CN116050716A - Intelligent park management control platform based on Internet - Google Patents
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Abstract
The invention relates to the technical field of campus management control, in particular to an internet-based intelligent campus management control platform, which comprises a campus on-line management control platform, a campus working condition analysis unit, a campus hardware monitoring unit, a campus sales volume processing unit, a processor and a campus management control regulating unit.
Description
Technical Field
The invention relates to the technical field of park management control, in particular to an intelligent park management control platform based on the Internet.
Background
Modern industrial parks, apart from the need to have basic production capacity, are increasingly concerned with the operational efficiency of the park; the construction of the intelligent park is based on cooperation, including cooperation between facilities and people, cooperation between people and cooperation between facilities.
At present, for management control of an intelligent park, related data in the park is manually input into a network platform through a manager, the data is calculated and summarized through the manager, the analysis is considered according to the input data and results, the efficiency is low, the collected data cannot be automatically subjected to association analysis, the accuracy of a data assistant is improved, and meanwhile, the value of the association analysis cannot be comprehensively calculated, so that the intelligent analysis management of the park is automatically performed.
For this reason we propose an internet-based intelligent park management control platform.
Disclosure of Invention
The invention aims to provide an intelligent park management control platform based on the Internet, which is used for acquiring and acquiring data of staff, operation equipment and overall marketing in a park, classifying and analyzing the acquired data, calculating and processing numerical signals of the staff, the equipment and the marketing, converting the calculated numerical values into the signals and the data values, enabling a manager to intuitively know the management condition in the park, facilitating the park manager to adjust the condition in the park in time, carrying out overall numerical conversion and calculation according to the specific conditions of the staff, the equipment and the marketing, judging whether the staff, the equipment and the marketing in the park need to be adjusted according to the final results of the numerical conversion and the calculation, facilitating reasonable management of the park, and improving the management efficiency in the park.
The aim of the invention can be achieved by the following technical scheme: the intelligent park management control platform based on the Internet comprises a park on-line management control platform, a park working condition analysis unit, a park hardware monitoring unit, a park sales volume processing unit, a processor and a park management control and adjustment unit;
the processor generates a personnel division signaling and transmits the personnel division signaling to the park working condition analysis unit, the park working condition analysis unit is used for collecting relevant state data of workers in the park, and working condition processing operation of a work is carried out according to the collected relevant state data, so that a working condition numerical value set is obtained, and the working condition numerical value set comprises a working condition normal value and a working condition abnormal value;
the processor generates a hard monitoring signal and transmits the hard monitoring signal to the park hardware monitoring unit, the data extraction is carried out on the operation condition of each production device in the park through the park hardware monitoring unit, and the hard monitoring analysis operation is carried out according to the operation condition of the data extraction production device, so as to obtain a device evaluation array, wherein the device evaluation array comprises a device normal value and a device abnormal value;
the processor generates a sales signal and transmits the sales signal to the park sales volume processing unit, extracts product sales data produced by equipment in the park through the park sales volume processing unit, and performs sales calculation operation according to the extracted product sales volume data to obtain a sales signal group, wherein the sales signal group comprises sales abnormal signals, sales normal signals and production abnormal signals;
the processor generates an adjusting signal and transmits the adjusting signal to the park management and control adjusting unit, the data results of working condition processing, hard supervision analysis and selling production calculation are extracted by the park management and control adjusting unit, adjusting processing operation is carried out according to the extracted values, and the adjusting signal is obtained and sent to the communication terminal of park manager.
Further, the specific operation process of the working condition processing operation is as follows:
the number of workers in a park in a period of time is calibrated as i, the value of i is a positive integer, the corresponding attendance time length of each worker is selected, and the corresponding attendance time length is respectively matched with the corresponding attendance standard to obtain a qualified signal, a late signal, an early-return signal, a normal signal and an overtime signal;
identifying and counting the times of occurrence of qualified signals, late signals, early-back signals, normal signals and overtime signals, judging that the attendance is unqualified twice if the late signals and the early-back signals occur at the same time and the time corresponding to the late signals and the early-back signals is the same day, judging that the attendance is qualified once if the qualified signals and the normal signals occur at the same time and the time corresponding to the qualified signals and the normal signals is one day, judging that the attendance is overtime once if the overtime signals are identified, calibrating the times of occurrence of the attendance unqualified as the times of the attendance unqualified times, calibrating the times of occurrence of the attendance overtime as the times of the attendance overtime;
marking the unqualified times of attendance as Kb i Marking the qualified number of times of checking in as Kh i Marking the attendance and shift times as Kj i According to the calculation formula:
work staff attendance rate is calculated
Hc is a deviation adjusting factor of the attendance rate, and i is a positive integer;
the working years of staff in the park are marked as working age data, the times of the staff in the park when serious mistakes occur in working are marked as error time data, and working conditions of the staff are processed together with the working age data and the error time data to obtain working condition abnormal values and working condition normal values.
Further, the specific process of matching the corresponding attendance time length with the corresponding attendance standard respectively comprises the following steps:
marking the starting time point of the attendance duration of the staff as A1, marking the starting time point in the attendance standard as B1, generating a qualified signal when A1 is more than or equal to B1, and generating a late signal when A1 is less than B1;
marking the ending time point of the attendance duration of the staff as A2, marking the ending time point in the attendance standard as B2, generating early-return signals when A2 is more than or equal to B2, and generating normal signals when A2 is less than B2;
and when the end time point of the attendance duration of the staff is greater than or equal to the overtime end time point in the attendance standard, generating an overtime signal.
Further, the specific processing procedure of the working condition processing is as follows:
carrying out work evaluation calculation on work age data and error time data together with the attendance rate of a worker, wherein the worker evaluation value= (work age data, work age weight coefficient, error time data, error time weight coefficient, attendance rate weight coefficient) evaluates the deviation regulating factor;
calculating the average value of the evaluation values of each employee, calculating an employee evaluation average value, respectively calculating the difference value of the employee evaluation average value and the corresponding employee evaluation value, calculating a plurality of employee evaluation difference values, calculating the average value of the employee evaluation difference values by carrying out average value calculation on the plurality of employee evaluation difference values, and substituting the employee evaluation average value and the employee evaluation average difference value into a calculation formula: the method comprises the steps of (1) comparing evaluation values of a plurality of staff with staff standard evaluation values, wherein staff standard evaluation values = staff evaluation average value ± (staff evaluation average value × evaluation conversion coefficient), generating a working condition disqualification signal when the evaluation values of the staff are smaller than the staff standard evaluation values, and generating a working condition qualification signal when the evaluation values of the staff are greater than or equal to the staff standard evaluation values;
calculating the number of times of occurrence of the working condition disqualification signals and the number of occurrence of the working condition qualification signals with the total staff of the park, calculating the ratio of the failed working condition, comparing the ratio of the failed working condition with a working condition ratio threshold, and when the ratio of the failed working condition is greater than or equal to the working condition ratio threshold, calibrating the failed working condition as a working condition abnormal state, selecting a working condition abnormal value, wherein the selection of the working condition abnormal value is as follows: and when the ratio of the failed working condition to the working condition is smaller than the threshold value of the working condition ratio, calibrating the ratio as a working condition normal state, selecting a working condition normal value, and selecting the working condition normal value as follows: the difference between the duty ratio of the failed condition and the duty ratio threshold.
Further, the specific operation process of the hard monitoring analysis operation is as follows:
collecting corresponding equipment data, setup and change data, setup and maintenance data, time-of-operation data and efficiency data in a period of time;
selecting maintenance data and time-of-operation data corresponding to the equipment data, summing the time-of-operation data, calculating the total duration of the time-of-operation data, calibrating the total duration as a total operation value, calculating the maintenance frequency of the total operation value and the maintenance data, and calculating a maintenance frequency value;
and carrying out replacement processing on the set-up data and the total operation value of the same equipment to obtain the normal value and the abnormal value of the equipment.
Further, the specific process of the replacement treatment is as follows:
calculating a set change mean value of set change data of the same equipment, selecting a total operation value corresponding to the same equipment when the set change is performed, calibrating the total operation value as a life duration, calculating the mean value of the life duration corresponding to a plurality of times of set change data of the same equipment, calculating the life mean value, calculating the difference value of the life mean value and a plurality of corresponding life durations, calculating a plurality of life difference values, calculating the mean value of a plurality of life difference values, and calculating the life average difference value;
according to the calculation formula: the equipment evaluation value = [ maintenance frequency value = [ conversion weight coefficient of maintenance frequency value + set average value + ] conversion weight coefficient of set average value + (life average value ± life average difference value) = life conversion weight coefficient + efficiency data × conversion weight coefficient of efficiency data ] = conversion deviation correction factor, calculate equipment evaluation value, calculate the equipment evaluation average value with the equipment evaluation value of the same equipment, compare the equipment evaluation average value with the equipment evaluation threshold, when the equipment evaluation average value is greater than or equal to the equipment evaluation threshold, mark it as equipment normal state, generate equipment normal signal, and select equipment normal value, the selection of equipment normal value is: when the equipment evaluation mean value is smaller than the equipment evaluation threshold value, calibrating the equipment evaluation mean value into an equipment abnormal state, generating an equipment abnormal signal, selecting an equipment abnormal value, and selecting the equipment abnormal value as follows: the difference between the device evaluation mean and the device evaluation threshold.
Further, the specific operation process of the sales product calculating operation is as follows:
collecting yield data, sales data and preset data in a period of time;
calculating the difference between the output data and the sales data, calculating the difference of the production and sales, marking the difference of the production and sales with positive and negative values, calibrating the difference of the production and sales to be a sales shortage when the difference of the production and sales is greater than zero, generating a sales shortage signal, calibrating the difference of the production and sales to be a sales good when the difference of the production and sales is equal to zero, generating a positive signal, and calibrating the difference of the production and sales to be a production shortage when the difference of the production and sales is less than zero;
comparing and analyzing the under-sales signal, the positive-production signal and the difference-production signal with preset data, wherein the method specifically comprises the following steps:
when the sales shortage signal appears and the preset data is larger than zero, a sales abnormal signal is generated, when the production positive signal appears and the preset data is larger than zero, a production sales normal signal is generated, and when the production difference signal appears and the preset data is larger than zero, a production abnormal signal is generated.
Further, the specific operation procedure of the adjustment treatment operation is as follows:
sequentially assigning sales abnormality signals, sales production abnormality signals and production abnormality signals to the values X1, X2 and X3, and uniformly marking the values of X1, X2 and X3 as XH c ，c=1，2，3；
Extracting abnormal values of working conditions and normal values of working conditions and uniformly marking the abnormal values as Gk e E=1, 2; device normal values and device outliers are extracted and collectively labeled Sk r ，r=1，2；
According to the calculation formula:
calculating a park evaluation value
β1 is expressed as a weight of an abnormal value or normal value of the working conditionThe weight coefficient, beta 2 is expressed as a weight coefficient of a normal value of equipment or an abnormal value of equipment, beta 3 is expressed as a weight coefficient of assignment of a sales abnormality signal, a production sales abnormality signal or a production abnormality signal, and glc is expressed as a conversion deviation correction factor of park evaluation;
park evaluation value
Comparing with threshold M1, when ∈1>
If M1 is not less than the standard, determining that management in the campus meets the requirement standard, and when +.>
And when the management value is less than M1, judging that the management in the park does not meet the standard, and generating a management regulation signal.
The invention has the beneficial effects that:
according to the invention, the personnel, the equipment and the marketing in the park are subjected to data acquisition, and the acquired data are subjected to classification analysis, so that the personnel, the equipment and the marketing are subjected to numerical signal calculation, and the calculated numerical value is subjected to signal and data value conversion, so that management personnel can more intuitively know the management condition in the park, the management personnel in the park can conveniently and timely adjust the condition in the park, the integral numerical conversion and calculation are performed according to the specific conditions of the personnel, the equipment and the marketing, and whether the personnel, the equipment and the marketing in the park need to be adjusted or not is judged according to the final results of the numerical conversion and the calculation, thereby being convenient for reasonably managing the park and improving the management efficiency in the park.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram 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 invention discloses an intelligent park management control platform based on internet, which comprises a park on-line management control platform, a park working condition analysis unit, a park hardware monitoring unit, a park sales volume processing unit, a processor and a park management control and adjustment unit;
the on-line management and control platform is used for acquiring and collecting data in the park, and comprehensively analyzing and processing the data according to the acquired data so as to judge the condition of the park, thereby carrying out management, control and adjustment on the park;
the processor is respectively in communication connection with the park working condition analysis unit, the park hardware monitoring unit, the park sales volume processing unit and the park management and control regulating unit, and the park working condition analysis unit, the park hardware monitoring unit, the park sales volume processing unit, the processor and the park management and control regulating unit are all arranged in the park management and control platform;
the processor generates a staff-division signaling and transmits the staff-division signaling to the campus working condition analysis unit, the campus working condition analysis unit carries out working condition processing operation on relevant state data of staff in the campus according to the staff-division signaling, and the specific operation process of the working condition processing operation is as follows:
the number of workers in a park in a period of time is calibrated as i, the value of i is a positive integer, the attendance time length corresponding to each worker is selected, and the corresponding attendance time length is respectively matched with the corresponding attendance standard, specifically:
judging that the attendance is qualified when the starting time point of the attendance duration of the staff is smaller than or equal to the starting time point in the attendance standard, generating a qualified signal, judging that the attendance is wrong when the starting time point of the attendance duration of the staff is larger than the starting time point in the attendance standard, generating a late signal, judging that the attendance is abnormal when the ending time point of the attendance duration of the staff is smaller than the ending time point in the attendance standard, generating an early-return signal, judging that the attendance is normal when the ending time point of the attendance duration of the staff is larger than or equal to the ending time point of the overtime in the attendance standard, generating a normal signal, judging that the staff is overtime when the ending time point of the attendance duration of the staff is larger than or equal to the overtime ending time point in the attendance standard, and generating an overtime signal, wherein the early-return signal and the overtime signal do not appear simultaneously;
the method comprises the steps of extracting an internal qualified signal, a late signal, an early-return signal, a normal signal and an overtime signal according to the time length of checking in, identifying, counting the times of occurrence of the qualified signal, the late signal, the early-return signal, the normal signal and the overtime signal, judging that the two times of checking in are unqualified if the late signal and the early-return signal occur simultaneously in a day, judging that the one time of checking in is qualified if the qualified signal and the normal signal occur simultaneously in the day, judging that the one time of checking in is overtime if the overtime signal is identified in the day, calibrating the times of occurrence of the unqualified checking in as the times of checking in, calibrating the times of occurrence of the qualified checking in as the times of checking in, and calibrating the times of occurrence of the overtime as the times of checking in and overtime;
substituting the unqualified times of checking in, the qualified times of checking in and the overtime times of checking in into a calculation formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
expressed as attendance rate, kh of staff i Expressed as the number of pass of attendance of the corresponding employee, kb i Expressed as the number of attendance failures of the corresponding employee, kj i The method comprises the steps that the attendance overtime times of corresponding staff are represented, u1 is represented as a conversion coefficient of the attendance overtime of the corresponding staff, hc is represented as a deviation adjusting factor of the attendance rate, and the value of i is a positive integer;
the working years of staff in a park are marked as working age data, the times of serious mistakes of the staff in the park are marked as error time data, the working age data and the error time data are evaluated and calculated together with the attendance rate of the staff, and a staff evaluation value= (working age data weight coefficient + error time data error time weight coefficient + attendance rate weight coefficient) evaluates a deviation regulating factor;
extracting the evaluation value of each employee, carrying out average calculation on the evaluation value of each employee, calculating an employee evaluation average value, carrying out difference calculation on the employee evaluation average value and the corresponding employee evaluation value respectively, calculating a plurality of employee evaluation differences, carrying out average calculation on the plurality of employee evaluation differences, calculating an employee evaluation average difference value, and substituting the employee evaluation average value and the employee evaluation average difference value into a calculation formula: the method comprises the steps of (1) comparing evaluation values of a plurality of staff with staff standard evaluation values, wherein when the evaluation values of the staff are smaller than the staff standard evaluation values, judging that the evaluation of the staff is low, working condition data of the staff are poor, generating a working condition unqualified signal, and when the evaluation values of the staff are larger than or equal to the staff standard evaluation values, judging that the evaluation of the staff is high, working condition data of the staff are good, and generating a working condition qualified signal;
calculating the unqualified ratio of the times of occurrence of the unqualified signals of the working condition and the times of occurrence of the qualified signals of the working condition to the total staff of the park, calculating the ratio of the unqualified rate of the working condition, comparing the ratio of the unqualified rate of the working condition with the threshold value of the ratio of the working condition, judging the abnormal state of the working condition when the ratio of the unqualified rate of the working condition is larger than or equal to the threshold value of the ratio of the working condition, selecting the abnormal value of the working condition, and selecting the abnormal value of the working condition as follows: and when the ratio of the failed working condition to the working condition is smaller than the threshold value of the working condition ratio, judging that the working condition is in a normal state, selecting a normal value of the working condition, and selecting the normal value of the working condition as follows: the difference value of the duty ratio of the working condition failure and the duty ratio threshold value refers to the first day of the last year to the last day of the last year;
the processor generates a hard monitoring signaling and transmits the hard monitoring signaling to the campus hardware monitoring unit, the campus hardware monitoring unit performs hard monitoring analysis operation on the running condition of equipment in the campus according to the hard monitoring signaling, and the specific operation process of the hard monitoring analysis operation is as follows:
marking each device as device data in a period of time, marking the device data as O, marking the value of O as a positive integer, marking the replacement times of the device in the period of time as set replacement data, marking the maintenance times of the device in the period of time as set maintenance data, marking the operation time of each device in each day in the period of time as time of operation data, and marking the production efficiency of the device in the period of time as efficiency data;
selecting maintenance data and time-of-operation data corresponding to the equipment data, summing the time-of-operation data, calculating the total duration of the time-of-operation data, calibrating the total duration as a total operation value, calculating the maintenance frequency of the total operation value and the maintenance data, and calculating a maintenance frequency value;
selecting corresponding equipment setting and changing data according to the equipment data, and carrying out the changing processing of the setting and changing data of the same equipment and the total operation value, wherein the specific process of the changing processing is as follows: calculating a set change mean value of set change data of the same equipment, selecting a total operation value corresponding to the same equipment when the set change is performed, calibrating the total operation value as a life duration, calculating the mean value of the life duration corresponding to a plurality of times of set change data of the same equipment, calculating the life mean value, calculating the difference value of the life mean value and a plurality of corresponding life durations, calculating a plurality of life difference values, calculating the mean value of a plurality of life difference values, and calculating the life average difference value;
substituting the maintenance frequency value, the set average value, the service life average value and the efficiency data into a calculation formula: the equipment evaluation value= [ maintenance frequency value x conversion weight coefficient of maintenance frequency value + set average value x conversion weight coefficient of set average value + (life average value ± life average difference value) xlife conversion weight coefficient of life conversion weight coefficient + efficiency data x conversion weight coefficient of efficiency data ] xconversion deviation correction factor, the equipment evaluation value of the same equipment is subjected to mean value calculation, the equipment evaluation mean value is calculated, the equipment evaluation mean value is compared with the equipment evaluation threshold value, when the equipment evaluation mean value is greater than or equal to the equipment evaluation threshold value, the equipment is judged to be in a normal state, an equipment normal signal is generated, the equipment normal value is selected, and the equipment normal value is selected as follows: and when the equipment evaluation mean value is smaller than the equipment evaluation threshold value, judging that the equipment is in an abnormal state, generating an equipment abnormal signal, selecting an equipment abnormal value, wherein the selection of the equipment abnormal value is as follows: the difference between the device evaluation mean and the device evaluation threshold is defined as a period of time from the first day of the last year to the last day of the last year;
the processor generates a sales signal and transmits the sales signal to the park sales volume processing unit, and the park sales volume processing unit performs sales calculation operation on production sales conditions in the park according to the sales signal, wherein the specific operation process of the sales calculation operation is as follows:
the production of the products in the park in a period of time is calibrated to be production data, the sales condition of the products in the park in a period of time is calibrated to be sales data, the pre-order of the products in the park in a period of time is calibrated to be preset data, and a period of time refers to the first day of the last year to the last day of the last year;
calculating the difference between the output data and the sales data, calculating the difference of the production and sales, marking the difference of the production and sales with positive and negative values, calibrating the difference of the production and sales to be a sales shortage when the difference of the production and sales is greater than zero, generating a sales shortage signal, calibrating the difference of the production and sales to be a sales good when the difference of the production and sales is equal to zero, generating a positive signal, and calibrating the difference of the production and sales to be a production shortage when the difference of the production and sales is less than zero;
extracting the under-sales signal, the positive-production signal and the difference-production signal, and comparing and analyzing the under-sales signal, the positive-production signal and the difference-production signal with preset data, wherein the specific steps are as follows:
when the sales undersignal appears and the preset data is larger than zero, the sales condition is judged to be abnormal, a sales abnormal signal is generated, when the sales positive signal appears and the preset data is larger than zero, the sales production condition is judged to be normal, a sales normal signal is generated, when the sales difference signal appears and the preset data is larger than zero, the production condition is judged to be seriously lacking, and a production abnormal signal is generated;
the processor generates an adjusting signaling and transmits the adjusting signaling to the park management and control adjusting unit, the park management and control adjusting unit adjusts and processes the working condition abnormal value, the working condition normal value, the equipment abnormal value, the sales abnormal signal, the production sales normal signal and the production abnormal signal according to the adjusting signaling, and the specific operation process of the adjusting and processing operation is as follows:
extracting sales abnormal signals, production and marketing normal signals and production abnormal signals, and sequentially assigning values, wherein the method specifically comprises the following steps: sequentially assigning sales abnormality signals, sales production abnormality signals and production abnormality signals to the values X1, X2 and X3, and uniformly marking the values of X1, X2 and X3 as XH c C=1, 2,3, when the value of c is 1, then XH c Represented by the value X1;
extracting abnormal values of working conditions and normal values of working conditions and uniformly marking the abnormal values as Gk e E=1, 2, when e takes on a value of 1, gk e Expressed as abnormal values of working conditions, and the normal values of equipment and the abnormal values of the equipment are extracted and are uniformly marked as Sk r R=1, 2, when r takes on a value of 1, sk r Expressed as a normal value of the device;
substituting the abnormal value of the working condition, the normal value of the equipment, the abnormal signal of sales, the normal signal of production and marketing and the assignment of the abnormal signal of production into a calculation formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
expressed as park evaluation value, gk e Expressed as an operating condition abnormal value or an operating condition normal value, and beta 1 is expressed as a weight coefficient of the operating condition abnormal value or the operating condition normal value, sk r Expressed as normal or abnormal values of the device, β2 is expressed as a weight coefficient of normal or abnormal values of the device, XH c Assigning a value represented as a sales abnormality signal, a production sales abnormality signal or a production abnormality signal, β3 represents a weight coefficient assigned as a sales abnormality signal, a production sales abnormality signal or a production abnormality signal, glc represents a conversion deviation correction factor for a campus evaluation;
and comparing the park evaluation value with a threshold value, when the park evaluation value is greater than or equal to the threshold value, judging that the management in the park meets the requirement standard, and when the park evaluation value is less than the threshold value, judging that the management in the park does not meet the standard, generating a management adjusting signal and sending the management adjusting signal to a communication terminal of a park manager.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (8)
1. The intelligent park management control platform based on the Internet is characterized by comprising a park on-line management control platform, a park working condition analysis unit, a park hardware monitoring unit, a park sales volume processing unit, a processor and a park management control and adjustment unit;
the processor generates a personnel division signaling and transmits the personnel division signaling to the park working condition analysis unit, the park working condition analysis unit is used for collecting relevant state data of workers in the park, and working condition processing operation of a work is carried out according to the collected relevant state data, so that a working condition numerical value set is obtained, and the working condition numerical value set comprises a working condition normal value and a working condition abnormal value;
the processor generates a hard monitoring signal and transmits the hard monitoring signal to the park hardware monitoring unit, the data extraction is carried out on the operation condition of each production device in the park through the park hardware monitoring unit, and the hard monitoring analysis operation is carried out according to the operation condition of the data extraction production device, so as to obtain a device evaluation array, wherein the device evaluation array comprises a device normal value and a device abnormal value;
the processor generates a sales signal and transmits the sales signal to the park sales volume processing unit, extracts product sales data produced by equipment in the park through the park sales volume processing unit, and performs sales calculation operation according to the extracted product sales volume data to obtain a sales signal group, wherein the sales signal group comprises sales abnormal signals, sales normal signals and production abnormal signals;
the processor generates an adjusting signal and transmits the adjusting signal to the park management and control adjusting unit, the data results of working condition processing, hard supervision analysis and selling production calculation are extracted by the park management and control adjusting unit, adjusting processing operation is carried out according to the extracted values, and the adjusting signal is obtained and sent to the communication terminal of park manager.
2. The intelligent park management control platform based on the internet of claim 1, wherein the specific operation process of the working condition processing operation is:
the number of workers in a park in a period of time is calibrated as i, the value of i is a positive integer, the corresponding attendance time length of each worker is selected, and the corresponding attendance time length is respectively matched with the corresponding attendance standard to obtain a qualified signal, a late signal, an early-return signal, a normal signal and an overtime signal;
identifying and counting the times of occurrence of qualified signals, late signals, early-back signals, normal signals and overtime signals, judging that the attendance is unqualified twice if the late signals and the early-back signals occur at the same time and the time corresponding to the late signals and the early-back signals is the same day, judging that the attendance is qualified once if the qualified signals and the normal signals occur at the same time and the time corresponding to the qualified signals and the normal signals is one day, judging that the attendance is overtime once if the overtime signals are identified, calibrating the times of occurrence of the attendance unqualified as the times of the attendance unqualified times, calibrating the times of occurrence of the attendance overtime as the times of the attendance overtime;
marking the unqualified times of attendance as Kb i Marking the qualified number of times of checking in as Kh i Marking the attendance and shift times as Kj i According to the calculation formula:
work staff attendance rate is calculated
Hc is a deviation adjusting factor of the attendance rate, and i is a positive integer;
the working years of staff in the park are marked as working age data, the times of the staff in the park when serious mistakes occur in working are marked as error time data, and working conditions of the staff are processed together with the working age data and the error time data to obtain working condition abnormal values and working condition normal values.
3. The intelligent park management control platform based on the internet of claim 2, wherein the specific process of matching the corresponding attendance duration with the corresponding attendance standard respectively is as follows:
marking the starting time point of the attendance duration of the staff as A1, marking the starting time point in the attendance standard as B1, generating a qualified signal when A1 is more than or equal to B1, and generating a late signal when A1 is less than B1;
marking the ending time point of the attendance duration of the staff as A2, marking the ending time point in the attendance standard as B2, generating early-return signals when A2 is more than or equal to B2, and generating normal signals when A2 is less than B2;
and when the end time point of the attendance duration of the staff is greater than or equal to the overtime end time point in the attendance standard, generating an overtime signal.
4. The intelligent park management control platform based on the internet of claim 2, wherein the specific processing procedure of the working condition processing is as follows:
carrying out work evaluation calculation on work age data and error time data together with the attendance rate of a worker, wherein the worker evaluation value= (work age data, work age weight coefficient, error time data, error time weight coefficient, attendance rate weight coefficient) evaluates the deviation regulating factor;
calculating the average value of the evaluation values of each employee, calculating an employee evaluation average value, respectively calculating the difference value of the employee evaluation average value and the corresponding employee evaluation value, calculating a plurality of employee evaluation difference values, calculating the average value of the employee evaluation difference values by carrying out average value calculation on the plurality of employee evaluation difference values, and substituting the employee evaluation average value and the employee evaluation average difference value into a calculation formula: the method comprises the steps of (1) comparing evaluation values of a plurality of staff with staff standard evaluation values, wherein staff standard evaluation values = staff evaluation average value ± (staff evaluation average value × evaluation conversion coefficient), generating a working condition disqualification signal when the evaluation values of the staff are smaller than the staff standard evaluation values, and generating a working condition qualification signal when the evaluation values of the staff are greater than or equal to the staff standard evaluation values;
calculating the number of times of occurrence of the working condition disqualification signals and the number of occurrence of the working condition qualification signals with the total staff of the park, calculating the ratio of the failed working condition, comparing the ratio of the failed working condition with a working condition ratio threshold, and when the ratio of the failed working condition is greater than or equal to the working condition ratio threshold, calibrating the failed working condition as a working condition abnormal state, selecting a working condition abnormal value, wherein the selection of the working condition abnormal value is as follows: and when the ratio of the failed working condition to the working condition is smaller than the threshold value of the working condition ratio, calibrating the ratio as a working condition normal state, selecting a working condition normal value, and selecting the working condition normal value as follows: the difference between the duty ratio of the failed condition and the duty ratio threshold.
5. The intelligent campus management control platform based on internet of claim 1, wherein the specific operation process of the hard supervision analysis operation is:
collecting corresponding equipment data, setup and change data, setup and maintenance data, time-of-operation data and efficiency data in a period of time;
selecting maintenance data and time-of-operation data corresponding to the equipment data, summing the time-of-operation data, calculating the total duration of the time-of-operation data, calibrating the total duration as a total operation value, calculating the maintenance frequency of the total operation value and the maintenance data, and calculating a maintenance frequency value;
and carrying out replacement processing on the set-up data and the total operation value of the same equipment to obtain the normal value and the abnormal value of the equipment.
6. The internet-based intelligent campus management control platform of claim 5, wherein the specific process of the replacement process is:
calculating a set change mean value of set change data of the same equipment, selecting a total operation value corresponding to the same equipment when the set change is performed, calibrating the total operation value as a life duration, calculating the mean value of the life duration corresponding to a plurality of times of set change data of the same equipment, calculating the life mean value, calculating the difference value of the life mean value and a plurality of corresponding life durations, calculating a plurality of life difference values, calculating the mean value of a plurality of life difference values, and calculating the life average difference value;
according to the calculation formula: the equipment evaluation value = [ maintenance frequency value = [ conversion weight coefficient of maintenance frequency value + set average value + ] conversion weight coefficient of set average value + (life average value ± life average difference value) = life conversion weight coefficient + efficiency data × conversion weight coefficient of efficiency data ] = conversion deviation correction factor, calculate equipment evaluation value, calculate the equipment evaluation average value with the equipment evaluation value of the same equipment, compare the equipment evaluation average value with the equipment evaluation threshold, when the equipment evaluation average value is greater than or equal to the equipment evaluation threshold, mark it as equipment normal state, generate equipment normal signal, and select equipment normal value, the selection of equipment normal value is: when the equipment evaluation mean value is smaller than the equipment evaluation threshold value, calibrating the equipment evaluation mean value into an equipment abnormal state, generating an equipment abnormal signal, selecting an equipment abnormal value, and selecting the equipment abnormal value as follows: the difference between the device evaluation mean and the device evaluation threshold.
7. The internet-based intelligent campus management control platform of claim 1, wherein the specific operation process of the sales computing operation is:
collecting yield data, sales data and preset data in a period of time;
calculating the difference between the output data and the sales data, calculating the difference of the production and sales, marking the difference of the production and sales with positive and negative values, calibrating the difference of the production and sales to be a sales shortage when the difference of the production and sales is greater than zero, generating a sales shortage signal, calibrating the difference of the production and sales to be a sales good when the difference of the production and sales is equal to zero, generating a positive signal, and calibrating the difference of the production and sales to be a production shortage when the difference of the production and sales is less than zero;
comparing and analyzing the under-sales signal, the positive-production signal and the difference-production signal with preset data, wherein the method specifically comprises the following steps:
when the sales shortage signal appears and the preset data is larger than zero, a sales abnormal signal is generated, when the production positive signal appears and the preset data is larger than zero, a production sales normal signal is generated, and when the production difference signal appears and the preset data is larger than zero, a production abnormal signal is generated.
8. The internet-based intelligent campus management control platform of claim 1, wherein the specific operation procedure for adjusting the processing operation is:
sequentially assigning sales abnormality signals, sales production abnormality signals and production abnormality signals to the values X1, X2 and X3, and uniformly marking the values of X1, X2 and X3 as XH c ，c=1，2，3；
Extracting abnormal values of working conditions and normal values of working conditions and uniformly marking the abnormal values as Gk e E=1, 2; device normal values and device outliers are extracted and collectively labeled Sk r ，r=1，2；
According to the calculation formula:
calculating a park evaluation value
β1 is represented as a weight coefficient of an abnormal value of a working condition or a normal value of the working condition, β2 is represented as a weight coefficient of a normal value of equipment or an abnormal value of the equipment, β3 is represented as a weight coefficient of a sales abnormality signal, a production sales abnormality signal or an assignment of a production abnormality signal, and glc is represented as a conversion deviation correction factor of a campus evaluation;
park evaluation value
Comparing with threshold M1, when ∈1>
If M1 is not less than the standard, determining that management in the campus meets the requirement standard, and when +.>
And when the management value is less than M1, judging that the management in the park does not meet the standard, and generating a management regulation signal. />
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