CN111105129A - Detection task intelligent allocation method based on detection mechanism evaluation - Google Patents
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Abstract
The invention relates to a detection task intelligent allocation method based on detection mechanism evaluation, which comprises the following steps: 1. inputting a detection plan; 2. and screening the detection mechanism capable of executing the detection task by detecting the material types in the plan and the detection requirements of the power distribution materials of the state network at the present stage. 3. And acquiring the data of the service quality, the real-time bearing capacity and the logistics real-time bearing capacity of each detection mechanism. 4. And (5) calculating a detection task distribution model by using the three indexes. 5. And outputting the sampling inspection strategy. The invention is suitable for provincial and national detection task distribution of power distribution material detection tasks. Compared with the traditional manual distribution method based on experience, the method has the advantages that intelligent planning, scientific and stable characteristic points can be realized, and meanwhile, the management of detection tasks and the utilization of detection resources are more efficient and controllable.
Description
Technical Field
The invention belongs to the field of dynamic resource planning, and particularly relates to a novel detection task intelligent allocation method and system based on detection mechanism evaluation.
Background
The national power grid company continuously increases the power grid material selective inspection force, the material selective inspection management range is continuously enlarged, and the management and control force is continuously strengthened. The determination and allocation of detection tasks is an important task in the whole detection process. However, at the present stage, the sharing of the detection mechanism and the detection resources is lacked, the detection task is manually distributed, and scientific guidance is lacked, so that the phenomenon of low detection efficiency and high cost at the present stage is caused. The system has the advantages that the system frees workers from heavy planning tasks, realizes intellectualization and automation of material detection task allocation, can effectively improve inspection delivery allocation efficiency and detection task timeliness, and is favorable for improving resource utilization rate of a detection base.
The method comprehensively considers the characteristics of detection task allocation under a provincial and municipal two-stage detection center system of a national power grid company, designs the detection task intelligent allocation method and system based on detection mechanism evaluation, and achieves high benefit, high efficiency and high reliability of detection task allocation.
Disclosure of Invention
Along with the construction of a two-stage detection center system of a national power grid in each grid province, city and province in recent years, the task amount of detection work of power distribution materials is more and more. The traditional mode of manually distributing detection tasks to each detection center in an empirical mode reflects the problems of low efficiency, poor scientificity and poor reliability. The intelligent detection task allocation mainly means that the allocation of detection tasks is realized on the basis of historical data accumulated by each detection mechanism and an information system and state data shared by the detection mechanisms in real time, and the traditional manual allocation to scientific intelligent allocation are realized, so that the high benefit, high efficiency and high reliability of the detection process are realized.
The invention provides a detection task intelligent distribution method and system based on detection mechanism evaluation, which realize intelligent distribution of detection tasks mainly through detection mechanism service quality, detection mechanism real-time bearing capacity and logistics real-time bearing capacity and a detection task intelligent distribution model.
The invention adopts the following technical scheme: a detection task intelligent allocation method based on detection mechanism evaluation comprises the following steps:
s1: inputting a detection plan comprising material types, material models, suppliers, winning lots, delivery lots, sample quantity, storage positions and detection levels;
s2: screening a detection mechanism capable of executing a detection task through the material types in the detection plan and the detection requirements of the power distribution materials of the state network at the present stage;
s3: for the detection mechanisms screened in the S2, acquiring the data of the service quality, the real-time bearing capacity and the logistics real-time bearing capacity of each detection mechanism;
s4: performing detection task allocation model calculation on the three indexes calculated in the step S3;
s5: finally, the calculation result of S4 outputs a spot check policy.
The analysis of the sampling plan in S1 includes obtaining the detection levels of the material type, the material model, the supplier, the winning lot, the arriving lot, the sample amount, the warehousing location and the related requirements in the sampling plan.
The material type and detection requirement analysis in the S2 includes the following steps:
(1) acquiring the type and the detection grade of a detection material in a detection plan;
(2) converting into corresponding experimental detection items according to the detection grade in the step (1);
(3) screening all detection mechanisms with the materials and corresponding experimental projects;
(4) and selecting detection mechanisms 4 times before the completion time of the detection mechanism according to the predicted completion time, and outputting the detection mechanisms.
In S3, the calculation and analysis of the detection mechanism service quality, the real-time carrying capacity and the logistics real-time carrying capacity includes:
(1) acquiring the service quality of a detection mechanism, wherein the detection mechanism mainly comprises the detection mechanism and a service quality Q-service;
(2) acquiring the detection efficiency P detection, the waiting time T and the like of a detection mechanism;
(3) and acquiring the fastest distribution time T of the detection mechanism.
The computational analysis of the detection task assignment model in S4 includes:
(1) calculating detection time Tcheck by using the detection efficiency P check, the waiting time T and the like of a detection center;
(2) the distribution of single detection sample, and the T of each detection mechanismDetection ofDetecting time, namely selecting the shortest detecting time detecting mechanism Oi, and then recalculating the waiting time Ti of the detecting mechanism;
(3) circulating (2) until all samples are distributed, and acquiring the number Di of the samples distributed by each detection mechanism;
(4) rejecting the detection mechanism with small sample quantity in the detection mechanism, and distributing the detection mechanism to other detection mechanisms;
(5) the total number of samples and the expected start detection time and the expected finish time for all assigned detection mechanisms were calculated.
The spot check strategy output in S5 includes the type of the delivered material, the type of the material, the storage location, the distribution detection mechanism, the expected start detection time, and the expected completion time.
The method for acquiring the detection room type and detection registration in the detection plan and converting the detection level into the corresponding experimental detection item comprises the following steps:
(1) acquiring a detection demand grade J and a material type M;
(2) searching the material M in the network material quality detection capability standard library, and if the material type exists, executing (3); if the material type does not exist, directly returning that the material type does not exist, and finishing the distribution calculation;
(3) and searching all corresponding detection items G in the material quality detection capability standard library through the material type M and the detection requirement level J.
The detection mechanism completion time is calculated by using the waiting time and the detection time, and the steps comprise:
(1) obtaining the detection efficiency P of the detection center, waiting time T and the like, and calculating the detection time T, wherein the calculation mode is as follows:
Tdetection of＝DDetection of×PDetection of+TEtc. of
(2) The detection completion time Th of each detection means is calculated as follows:
(3)*Tetc. ofWaiting time, TFitting for mixingDelivery time, TDetection ofDetecting the time required
Calculating by using the planned expected completion time and the actual completion time to obtain the service quality Q service corresponding to the detection mechanism;
and calculating the distribution time by using the distribution efficiency and the distance, and adding the actual distribution waiting time to obtain the fastest distribution time T distribution.
Further, when the detection means redistributes the samples of the rejected detection means, the rejected detection means are removed from the distributed samples and distributed, and the method is as follows:
(1) removing the removed detection mechanisms from the screened detection to generate a residual detection mechanism combination;
(2) calculating various weights and parameters after the samples are distributed;
(3) and redistributing according to a calculation method of the intelligent distribution model of the detection tasks.
The invention has the positive effects that: the invention is suitable for provincial and national detection task distribution of power distribution material detection tasks. Compared with the traditional manual distribution method based on experience, the method has the advantages that intelligent planning, scientific and stable characteristic points can be realized, and meanwhile, the management of detection tasks and the utilization of detection resources are more efficient and controllable.
Drawings
FIG. 1 is a flow chart of the work flow of the detection task intelligent allocation method based on the detection mechanism evaluation;
FIG. 2 is a model design diagram of the detection task intelligent allocation method based on detection mechanism evaluation according to the present invention;
FIG. 3 is a diagram illustrating a time difference calculation weight according to the present invention.
Detailed Description
The present invention will be described in detail with reference to the following examples and drawings. The scope of protection of the invention is not limited to the embodiments, and any modification made by those skilled in the art within the scope defined by the claims also falls within the scope of protection of the invention.
As shown in fig. 1 and 2, the method comprises the following steps:
s1: inputting a detection plan, which mainly comprises material types, material models, suppliers, winning lots, arriving lots, sample quantity, storage positions and detection levels;
s2: screening a detection mechanism capable of executing a detection task through the material types in the detection plan and the detection requirements of the power distribution materials of the state network at the present stage;
s3: for the detection mechanisms screened in the S2, acquiring the data of the service quality, the real-time bearing capacity and the logistics real-time bearing capacity of each detection mechanism;
s4: performing detection task allocation model calculation on the three indexes calculated in the step S3;
s5: finally, the calculation result of S4 outputs a spot check strategy
The analysis of the spot check plan in step S1 obtains the detection levels of the material type, the material model, the supplier, the winning lot, the arriving lot, the sample quantity, the storage location and the related requirements in the spot check plan.
The method for analyzing the material types and the detection requirements in the step S2 specifically comprises the following steps:
s21: acquiring the detection material type and the detection grade in the detection plan in the S1;
s22: converting the test level into a corresponding experimental test item according to the test level in the S1;
s22: screening all detection mechanisms with the materials and corresponding experimental projects;
s23: according to the detection completion time T of each detection mechanismhThe calculation method is as follows:
*Tetc. ofWaiting time, TFitting for mixingDelivery time, TDetection ofDetecting the time required
S24: and selecting detection mechanisms 4 times before the completion time of the detection mechanism, and outputting the detection mechanisms.
The step S3 of computing and analyzing the service quality, the real-time carrying capacity, and the real-time logistics carrying capacity of the detection mechanism includes:
s31: obtaining the service quality of the detection mechanism, mainly including the detection mechanism and the service quality QGarment；
S32: obtaining the detection efficiency P of the detection mechanismDetection ofAnd a waiting time TEtc. of；
S33: obtaining the fastest delivery time T for a detection mechanismFitting for mixing。
The calculation analysis of the intelligent task allocation model in step S4 includes:
s41: detecting efficiency P of detecting center obtained in S3Detection ofWaiting time TEtc. ofCalculating the detection time T, the calculating partyThe formula is as follows:
Tdetection of＝DDetection of×PDetection of+TEtc. of
*TEtc. ofWaiting time, TFitting for mixingDelivery time, TDetection ofTime required for detection, DDetection ofDetecting the quantity of materials;
s42: the distribution of single detection sample, and the T of each detection mechanismDetection ofDetecting time, selecting the shortest detecting time detecting mechanism OiThen recalculate the waiting time T of the detection mechanismi；
S43: and circulating S42 until all samples are completely distributed, and acquiring the distributed sample quantity Di of each detection mechanism.
S44: the detection mechanism with the small number of samples is rejected in the detection mechanism and is distributed to other detection mechanisms. The samples were counted for less than 5 and less than one quarter of the total number, i.e. labeled as reject detection mechanisms.
S45: the total number of samples and the expected start detection time and the expected finish time for all assigned detection mechanisms were calculated.
In step S5, the sampling inspection strategy is generated using the result calculated in step S4, and the strategy mainly includes the type of the delivered materials, the type of the materials, the storage location, the distribution detection mechanism, the expected start detection time, and the expected completion time.
The step of converting the detection grade into the corresponding experimental detection item in the step S22 includes:
s221, acquiring the detection demand level J and the material type M acquired in the step S21;
s222, retrieving the material M in the network material quality detection capability standard library, and executing S223 if the material type exists; if the material type does not exist, directly returning that the material type does not exist, and finishing the distribution calculation;
s223: and searching all corresponding detection items G in the material quality detection capability standard library through the material type M and the detection requirement level J.
The detection mechanism completion time is calculated by using the waiting time and the detection time, and the steps comprise:
(1) obtaining the detection efficiency P of the detection centerDetection ofWaiting time TEtc. ofCalculating the detection time T detection in the following way:
Tdetection of＝DDetection of×PDetection of+TEtc. of
(2) Detection completion time T of each detection mechanismhThe calculation method is as follows:
(3)*Tetc. ofWaiting time, TFitting for mixingDelivery time, TDetection ofThe time required for detection.
Calculating by using the planned expected completion time and the actual completion time to obtain the service quality Q corresponding to the detection mechanismGarment；
Calculating the fastest distribution time T by adding the distribution time calculated by the distribution efficiency and the distance and the actual distribution waiting timeFitting for mixing。
When the detection mechanism distributes the samples of the eliminating mechanism again, the detection mechanism is characterized in that the eliminating mechanism is removed on the basis of distribution, and the distribution is carried out, and the method comprises the following steps:
(1) removing the removing detection mechanism from the screened detection to generate a residual detection mechanism combination;
(2) calculating various weights and parameters after the samples are distributed;
(3) and redistributing according to a calculation method of the intelligent distribution model of the detection tasks.
The invention mainly adopts big data analysis technology, decision tree technology and dynamic planning technology to realize scientific and effective analysis of data of a historical detection mechanism, detection capability of the detection mechanism and bearing capacity of the detection mechanism, extracts the internal incidence relation of the data and the incidence relation between the data and a result, then evaluates the service quality of the detection mechanism, and utilizes a sampling strategy to sample and examine the types and the quantity of goods and materials and test items required to be carried out to match with the real-time detection capability and the detection bearing capacity of the detection mechanism at the present stage. And determining the detection mechanism meeting the requirement, and finally determining the task allocation of the detection mechanism according to the sample storage position and the service quality of the detection mechanism.
And (3) matching calculation of a detection mechanism: the detection mechanism matching calculation mainly realizes the search of the detection mechanism meeting the detection requirement in the sampling detection scheme, and the main calculation process is as follows:
inputting data: the spot check plan (mainly including material name, material model, manufacturer, winning lot, arriving lot and sample amount) is as follows:
detecting mechanism data: mainly comprises a detection mechanism and a grade of a detection test which can be carried out on corresponding detection materials.
| Serial number | Detection | Detection level | |
| 1 | | B | |
| 2 | XXXX2 | B | |
| 3 | XXXX3 | A | |
| 4 | XXXX4 | C | |
| … | … | … |
And (3) data output: the sampling inspection is performed on 90 220KV transformers, and the 90 detection mechanisms are divided into 4 companies and 6 batches. A level detection is carried out according to 10% of requirements, B level detection is carried out according to 80% of requirements, and basic detection requirement data can be obtained according to 10% of requirements for C level detection. And accompanied by the following data sheet:
that is, 13A-level detection units, 64B-level detection units and 13C-level detection units are required for the sampling inspection of the material.
The mechanism that satisfies the demand: the matching of the detection means with respect to the generated detection demand and the detection capability of the detection means is as follows.
| Serial number | Detection mechanism | Class A | Class | Class C | |
| 1 | XXXX1 | 64 | 13 | ||
| 2 | XXXX2 | 64 | 13 | ||
| 3 | XXXX3 | 13 | 64 | 13 | |
| 4 | XXXX4 | 13 | |||
| … | … | … | … | … |
Detection task allocation efficiency calculation: the detection task allocation high efficiency calculation mainly takes the fastest sampling time as a principle when the detection task is allocated, namely the detection task completion high efficiency is ensured. And (3) detecting real-time data of the mechanism, performing sampling inspection strategy through the real-time data of the mechanism, performing task simulation distribution according to the bearing capacity and the detection efficiency of the mechanism, and calculating the sample collection efficiency of the mechanism, so that the calculation is performed in an efficient principle. The main calculation process is as follows:
inputting data: the method mainly comprises the name of materials, the model of the materials, manufacturers, a winning batch, a delivery batch and the number of samples. As in the following table:
detecting mechanism data:
first, considering the detection items with relatively poor detection resources, such as class a detection items, 13 devices among 90 devices that need to be detected need to be directly allocated to XXXX3 for detection. The real-time bearer capability after delivery is as follows:
then, considering the items with a small detection quantity, such as the C-level detection items, wherein the C-level detection items are 13 items, and according to the fastest calculation efficiency, the XXXXXX 1 needs to be completed after 25 days, the male security detection center needs to be completed after 10 days, the XXXX2 needs to be completed after 9 days, and the XXXX4 needs to be completed after 2 days. A 12 day distribution transformer requiring a class C test item is distributed to XXXX 4. Then the real-time bearing capacity of the detection mechanism at this time is as follows:
the last 64 distribution transformers need to be subjected to class B inspection items, such as XXXX1, XXXX2 and XXXX 3. And distributing 64 distribution transformers by adopting a dynamic programming calculation method. The process is as follows:
and calculating the detection amount of each detection mechanism by taking the detection mechanism with the largest waiting days as a criterion. As shown, calculated on a XXXX 122 day basis:
| serial number | Detection mechanism | Calculation process | Amount of |
| 1 | XXXX1 | (22-22)*4 | 0 table |
| 2 | XXXX2 | (22-8)*5 | 70 tables |
| 3 | XXXX3 | (22-7)*6 | 90 stands |
| … | … | … |
Then, it is judged that the sum of the detection amounts of the respective detection mechanisms is larger than the demand. If the detected amount sum is greater than the demanded amount, then no detection packetization of XXXX1 is required. Otherwise the detection mechanism needs to be added to the assignment of the task. Since this calculation is being made
(75+90)>64
Then XXXX1 need not be added to the assignment at this time. Thus, two detection mechanisms XXXX2 and XXXX3 remain, and the above operation is repeated again, the sum of the detection amounts of the two mechanisms is 6, and if 6 is less than 64, two detection mechanisms are required for task allocation.
And (3) task allocation process: the excess is first removed, i.e. after 8 blocks are allocated to XXXX3, the waiting time of XXXX2 and XXXX3 is the same, and then the two detection mechanisms are allocated with full daily quota, i.e. the sampling time is calculated for both detection mechanisms.
The sampling time (day) ═ 64-6)/(5+6) ═ 5.3 (day)
That is, the maximum sampling time is 5.3 days, the B-level detection amount of the distribution of the male safety is as follows:
XXXX2 dispense 5.5.3 27 stations
Class B measurements of XXXX3 were: (6+1) × 5.3 ═ 37 (mesa)
And (3) data output: the sampling inspection is performed on 90 220KV transformers, and the 90 detection mechanisms are divided into 4 companies and 6 batches. A level detection is carried out according to 10% of requirements, B level detection is carried out according to 80% of requirements, and basic detection requirement data can be obtained according to 10% of requirements for C level detection. The workload is mainly borne by XXXX2, XXXX3 and XXXX4, and the final sampling time is 14 working days. The results are as follows:
and (3) calculating distribution cost: in the distribution process, the distribution time is also an important factor influencing the sample collection efficiency, therefore, the distribution problem of the detection mechanism and the storage quality inspection needs to be fully considered, when the detection mechanism is selected, the closer the detection mechanism to the storage place, the higher the selected priority level is, the closer principle is the main principle of reducing the logistics period and the logistics cost, the maximum reliability and the lowest cost of logistics are ensured, and the reliability of the detection process is ensured to a certain extent.
Inputting data: the detection plan and the storage condition thereof mainly comprise material names, material models, manufacturers, bid winning batches, delivery batches and storage positions, and the data thereof are as follows:
real-time logistics distribution condition of each detection mechanism and storage quality inspection
And (3) calculating: this time, taking the distribution of 64 distribution transformers as an example, XXXX2 distributes 64 distribution transformers to different detection mechanisms, and the distribution time is calculated as:
delivery time ═ (total delivery/single delivery) delivery cycle + wait time
The result center library of data output distributes the material to the distribution time length required by different detection centers, and the table is as follows:
calculating the optimal principle of the service: the optimal service calculation is to analyze the data of the historical detection data of the detection equipment by aiming at different detection mechanisms so as to evaluate the detection service quality of the products by the different detection mechanisms. And (3) a principle of distributing detection tasks by considering detection services and detection qualities provided by different detection mechanisms, namely selecting the detection mechanism with good service quality of the detection mechanism as far as possible under the allowable condition to distribute the detection tasks. The service quality assessment of different detection mechanisms is formed mainly by using data analysis principles through historical detection data of the detection mechanisms, and analysis is carried out through assessment results.
Inputting data: the historical inspection results mainly comprise the predicted completion time, the actual completion time and the like of the spot-checking plan.
And (3) calculating: acquiring historical detection plan data, and calculating the task completion time difference Q of each time in the following way:
m is the predicted completion time and N is the actual completion time
Then, the weighted value is calculated according to the fact that the closer the current time is, the higher the weighted value is, and the weighted value is removed as shown in fig. 3.
Then, the total qos F is calculated as follows:
outputting information
Quality of service of the detection mechanism: the method mainly comprises the following steps of detecting the service quality assessment of an organization. The results are as follows:
and (3) calculating a distribution strategy: the distribution strategy is mainly calculated by fully considering the efficiency, the distribution cost and the service quality on the basis of distribution matching. The main idea is to add the service quality factor into the time period in a certain proportion based on the principle of fastest sampling period, and finally find the optimal time efficiency.
Inputting information: basic distribution strategy
And (4) distribution cost:
quality of service:
and (3) calculating: the efficiency period refers to a period required for the assignment of tasks to the detection means and a reflection of a combined effect of the detection quality of the detection means. The system mainly comprises the sum of a detection period and a distribution period, is influenced by the service quality of a detection mechanism, has a smaller efficiency period when the detection service quality is higher, and has a larger efficiency period when the detection service quality is worse. The calculation method is as follows:
efficiency period (detection period + delivery period) quality of service/100
Quality of service/100 + delivery cycle/100 ═ detection cycle
Firstly, the service quality measurement period is classified into a detection period and a distribution period, and the result is as follows:
the weighted delivery cycle calculation results are as follows:
calculating the total difference between the logistics waiting time and the distribution waiting time to obtain the waiting time, wherein the calculation mode is as follows:
if the detection waiting time is larger than the logistics waiting time, the detection waiting time is the logistics waiting time, and otherwise, the detection waiting time is not changed. The new detection latency is obtained as follows:
and calculating the average sampling time of each distribution transformer, namely the sampling efficiency is equal to the detection weighted efficiency plus the distribution weighted efficiency. The results are as follows:
the availability detection task allocation strategy is then calculated by detection task allocation efficiency.
The later condition after the A-level detection task is allocated is as follows:
after the C-level detection task is distributed, the following steps are carried out:
after the B-level detection task is distributed, the following steps are carried out:
outputting data: and outputting the data into a final distribution strategy and a sampling period thereof.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A detection task intelligent allocation method based on detection mechanism evaluation is characterized by comprising the following steps:
s1: inputting a detection plan comprising material types, material models, suppliers, winning lots, delivery lots, sample quantity, storage positions and detection levels;
s2: screening a detection mechanism capable of executing a detection task through the material types in the detection plan and the detection requirements of the power distribution materials of the state network at the present stage;
s3: for the detection mechanisms screened in the S2, acquiring the data of the service quality, the real-time bearing capacity and the logistics real-time bearing capacity of each detection mechanism;
s4: performing detection task allocation model calculation on the three indexes calculated in the step S3;
s5: finally, the calculation result of S4 outputs a spot check policy.
2. The method according to claim 1, wherein the analysis of the sampling plan in S1 includes obtaining the detection levels of the material type, the material model, the supplier, the winning lot, the arriving lot, the sample amount, the warehousing location and the related requirements in the sampling plan.
3. The method for intelligently allocating the detection tasks based on the detection mechanism evaluation as claimed in claim 1, wherein the analysis of the material types and the detection requirements in the step S2 comprises the following steps:
(1) acquiring the type and the detection grade of a detection material in a detection plan;
(2) converting into corresponding experimental detection items according to the detection grade in the step (1);
(3) screening all detection mechanisms with the materials and corresponding experimental projects;
(4) and selecting detection mechanisms 4 times before the completion time of the detection mechanism according to the predicted completion time, and outputting the detection mechanisms.
4. The method for intelligently allocating detection tasks based on detection mechanism evaluation according to claim 1, wherein in S3, the computational analysis of the detection mechanism service quality, the real-time bearing capacity and the logistics real-time bearing capacity includes:
(1) obtaining the service quality of the detection mechanism, mainly including the detection mechanism and the service quality QGarment；
(2) Obtaining the detection efficiency P of the detection mechanismDetection ofAnd a waiting time TEtc. of；
(3) Obtaining the fastest delivery time T for a detection mechanismFitting for mixing。
5. The method for intelligently allocating the detection tasks based on the detection mechanism evaluation as claimed in claim 1, wherein the computational analysis of the detection task allocation model in the step S4 includes:
(1) detection efficiency P using detection centerDetection ofWaiting time TEtc. ofCalculating the detection time TDetection of；
(2) The distribution of single detection sample, and the T of each detection mechanismDetection ofDetecting time, selecting the shortest detecting time detecting mechanism OiThen recalculate the waiting time T of the detection mechanismi；
(3) And (2) circulating until all samples are distributed, and acquiring the quantity D of the samples distributed by each detection mechanismi；
(4) Rejecting the detection mechanism with small sample quantity in the detection mechanism, and distributing the detection mechanism to other detection mechanisms;
(5) the total number of samples and the expected start detection time and the expected finish time for all assigned detection mechanisms were calculated.
6. The method according to claim 1, wherein the spot check strategy outputted in S5 includes a type of delivered material, a type of material, a storage location, a distribution detection mechanism, an expected start detection time, and an expected completion time.
7. The intelligent detection task allocation method based on detection mechanism evaluation as claimed in claim 3, wherein the steps of obtaining the detection room type and detection registration in the detection plan, and converting the detection level into the corresponding experimental detection item comprise:
(1) acquiring a detection demand grade J and a material type M;
(2) searching the material M in the network material quality detection capability standard library, and if the material type exists, executing (3); if the material type does not exist, directly returning that the material type does not exist, and finishing the distribution calculation;
(3) and searching all corresponding detection items G in the material quality detection capability standard library through the material type M and the detection requirement level J.
8. The intelligent detection task allocation method based on detection mechanism evaluation as claimed in claim 3, wherein the detection mechanism completion time is calculated by using the waiting time and the detection time, and the method comprises the following steps:
(1) obtaining the detection efficiency P of the detection centerDetection ofWaiting time TEtc. ofCalculating the detection time T detection in the following way:
Tdetection of＝DDetection of×PDetection of+TEtc. of
(2) Detection completion time T of each detection mechanismhThe calculation method is as follows:
(3)*Tetc. ofWaiting time, TFitting for mixingDelivery time, TDetection ofThe time required for detection.
9. The method as claimed in claim 4, wherein the planned expected completion time and the actual completion time are used for calculating to obtain the service quality Q corresponding to the detection mechanismGarment；
Calculating the fastest distribution time T by adding the distribution time calculated by the distribution efficiency and the distance and the actual distribution waiting timeFitting for mixing。
10. The method for intelligently allocating the inspection tasks based on the inspection mechanism evaluation as claimed in claim 5, wherein when the inspection mechanism allocates the rejected inspection mechanism sample again, the rejected inspection mechanism is removed and allocated on the basis of the allocated inspection mechanism sample, and the method comprises the following steps:
(1) removing the removed detection mechanisms from the screened detection to generate a residual detection mechanism combination;
(2) calculating various weights and parameters after the samples are distributed;
(3) and redistributing according to a calculation method of the intelligent distribution model of the detection tasks.
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