CN114386655A - Transformer quality detection task scheduling method and system - Google Patents
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Abstract
The invention belongs to the technical field of detection of distribution transformers, and particularly relates to a transformer quality detection task scheduling method and system. The method comprises the following steps: step 1, modeling a detection project; step 2, modeling the detection resources; step 3, automatically allocating detection tasks; step 4, automatically processing abnormal conditions; step 5, adjusting detection items; step 6, automatically arranging preparation work; and 7, carrying out statistics and optimization. The method can reduce resource idleness, improve the detection efficiency, greatly shorten the average detection time of each distribution transformer and improve the efficiency by 17 percent; the number of times of carrying can also be reduced, the cost is reduced, the number of times of power transmission and power failure is reduced, the service life of related equipment is prolonged, and the labor cost is saved. The invention is suitable for various large, medium and small distribution transformer detection mechanisms.
Description
Technical Field
The invention belongs to the technical field of detection of distribution transformers, and particularly relates to a transformer quality detection task scheduling method and system.
Background
With the advance of power grid construction, a large number of newly installed or replaced distribution transformers (distribution transformers for short) exist in various regions, and quality detection of the distribution transformers is an important link for ensuring power supply reliability and power quality. The detection process of the distribution transformer is complex, the number of detection projects is large, the standards, equipment, processes, safety and the like related to each detection project are different, the technical requirements on detection personnel are different, and abnormal conditions affecting progress in the detection process also often occur.
At present, the detection tasks of the distribution transformer are generally scheduled in a manual scheduling mode, namely, a detailed detection plan is made in advance, and resources such as equipment, a field and personnel are arranged in a unified mode to carry out detection according to steps. This method is simple and easy to implement, but has major defects, mainly including two aspects:
first, there is a problem that the utilization efficiency of resources is too low.
The detection process of the distribution transformer is complex, abnormal conditions are many, and the detection time of the project is difficult to accurately estimate. If a project is completed in advance, the relevant resources are idle for a period of time, and if the project is delayed, other samples are queued up, and resources of other projects are idle, which causes process confusion.
Second, there is a problem in that it is not possible to flexibly handle an emergency.
The method is characterized in that a part of samples are returned to a factory for rectification, or a detection process is temporarily changed, or a certain sample needs to be subjected to urgent treatment, and the sample needs to be rescheduled, so that the method is time-consuming and labor-consuming.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a transformer quality detection task scheduling method and a transformer quality detection task scheduling system, and aims to realize the invention aims at automatically scheduling resources such as samples, equipment, fields, personnel and the like in the detection process by adopting an intelligent algorithm aiming at the detection process of a distribution transformer, processing emergencies at any time, automatically reallocating the resources, meeting the professional requirements of distribution transformer detection, avoiding resource waste to the maximum extent and improving the detection efficiency.
The technical scheme adopted by the invention for realizing the purpose is as follows:
the transformer quality detection task scheduling method comprises the following steps:
step 1, modeling a detection project;
step 2, modeling the detection resources;
step 3, automatically allocating detection tasks;
step 4, automatically processing abnormal conditions;
step 5, adjusting detection items;
step 6, automatically arranging preparation work;
and 7, carrying out statistics and optimization.
Furthermore, the modeling of the detection items is to establish a digital model of each detection item according to the detection process and the specification of the distribution transformer.
Further, the detection resource includes: detecting equipment, sites, personnel, logistics tools and power supply facility resources, wherein each resource is provided with a weighting coefficient which represents the importance degree of a detection project and indicates whether the resources can be temporarily allocated and matched with each other.
Furthermore, the automatic distribution detection task is a detection task for automatically compiling the distribution transformer by adopting an intelligent scheduling algorithm;
the intelligent scheduling algorithm is that the system uses parameters or variables of multiple dimensions and weighting factors thereof, comprehensively considers various factors, and performs weighting calculation to obtain an optimal task allocation scheme;
the intelligent scheduling algorithm comprises the following steps:
step 3.1, automatically selecting detection items according to the sample models and the detection types, and labeling various attributes;
3.2, automatically calculating the priority of the sample according to the detection type and the property of the sample sending unit, integrating the sample priority into the project priority and generating a new project priority;
the specific algorithm is as follows: if the sample priority is the highest (deadline), the priorities of the related items are all set to be the highest (deadline); if the sample priority is the second level (urgency), the item priority is floated by 33%; if the sample priority is in season III (urgent), the project priority remains unchanged; if the sample priority is the lowest level (general), setting the priority of the related items to be the lowest;
step 3.3, circularly traversing each test bed and each sample, and calculating an optimal scheduling result;
let the number of test stands be m, each test stand is labeled as Ti, where i =1, 2.. multidot.m; the number of samples is n, the sample currently placed on the test stand Ti is Ti0, each sample is labeled Yj, where j =1, 2.. multidot.n; calculating a weight value for allocating Yj to Ti by adopting a circular traversal method, and marking as R (i, j);
the calculation method is as follows:
(1) if the number of items with the highest priority can be increased after Yj- > Ti, namely the items with the highest priority are detected in advance, R is set as the maximum value of 100;
(2) presetting the R value as Q (Yj) -Q (Yi0), wherein Q (Yj) represents the weight of the detection item of the sample Yj on the test bed;
(3) if Yj- > Ti, adding one time of transportation, subtracting the transportation weight value from the R value, and optimizing to obtain an initial value of 10; wherein, the judgment basis of whether the transportation is needed is the values of j and i0, if the two values are equal, the transportation is not needed;
(3) if Yj- > Ti, increasing one power supply and power off, subtracting the power supply and power off weight value from the R value, and optimizing to obtain an initial value of 20; the judgment basis of whether power is required to be supplied or not is the voltage and current parameters of the two samples before and after scheduling, and if the parameters are the same, power can be continuously supplied;
(4) in the above calculation, the process specification and mandatory detection sequence specification are also checked, and if the specification is violated, the R value is directly set to be-100 as the minimum value;
and after all R values are obtained, selecting the n largest and non-repeated scheduling items to form the optimal scheduling scheme.
Furthermore, the automatic processing of the abnormal condition means that if the abnormal condition occurs in the process of detecting the distribution transformer, automatic processing is performed, an optimal path is automatically searched, and a detection task is redistributed;
the abnormal condition includes: the factory needs to be returned for rectification, an expert committee is needed to discuss decisions, the situation that detection personnel with related skills are temporarily lack of positions is filled in the system, and the system starts a scheduling algorithm to reallocate resources;
automatically finding an optimal path and redistributing tasks, comprising the following steps:
the method comprises the following steps that (1) according to actual conditions, the abnormity is divided into four categories of 1-condition feedback, 2-consignor rectification, 3-unqualified report and 4-application sample withdrawal;
step (2) for the type 1 exception, the system does not start a rescheduling program, only records are made, and once the type 1 exception occurs twice in the same sample, the type 1 exception is automatically upgraded to the type 3 exception;
step (3) for the 2 nd type of abnormity, the system calls the same consignor to correct the record of the same type of abnormity, and calculates the average correction period, if the period is less than the set threshold value, the initial value is 2 days, and no urgent sample exists in the sample to be detected, the rescheduling program is not started, otherwise, the rescheduling program is started;
and (4) immediately starting a rescheduling program for the 3 rd type and 4 th type exceptions.
Furthermore, the adjustment of the detection items refers to the adjustment of the detection items and the modification of the sample priority before and during the detection; after adjusting the items or modifying the priority, the dispatching system can automatically adjust the detection sequence, simultaneously considers the factors of detection quality, carrying cost and power supply and power off times, and combines all the factors with the weighting factors to obtain the optimal result.
Furthermore, the automatic arrangement of the preparation work means that after the detection task is generated, the scheduling system automatically arranges the preparation work at a specified time according to the setting.
Furthermore, the counting and optimizing means that after the sample detection is finished, the dispatching system counts the indexes of equipment idling, carrying times and power failure times, performs transverse and longitudinal comparison, and optimizes the dispatching algorithm.
The transformer quality detection task scheduling system adopts a single-server single-network architecture, and the control platform is respectively in information interaction with a laboratory through a switch; the scheduling system comprises the following modules: the system comprises a data management module, a real-time calculation module, a man-machine interaction module and a result notification module;
the execution sequence of the modules is as follows:
(1) executing data management module to prepare basic data;
(2) the real-time computing module is executed to compute the scheduling result of the transformer quality detection task;
(3) executing a human-computer interaction module, and confirming by a user;
(4) finally, the execution result notification module notifies the calculated task scheduling scheme to an operator;
the hardware of the system is connected by adopting a local area network;
the data management module is responsible for inputting and storing various archive data, detection data, service flows and historical records;
the real-time computing module is responsible for calling an algorithm and computing a scheduling result of a detection task of the distribution transformer;
the human-computer interaction module is responsible for human-computer interaction and performs human intervention on a scheduling process;
and the result notification module feeds the scheduling result back to field workers in a field indication screen, a mobile client and the like.
A computer storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of the transformer quality detection task scheduling method.
The invention has the following beneficial effects and advantages:
the transformer quality detection task scheduling method provided by the invention utilizes an intelligent scheduling algorithm and adopts a multidimensional vector model and a machine learning algorithm, can evaluate various indexes of a detection result, such as detection duration, carrying times, power transmission and outage times and the like, and automatically feeds back the indexes to the scheduling algorithm, adjusts relevant calculation parameters, and performs self-optimization.
The invention is suitable for various large, medium and small distribution transformer detection mechanisms, and obtains better effect after trial by the detection mechanisms. The method mainly comprises the following aspects:
(1) the resource idleness is reduced, the detection efficiency is improved, the average detection time of each distribution transformer is reduced from 12 working days to 10 working days, and the efficiency is improved by 17%.
(2) The carrying times are reduced, the cost is reduced, and the average carrying times of each distribution transformer is reduced from 6 times to 5.5 times.
(3) The power transmission and outage frequency is reduced, the average power transmission and outage frequency of each distribution transformer is reduced from 9 to 8, the service life of related equipment can be prolonged, and the labor cost is saved.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a system architecture diagram of the present invention;
fig. 2 is a flow chart of an implementation of the automatic dispatching system for the distribution transformer detection process of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
The solution of some embodiments of the invention is described below with reference to fig. 1 and 2.
Example 1
The invention provides an embodiment, which is a transformer quality detection task scheduling method, and as shown in fig. 2, fig. 2 is a flow chart of an implementation of an automatic scheduling system in a distribution transformer detection process of the invention.
The scheduling method specifically comprises the following steps:
step 1, modeling a detection project;
specifically, a digital model of each detection item is established according to the detection process and the specification of the distribution transformer.
The inspection process and specification include sequence information of inspection items, such as which items must be performed before the short-circuit test, which items must be performed after the short-circuit test, which items must be performed in sequence, which items can be performed at any time, and the like. The method comprises the following steps: the scheduling algorithm comprises the following steps of detecting procedures, required equipment, required sites, required personnel skills, average duration, shortest duration, abnormal conditions, abnormal probability and the like, and the parameters are required to be used when the optimal result is calculated by the scheduling algorithm.
Step 2, modeling the detection resources;
the detection resources of the distribution transformer include: resources such as detection equipment, places, personnel, logistics tools, power facilities and the like are detected, and when a detection task is scheduled, the resources need to be considered fully, so that resource conflict is avoided, resource idling is avoided to the maximum extent, and efficiency is improved. Each resource is provided with a weighting coefficient which represents the importance degree of the detection item and indicates information such as whether the resources can be temporarily allocated and matched with each other.
Step 3, automatically allocating detection tasks;
the automatic distribution detection task is specifically a detection task for automatically compiling the distribution transformer by adopting an intelligent scheduling algorithm.
The intelligent scheduling algorithm is that the system uses parameters or variables of various dimensions and weighting factors thereof, and comprehensively considers: the method comprises the following steps of carrying out weighted calculation on various factors such as sample types, detection types, the properties of sample sending units, the number of current stock samples, laboratory states, the service conditions of detection equipment, the maintenance plans of the detection equipment, the skill conditions of the qualification and the like of related detection personnel, the emergency degree of samples and the like to obtain an optimal task allocation scheme.
The laboratory state specifically includes the number of samples under test and the like.
The sample model comprises a laminated iron core, amorphous iron core, three-dimensional coil iron core, high overload, dry type and the like.
The detection types comprise national network sampling test, provincial sampling test, type test, entrustment test and the like.
Unit properties include a unit of use, a unit of production, a unit of commission, a management department, and the like.
The specific process of the intelligent scheduling algorithm comprises the following steps:
and 3.1, automatically selecting detection items according to the sample models and the detection types, and labeling various attributes.
The various attributes include: whether it is necessary to check, whether it is destructive, whether it is necessary to precede or follow an item, etc., and a priority is assigned to each item.
And 3.2, automatically calculating the priority of the sample according to the detection type and the property of the sample sending unit, integrating the priority of the sample into the priority of the project, and generating a new project priority.
The specific algorithm is as follows: if the sample priority is the highest (deadline), the priorities of the related items are all set to be the highest (deadline); if the sample priority is the second level (urgency), the item priority is floated by 33%; if the sample priority is in season III (urgent), the project priority remains unchanged; if the sample priority is the lowest (general), the associated item priorities are all set to be the lowest.
And 3.3, circularly traversing each test bed and each sample, and calculating an optimal scheduling result.
Let the number of test beds be m, each test bed is marked as Ti (where i =1, 2.. multidot.m; the number of samples is n, the sample currently placed on the test bed Ti is Ti0, and each sample is marked as Yj (where j =1, 2.. multidot.n).
(1) If the number of items with the highest priority can be increased after Yj- > Ti, i.e. the items with the highest priority are detected earlier, then R is set to the maximum value of 100.
(2) The R value is preset to Q (Yj) -Q (Yi0), wherein Q (Yj) represents the weight of the detection item of the sample Yj on the test bed.
(3) If the transport weight needs to be increased once after Yj- > Ti, the R value is subtracted by the transport weight (which can be optimized, and the initial value is 10). The judgment of whether the transport is required is based on the values of j and i0, and if the values are equal, the transport is not required.
(3) If power supply and power off needs to be added once after Yj- > Ti, the power supply and power off weight is subtracted from the R value (which can be optimized and has an initial value of 20). The judgment basis of whether power supply and power off are needed is the voltage and current parameters of the two samples before and after scheduling, if the parameters are the same, power can be continuously supplied, and power supply and power off operation are not needed.
(4) In the above calculation, the process specification and mandatory inspection sequence specification are also checked, and if the specification is violated, the R value is directly set to the minimum value of-100. The process specification and mandatory detection sequence specification refer to a sample detection specification made by a national grid company, wherein the sequence of a part of detection items is specified, for example, an A item must be performed before a B item and cannot be performed after the B item.
And after all R values are obtained, selecting the n largest and non-repeated scheduling items to form the optimal scheduling scheme.
The intelligent scheduling algorithm adopts a multidimensional vector model and a machine learning algorithm, can evaluate various indexes of a detection result, such as detection duration, carrying times, power transmission and outage times and the like, and automatically feeds back the indexes to the scheduling algorithm, adjusts relevant calculation parameters, and performs self-optimization.
Step 4, automatically processing abnormal conditions;
if an abnormal condition occurs in the process of detecting the distribution transformer, the dispatching system and the dispatching method can automatically process, automatically find the optimal path and redistribute the detection task.
The abnormal condition includes: and the conditions such as returning to the factory, correcting, requiring expert committees to discuss decisions, temporary missing of detection personnel with related skills and the like can be filled in the system, and the system starts a scheduling algorithm to reallocate resources. Various factors can be comprehensively considered during redistribution, and on the premise of meeting the detection process requirement, the resource idling is reduced as much as possible, and the times of carrying, power transmission and power failure are reduced.
The following factors need to be considered comprehensively when the optimal path is found, and a weighted optimal value is obtained:
(1) new tasks cannot conflict with detection processes, equipment functions, site capacity, personnel skills, etc.;
(2) the resource idling is avoided as much as possible, and the detection efficiency is improved;
(3) avoid carrying the sample repeatedly, power transmission outage repeatedly as far as possible to practice thrift manpower and materials cost.
Automatically searching an optimal path and redistributing tasks after the abnormity occurs, and specifically comprising the following steps:
firstly, dividing the abnormity into four major categories of 1-condition feedback, 2-consignor rectification, 3-unqualified report, 4-application sample withdrawal and the like according to the actual condition;
step (2) for the 1 st type exception, the system does not start the rescheduling program, but only records, once the 1 st type exception occurs twice in the same sample, the type 1 exception is automatically upgraded to the 3 rd type exception;
step (3) for the 2 nd type of abnormity, the system calls the same consignor to correct the record of the same type of abnormity, and calculates the average correction period, if the period is less than the set threshold (the initial value is 2 days) and no urgent sample exists in the sample to be detected, the rescheduling program is not started, otherwise, the rescheduling program is started;
and (4) immediately starting a rescheduling program for the 3 rd type and 4 th type exceptions. The algorithm of the rescheduling program is the same as the scheduling algorithm when the tasks are allocated.
Step 5, adjusting detection items;
before and during detection, detection items can be manually adjusted, and the sample priority can be modified. After adjusting the items or modifying the priority, the dispatching system can automatically adjust the detection sequence, simultaneously considers the factors such as detection quality, carrying cost, power transmission and outage times and the like, and combines all the factors with the weighting factors to obtain the optimal result.
The manual adjustment of the detection items is performed by manually adjusting and intervening the detection tasks, such as temporarily changing the priority of the samples. The system sets the priority of the sample to be in multiple levels such as 'normal', 'emergency', 'time limit', and the like, the user can adjust the priority after obtaining authorization, and the system can automatically trigger a scheduling algorithm to redistribute the detection tasks.
Step 6, automatically arranging preparation work;
after the detection task is generated, the scheduling system automatically schedules preparation work at a specified time according to the setting so as to ensure that the detection project is smoothly carried out.
The preparation work refers to various preparation works which need to be performed in advance before the detection project starts, and specifically includes: the system can automatically send out related notice according to task scheduling results to enable related personnel to make preparation in advance.
In specific implementation, the preparation work in advance comprises the following steps: if the location of the sample needs to be changed, the system automatically notifies the logistics personnel 30 minutes before the project begins, and the sample is ready to be carried; if the detection item needs high voltage electricity, the system automatically informs a power supply manager 15 minutes before the item starts and after the item finishes, and prepares to carry out power-off operation; at present, the test items such as lightning impulse, short circuit bearing capacity and the like automatically send out safety warning to avoid potential safety hazards.
And 7, carrying out statistics and optimization.
After the sample detection is finished, the dispatching system carries out statistics on indexes such as equipment idling, carrying times, power failure and transmission times and the like, carries out transverse and longitudinal comparison and continuously optimizes a dispatching algorithm.
In specific implementation, after the detection task is finished, the system starts a statistical evaluation program to perform statistics and comparison on various indexes in the sample detection process so as to optimize a related algorithm.
Example 2
The invention further provides an embodiment, which is a transformer quality detection task scheduling system, as shown in fig. 1, fig. 1 is a system architecture diagram of the invention.
The dispatching system adopts a single-server single-network architecture, and the control platform is respectively in information interaction with 10 laboratories through the switch. The system of the invention relates to more than 200 detection devices, and about 50 detection persons.
The dispatching system can detect the distribution transformer according to the items including: the method comprises the following steps of measuring insulation resistance, measuring voltage ratio and connecting group labels, measuring winding resistance, externally applying voltage resistance test, inducing voltage resistance test, measuring no-load current and no-load loss, measuring short-circuit impedance and load loss, temperature rise test, measuring sound level, testing short-circuit bearing capacity, testing lightning impulse, testing partial discharge, testing insulation heat resistance grade, overload capacity, testing insulating oil and the like, wherein the testing items comprise more than 10 detection items.
The transformer quality detection task scheduling system comprises the following functional modules: the system comprises a data management module, a real-time calculation module, a man-machine interaction module and a result notification module.
The execution sequence of the modules is as follows:
(1) executing data management module to prepare basic data;
(2) the real-time computing module is executed to compute the scheduling result of the transformer quality detection task;
(3) executing a human-computer interaction module, and confirming by a user;
(4) and finally, the execution result notification module notifies the calculated task scheduling scheme to the staff.
The hardware of the system is connected by adopting a local area network.
The data management module is responsible for inputting and storing various archive data, detection data, business processes, historical records and the like.
And the real-time computing module is responsible for calling an algorithm and computing a scheduling result of a detection task of the distribution transformer.
The human-computer interaction module is responsible for human-computer interaction and carries out human intervention on the scheduling process.
And the result notification module feeds the scheduling result back to field workers in a field indication screen, a mobile client and the like.
Example 3
Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, where a computer program is stored on the computer storage medium, and when the computer program is executed by a processor, the steps of the transformer quality detection task scheduling method described in embodiment 1 are implemented.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. The transformer quality detection task scheduling method is characterized by comprising the following steps: the method comprises the following steps:
step 1, modeling a detection project;
step 2, modeling the detection resources;
step 3, automatically allocating detection tasks;
step 4, automatically processing abnormal conditions;
step 5, adjusting detection items;
step 6, automatically arranging preparation work;
and 7, carrying out statistics and optimization.
2. The transformer quality detection task scheduling method according to claim 1, wherein: the modeling of the detection items is to establish a digital model of each detection item according to the detection process and the specification of the distribution transformer.
3. The transformer quality detection task scheduling method according to claim 1, wherein: the detection resources include: detecting equipment, sites, personnel, logistics tools and power supply facility resources, wherein each resource is provided with a weighting coefficient which represents the importance degree of a detection project and indicates whether the resources can be temporarily allocated and matched with each other.
4. The transformer quality detection task scheduling method according to claim 1, wherein: the automatic distribution detection task is a detection task for automatically compiling the distribution transformer by adopting an intelligent scheduling algorithm;
the intelligent scheduling algorithm is that the system uses parameters or variables of multiple dimensions and weighting factors thereof, comprehensively considers various factors, and performs weighting calculation to obtain an optimal task allocation scheme;
the intelligent scheduling algorithm comprises the following steps:
step 3.1, automatically selecting detection items according to the sample models and the detection types, and labeling various attributes;
3.2, automatically calculating the priority of the sample according to the detection type and the property of the sample sending unit, integrating the sample priority into the project priority and generating a new project priority;
the specific algorithm is as follows: if the sample priority is the highest (deadline), the priorities of the related items are all set to be the highest (deadline); if the sample priority is the second level (urgency), the item priority is floated by 33%; if the sample priority is in season III (urgent), the project priority remains unchanged; if the sample priority is the lowest level (general), setting the priority of the related items to be the lowest;
step 3.3, circularly traversing each test bed and each sample, and calculating an optimal scheduling result;
let the number of test stands be m, each test stand is labeled as Ti, where i =1, 2.. multidot.m; the number of samples is n, the sample currently placed on the test stand Ti is Ti0, each sample is labeled Yj, where j =1, 2.. multidot.n; calculating a weight value for allocating Yj to Ti by adopting a circular traversal method, and marking as R (i, j);
the calculation method is as follows:
(1) if the number of items with the highest priority can be increased after Yj- > Ti, namely the items with the highest priority are detected in advance, R is set as the maximum value of 100;
(2) presetting the R value as Q (Yj) -Q (Yi0), wherein Q (Yj) represents the weight of the detection item of the sample Yj on the test bed;
(3) if Yj- > Ti, adding one time of transportation, subtracting the transportation weight value from the R value, and optimizing to obtain an initial value of 10; wherein, the judgment basis of whether the transportation is needed is the values of j and i0, if the two values are equal, the transportation is not needed;
(3) if Yj- > Ti, increasing one power supply and power off, subtracting the power supply and power off weight value from the R value, and optimizing to obtain an initial value of 20; the judgment basis of whether power is required to be supplied or not is the voltage and current parameters of the two samples before and after scheduling, and if the parameters are the same, power can be continuously supplied;
(4) in the above calculation, the process specification and mandatory detection sequence specification are also checked, and if the specification is violated, the R value is directly set to be-100 as the minimum value;
and after all R values are obtained, selecting the n largest and non-repeated scheduling items to form the optimal scheduling scheme.
5. The transformer quality detection task scheduling method according to claim 1, wherein: the automatic processing of the abnormal condition refers to automatic processing, automatic searching of an optimal path and redistribution of detection tasks if the abnormal condition occurs in the process of detecting the distribution transformer;
the abnormal condition includes: the factory needs to be returned for rectification, an expert committee is needed to discuss decisions, the situation that detection personnel with related skills are temporarily lack of positions is filled in the system, and the system starts a scheduling algorithm to reallocate resources;
automatically finding an optimal path and redistributing tasks, comprising the following steps:
the method comprises the following steps that (1) according to actual conditions, the abnormity is divided into four categories of 1-condition feedback, 2-consignor rectification, 3-unqualified report and 4-application sample withdrawal;
step (2) for the type 1 exception, the system does not start a rescheduling program, only records are made, and once the type 1 exception occurs twice in the same sample, the type 1 exception is automatically upgraded to the type 3 exception;
step (3) for the 2 nd type of abnormity, the system calls the same consignor to correct the record of the same type of abnormity, and calculates the average correction period, if the period is less than the set threshold value, the initial value is 2 days, and no urgent sample exists in the sample to be detected, the rescheduling program is not started, otherwise, the rescheduling program is started;
and (4) immediately starting a rescheduling program for the 3 rd type and 4 th type exceptions.
6. The transformer quality detection task scheduling method according to claim 1, wherein: the adjustment of the detection items refers to the adjustment of the detection items and the modification of the sample priority before and during the detection; after adjusting the items or modifying the priority, the dispatching system can automatically adjust the detection sequence, simultaneously considers the factors of detection quality, carrying cost and power supply and power off times, and combines all the factors with the weighting factors to obtain the optimal result.
7. The transformer quality detection task scheduling method according to claim 1, wherein: the automatic preparation scheduling means that after the detection task is generated, the scheduling system automatically schedules the preparation at a specified time according to the setting.
8. The transformer quality detection task scheduling method according to claim 1, wherein: the counting and optimizing means that after the sample detection is finished, the dispatching system counts the indexes of equipment idling, carrying times and power failure times, compares the indexes transversely and longitudinally and optimizes a dispatching algorithm.
9. Transformer quality testing task scheduling system, characterized by: a single-server single-network architecture is adopted, and the control platform is respectively in information interaction with a laboratory through a switch; the scheduling system comprises the following modules: the system comprises a data management module, a real-time calculation module, a man-machine interaction module and a result notification module;
the execution sequence of the modules is as follows:
(1) executing data management module to prepare basic data;
(2) the real-time computing module is executed to compute the scheduling result of the transformer quality detection task;
(3) executing a human-computer interaction module, and confirming by a user;
(4) finally, the execution result notification module notifies the calculated task scheduling scheme to an operator;
the hardware of the system is connected by adopting a local area network;
the data management module is responsible for inputting and storing various archive data, detection data, service flows and historical records;
the real-time computing module is responsible for calling an algorithm and computing a scheduling result of a detection task of the distribution transformer;
the human-computer interaction module is responsible for human-computer interaction and performs human intervention on a scheduling process;
and the result notification module feeds the scheduling result back to field workers in a field indication screen, a mobile client and the like.
10. A computer storage medium, characterized by: the computer storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the transformer quality inspection task scheduling method of claims 1-8.
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