistent with a matched evaluation resource item, marking the evaluation resource item as an identical item, and counting the number of the identical items of each analysis green plant ；
Statistics of green plant setting adaptation index，/>，/>For the set allowable growth temperature deviation degree,/>The set reference growth fitness is set;
The analyzing green plants to set energy-saving optimization indexes comprises the following steps:
The starting monitoring day and the stopping monitoring day form a monitoring period;
positioning a water guide treatment tracking log positioned in a composition monitoring period from the structure related data, and further extracting index values and water storage volumes of each monitoring water body in the corresponding water guide tank during each water guide;
The water impurity degree during each water diversion is estimated and obtained through a water state estimation model, and the reference impurity removal power consumption corresponding to the water impurity degree during each water diversion under unit volume is extracted from an information base and is recorded as ，/>The number of the water guiding sequence is indicated,；
The water storage volume in the corresponding water guide tank during each water guide is recorded asStatistics of accumulated filtered power consumption of water body/>，；
Based on the internal environment information of each monitoring day, the temperature-saving power consumption of the roof green plants in each monitoring day is predicted, and the current accumulated temperature-saving power consumption is obtained by summation；
Based on the meteorological information of each monitoring day, the green plant ecological regulation power consumption of each monitoring day is predicted, and the current accumulated predicted green plant ecological regulation power consumption is obtained by summation；
The energy saving and power consumption of the currently set energy saving optimization scheme on each monitoring day is summed and recorded asFurther, the energy-saving optimization index/>, which is set by green planting, is counted，/>，/>And the electric energy is saved for the effective optimization of the set reference.
2. A green logistics warehouse optimization system of claim 1, wherein: the analyzing the illumination resource fullness of the target area includes:
Recording each monitoring diary with the weather type of sunny day as each illumination day, and comparing each illumination day to obtain the interval days among each illumination day;
Constructing illumination interval curve by taking illumination day as abscissa and interval day as ordinate, and performing slope And amplitude/>Extracting and setting an illumination interval error compensation factor/>And confirm the reference illumination interval days based on this, and record as/>；
The illumination intensity and the illumination time length are screened from the meteorological parameters of each illumination day, each illumination day is effectively assessed through an effective illumination assessment rule, the illumination diary with the assessment result being effective is used as the effective illumination day, and then the illumination resource assessment compensation factor is set based on the illumination intensity and the illumination time length of the effective illumination day；
The number of the interval days between the first monitoring day and the last monitoring day is recorded asSimultaneously, the number of illumination days and the number of effective illumination days are respectively recorded as/>And/>；
Counting the sufficiency of illumination resources of a target area，/>，Respectively set reference illumination interval ratio, illumination ratio and effective illumination ratio,/>And respectively evaluating the compensation factors for the set unit illumination resources to correspondingly compensate the sufficiency of the illumination resources.
3. A green logistics warehouse optimization system of claim 1, wherein: the analyzing the water resource fullness of the target area comprises:
marking each monitoring diary with weather type of rainy day as each drop rain day, and marking each drop rain day on the electronic calendar;
If a marked drop rain day exists in a certain month, the month is marked as a rainfall month, the number of drops rain day of each rainfall month is counted, the number of drops rain day of each rainfall month is compared with the number of days of each rainfall month, and the ratio is marked as rainfall frequency;
constructing a rainfall frequency change curve by taking the rainfall month as an abscissa and the rainfall frequency as an ordinate, and positioning the length of the rainfall frequency change curve And a curve length/>, above the set rainfall frequency；
Extracting accumulated rainfall from the meteorological parameters, extracting the gradient, the material and the area of the roof from the structure related data, and further evaluating through a green-Amsterm formula and a hydraulics model to obtain rainfall runoff loss of each drop rain day, wherein the rainfall runoff loss is taken as the allowable water storage capacity of each drop rain day and is recorded as，/>Number of rainfall day is shown,/>；
The accumulated rainfall of each drop rain day is recorded asAnd extracting the capacity/>, of the water guide tank from the structure-related data；
Counting the fullness of water resources of a target area，，/>For the set reference rainfall,/>To round down the sign,/>For the number of rainfall days,/>For a set excess tank capacity value.
4. A green logistics warehouse optimization system of claim 1, wherein: the analyzing the wind interference trend degree of the target area comprises the following steps:
extracting maximum wind speed from meteorological information of each monitoring day, and further screening maximum value Meanwhile, the maximum wind speed of each monitoring day is subjected to mean value calculation, and the calculation result is recorded as/>；
The maximum wind speed and the set interference wind speed of each monitoring dayComparing, to be greater than or equal to the set disturbance wind speed/>Is recorded as wind speed interference days;
Comparing the wind speed interference days to obtain the number of interval days among the wind speed interference days, further calculating the variance of the interval days, and recording the calculation result as And will/>As wind speed disturbance duration/>；
Statistics of wind interference tendencies of target areas，/>，/>For a set reference wind speed difference,/>To set the reference wind speed disturbance duration.
5. A green logistics warehouse optimization system of claim 1, wherein: the statistical analysis of the deviation degree of the growth temperature of the green plants comprises the following steps:
Marking the actual temperature interval of each monitoring day and the suitable growth temperature interval of each analysis green plant on a number axis respectively;
Extracting the length of the marked area of each monitoring day and marking the length as And extracting the length of the overlapping marking area corresponding to each monitoring day and each analysis green plant, and marking as/>，/>Indicates the monitoring day number,/>；
Counting the deviation degree of the growth temperature of each analysis green plant，/>，/>Indicating the number of monitoring days.
6. A green logistics warehouse optimization system of claim 1, wherein: the green plant ecological regulation power consumption of each monitoring day is predicted, including:
The accumulated rainfall of each monitoring day and the set green plant bearing rainfall are combined By contrast, if the accumulated rainfall on a certain monitoring day is greater than/>The cumulative rainfall on this monitoring day is denoted as/>；
Extracting roof height from the structural correlation dataThe area and gradient of the roof corresponding to the target logistics warehouse are respectively recorded as/>And/>And will/>The green plant ecology used as the drainage day is used for regulating the power consumption and is recorded as/>，/>The unit water discharge amount under the set unit height is correspondingly consumed electric energy,Roof area and roof slope of the set reference respectively;
If the accumulated rainfall on a certain monitoring day is less than or equal to And is greater than or equal to the set proper water supplementing amount/>, for green plantingTaking 0 as the green plant ecological regulation power consumption of the monitoring day;
if the accumulated rainfall on a certain monitoring day is smaller than The cumulative rainfall on this monitoring day is denoted as/>And then willThe green plant ecology used as the water supplementing day is used for regulating the power consumption and is recorded as/>，/>Corresponding consumption of electric energy for unit water supplementing quantity under set unit height,/>。
7. A green logistics warehouse optimization system of claim 1, wherein: the prediction is monitored the green power consumption of adjusting the temperature of planting of saving in the roof in each day, include:
Will be As the growth fitness of each analysis green plant, taking the analysis green plant with the maximum growth fitness as a target planting green plant;
Extracting a proper internal temperature interval of a target logistics warehouse from an information base, and extracting the predicted heat insulation temperature of the green plant of the target planting ；
Extracting the temperature of each monitoring time point from the internal environment information of each monitoring day, judging the high-temperature regulation and control duration, the low-temperature regulation and control duration, the regulation and control high-temperature value and the regulation and control low-temperature value of each monitoring day according to a temperature regulation judgment rule, and respectively marking as、/>、And/>；
Extracting the volume of the target logistics warehouse from the structure related data, which is recorded asStatistics of roof green plants in each monitoring day to save high-temperature regulation and control power consumption/>，/>，/>The power consumption is regulated and controlled according to the unit regulation and control time length corresponding to the unit regulation and control temperature value under the set unit regulation and control space volume;
According to The statistical mode of the system is similar to that of the system for monitoring the roof green planting in each day to save low-temperature regulation and control power consumption/>Will/>The method is used for saving temperature and power consumption of roof green plants in each monitoring day.
8. A green logistics warehouse optimization system of claim 1, wherein: the confirmation of the adaptive energy-saving optimization scheme comprises the following steps:
If it is Green plant layout is used as an adaptive energy-saving optimization scheme,/>Optimizing an index for the set reference energy conservation;
If it is And taking the currently set energy-saving optimization scheme as an adaptive energy-saving optimization scheme.
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