CN113482769A - Engine group remote control system based on Internet of things - Google Patents
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02B—INTERNAL-COMBUSTION PISTON ENGINES; COMBUSTION ENGINES IN GENERAL
- F02B63/00—Adaptations of engines for driving pumps, hand-held tools or electric generators; Portable combinations of engines with engine-driven devices
- F02B63/04—Adaptations of engines for driving pumps, hand-held tools or electric generators; Portable combinations of engines with engine-driven devices for electric generators
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02B—INTERNAL-COMBUSTION PISTON ENGINES; COMBUSTION ENGINES IN GENERAL
- F02B77/00—Component parts, details or accessories, not otherwise provided for
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02B—INTERNAL-COMBUSTION PISTON ENGINES; COMBUSTION ENGINES IN GENERAL
- F02B77/00—Component parts, details or accessories, not otherwise provided for
- F02B77/08—Safety, indicating, or supervising devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D45/00—Electrical control not provided for in groups F02D41/00 - F02D43/00
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Abstract
The invention discloses an engine group remote control system based on the Internet of things, which comprises an engine group, a data acquisition unit, an initial judgment unit, an action monitoring unit, a processor, a display, a storage unit, a loss assessment comparison unit and a loss model library, wherein the engine group comprises a plurality of engine groups; the method comprises the steps of collecting operating parameters of the engine through a data collection unit; meanwhile, instruction operation of a user on the generator set is obtained by means of the action monitoring unit, and the instruction operation is that a corresponding action command is transmitted to the generator set and used for the generator set to execute; then, the operation parameters are compared with each other in advance by means of the initial judgment unit and the action monitoring unit, and all disorder parameters are obtained and fused to form a disorder parameter group; analyzing the disturbance parameter group by using a loss assessment comparison unit, analyzing the cause of the disturbance parameter by combining a loss model in a loss model library while analyzing, and generating a target record and a field inspection signal according to an analysis result; relevant suggestions and operations are given.
Description
Technical Field
The invention belongs to the field of engine unit control, relates to a remote control technology, and particularly relates to an engine unit remote control system based on the Internet of things.
Background
Patent publication No. CN107143453A discloses a generator set control system and a power generation system. This generating set control system includes: the generating set controller is used for detecting an operation signal of the generating set and controlling the generating set; the ignition controller is used for detecting an ignition signal of the engine unit and controlling an ignition device of the engine unit; the knock controller is used for detecting a knock signal of the engine unit and controlling the ignition time of the engine unit; and the main controller is electrically connected with the generator set controller, the ignition controller and the knock controller respectively and is used for acquiring the working state data of the generator set and generating a control strategy for controlling the generator set. The main controller is used for overall control of the generator set. According to the invention, the data of the generator set is subjected to overall operation and the corresponding control strategy is generated, so that the control precision and the control flexibility are improved.
However, although the control mode and logic of the current engine unit are comprehensive, a corresponding technical scheme is provided for monitoring the state of the engine unit in real time and diagnosing the state of the engine unit in the control process, which is a difficult problem; based on this, a solution is now provided.
Disclosure of Invention
The invention aims to provide an engine group remote control system based on the Internet of things.
The purpose of the invention can be realized by the following technical scheme:
the engine group remote control system based on the Internet of things comprises an engine group, a data acquisition unit, an initial judgment unit, an action monitoring unit, a processor, a display, a storage unit, a loss assessment comparison unit and a loss model library;
the system comprises a data acquisition unit, a preliminary judgment unit and a control unit, wherein the data acquisition unit is used for acquiring the operating parameters of an engine and transmitting the operating parameters to the preliminary judgment unit;
the action monitoring unit is in communication connection with the initial judging unit and is used for acquiring instruction operation of a user on the generator set, wherein the instruction operation is that a corresponding action command is transmitted to the generator set and is used for the generator set to execute; the initial judgment unit receives the operation parameters transmitted by the data acquisition unit and compares the operation parameters with the action monitoring unit in advance to obtain a disturbance parameter group formed by fusing all the disturbance parameters;
the initial judgment unit is used for transmitting the disturbance parameter group to the processor, the processor is used for transmitting the disturbance parameter group to the damage assessment comparison unit, and the damage assessment comparison unit receives the disturbance parameter group transmitted by the processor; the loss model is arranged in the loss model library and used for analyzing the cause of the disorder parameter by combining the loss model with the loss assessment comparison unit and generating a target record and a field inspection signal according to the analysis result;
the loss assessment comparison unit is used for transmitting the target record and the field inspection signal to the processor; the processor transmits the target record and the field inspection signal to the display unit and the storage unit when receiving the target record and the field inspection signal transmitted by the loss assessment comparison unit;
the display unit automatically displays that 'error occurs, please check on site and related suggestions cannot be provided' when receiving a site checking signal transmitted by the processor;
the display unit displays the target record in real time when receiving the target record transmitted by the processor;
and the storage unit automatically stamps the target record and the field inspection signal transmitted by the processor when receiving the target record and the field inspection signal to form an analysis record, and stores the analysis record in real time.
Further, the operation parameters comprise the voltage of a storage battery, the voltage and the rotating speed of no-load for 10 seconds, the excitation voltage, the electrically-regulated output voltage and the bearing temperature.
Further, the specific steps of the self-alignment are as follows:
the method comprises the following steps: acquiring a specified operation parameter, acquiring a corresponding operation parameter value once every T1 time to acquire an operation parameter value group, marking the operation parameter value group as Yi, i is 1, n is a positive integer, and T1 is a preset value;
step two: then obtaining the latest value Yn of Yi, then obtaining Yn, sequentially pushing the Yn at intervals of two values, and selecting X1 values; sequentially selecting Yn-3, Yn-6 and Yn-9 until obtaining the X1 numerical value; x1 is a preset value;
step three: subtracting the selected X1 numerical values from Yn in sequence to obtain a difference value set Cj, wherein j is 1.. X1;
step four: calculating the average value of the difference value groups Cj, marking the average value as P, then calculating the number of the numerical values of which the difference values of all Cj and P exceed X2, and dividing the number by X1 to obtain the deviation ratio; x2 is a preset value;
step five: generating a disturbance signal when the deviation ratio exceeds X3; x3 is less than 1 and is a preset value; otherwise, no processing is carried out;
step six: processing all the operation parameters in the second step to the seventh step to obtain all the operation parameters generating the turbulence signals;
step seven: and acquiring instruction operation of a user by using an action monitoring unit, removing the operation parameters with numerical value change caused by the instruction operation, summarizing the rest operation parameters to obtain the disorder parameters, and fusing all the disorder parameters to form a disorder parameter group.
Further, the specific analysis mode for generating the target record and the field inspection signal according to the analysis result is as follows:
s1: acquiring all maintenance records of the current generator set, wherein the maintenance records comprise parameter abnormal data, damaged parts and maintenance schemes, and the maintenance schemes specifically refer to the names and the number of the replaced parts; the maintenance records are not recorded in the schemes of other non-replaced parts;
s2: selecting a maintenance record, and acquiring parameter abnormal data in the maintenance record;
s3: then acquiring a disturbance parameter set; optionally a disorder parameter;
s4: comparing the disorder parameters with corresponding parameters in the parameter abnormal data, and marking a matching signal when the difference value of the abnormal values of the disorder parameters and the parameter abnormal data is lower than X5, wherein X5 is a preset numerical value;
s5: selecting the next disorder parameter, repeating the steps S3-S5 until all the disorder parameters are processed to obtain the number of generated matching signals, and dividing the number by the total number of the disorder parameters to obtain a coincidence ratio;
s6: optionally selecting the next maintenance record, and repeating the steps S2-S6 to obtain the coincidence ratio of all the maintenance records;
s7: marking the maintenance record corresponding to the coincidence ratio larger than 0.75 as a primary selection record;
s8: when the number of the primary selection records is more than or equal to three, sorting the first three primary selection records from large to small according to the coincidence ratio values of the primary selection records, and marking the first three primary selection records as target records;
when the primary records are less than three and more than zero, marking all the primary records as target records;
otherwise, a field ping signal is generated.
Further, the abnormal value of the disturbance parameter in step S4 is a specific value of the disturbance parameter varying from the normal parameter, and the abnormal value of the corresponding parameter in the parameter abnormal data is also a specific value of the parameter varying from the normal parameter.
Further, the device also comprises a data synchronization unit and an action unit;
the data synchronization unit is used for synchronizing the real-time position, the working state value and the area map of a maintenance worker, and the working state value refers to the number of maintenance tasks of the current maintenance worker; the regional map is a city map of the engine unit, and the map is internally marked with the position of a supply shop related to maintenance accessories of the engine unit;
the processor is also used for transmitting the target record and the field inspection signal to the action unit when receiving the target record and the field inspection signal transmitted by the loss-to-assessment comparison unit, the action unit is used for carrying out maintenance analysis on the target record and the field inspection signal by combining the data synchronization unit, and the specific maintenance analysis steps are as follows:
SS 1: when the target record is received, the real-time positions of all maintenance personnel can be automatically obtained;
SS 2: meanwhile, the position of a supply shop of engine unit maintenance accessories needing to be replaced in the target record is obtained, when corresponding maintenance accessories exist in the warehouse, the position does not need to be obtained, and the position can be obtained by synchronizing with a warehouse management part, is not the key point of the application, and is not repeated;
SS 3: then automatically calculating the shortest path distance from the real-time position of the maintenance personnel to the position of the supply store and then to the position of the generator set, and marking the distance as the rush-up distance;
SS 4: then acquiring the rush-up distance of all maintenance personnel, and simultaneously acquiring working state values corresponding to all maintenance personnel;
SS 5: and (3) calculating the selected value by using a formula, wherein the specific formula is as follows:
selecting a value of 0.578 to rush to a distance +0.422 to work state value;
SS 6: marking the maintenance personnel with the maximum selection value as target personnel;
SS 7: the action unit automatically sends maintenance information to the target personnel, and the maintenance information comprises a target record and the position of the engine unit.
The invention has the beneficial effects that:
the method comprises the steps of collecting operating parameters of the engine through a data collection unit; meanwhile, instruction operation of a user on the generator set is obtained by means of the action monitoring unit, and the instruction operation is that a corresponding action command is transmitted to the generator set and used for the generator set to execute; then, the operation parameters are compared with each other in advance by means of the initial judgment unit and the action monitoring unit, and all disorder parameters are obtained and fused to form a disorder parameter group;
analyzing the disturbance parameter group by using a loss assessment comparison unit, analyzing the cause of the disturbance parameter by combining a loss model in a loss model library while analyzing, and generating a target record and a field inspection signal according to an analysis result; relevant suggestions and operations are given, and meanwhile maintenance of the engine unit by nearby maintenance personnel is automatically recommended in a fastest mode, so that the engine unit is prevented from being in an error state for a long time.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
As shown in fig. 1, an engine block remote control system based on the internet of things,
the user carries out remote fault diagnosis to guide the user to eliminate simple faults; recording data at each second instant before the fault occurs, comprising: engine data, generator data, load data, I/O port status
The device comprises an engine unit, a data acquisition unit, an initial judgment unit, an action monitoring unit, a processor, a display, a storage unit, a loss assessment comparison unit and a loss model library, wherein the initial judgment unit is used for judging the initial judgment of the engine unit;
the data acquisition unit is used for acquiring the operation parameters of the engine, wherein the operation parameters comprise storage battery voltage, voltage and rotating speed of no-load for 10 seconds, excitation voltage, electrically-regulated output voltage, bearing temperature and the like; the data acquisition unit is used for transmitting the operation parameters to the initial judgment unit;
the action monitoring unit is in communication connection with the initial judging unit and is used for acquiring instruction operation of a user on the generator set, wherein the instruction operation is that a corresponding action command is transmitted to the generator set and is used for the generator set to execute; the initial judgment unit receives the operation parameters transmitted by the data acquisition unit and carries out automatic comparison on the operation parameters by combining the action monitoring unit, and the automatic comparison comprises the following specific steps:
the method comprises the following steps: uniformly adopting a subsequent step processing mode for all the operation parameters, wherein only all the operation parameters are written in a summary mode to form a processing step; the details are shown in the next step;
step two: acquiring a specified operation parameter, acquiring a corresponding operation parameter value once every T1 time to acquire an operation parameter value group, marking the operation parameter value group as Yi, i is 1, n is a positive integer, and T1 is a preset value;
step three: then obtaining the latest value Yn of Y i, then obtaining that Yn is pushed forward by two values in sequence, and selecting X1 values; sequentially selecting Yn-3, Yn-6 and Yn-9 until obtaining the X1 numerical value; x1 is a preset value;
step four: subtracting the selected X1 numerical values from Yn in sequence to obtain a difference value set Cj, wherein j is 1.. X1;
step five: calculating the average value of the difference value groups Cj, marking the average value as P, then calculating the number of the numerical values of which the difference values of all Cj and P exceed X2, and dividing the number by X1 to obtain the deviation ratio; x2 is a preset value;
step six: generating a disturbance signal when the deviation ratio exceeds X3; x3 is less than 1 and is a preset value; otherwise, no processing is carried out;
step seven: processing all the operation parameters in the second step to the seventh step to obtain all the operation parameters generating the turbulence signals;
step eight: acquiring instruction operation of a user by using an action monitoring unit, removing operation parameters with numerical value change caused by the instruction operation, summarizing the rest operation parameters to obtain disorder parameters, and fusing all the disorder parameters to form a disorder parameter group;
the initial judgment unit is used for transmitting the disturbance parameter group to the processor, the processor is used for transmitting the disturbance parameter group to the damage assessment comparison unit, and the damage assessment comparison unit receives the disturbance parameter group transmitted by the processor; the loss model library is internally provided with a loss model for analyzing the cause of the disorder parameter by combining the loss model with the loss assessment comparison unit, and the specific analysis mode is as follows:
s1: acquiring all maintenance records of the current generator set, wherein the maintenance records comprise parameter abnormal data, damaged parts and maintenance schemes, and the maintenance schemes specifically refer to the names and the number of the replaced parts; the maintenance records are not recorded in the schemes of other non-replaced parts;
s2: selecting a maintenance record, and acquiring parameter abnormal data in the maintenance record;
s3: then acquiring a disturbance parameter set; optionally a disorder parameter;
s4: comparing the disorder parameters with corresponding parameters in the parameter abnormal data, and marking a matching signal when the difference value of the abnormal values of the disorder parameters and the parameter abnormal data is lower than X5, wherein X5 is a preset numerical value; the abnormal value of the disturbance parameter is a specific value of the disturbance parameter changed compared with the normal parameter, and the abnormal value of the corresponding parameter in the parameter abnormal data is also a specific value of the parameter changed compared with the normal parameter;
s5: selecting the next disorder parameter, repeating the steps S3-S5 until all the disorder parameters are processed to obtain the number of generated matching signals, and dividing the number by the total number of the disorder parameters to obtain a coincidence ratio;
s6: optionally selecting the next maintenance record, and repeating the steps S2-S6 to obtain the coincidence ratio of all the maintenance records;
s7: marking the maintenance record corresponding to the coincidence ratio larger than 0.75 as a primary selection record;
s8: when the number of the primary selection records is more than or equal to three, sorting the first three primary selection records from large to small according to the coincidence ratio values of the primary selection records, and marking the first three primary selection records as target records;
when the primary records are less than three and more than zero, marking all the primary records as target records;
otherwise, generating a field inspection signal;
the loss assessment comparison unit is used for transmitting the target record and the field inspection signal to the processor; the processor transmits the target record and the field inspection signal to the display unit and the storage unit when receiving the target record and the field inspection signal transmitted by the loss assessment comparison unit;
the display unit automatically displays that 'error occurs, please check on site and related suggestions cannot be provided' when receiving a site checking signal transmitted by the processor;
the display unit displays the target record in real time when receiving the target record transmitted by the processor;
the storage unit automatically stamps a time stamp when receiving the target record and the field inspection signal transmitted by the processor to form an analysis record and stores the analysis record in real time;
as a second embodiment of the present invention, on the basis of the first embodiment, the storage unit and the loss model library of the present invention may also perform data exchange, so as to provide data support for the loss model in the loss model library;
as a third embodiment of the present invention, on the basis of the first embodiment, the present invention further includes a data synchronization unit and an action unit;
the data synchronization unit is used for synchronizing the real-time position, the working state value and the area map of a maintenance worker, wherein the working state value refers to the number of maintenance tasks of the current maintenance worker, namely the number of the maintenance tasks which are remained by the maintenance worker and the engine unit which is being maintained; the regional map is a city map of the engine unit, and the map is internally marked with the position of a supply shop related to maintenance accessories of the engine unit;
the processor is also used for transmitting the target record and the field inspection signal to the action unit when receiving the target record and the field inspection signal transmitted by the loss-to-assessment comparison unit, the action unit is used for carrying out maintenance analysis on the target record and the field inspection signal by combining the data synchronization unit, and the specific maintenance analysis steps are as follows:
SS 1: when the target record is received, the real-time positions of all maintenance personnel can be automatically obtained;
SS 2: meanwhile, the position of a supply shop of engine unit maintenance accessories needing to be replaced in the target record is obtained, when corresponding maintenance accessories exist in the warehouse, the position does not need to be obtained, and the position can be obtained by synchronizing with a warehouse management part, is not the key point of the application, and is not repeated;
SS 3: then automatically calculating the shortest path distance from the real-time position of the maintenance personnel to the position of the supply store and then to the position of the generator set, and marking the distance as the rush-up distance;
SS 4: then acquiring the rush-up distance of all maintenance personnel, and simultaneously acquiring working state values corresponding to all maintenance personnel;
SS 5: and (3) calculating the selected value by using a formula, wherein the specific formula is as follows:
selecting a value of 0.578 to rush to a distance +0.422 to work state value;
SS 6: marking the maintenance personnel with the maximum selection value as target personnel;
SS 7: the action unit automatically sends maintenance information to the target personnel, and the maintenance information comprises a target record and the position of the engine unit.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (7)
1. Engine group remote control system based on thing networking, its characterized in that includes:
a data acquisition unit: the engine management system is used for acquiring the operating parameters of the engine and transmitting the operating parameters to the initial judgment unit;
an action monitoring unit: the device is in communication connection with the initial judgment unit and is used for acquiring instruction operation of a user on the generator set, wherein the instruction operation is that a corresponding action command is transmitted to the generator set and is used for the generator set to execute;
the initial judgment unit is combined with the action monitoring unit to compare the operation parameters in advance to obtain a disturbance parameter set formed by fusing all the disturbance parameters and transmitting the disturbance parameter set to the loss assessment comparison unit through the processor;
and the loss assessment comparison unit analyzes the cause of the disorder parameter by combining the loss model in the loss model library, generates a target record and a field inspection signal according to the analysis result, and transmits the target record and the field inspection signal to the processor.
2. The internet of things based engine block remote control system of claim 1, wherein the processor transmits a target record and a field ping signal to the display unit and the storage unit upon receipt thereof;
the display unit automatically displays that 'error occurs, please check on site and related suggestions cannot be provided' when receiving a site checking signal transmitted by the processor;
the display unit displays the target record in real time when receiving the target record transmitted by the processor;
and the storage unit automatically stamps the target record and the field inspection signal transmitted by the processor when receiving the target record and the field inspection signal to form an analysis record, and stores the analysis record in real time.
3. The internet-of-things-based engine block remote control system according to claim 1, wherein the operating parameters include battery voltage, voltage and rotational speed at 10-second idle, excitation voltage, electrically regulated output voltage, and bearing temperature.
4. The internet of things-based engine group remote control system according to claim 1, wherein the specific steps of the automatic comparison are as follows:
the method comprises the following steps: acquiring a specified operation parameter, acquiring a corresponding operation parameter value once every T1 time to acquire an operation parameter value group, marking the operation parameter value group as Yi, i is 1, n is a positive integer, and T1 is a preset value;
step two: then obtaining the latest value Yn of Yi, then obtaining Yn, sequentially pushing the Yn at intervals of two values, and selecting X1 values; sequentially selecting Yn-3, Yn-6 and Yn-9 until obtaining the X1 numerical value; x1 is a preset value;
step three: subtracting the selected X1 numerical values from Yn in sequence to obtain a difference value set Cj, wherein j is 1.. X1;
step four: calculating the average value of the difference value groups Cj, marking the average value as P, then calculating the number of the numerical values of which the difference values of all Cj and P exceed X2, and dividing the number by X1 to obtain the deviation ratio; x2 is a preset value;
step five: generating a disturbance signal when the deviation ratio exceeds X3; x3 is less than 1 and is a preset value; otherwise, no processing is carried out;
step six: processing all the operation parameters in the second step to the seventh step to obtain all the operation parameters generating the turbulence signals;
step seven: and acquiring instruction operation of a user by using an action monitoring unit, removing the operation parameters with numerical value change caused by the instruction operation, summarizing the rest operation parameters to obtain the disorder parameters, and fusing all the disorder parameters to form a disorder parameter group.
5. The internet of things-based engine group remote control system according to claim 1, wherein the specific analysis mode for generating the target record and the field inspection signal according to the analysis result is as follows:
s1: acquiring all maintenance records of the current generator set, wherein the maintenance records comprise parameter abnormal data, damaged parts and maintenance schemes, and the maintenance schemes specifically refer to the names and the number of the replaced parts; the maintenance records are not recorded in the schemes of other non-replaced parts;
s2: selecting a maintenance record, and acquiring parameter abnormal data in the maintenance record;
s3: then acquiring a disturbance parameter set; optionally a disorder parameter;
s4: comparing the disorder parameters with corresponding parameters in the parameter abnormal data, and marking a matching signal when the difference value of the abnormal values of the disorder parameters and the parameter abnormal data is lower than X5, wherein X5 is a preset numerical value;
s5: selecting the next disorder parameter, repeating the steps S3-S5 until all the disorder parameters are processed to obtain the number of generated matching signals, and dividing the number by the total number of the disorder parameters to obtain a coincidence ratio;
s6: optionally selecting the next maintenance record, and repeating the steps S2-S6 to obtain the coincidence ratio of all the maintenance records;
s7: marking the maintenance record corresponding to the coincidence ratio larger than 0.75 as a primary selection record;
s8: when the number of the primary selection records is more than or equal to three, sorting the first three primary selection records from large to small according to the coincidence ratio values of the primary selection records, and marking the first three primary selection records as target records;
when the primary records are less than three and more than zero, marking all the primary records as target records;
otherwise, a field ping signal is generated.
6. The internet-of-things-based engine block remote control system according to claim 5, wherein the abnormal value of the disturbance parameter in step S4 is a specific value of the disturbance parameter varied from the normal parameter, and the abnormal value of the corresponding parameter in the parameter abnormal data is a specific value of the parameter varied from the normal parameter.
7. The internet of things based engine block remote control system of claim 1, further comprising a data synchronization unit and an action unit;
the data synchronization unit is used for synchronizing the real-time position, the working state value and the area map of a maintenance worker, and the working state value refers to the number of maintenance tasks of the current maintenance worker; the regional map is a city map of the engine unit, and the map is internally marked with the position of a supply shop related to maintenance accessories of the engine unit;
the processor is also used for transmitting the target record and the field inspection signal to the action unit when receiving the target record and the field inspection signal transmitted by the loss-to-assessment comparison unit, the action unit is used for carrying out maintenance analysis on the target record and the field inspection signal by combining the data synchronization unit, and the specific maintenance analysis steps are as follows:
SS 1: when the target record is received, the real-time positions of all maintenance personnel can be automatically obtained;
SS 2: meanwhile, the position of a supply shop of engine unit maintenance accessories needing to be replaced in the target record is obtained, when corresponding maintenance accessories exist in the warehouse, the position does not need to be obtained, and the position can be obtained by synchronizing with a warehouse management part, is not the key point of the application, and is not repeated;
SS 3: then automatically calculating the shortest path distance from the real-time position of the maintenance personnel to the position of the supply store and then to the position of the generator set, and marking the distance as the rush-up distance;
SS 4: then acquiring the rush-up distance of all maintenance personnel, and simultaneously acquiring working state values corresponding to all maintenance personnel;
SS 5: and (3) calculating the selected value by using a formula, wherein the specific formula is as follows:
selecting a value of 0.578 to rush to a distance +0.422 to work state value;
SS 6: marking the maintenance personnel with the maximum selection value as target personnel;
SS 7: the action unit automatically sends maintenance information to the target personnel, and the maintenance information comprises a target record and the position of the engine unit.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114294797A (en) * | 2021-12-30 | 2022-04-08 | 通用空气(辽宁)有限公司 | Intelligent air circulation and purification system for closed space |
-
2021
- 2021-07-27 CN CN202110851108.1A patent/CN113482769A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114294797A (en) * | 2021-12-30 | 2022-04-08 | 通用空气(辽宁)有限公司 | Intelligent air circulation and purification system for closed space |
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Application publication date: 20211008 |