CN118396601A - Intelligent area operation and maintenance system optimization method and device and computer equipment - Google Patents
Intelligent area operation and maintenance system optimization method and device and computer equipment Download PDFInfo
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Abstract
The application relates to an operation and maintenance system optimization method for an intelligent area, which comprises the following steps: acquiring each index item of a preset area operation and maintenance system, a reference score of each index item, a scoring function corresponding to each index item and a weight of each index item; acquiring data of the current period of each index item, and determining a first score of the current period of each index item based on the data of the index item and a scoring function; calculating absolute values of differences between the first scores and the corresponding reference scores of the index items, and determining improvement factors of the index items in the preset region operation system based on the absolute values and the corresponding weights; and determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item. The method can obtain the improved degree of urgency of each index item, further gives out a warning whether the weight is reasonably set, and is convenient for an operation and maintenance system to adjust the relatively unreasonable weight according to the change and the gap of the actual monitoring scene.
Description
Technical Field
The application relates to the technical field of the internet of things, in particular to an operation and maintenance system optimization method and device for an intelligent area, computer equipment and a storage medium.
Background
With the continuous improvement of the operation and maintenance requirements of the intelligent parks of enterprises, municipal administration or urban underground comprehensive pipe racks, the continuous expansion of the management dimension of the intelligent parks, the continuous coping with the environmental changes of the inside and the outside, and the operation and maintenance management not only consists in the maintenance management and the upgrading of an intelligent system, but also needs to monitor personnel training, standardization, regular investigation, exercise and the like.
In the related art, a monitoring system of an operation and maintenance system is generally a fixed authority and operation and maintenance work item. Different geographical areas or different monitoring scenes of the same geographical area are usually used for monitoring indexes by a set of standards, and in this case, the situation that the weight setting of each index is relatively unreasonable exists. When the operation and maintenance system faces to environmental changes caused by different monitoring scenes, the improved forcing degree of each index item cannot be obtained, so that relatively unreasonable weights are difficult to adjust according to the change and the gap of the actual monitoring scenes, and the maintenance management and the upgrading of the intelligent system are not facilitated.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, computer device and storage medium for optimizing an operation and maintenance system in an intelligent area.
In a first aspect, the present application provides a method for optimizing an operation and maintenance system in an intelligent area, the method comprising:
acquiring each index item of a preset area operation and maintenance system, a reference score of each index item, a scoring function corresponding to each index item and a weight of each index item; the weight of each index item in the operation and maintenance system is related to the attribute of the preset area;
acquiring the data of the current period of each index item, and determining a first score of the current period of each index item based on the data of the index item and the scoring function;
calculating the absolute value of the difference between the first score of each index item and the corresponding reference score, and determining the improvement factor of each index item in the preset region operation system based on the absolute value and the corresponding weight;
And determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item.
In one embodiment, the determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factor of each index item includes:
predicting a second score of each index item after the current period is improved based on the improvement factors of each index item;
and determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item and the corresponding second scores.
In one embodiment, the determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factor of each index item and the corresponding second score includes:
acquiring a third score of each index item in the preset area operation and maintenance system in the next period;
And calculating the absolute value of the difference between the improvement factor of each index item and the corresponding second score, and determining the weight offset rate of each index item in the preset area operation and maintenance system based on the difference value and the corresponding third score.
In one embodiment, the method further comprises:
determining a first total score of the current period of the preset area operation and maintenance system based on the weight of each index item and the first score of each index item;
and predicting a second total score of the preset area operation and maintenance system after the current period is improved based on the first total score and the improvement factors of the index items.
In one embodiment, the predicting, based on the first total score and the improvement factors of the respective index items, the second total score of the preset area operation and maintenance system after the current period improvement includes:
determining an expected difference value of each index item based on the improvement factor of each index item and the first score of each index item;
determining an improvement value of each index item based on the expected difference value of each index item and the weight of each index item, and determining an improvement value of the preset area operation and maintenance system based on the improvement value of each index item;
And predicting a second total score of the preset area operation and maintenance system after the current period is improved based on the first total score and the improvement value of the preset area operation and maintenance system.
In one embodiment, the determining the improvement factor of each index item in the preset area operation system based on the absolute value and the corresponding weight includes: calculating the ratio of the absolute value to the corresponding weight, and determining the improvement factors of each index item in the preset region operation system;
the method further comprises arranging the weight offset rates in order of magnitude; the larger the weight offset rate is, the more urgent the weight adjustment of the corresponding index item is.
In a second aspect, the present application also provides an operation and maintenance system optimization device for an intelligent area, where the device includes:
The first acquisition module is used for acquiring each index item of the operation and maintenance system in the preset area, the reference score of each index item, the scoring function corresponding to each index item and the weight of each index item; the weight of each index item in the operation and maintenance system is related to the attribute of the preset area;
the second acquisition module is used for acquiring the data of the current period of each index item and determining a first score of the current period of each index item based on the data of the index item and the scoring function;
The analysis module is used for calculating the absolute value of the difference between the first score of each index item and the corresponding reference score; and determining an improvement factor of each index item in the preset region operation system based on the absolute value and the corresponding weight, and determining a weight offset rate of each index item in the preset region operation and maintenance system based on the improvement factor of each index item.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method of any of the above first aspects when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects described above.
The method, the device, the computer equipment and the storage medium for optimizing the operation and maintenance system of the intelligent area are characterized in that the weight of each index item in the operation and maintenance system related to the attribute of the preset area is obtained, and the first score of the current period of each index item in the operation and maintenance system of the preset area is determined based on the current period data of each index item and the scoring function; further, the absolute value of the difference between the first score of each index item and the corresponding reference score is calculated, the improvement factors of each index item in the preset area operation system are determined according to the absolute value and the corresponding weight, the weight offset rate of each index item in the preset area operation and maintenance system is determined by using the improvement factors of each index item, further, the improved forced relevance of each index item in the operation and maintenance system can be obtained, further, a warning about whether the weight is reasonably set is given, and the operation and maintenance system is convenient to adjust the relatively unreasonable weight according to the change and the difference of the actual monitoring scene.
Drawings
FIG. 1 is an application environment diagram of an operation and maintenance system optimization method of intelligent regions in one embodiment;
FIG. 2 is a flow chart of an operation and maintenance system optimization method of intelligent region in one embodiment;
FIG. 3 is a flowchart illustrating a step of determining weight offset rates of respective index items according to one embodiment;
FIG. 4 is a flowchart illustrating a step of determining weight offset rates of respective index items according to another embodiment;
FIG. 5 is a flow diagram of a post-improvement scoring step in one embodiment;
FIG. 6 is a flow chart of a scoring step after prediction improvement in another embodiment;
FIG. 7 is a flowchart of another embodiment of an operation and maintenance system optimization method for intelligent area;
FIG. 8 is a flowchart of an operation and maintenance system optimization method for intelligent area according to another embodiment;
FIG. 9 is a block diagram of an operation and maintenance system optimizing apparatus for intelligent area in one embodiment;
Fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The operation and maintenance system optimization method for the intelligent region provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 101 communicates with the server 102 via a network. The data storage system may store data that the server 102 needs to process. The data storage system may be integrated on the server 102 or may be located on a cloud or other network server. Specifically, the terminal may acquire each index item of the operation and maintenance system in the preset area and a reference score of each index item, determine a weight of each index item based on an attribute of the preset area, collect data of the index item in a current period, and determine a first score of each index item in the current period based on the data of the index item and the weight of the index item. Calculating, by the server 102, absolute values of differences between the first scores and the corresponding reference scores of the respective index items; determining improvement factors of all index items in a preset area operation system based on the absolute value and the corresponding weight; and determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item. The terminal 101 may be, but not limited to, a sensor (temperature, humidity, air quality sensor, etc.), and the terminal 101 may also be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, and the internet of things devices may be smart speakers, smart car devices, etc. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 102 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, there is provided an operation and maintenance system optimization method for an intelligent area, including the steps of:
Step 201, obtaining each index item of a preset area operation and maintenance system, a reference score of each index item, a scoring function corresponding to each index item and a weight of each index item; the weight of each index item in the operation and maintenance system is related to the attribute of the preset area.
The preset area may be a certain park of the first province, such as an enterprise smart park, or may be an urban underground utility tunnel of a certain area of the first province. Specifically, the index items of the enterprise intelligent park comprise subsystems such as security protection, fire protection, access control, parking lots and the like, and also comprise systems such as building, patrol and the like, and meanwhile, the establishment and execution of the systems such as park property personnel training, property duty, equipment facility maintenance and the like can also be used as examination items. The index item of the urban underground comprehensive pipe rack comprises subsystems such as environment and equipment monitoring, fire alarm control, emergency management and the like, and also comprises information such as personnel qualification, training, management system, emergency exercise and the like. The reference score of each index item may be preset to be 100 full.
It should be noted that the attributes of different preset areas are different, and the weights of the index items in the operation and maintenance system are different. The attribute of the preset area may be understood as a characteristic of the preset area, and the index item of comparative interest in the preset area can be determined based on the attribute of the preset area. For example, the urban underground utility tunnel is used as a special scene of urban underground space operation and maintenance, and focuses on space safety, equipment facility management, emergency management and the like, so that environmental risk factors, management capacity, system operation completeness and emergency treatment capacity are used as important assessment items, namely, the occupied weight is larger. The intelligent park is a common business state, and the attention points of the intelligent park are equipment and facility management, safety management and the like, so that the management capability, the completeness of system operation and technical protection measures are taken as important assessment items, namely the intelligent park is more weighted. Further, the preset area may be a city, and illustratively, from the basic characteristics of two cities, the Gansu is entirely dry, guangzhou is hot and typhoons are many, so that the importance of index items of the two cities is different, and the Gansu focuses on environmental fire and intelligent monitoring system construction (such as a security system, a fire protection system, an equipment and facility management system and the like). Guangzhou focuses on pre-warning, budget and emergency management of extreme weather such as typhoons.
Step 202, obtaining the data of the current period of each index item, and determining the first score of the current period of each index item based on the data of the index item and the scoring function.
The period may be one month or one quarter. The data of the current period comprises data collected in real time and also comprises preset setting or recorded data. Specifically, the environmental risk factors can be acquired through various sensors, such as temperature, humidity, gas detector, water level sensor and the like, and the energy consumption monitoring is acquired through data of water meters and electric meters; the historical security record may be the number of fire alarms that occurred over the past year; the management capability may be a fire check task completion rate. For example, 7 index items are provided in a certain preset area, and the index items are respectively: environmental risk factor a (e.g., temperature a1, smoke concentration a2, combustible gas concentration a 3); basic information integrity B (unit basic information B1, dangerous property B2, service life B3, service condition B4, regulation B5); management capability C (fire check task completion rate C1, equipment inspection completion rate C2, maintenance task completion rate C3, security training C4, emergency drill completion rate C5, personnel on duty rate C6, channel unobstructed rate C7); the system operation completeness D (alarm system failure rate D1, fire extinguishing system failure rate D2, ventilation system failure rate D3, water supply system failure rate D4, power supply and distribution system failure rate D5 and emergency broadcast industrial telephone failure rate D6); emergency handling capability E (emergency plan completeness E1, emergency system completeness E2, material warehouse distance E3); historical security records F (number of fire alarms F1, degree of fire loss F2 occurring in the past year); technical measure G (security monitoring G1, energy consumption monitoring G2 and remote control G3). Illustratively, the weight of the index term technical measure G is 15%, and the sum of the weights of the sub-terms in the index term technical measure G is 15%, that is, the sum of the weight of G1 plus the weight of G2 plus the weight of G3 is 15%. The first scores of the current periods of the respective index items are (si (i=1, 2,..and n)), and the calculated scores are (i=1, 2,..and n):
Score—1= FunctionA (a 1, a2, a 3) (scoring function of environmental risk factor, calculated from actual monitoring data),
Score—2= FunctionB (b 1, b2, b3, b4, b 5) (basis information completeness scoring function),
Score—3= FunctionC (c 1, c2, c3, c4, c5, c6, c 7) (management capability scoring function),
Score—4= FunctionD (d 1, d2, d3, d4, d5, d 6) (system run completeness scoring function),
Score _ 5= FunctionE (e 1, e2, e 3) (emergency treatment capability scoring function),
Score—6= FunctionF (f 1, f 2) (historical security record scoring function),
Score_7= FunctionG (g 1, g2, g 3) (technical measure scoring function). It should be noted that, the scoring function may be selected according to characteristics of different industries, i.e. selecting a calculation mode.
And 203, calculating the absolute value of the difference between the first score and the corresponding reference score of each index item, and determining the improvement factor of each index item in the preset region operation system based on the absolute value and the corresponding weight.
Specifically, each index item has a corresponding benchmark score value mi (i=1, 2, the term "n") and the improvement factor pi (i=1, 2. w1, w2, w3, w4, w5, w6, w7 respectively represent weights of the above indicators, satisfying w1+w2+w3+w4+w5+w6+w7=1. It should be noted that, the improvement factor of each index item in the preset area operation system may be determined based on the absolute value and the corresponding weight ratio, or may be determined based on the product of the absolute value and the corresponding weight.
And 204, determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item.
By way of example, taking a preset area as an urban underground utility tunnel as an example, the operation and maintenance system comprises an environment and equipment monitoring system, an inspection system, equipment fault monitoring, an emergency command system and the like, and meanwhile, daily operation and maintenance work of personnel, such as manual inspection, equipment maintenance, equipment fault treatment, plan management and the like, are also included in the index items. Assuming that the above mentioned monitoring system and daily operation and maintenance work are in place, i.e. under the condition that the intelligent monitoring system is perfect and the operation and maintenance work is in place, according to the determined weight deviation rate, the weight deviation rate can be relatively large, for example, the weight of an environmental risk factor A, one of the subdivisions in the index item is aimed at methane monitoring, and at present, a methane detector is available, and a specific numerical value can be received in real time for early warning, which is very important but has very low weight, so that the proper weight is adjusted. For another example, the temperature and humidity are detected, the underground humidity of the coast is relatively large, but the threshold value is not set high (the threshold value regulated by the standard) and the alarm is always given, the alarm is buckled once, the objective condition cannot be changed, and the weight can be reduced at the moment.
According to the operation and maintenance system optimization method of the intelligent region, the weight of each index item in the operation and maintenance system related to the attribute of the preset region is obtained, and the first score of the current period of each index item in the operation and maintenance system of the preset region is determined based on the current period data of each index item and the scoring function; further, the absolute value of the difference between the first score of each index item and the corresponding reference score is calculated, the improvement factors of each index item in the preset area operation system are determined according to the absolute value and the corresponding weight, the weight offset rate of each index item in the preset area operation and maintenance system is determined by using the improvement factors of each index item, further, the improved forced relevance of each index item in the operation and maintenance system can be obtained, further, a warning about whether the weight is reasonably set is given, and the operation and maintenance system is convenient to adjust the relatively unreasonable weight according to the change and the difference of the actual monitoring scene.
In one embodiment, determining the improvement factor for each index item in the preset zone operating system based on the absolute value and the corresponding weight includes: and calculating the ratio of the absolute value to the corresponding weight, and determining the improvement factors of all index items in the operation system of the preset area. The improvement factor of each index item is pi= |si-mi|/wi (i=1, 2,., n). By the improvement factors of the items, it can be clear which index item has the greatest improvement factor, namely which index item needs to be focused on and needs to be improved.
In one embodiment, the weight offset rates may be arranged in order of magnitude; the larger the weight offset rate is, the more urgent the weight adjustment of the corresponding index item is. Specifically, the weight offset rates of the index items are sorted from big to small, weight difference offset ratio is ri (i=1, 2,., n), if: r1> r2> r3> r4> r5, then the adjustment of the forcing degree is w1> w2> w3> w4> w5.
In one embodiment, as shown in fig. 3, determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factor of each index item includes:
step 301, predicting a second score of each index item after the current period improvement based on the improvement factors of each index item.
Wherein, improvement factor pi (i=1, 2,., n) of each index item, and the second score of each index item after improvement in the current period is si+ (pi-si) ×wi.
Step 302, determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item and the corresponding second scores.
The second score is a score of each index item after the current period is improved, and the improvement factor is determined by each index item based on the reference score and the score of the current period.
In this embodiment, the weight offset rate of each index item determined by the score of each index item after the improvement of the current period and the improvement factor of each index item is more reasonable and more accurate.
In one embodiment, as shown in fig. 4, determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factor of each index item and the corresponding second score value includes:
Step 401, obtaining a third score of a next period of each index item in a preset area operation and maintenance system;
The method comprises the steps of firstly obtaining data of each index item in a next period in a preset region operation and maintenance system, and determining the score of the next period of each index item based on the data of the index item and a scoring function.
Step 402: and calculating the absolute value of the difference between the improvement factor of each index item and the corresponding second score, and determining the weight offset rate of each index item in the operation and maintenance system of the preset area based on the absolute value and the corresponding third score.
Specifically, the weight shift rate of each index item in the preset area operation and maintenance system is ri (i=1, 2,., n), the third score is si ', the weight offset rate can be ri= |pi-si|/si', pi is an improvement factor, and si is a second score, namely a score of each index item after improvement in the current period.
In this embodiment, the absolute value of the difference between the improvement factor of each index item and the score of each index item corresponding to the improvement of the index item in the current period is used, and the weight offset rate of each index item in the operation and maintenance system in the preset area is determined based on the difference and the score of each index item in the next period, so that the weight offset rate of each index item in the operation and maintenance system in the preset area is more accurate and objective, and when the intelligent monitoring system is perfect and the operation and maintenance work in place, the score has deviation due to unreasonable weight, the user can conveniently obtain the improved degree of forcing each index item according to the weight offset rate, and further relatively unreasonable weight is adjusted or some meaningless index items are removed.
In one embodiment, as shown in fig. 5, the optimization method further includes:
step 501: and determining a first total score of the current period of the operation and maintenance system of the preset area based on the weight of each index item and the first score of each index item.
In particular, the method comprises the steps of, total current period Score TotleScore =w1×score for preset area operation and maintenance system 1+w2 x score_2+w3 x score_3+w4 x score_4+w5 x score_5+w6 x score_6+w7 x score_7. Further, the method also comprises setting a scoring threshold of the operation and maintenance system of the preset area, in particular, excellent (90-100 minutes), good (70-89 minutes), qualified (60-69 minutes) and unqualified (< 60 minutes).
Based on the scoring threshold value, the user can conveniently judge whether the total score of the current period of the operation and maintenance system in the preset area is qualified or not. Specifically, the total score of the preset area operation and maintenance system is the total score result of all index items, and the total score is compared with a score threshold value to obtain excellent, good, qualified or unqualified operation and maintenance evaluation results. The operation and maintenance evaluation results are read by combining with actual operation and maintenance work, and the following common situations exist: 1) The operation and maintenance work is in place, the operation and maintenance evaluation result is relatively objective, and the weight of the existing index item is kept; 2) The operation and maintenance work is not in place, the operation and maintenance evaluation result is not ideal, the whole operation and maintenance evaluation model is also objective, and a user can lock specific index items to optimize specific operation and maintenance work; 3) The operation and maintenance work is inconsistent with the operation and maintenance evaluation result, for example, the total score of the operation and maintenance system is very high when the operation and maintenance work is not operated, or the total score of the operation and maintenance system is not operated in place when the operation and maintenance work is operated in place, the weight of each index item and the index item subdivision item in the operation and maintenance system are required to be adjusted.
For example, the urban underground utility tunnel is used as a special scene of urban underground space operation and maintenance, and focuses on space safety, equipment and facility management, emergency management and the like, so an environmental risk factor A, a management capacity C, a system operation completeness D and an emergency treatment capacity E are used as important assessment items. Therefore, the operation and maintenance system comprises index items such as an environment and equipment monitoring system, a patrol system, equipment fault monitoring, an emergency command system and the like, and meanwhile, the daily operation and maintenance work of personnel such as manual patrol, equipment maintenance and maintenance, equipment fault treatment, plan management and the like are also taken into consideration. It is assumed that the above-mentioned system and daily operation and maintenance work are in place, but the result of the total score of the urban underground utility tunnel operation and maintenance system is not satisfactory, i.e., is not in compliance with objective conditions. At this time, the improvement factors of the index items can be calculated based on the index items, so that it is clear and objective that which improvement factor is the largest and which index item needs to be focused on and needs to be improved. And combining the improvement factors, comparing the improved score with the actual score of the next period, and determining the weight offset rate, namely, under the condition that the monitoring system of the operation and maintenance system is perfect and the operation and maintenance work is in place, the score has deviation due to unreasonable weight. The maximum weight offset rate, such as the weight of an environmental risk factor A, is determined, one of the subdivision items in the index item is aimed at methane monitoring, and a methane detector is available at present, and can receive specific values in real time for early warning, and the weight is very important but very low, so that the proper weight is adjusted. It should be noted that, the weights of the index items in the preset area operation and maintenance system may be initially set by the user according to experience, or may be automatically set according to the history record, and the set weights may be different from the actual situation and are not objective. There are some weight adjustments that need to be made depending on the improvement factor. For example, the temperature and humidity are detected, the underground humidity of coastal is relatively large, but the threshold value is not set high (the threshold value regulated by the standard), the alarm is always given, the alarm is buckled once, the objective condition cannot be changed, and the weight is reduced.
Step 502: and predicting a second total score after the current period of the preset area operation and maintenance system is improved based on the first total score and the improvement factors of the index items.
It should be noted that the second total score can be used to predict an ideal score that can be achieved after the subsequent weight improvement, so as to remind the user of the space that can be lifted.
In this embodiment, by determining the total score of the current period of the preset area operation and maintenance system, it is convenient to determine whether the operation and maintenance of the operation and maintenance system meets the expectations, and further predict the total score of the preset area operation and maintenance system after the current period is improved based on the first total score and the improvement factors of each index item, so as to predict the ideal score that can be reached after the subsequent weight improvement, so as to remind the user of the liftable space, and give a safety warning with the minimum cost and the method closest to the real environment, and instruct whether the weight setting is reasonable by the difference between the predicted total score and the actual difference.
In one embodiment, as shown in fig. 6, predicting the second total score after the current period of the preset area operation and maintenance system is improved based on the first total score and the improvement factors of the respective index items includes:
step 601: an expected difference for each index item is determined based on the improvement factor for each index item and the first score for each index item.
Specifically, the expected difference for each index term is pi-si (i=1, 2, once again, n), pi is an improvement factor of each index item in the operation and maintenance system, si is a score of each index item in the current period.
Step 602: and determining an improvement value of each index item based on the expected difference value of each index item and the weight of each index item, and determining an improvement value of the operation and maintenance system of the preset area based on the improvement value of each index item.
Specifically, the improvement value of each index item is (pi-si) wi. For example, a preset area has 7 index items, and the improvement value of the operation and maintenance system of the preset area is (p 1-s 1) w1+ (p 2-s 2) w2+ (p 3-s 3) w3+ (p 4-s 4) w4+ (p 5-s 5) w5+ (p 6-s 6) w6+ (p 7-s 7) w7.
Step 603: and predicting a second total score after the current period of the preset area operation and maintenance system is improved based on the first total score and the improvement value of the preset area operation and maintenance system.
Wherein the second total score TotalScore'=TotalScore+(p1-s1)*w1+(p2–s2)*w2+(p2–s2)*w2+(p3–s3)*w3+(p4–s4)*w4+(p5–s5)*w5+(p6–s6)*w6+(p7–s7)*w7;TotalScore is the first total score.
In this embodiment, the improvement value of the preset area operation and maintenance system is determined based on the improvement value of each index item, and the total score after improvement is predicted based on the total score before the improvement of the current period of the preset area operation and maintenance system and the improvement value of the preset area operation and maintenance system, which is more accurate.
In one embodiment, as shown in fig. 7, there is provided an operation and maintenance system optimization method for an intelligent area, including the steps of:
Step 701: acquiring each index item in a preset area operation and maintenance system; the preset area may be defined by the user.
Step 702: the weight of each index item is set.
Step 703: and collecting index item data.
Step 704: and calculating the score of each index item in the operation and maintenance system of the preset area according to the weight.
Step 705: judging whether an abnormal index item exists or not; if yes, go to step 703, if no, go to step 706.
Step 706: the improvement factor is calculated.
Step 707: and calculating the total score of the predicted operation and maintenance system of the preset area.
Step 708: judging whether the index item weight in the operation and maintenance system is optimized or not; if yes, executing step 709, if not, ending execution; it should be noted that, the ideal score that can be achieved after the subsequent weight improvement is predicted is used to remind the user of the liftable space, so that the user can select whether to optimize the index item weight in the operation and maintenance system according to the liftable space. Or the operation and maintenance system can set a certain threshold value, specifically, when the liftable space is larger than a certain threshold value, the index item weight in the operation and maintenance system is optimized.
Step 709: and calculating the weight offset rate of each index item in the preset area operation and maintenance system.
In one embodiment, as shown in fig. 8, there is provided an operation and maintenance system optimization method for an intelligent area, including the steps of:
step 801: and acquiring each index item in the operation and maintenance system of the preset area.
Step 802: and setting the weight of each index item.
Step 803: and collecting data of each index item.
Step 804: and calculating the score of each index item in the operation and maintenance system of the preset area according to the weight.
Step 805: judging whether an abnormal index item exists or not; if yes, go to step 803, if no, go to step 806.
Step 806: and calculating the total score of the operation and maintenance system of the preset area.
Step 807: judging whether the index item weight in the operation and maintenance system is optimized or not; if yes, go to step 808, if not, end. Specifically, the total score of the preset area operation and maintenance system is the total score result of all index items, and the total score can be compared with a score threshold value to obtain excellent, good, qualified or unqualified operation and maintenance evaluation results. Under the condition that the total score is lower than the expected score, firstly determining whether the operation and maintenance work is in place, and if the operation and maintenance work is not in place, lifting the operation and maintenance work; if the operation and maintenance work in place, optimizing the index item weight in the operation and maintenance system. There are therefore two cases: 1. and the operation and maintenance work in place, the operation and maintenance evaluation result, namely the total score, does not meet the expectations, and the improvement factor is calculated. 2. Operation and maintenance work is not in place, the operation and maintenance evaluation result does not meet the expectations, and operation and maintenance work improvement is needed first instead of calculating an improvement factor. The standard of judgment is whether the actual operation and maintenance work is in place, whether the intelligent system construction, the hidden trouble investigation work and the like are in place, if the operation and maintenance work is in place, but the operation and maintenance evaluation result is not high, namely the objective condition is not met, the improvement factor needs to be calculated.
Step 808: the improvement factor is calculated.
Step 809: and calculating the weight offset rate of each index item in the preset area operation and maintenance system. The weight offset rate of each index item is ranked from high to low, so that it can be clear which index items of a user are perfect, but the integral grading improvement cannot be obviously improved, for example, in drought places, some monitoring indexes of flood disasters are meaningless, and the user can be guided to reject some meaningless index items.
It should be noted that, in the embodiment of the operation and maintenance system optimization method for one or more intelligent areas provided above, when the total score of the operation and maintenance system in the preset area is lower than the expected score and the operation and maintenance work is in place, the weight of each index item can be optimized, and the scoring function of the index item can be adjusted. For example, there are other scoring functions, and the existing scoring function is selected according to the characteristics of different regions, i.e. the calculation mode is selected. The subdivision of the index item may also be adjusted, for example, without methane or other toxic or harmful gases in the predetermined area, where the weight of one subdivision of the index item may be reduced or directly left out of consideration. Or the integral grading improvement can not be obviously improved, for example, in arid places, some monitoring indexes of flood disasters are meaningless, and users can be guided to reject some meaningless index items through the weight deviation rate.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an operation and maintenance system optimizing device for realizing the operation and maintenance system optimizing method of the intelligent area. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the operation and maintenance system optimization device for one or more intelligent areas provided below may be referred to the limitation of the operation and maintenance system optimization method for the intelligent area hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 9, there is provided an operation and maintenance system optimizing apparatus for an intelligent area, including: a first acquisition module 901, a first acquisition module 902, and an analysis module 903, wherein:
The first obtaining module 901 is configured to obtain each index item of the preset area operation and maintenance system, a reference score of each index item, a scoring function corresponding to each index item, and a weight of each index item; the weight of each index item in the operation and maintenance system is related to the attribute of the preset area.
And a second obtaining module 902, configured to obtain data of the current period of each index item, and determine a first score of the current period of each index item based on the data of the index item and the scoring function.
An analysis module 903 for calculating an absolute value of a difference between the first score of each index item and the corresponding reference score; and determining an improvement factor of each index item in the preset region operation system based on the absolute value and the corresponding weight, and determining a weight offset rate of each index item in the preset region operation and maintenance system based on the improvement factor of each index item.
In one embodiment, the analysis module 903 determining the weight offset rate for each index item in the preset zone operation and maintenance system based on the improvement factor for each index item includes: predicting a second score of each index item after the current period of each index item is improved based on the improvement factors of each index item; and determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item and the corresponding second scores.
In one embodiment, the analysis module 903 determines the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factor of each index item and the corresponding second score value includes: acquiring a third score of the next period of each index item in the operation and maintenance system of the preset area; and calculating the absolute value of the difference between the improvement factor of each index item and the corresponding second score, and determining the weight offset rate of each index item in the operation and maintenance system of the preset area based on the absolute value and the corresponding third score.
In one embodiment, the analysis module 903 is further configured to determine a first total score of the current period of the preset area operation and maintenance system based on the weight of each index item and the first score of each index item; and predicting a second total score after the current period of the preset area operation and maintenance system is improved based on the first total score and the improvement factors of the index items.
In one embodiment, the analyzing module 903 predicts a second total score of the current period improvement of the preset area operation and maintenance system based on the first total score and the improvement factors of the respective index items, including: determining an expected difference value for each index item based on the improvement factor for each index item and the first score for each index item; determining an improvement value of each index item based on the expected difference value of each index item and the weight of each index item, and determining an improvement value of a preset area operation and maintenance system based on the improvement value of each index item; and predicting a second total score after the current period of the preset area operation and maintenance system is improved based on the first total score and the improvement value of the preset area operation and maintenance system.
In one embodiment, the analysis module 903 determines the improvement factor of each index item in the preset area operation and maintenance system based on the absolute value and the corresponding weight includes: and calculating the ratio of the absolute value to the corresponding weight, and determining the improvement factors of all index items in the operation system of the preset area. The analysis module is also used for arranging the weight offset rates according to the order of magnitude; the larger the weight offset rate is, the more urgent the weight adjustment of the corresponding index item is.
The modules in the operation and maintenance system optimizing device in the intelligent area can be all or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 10. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for optimizing an operation and maintenance system in an intelligent area.
It will be appreciated by those skilled in the art that the structure shown in FIG. 10 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive Memory, magnetic Memory, ferroelectric Memory, phase change Memory, graphene Memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method for optimizing an operation and maintenance system in an intelligent area, the method comprising:
acquiring each index item of a preset area operation and maintenance system, a reference score of each index item, a scoring function corresponding to each index item and a weight of each index item; the weight of each index item in the operation and maintenance system is related to the attribute of the preset area;
acquiring the data of the current period of each index item, and determining a first score of the current period of each index item based on the data of the index item and the scoring function;
calculating the absolute value of the difference between the first score of each index item and the corresponding reference score, and determining the improvement factor of each index item in the preset region operation system based on the absolute value and the corresponding weight;
And determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item.
2. The method of claim 1, wherein the determining the weight offset rate for each index item in the preset zone operation and maintenance system based on the improvement factor for each index item comprises:
predicting a second score of each index item after the current period of each index item is improved based on the improvement factors of each index item;
and determining the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item and the corresponding second scores.
3. The method of claim 2, wherein determining the weight offset rate for each of the index items in the preset zone operation and maintenance system based on the improvement factor and the corresponding second score for each of the index items comprises:
acquiring a third score of the next period of each index item in the preset area operation and maintenance system;
And calculating the absolute value of the difference between the improvement factor of each index item and the corresponding second score, and determining the weight offset rate of each index item in the preset area operation and maintenance system based on the absolute value and the corresponding third score.
4. The method according to claim 1, wherein the method further comprises:
Determining a first total score of the current period of the preset area operation and maintenance system based on the weight of each index item and the first score of each index item;
And predicting a second total score after the current period of the preset area operation and maintenance system is improved based on the first total score and the improvement factors of the index items.
5. The method of claim 4, wherein predicting a second total score for the current period improvement of the preset zone operation and maintenance system based on the first total score and the improvement factor for each of the index items comprises:
determining an expected difference value of each index item based on the improvement factor of each index item and the first score of each index item;
determining an improvement value of each index item based on the expected difference value of each index item and the weight of each index item, and determining an improvement value of the preset area operation and maintenance system based on the improvement value of each index item;
And predicting a second total score after the current period of the preset area operation and maintenance system is improved based on the first total score and the improvement value of the preset area operation and maintenance system.
6. The method of claim 1, wherein the step of determining the position of the substrate comprises,
The determining the improvement factors of each index item in the preset area operation and maintenance system based on the absolute value and the corresponding weight comprises the following steps: calculating the ratio of the absolute value to the corresponding weight, and determining the improvement factors of each index item in the preset region operation system;
the method further comprises arranging the weight offset rates in order of magnitude; the larger the weight offset rate is, the more urgent the weight adjustment of the corresponding index item is.
7. An operation and maintenance system optimizing device for an intelligent area, the device comprising:
The first acquisition module is used for acquiring each index item of the operation and maintenance system in the preset area, the reference score of each index item, the scoring function corresponding to each index item and the weight of each index item; the weight of each index item in the operation and maintenance system is related to the attribute of the preset area;
the second acquisition module is used for acquiring the data of the current period of each index item and determining a first score of the current period of each index item based on the data of the index item and the scoring function;
The analysis module is used for calculating the absolute value of the difference between the first score of each index item and the corresponding reference score; and determining an improvement factor of each index item in the preset region operation system based on the absolute value and the corresponding weight, and determining a weight offset rate of each index item in the preset region operation and maintenance system based on the improvement factor of each index item.
8. The apparatus of claim 7, wherein the device comprises a plurality of sensors,
The analysis module predicts the second score of each index item after the current period is improved based on the improvement factors of each index item, and determines the weight offset rate of each index item in the preset area operation and maintenance system based on the improvement factors of each index item and the corresponding second scores.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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