CN113469382A - Production equipment debugging management system based on Internet of things - Google Patents
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Abstract
The invention discloses a production equipment debugging management system based on the Internet of things, which relates to the technical field of production equipment management and comprises an equipment management module, a task release module, a task distribution module, a field database, a personnel management module and a display module; the equipment management module is used for displaying the running state information of the production equipment and uploading and issuing maintenance information of the production equipment; when the production equipment is abnormal and needs to be debugged, the task issuing module is used for issuing debugging tasks of the production equipment corresponding to operators of the production equipment and intelligently sequencing the debugging tasks; the task distribution module is used for sequentially distributing the debugging tasks of the production equipment to corresponding debugging personnel according to the sequencing of the debugging tasks, so that the selected personnel can accurately master the technical parameters of the production equipment at the first time, the debugging period is greatly shortened, the debugging efficiency of the production equipment is improved, and the maximum potential is exerted to improve the production efficiency of individuals and enterprises.
Description
Technical Field
The invention relates to the technical field of production equipment management, in particular to a production equipment debugging management system based on the Internet of things.
Background
Production equipment comprises a blast furnace, a machine tool, a reactor, a dyeing machine and the like, the production equipment is subjected to various complex problems after leaving a factory, a large number of production equipment is distributed all over the country, various problems can occur in the use process of the production equipment due to the factors such as the complexity of engineering machinery products, the technical literacy of operators of the production equipment is uneven, the uncertainty of an operation method and the like, the problems are fed back to a debugging department, and the debugging technicians cannot accurately master the technical parameters of the production equipment at the first time due to the lack of direct effective operation and debugging data reference, so that the debugging cycle time is long, and time and human resources are greatly wasted;
most of the existing production equipment debugging management systems only carry out simple production equipment debugging, and the problem that debugging tasks of production equipment cannot be sequenced according to debugging coefficients of the production equipment and corresponding debugging personnel are distributed to get the debugging tasks of the production equipment exists, so that the maximum potential is brought into play to improve the production efficiency of individuals and enterprises.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a production equipment debugging and managing system based on the Internet of things. The equipment management module is used for displaying the running state information of the production equipment and uploading and issuing the maintenance information of the production equipment; when the production equipment is abnormal and needs to be debugged, an operator corresponding to the production equipment issues debugging tasks of the production equipment and intelligently sorts the debugging tasks, so that the processing of the debugging tasks is more hierarchical and orderly; meanwhile, the task allocation module is used for sequentially allocating the debugging tasks to corresponding debugging personnel according to the sequencing of the debugging tasks, so that the debugging efficiency of the debugging personnel is further improved; after receiving the debugging task, a selected person calls real-time data related to the running state of the corresponding production equipment from the field database, so that the technical parameters of the production equipment can be accurately mastered at the first time, the debugging period is greatly shortened, and the debugging efficiency of the production equipment is improved; thereby exerting the maximum potential to improve the production efficiency of individuals and enterprises.
The purpose of the invention can be realized by the following technical scheme:
a production equipment debugging management system based on the Internet of things comprises a data acquisition module, a server, an equipment management module, a task release module, a task distribution module, a field database, a personnel management module and a display module;
a data acquisition module: the system comprises a server, a server and a client side, wherein the server is used for acquiring manufacturing information of production equipment and sending the manufacturing information of the production equipment to the server for storage through the Internet of things;
a device management module: the system is used for displaying the uploading and issuing of the operation state information of the production equipment and the maintenance information of the production equipment; when the production equipment is abnormal and needs to be debugged, the task issuing module is used for issuing debugging tasks of the production equipment corresponding to operators of the production equipment and intelligently sequencing the debugging tasks;
the task allocation module is used for sequentially allocating the debugging tasks of the production equipment to corresponding debugging personnel according to the sequencing of the debugging tasks, and the specific allocation steps are as follows:
marking the debugging personnel in the idle state as a primary selection personnel, acquiring debugging equipment corresponding to a debugging task, counting the total debugging times of the primary selection personnel aiming at the debugging equipment, and marking the debugging equipment as Cs;
counting all debugging time lengths of the primary selection personnel within one month before the current time of the system and summing to obtain total debugging time length T2; marking the working age of the primary election personnel as N1; setting the age of the primary selected person as N2; acquiring a debugging learning value WX of a primary selection person;
calculating the blending value GP of the primary candidate by using a formula GP ═ WX × d1+ Cs × d2+ N1 × d3)/(T2 × d4) - | N2-35| × d5, wherein d1, d2, d3, d4 and d5 are coefficient factors; selecting the primary selected person with the largest allocation value GP as a selected person, and sending the debugging task to the mobile phone terminal of the selected person;
after receiving the debugging task, a selected person invokes real-time data related to the running state of the corresponding production equipment from the field database, debugs the debugging equipment after the real-time data arrives at the position of the debugging equipment, records the debugging process through the mobile phone terminal, and sends the recorded debugging video to the cloud platform; other debugging personnel access the debugging video of the cloud platform through the mobile phone terminal and watch the debugging video;
calculating the time difference between the debugging finishing time and the debugging starting time to obtain the debugging time length of the selected person, and marking the debugging time length as the auxiliary time length corresponding to the operator of the production equipment;
the personnel management module is used for training and managing operators of the production equipment.
Further, the specific sequencing method of the task issuing module comprises the following steps:
acquiring the release time of the debugging task, and calculating the time difference between the release time and the current time of the system to obtain a release time length T1;
acquiring production equipment corresponding to the debugging task, marking the production equipment as debugging equipment, acquiring manufacturing information of the debugging equipment from the server, and analyzing the manufacturing information to obtain a production coefficient SC of the debugging equipment;
using formulas
Calculating to obtain a debugging coefficient GS of the debugging task, wherein both b1 and b2 are coefficient factors; and sequencing the debugging tasks from large to small according to the debugging coefficient GS.
Further, the manufacturing information of the debugging device is obtained from the server and analyzed, and the specific analysis steps are as follows:
s21: manufacturing information of the debugging equipment is obtained, the frequency of the debugging equipment receiving the product manufacturing orders in one month is marked as P1, and the average order quantity of each product manufacturing order is marked as P2;
marking the profit amount of each product manufacturing order as P3, and marking the average refund speed corresponding to the profit amount as D1; marking the average per product manufacturing order yield as P4;
s22: acquiring the ratio of the actual production capacity of the debugging equipment relative to the theoretical capacity and marking as B1;
the production coefficient SC of the commissioning apparatus is calculated by using the formula SC ═ P1 × a1+ P2 × a2+ P3 × a3+ P4 × a4+ B1 × a5)/(D1 × a6), where a1, a2, a3, a4, a5, and a6 are all coefficient factors.
Further, the method for calculating the debugging learning value WX includes:
collecting the watching record of debugging videos of the primary selection personnel in the cloud platform within one month before the current time of the system;
counting the total times of the debugging video watched by the primary selector and marking as C1, and summing the time lengths of the debugging video watched by the primary selector each time to obtain the total watching time length and marking as C2; calculating the time difference between the latest watching ending time of the primary selection personnel and the current time of the system to obtain a buffer duration HT;
and calculating a debugging learning value WX of the primary candidate by using a formula WX (C1 xq 1+ C2 xq 2)/(HT xq 3), wherein q1, q2 and q3 are all preset proportionality coefficients.
Further, the product yield G1 is further analyzed; the method specifically comprises the following steps:
comparing the product percent of pass G1 with a preset percent of pass threshold, and if the product percent of pass G1 is less than the preset percent of pass threshold, marking the corresponding product percent of pass as the influenced percent of pass; counting the number of times of influencing the qualified rate to be G2;
calculating the difference between the affected qualified rate and a preset qualified rate threshold to obtain a difference value, and summing all the difference values to obtain a total difference value W1;
calculating a difference rate coefficient CW by using a formula CW which is G2 × G1+ W1 × G2, wherein G1 and G2 are coefficient factors;
further, the specific management method of the personnel management module is as follows:
marking the working life of the operator as Ns; counting all auxiliary time lengths of an operator in one month before the current time of the system, summing the auxiliary time lengths to obtain an auxiliary total time length, and marking the auxiliary total time length as FT;
obtaining the daily product qualification rate G1 of the corresponding production equipment of an operator in one month before the current time of the system, further analyzing the product qualification rate G1, and calculating to obtain a difference rate coefficient CW;
calculating a kissing coefficient CZ of the operator by using a formula CZ (FT × g3+ CW × g 4), wherein g3 and g4 are coefficient factors;
setting a plurality of kiss coefficient thresholds, wherein each kiss coefficient threshold corresponds to a preset working year limit range, and determining the corresponding kiss coefficient threshold to be Km according to the working year limit Ns; wherein Km is a preset value;
when CZ is larger than Km, a reminding signal is generated, the technical literacy of an operator and the operation method are unqualified, and the operator needs to perform operation training of corresponding production equipment again;
the personnel management module is used for transmitting a reminding signal and the operation and kiss coefficient CZ to the server, and the server is used for transmitting the operation and kiss coefficient CZ with a timestamp to the display module for real-time display.
Compared with the prior art, the invention has the beneficial effects that:
1. the equipment management module is used for displaying the running state information of the production equipment and uploading and issuing the maintenance information of the production equipment; when the production equipment is abnormal and needs to be debugged, the task issuing module is used for issuing debugging tasks of the production equipment corresponding to an operator of the production equipment, intelligently sequencing the debugging tasks, and sequencing the debugging tasks from large to small according to a debugging coefficient GS, so that the processing of the debugging tasks is more hierarchical and orderly;
2. the task allocation module is used for sequentially allocating debugging tasks to corresponding debugging personnel according to the sequencing of the debugging tasks, marking the debugging personnel in an idle state at present as primary selection personnel, and combining the total debugging times, the total debugging time, the working life, the age and the debugging learning value WX of the primary selection personnel; the allocation value GP of the primary selection personnel is obtained through formula calculation, the primary selection personnel with the largest allocation value GP is selected as the selected personnel, and the debugging efficiency of debugging personnel is further improved; after receiving the debugging task, a selected person calls real-time data related to the running state of the corresponding production equipment from the field database, so that the technical parameters of the production equipment can be accurately mastered at the first time, the debugging period is greatly shortened, and the debugging efficiency of the production equipment is improved; thereby exerting the maximum potential to improve the production efficiency of individuals and enterprises;
3. the personnel management module is used for training and managing operators of the production equipment, the operation technology of the operators is examined by combining the auxiliary total duration of the operators and analyzing the product qualification rate G1, and if the operation coefficient CZ is larger than the corresponding operation coefficient threshold, the operators need to perform operation training of the corresponding production equipment again, so that the technical literacy of the operators is improved, and the operation quality is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of a production equipment debugging management system based on the internet of things.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a production equipment debugging management system based on the internet of things includes a data acquisition module, a server, an equipment management module, a task release module, a task allocation module, a field database, a personnel management module, and a display module;
a data acquisition module: the system comprises a server, a server and a client side, wherein the server is used for acquiring manufacturing information of production equipment and sending the manufacturing information of the production equipment to the server for storage through the Internet of things; the manufacturing information includes frequency data, quantity data, profitability data, and yield; the method comprises the steps that frequency data are expressed as frequency of receiving product manufacturing orders by a production device within a month, quantity data are expressed as order quantity of each average product manufacturing order by the production device within the month, profit data are expressed as profit amount of each average product manufacturing order by the production device within the month and withdrawal speed corresponding to the profit amount, and yield is expressed as qualification rate of each average product manufacturing order by the production device within the month;
a device management module: the system is used for displaying the uploading and issuing of the operation state information of the production equipment and the maintenance information of the production equipment; the device management module includes:
the state monitoring unit is used for acquiring real-time data related to the running state of the production equipment and transmitting the real-time data to the field database for storage;
the operation management unit is used for operating the equipment, and the operation comprises starting, stopping, resetting, maintaining or debugging;
the analysis unit is used for acquiring the ratio of the actual production capacity of the production equipment relative to the theoretical capacity;
the production data management unit is used for counting the yield and the production time of the production equipment and uploading the production equipment yield and the production time to the server;
the shutdown management unit is used for displaying and recording shutdown and alarm information of the production equipment on an interface;
the equipment management module is connected with the task issuing module, when the production equipment is abnormal and needs to be debugged, the task issuing module is used for issuing debugging tasks of the production equipment corresponding to operators of the production equipment and intelligently sequencing the debugging tasks, wherein the debugging tasks comprise names and positions of the production equipment; the specific sorting method comprises the following steps:
s1: acquiring the release time of the debugging task, and calculating the time difference between the release time and the current time of the system to obtain a release time length T1;
s2: acquiring production equipment corresponding to the debugging task, marking the production equipment as debugging equipment, acquiring manufacturing information of the debugging equipment from the server, and analyzing the manufacturing information to obtain a production coefficient SC of the debugging equipment; the specific analysis steps are as follows:
s21: manufacturing information of the debugging equipment is obtained, the frequency of the debugging equipment receiving the product manufacturing orders in one month is marked as P1, and the average order quantity of each product manufacturing order is marked as P2;
marking the profit amount of each product manufacturing order as P3, and marking the average refund speed corresponding to the profit amount as D1, wherein the smaller D1 is, the faster the refund speed is;
marking the average per product manufacturing order yield as P4;
s22: acquiring the ratio of the actual production capacity of the debugging equipment relative to the theoretical capacity and marking as B1; calculating a production coefficient SC of the debugging equipment by using a formula SC ═ P1 × a1+ P2 × a2+ P3 × a3+ P4 × a4+ B1 × a5)/(D1 × a6), wherein a1, a2, a3, a4, a5 and a6 are all coefficient factors;
s3: normalizing the release time and the production coefficient, taking the values, and utilizing a formula
Calculating to obtain a debugging coefficient GS of the debugging task, wherein both b1 and b2 are coefficient factors;
s4: sequencing the debugging tasks according to the debugging coefficient GS from big to small;
the task allocation module is used for allocating debugging tasks of the production equipment to corresponding debugging personnel, and the specific allocation steps are as follows:
the method comprises the following steps: acquiring a debugging task with a first sequence, and distributing debugging personnel for the debugging task, wherein the method specifically comprises the following steps:
v1: marking the debugging personnel in the idle state as a primary selection personnel, acquiring debugging equipment corresponding to a debugging task, counting the total debugging times of the primary selection personnel aiming at the debugging equipment, and marking the debugging equipment as Cs;
counting all debugging time lengths of the primary selection personnel within one month before the current time of the system, summing the debugging time lengths to obtain a total debugging time length, and marking the total debugging time length as T2;
marking the working age of the primary election personnel as N1; setting the age of the primary selected person as N2;
v2: acquiring a debugging learning value WX of a primary selection person; the method specifically comprises the following steps:
v21: collecting the watching record of debugging videos of the primary selection personnel in the cloud platform within one month before the current time of the system; the watching records comprise watching times, watching starting time and watching ending time;
v22: counting the total times of the debugging video watched by the primary selector and marking as C1, and summing the time lengths of the debugging video watched by the primary selector each time to obtain the total watching time length and marking as C2; calculating the time difference between the latest watching ending time of the primary selection personnel and the current time of the system to obtain a buffer duration HT;
v23: calculating a debugging learning value WX of the primary selector by using a formula WX (C1 xq 1+ C2 xq 2)/(HT xq 3), wherein q1, q2 and q3 are all preset proportionality coefficients;
v3: calculating the blending value GP of the primary candidate by using a formula GP ═ WX × d1+ Cs × d2+ N1 × d3)/(T2 × d4) - | N2-35| × d5, wherein d1, d2, d3, d4 and d5 are coefficient factors;
v4: selecting the primary selected person with the largest allocation value GP as a selected person, and sending the debugging task to the mobile phone terminal of the selected person;
step two: acquiring a second sequenced debugging task, and distributing debugging personnel for the second sequenced debugging task in the same way until corresponding debugging personnel are distributed for all debugging tasks;
step three: after receiving the debugging task, a selected person calls real-time data related to the running state of the corresponding production equipment from the field database, so that the technical parameters of the production equipment can be accurately mastered at the first time, the debugging period is greatly shortened, and the debugging efficiency of the production equipment is improved;
after the selected person arrives at the position of the debugging equipment, debugging the debugging equipment, simultaneously recording a debugging process through the mobile phone terminal, and sending a recorded debugging video to the cloud platform; other debugging personnel access the debugging video of the cloud platform through the mobile phone terminal and watch the debugging video;
calculating the time difference between the debugging finishing time and the debugging starting time to obtain the debugging time length of the selected person, and marking the debugging time length as the auxiliary time length of the operator;
in the invention, the task issuing module sequences the debugging tasks of the production equipment from large to small according to the debugging tasks GS and distributes the debugging tasks to corresponding debugging personnel in sequence, so that the processing of the debugging tasks is more hierarchical and orderly, thereby exerting the maximum potential and improving the production efficiency of individuals and enterprises;
meanwhile, the task allocation module can analyze the allocation value of the debugging personnel, and the primary personnel with the maximum allocation value GP is selected as the selected personnel, so that the debugging efficiency of the debugging personnel is further improved;
the personnel management module is used for training and managing operators of the production equipment, and the specific management method comprises the following steps:
SS 1: acquiring an operator corresponding to the production equipment, and marking the working life of the operator as Ns;
counting all auxiliary time lengths of an operator in one month before the current time of the system, summing the auxiliary time lengths to obtain an auxiliary total time length, and marking the auxiliary total time length as FT;
SS 2: obtaining the daily product qualification rate G1 of the corresponding production equipment of an operator in one month before the current time of the system, and further analyzing the product qualification rate G1; the method specifically comprises the following steps:
comparing the product percent of pass G1 with a preset percent of pass threshold, and if the product percent of pass G1 is less than the preset percent of pass threshold, marking the corresponding product percent of pass as the influenced percent of pass; counting the number of times of influencing the qualified rate to be G2;
calculating the difference between the affected qualified rate and a preset qualified rate threshold to obtain a difference value, and summing all the difference values to obtain a total difference value W1;
calculating a difference rate coefficient CW by using a formula CW which is G2 × G1+ W1 × G2, wherein G1 and G2 are coefficient factors;
SS 3: carrying out normalization processing on the auxiliary total duration and the difference rate coefficient and taking the values;
calculating a kissing coefficient CZ of the operator by using a formula CZ (FT × g3+ CW × g 4), wherein the larger the kissing coefficient CZ is, the lower the technical literacy of the operator is, the more operation method problems are shown, and g3 and g4 are coefficient factors;
SS 4: setting a plurality of operation coefficient threshold values and marking the operation coefficient threshold values as Km; m is 1, … …, j; k1 > K2 > … … > Kj; each operation and kiss coefficient threshold corresponds to a preset working age range, which is respectively (k1, k2, … …, (km, km + 1), wherein k1 is more than k2 and more than … … is more than km +1, wherein the longer the working age, the smaller the corresponding operation and kiss coefficient threshold is;
when Ns belongs to the range of (Km, Km +1], the threshold value of the operation and kiss coefficient corresponding to the preset working age limit range is Km;
when CZ is larger than Km, a reminding signal is generated, the technical literacy of an operator and the operation method are unqualified, and the operator needs to perform operation training of corresponding production equipment again;
the personnel management module is used for transmitting the reminding signal and the operation and kiss coefficient CZ to the server, and the server is used for transmitting the operation and kiss coefficient CZ to the display module for real-time display by stamping a timestamp;
the personnel management module is used for training and managing operators of the production equipment, the operation technology of the operators is examined by combining the auxiliary total duration of the operators and analyzing the product qualification rate G1, and if the operation coefficient CZ is larger than the corresponding operation coefficient threshold, the operators need to perform operation training of the corresponding production equipment again, so that the technical literacy of the operators is improved, and the operation quality is improved.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
a production equipment debugging management system based on the Internet of things is characterized in that when the system works, an equipment management module is used for displaying the uploading and the issuing of production equipment running state information and production equipment maintenance information; when the production equipment is abnormal and needs to be debugged, the task issuing module is used for issuing debugging tasks of the production equipment corresponding to an operator of the production equipment, intelligently sequencing the debugging tasks, and sequencing the debugging tasks from large to small according to a debugging coefficient GS, so that the processing of the debugging tasks is more hierarchical and orderly;
the task allocation module is used for sequentially allocating debugging tasks to corresponding debugging personnel according to the sequencing of the debugging tasks, marking the debugging personnel in an idle state at present as primary selection personnel, and combining the total debugging times, the total debugging time, the working life, the age and the debugging learning value WX of the primary selection personnel; the allocation value GP of the primary selection personnel is obtained through formula calculation, the primary selection personnel with the largest allocation value GP is selected as the selected personnel, and the debugging efficiency of debugging personnel is further improved; after receiving the debugging task, a selected person calls real-time data related to the running state of the corresponding production equipment from the field database, so that the technical parameters of the production equipment can be accurately mastered at the first time, the debugging period is greatly shortened, and the debugging efficiency of the production equipment is improved; thereby exerting the maximum potential to improve the production efficiency of individuals and enterprises;
the personnel management module is used for training and managing operators of the production equipment, analyzing the product qualification rate G1 by combining the auxiliary total duration of the operators, checking the operation technology of the operators, and if the operation coefficient CZ is larger than the corresponding operation coefficient threshold value, the operators need to perform operation training of the corresponding production equipment again, so that the technical literacy of the operators is improved, and the operation quality is improved.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A production equipment debugging management system based on the Internet of things is characterized by comprising a data acquisition module, a server, an equipment management module, a task release module, a task distribution module, a field database, a personnel management module and a display module;
a data acquisition module: the system comprises a server, a server and a client side, wherein the server is used for acquiring manufacturing information of production equipment and sending the manufacturing information of the production equipment to the server for storage through the Internet of things;
a device management module: the system is used for displaying the uploading and issuing of the operation state information of the production equipment and the maintenance information of the production equipment; when the production equipment is abnormal and needs to be debugged, the task issuing module is used for issuing debugging tasks of the production equipment corresponding to operators of the production equipment and intelligently sequencing the debugging tasks;
the task allocation module is used for sequentially allocating the debugging tasks of the production equipment to corresponding debugging personnel according to the sequencing of the debugging tasks; after receiving the debugging task, the selected personnel call real-time data related to the running state of the corresponding production equipment from the field database;
after the selected person arrives at the position of the debugging equipment, debugging the debugging equipment, simultaneously recording a debugging process through the mobile phone terminal, and sending a recorded debugging video to the cloud platform; other debugging personnel access the debugging video of the cloud platform through the mobile phone terminal and watch the debugging video;
calculating the time difference between the debugging finishing time and the debugging starting time to obtain the debugging time length of the selected person, and marking the debugging time length as the auxiliary time length corresponding to the operator of the production equipment;
the personnel management module is used for training and managing operators of the production equipment.
2. The production equipment debugging management system based on the Internet of things of claim 1, wherein the specific sequencing method of the task issuing module is as follows:
acquiring the release time of the debugging task, and calculating the time difference between the release time and the current time of the system to obtain a release time length T1;
acquiring production equipment corresponding to the debugging task, marking the production equipment as debugging equipment, acquiring manufacturing information of the debugging equipment from the server, and analyzing the manufacturing information to obtain a production coefficient SC of the debugging equipment;
using formulas
Calculating to obtain a debugging coefficient GS of the debugging task, wherein both b1 and b2 are coefficient factors; and sequencing the debugging tasks from large to small according to the debugging coefficient GS.
3. The production equipment debugging management system based on the internet of things as claimed in claim 2, wherein the manufacturing information of the debugging equipment is obtained from the server and analyzed, and the specific analysis steps are as follows:
s21: manufacturing information of the debugging equipment is obtained, the frequency of the debugging equipment receiving the product manufacturing orders in one month is marked as P1, and the average order quantity of each product manufacturing order is marked as P2;
marking the profit amount of each product manufacturing order as P3, and marking the average refund speed corresponding to the profit amount as D1; marking the average per product manufacturing order yield as P4;
s22: acquiring the ratio of the actual production capacity of the debugging equipment relative to the theoretical capacity and marking as B1;
the production coefficient SC of the commissioning apparatus is calculated by using the formula SC ═ P1 × a1+ P2 × a2+ P3 × a3+ P4 × a4+ B1 × a5)/(D1 × a6), where a1, a2, a3, a4, a5, and a6 are all coefficient factors.
4. The production equipment debugging management system based on the Internet of things of claim 1, wherein the specific allocation steps of the task allocation module are as follows:
marking the debugging personnel in the idle state as primary selection personnel, counting the total debugging times of the primary selection personnel aiming at the corresponding debugging equipment, and marking the debugging personnel as Cs;
counting all debugging time lengths of the primary selection personnel within one month before the current time of the system and summing to obtain total debugging time length T2; marking the working age of the primary election personnel as N1; setting the age of the primary selected person as N2;
acquiring a debugging learning value WX of a primary selection person; calculating the blending value GP of the primary selected person by using a formula GP ═ WX × d1+ Cs × d2+ N1 × d3)/(T2 × d4) - | N2-35| × d 5; and selecting the primary selected person with the largest deployment value GP as the selected person, and sending the debugging task to the mobile phone terminal of the selected person.
5. The Internet of things-based production equipment debugging management system according to claim 4, wherein the debugging learning value WX is calculated by the following method:
collecting the watching record of debugging videos of the primary selection personnel in the cloud platform within one month before the current time of the system;
counting the total times of the debugging video watched by the primary selector and marking as C1, and summing the time lengths of the debugging video watched by the primary selector each time to obtain the total watching time length and marking as C2; calculating the time difference between the latest watching ending time of the primary selection personnel and the current time of the system to obtain a buffer duration HT;
and calculating a debugging learning value WX of the primary candidate by using a formula WX (C1 xq 1+ C2 xq 2)/(HT xq 3), wherein q1, q2 and q3 are all preset proportionality coefficients.
6. The production equipment debugging management system based on the Internet of things of claim 1, wherein the specific management method of the personnel management module is as follows:
marking the working life of the operator as Ns; counting all auxiliary time lengths of an operator in one month before the current time of the system, summing the auxiliary time lengths to obtain an auxiliary total time length, and marking the auxiliary total time length as FT;
obtaining the daily product qualification rate G1 of the corresponding production equipment of an operator in one month before the current time of the system, further analyzing the product qualification rate G1, and calculating to obtain a difference rate coefficient CW;
calculating a kiss coefficient CZ of the operator by using a formula CZ (FT × g3+ CW × g 4);
setting a plurality of kiss coefficient thresholds, wherein each kiss coefficient threshold corresponds to a preset working year limit range, and determining the corresponding kiss coefficient threshold to be Km according to the working year limit Ns; wherein Km is a preset value;
when CZ is larger than Km, a reminding signal is generated, the technical literacy of an operator and the operation method are unqualified, and the operator needs to perform operation training of corresponding production equipment again;
the personnel management module is used for transmitting a reminding signal and the operation and kiss coefficient CZ to the server, and the server is used for transmitting the operation and kiss coefficient CZ with a timestamp to the display module for real-time display.
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| CN202110750740.7A CN113469382A (en) | 2021-07-02 | 2021-07-02 | Production equipment debugging management system based on Internet of things |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115660382A (en) * | 2022-12-08 | 2023-01-31 | 长沙润伟机电科技有限责任公司 | Vehicle section debugging management system based on Internet of things |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115660382A (en) * | 2022-12-08 | 2023-01-31 | 长沙润伟机电科技有限责任公司 | Vehicle section debugging management system based on Internet of things |
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