CN111984933A - Risk assessment method and device for water delivery system of hydraulic ship lift based on cloud model - Google Patents
Risk assessment method and device for water delivery system of hydraulic ship lift based on cloud model Download PDFInfo
- Publication number
- CN111984933A CN111984933A CN202010878759.5A CN202010878759A CN111984933A CN 111984933 A CN111984933 A CN 111984933A CN 202010878759 A CN202010878759 A CN 202010878759A CN 111984933 A CN111984933 A CN 111984933A
- Authority
- CN
- China
- Prior art keywords
- risk
- water head
- shaft water
- value
- cloud
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 312
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000012502 risk assessment Methods 0.000 title claims abstract description 25
- 238000012544 monitoring process Methods 0.000 claims abstract description 51
- 238000009826 distribution Methods 0.000 claims description 9
- 235000020681 well water Nutrition 0.000 claims description 6
- 239000002349 well water Substances 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims 1
- 230000008859 change Effects 0.000 description 8
- 238000011156 evaluation Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 229910000831 Steel Inorganic materials 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012854 evaluation process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Data Mining & Analysis (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Physics (AREA)
- Game Theory and Decision Science (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Quality & Reliability (AREA)
- Mathematical Optimization (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Computational Biology (AREA)
- Algebra (AREA)
- Life Sciences & Earth Sciences (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Emergency Alarm Devices (AREA)
Abstract
The embodiment of the invention provides a risk assessment method and a device for a water delivery system of a hydraulic ship lift, wherein the method comprises the following steps: acquiring vertical shaft water level difference monitoring data of a water delivery system of the hydraulic ship lift; calling a cloud model reverse cloud generator according to the vertical shaft water head monitoring data to obtain digital characteristic values Ex, En and He of the vertical shaft water head monitoring data, wherein Ex is an expected value, En is an entropy value, and He is an ultra-entropy value; calling a cloud model forward cloud generator according to the digital characteristic values Ex, En and He to generate a vertical shaft water level difference data cloud droplet group; determining the contribution rate of the shaft water head value of any interval to the shaft water head risk based on the shaft water head data cloud drop group, and confirming a plurality of shaft water head risk intervals according to the contribution rate; and determining a shaft water head risk early warning threshold value according to the shaft water head risk interval.
Description
Technical Field
The invention relates to the technical field of hydraulic engineering, in particular to a risk assessment method and device for a water delivery system of a hydraulic ship lift based on a cloud model.
Background
The ship lift is used as an indispensable hydraulic building in navigation transportation, is an important supporting facility in hydropower station construction, is applied to help ships to pass a dam, and has a safety condition directly related to personal and property safety and navigation efficiency of passengers. The hydraulic ship lift is a novel hydraulic building taking water power as a driving mode, and compared with the traditional electric driving ship lift, the hydraulic ship lift not only can thoroughly solve the accident risk of 'runaway' caused by the damage of a balance system generated by water leakage of a ship chamber, but also can adapt to the difficult butt joint of the ship chamber caused by the rapid and large-scale change of the water level of a river channel. The hydraulic power is used as a power improving and safety guaranteeing measure, and the hydraulic ship lift realizes the self-adaptive full balance in the development history of the ship lift. The significant advantage of the hydraulic ship lift is attributed to the fact that the hydraulic ship lift drives the core water delivery system. However, the hydraulic ship lift operates under complex hydrodynamic conditions for a long time, which increases the risk of accidents, especially the risk of unsynchronized water level of the vertical shaft in the driving core water delivery system. Therefore, it is necessary to perform a risk analysis on the operational safety of the water delivery system of the hydraulic ship lift.
For a long time, the risk analysis method applied to the hydraulic engineering field at home and abroad mainly focuses on dam safety risk assessment or flood risk early warning, and few safety risk researches in the field of ship lifts are carried out. To ensure safe operation of a hydraulic ship lift, a feasible risk assessment method for a driving core water delivery system of the hydraulic ship lift is still lacked. The operation risk of the water delivery system is caused by the uncertainty of the phenomenon of unsynchronized water level of the vertical shaft, the unsynchronized water level of the water delivery system causes the torque of the synchronizing shaft of the mechanical system to be increased, the exceeding of the torque of the synchronizing shaft brings the risk potential of the breakage of the synchronizing shaft, and the operation safety of the ship lift is threatened (as shown in figure 3). In recent years, most of the running risk state evaluation of ship lifts is based on a fuzzy comprehensive evaluation theory, in the evaluation process, the membership degree of a risk index to the evaluation state is obtained by adopting a strategy of expert scoring to determine an evaluation matrix, so that the influence of human factors is difficult to eliminate, and the one-sidedness exists.
Based on the above consideration, it is an urgent need to solve the problem of the technical personnel in the field to provide an evaluation method capable of quantitatively describing the operation risk of the water delivery system aiming at the risk of asynchronous water level of the vertical shaft existing in the water delivery system of the hydraulic ship lift.
Disclosure of Invention
The embodiment of the invention provides a risk assessment method and a risk assessment device for a water delivery system of a hydraulic ship lift.
The invention adopts a technical scheme for solving the technical problems that, on one hand, a risk assessment method for a water delivery system of a hydraulic ship lift is provided, and the method comprises the following steps:
acquiring vertical shaft water level difference monitoring data of a water delivery system of the hydraulic ship lift;
calling a cloud model reverse cloud generator according to the vertical shaft water head monitoring data to obtain digital characteristic values Ex, En and He of the vertical shaft water head monitoring data, wherein Ex is an expected value, En is an entropy value, and He is an ultra-entropy value;
calling a cloud model forward cloud generator according to the digital characteristic values Ex, En and He to generate a vertical shaft water level difference data cloud droplet group;
determining the contribution rate of the shaft water head value of any interval to the shaft water head risk based on the shaft water head data cloud drop group, and confirming a plurality of shaft water head risk intervals according to the contribution rate;
and determining a shaft water head risk early warning threshold value according to the shaft water head risk interval.
Preferably, the method further comprises determining a shaft water head risk early warning threshold according to the shaft hoisting height level.
Preferably, the method further comprises performing a shaft water head risk warning when the monitored value of the shaft water head is within a shaft water head risk warning threshold.
Preferably, the step of calling the cloud model forward cloud generator according to the numerical characteristic values Ex, En and He to generate the mathematical representation of the vertical shaft water level difference data cloud droplet group comprises the following steps:
generating a normal distribution random function E 'n according to En and He, wherein the mathematical expression of the E' n is
The mathematical expression for generating normal random numbers xi, xi according to Ex and He is
Calculating the membership degree of xi according to the formula
Wherein x is cloud drop quantitative data reflecting the risk of the vertical shaft water head, and mu (x) is cloud drop membership data for calculating the vertical shaft water head monitoring data x.
Preferably, the shaft water head risk zone comprises:
the safety element interval is a numerical value interval in which the value of the water level difference of the vertical shaft is smaller than Ex + En;
the safer element interval is a numerical value interval of the value of the water level difference of the vertical shaft between Ex + En and Ex +2 En;
the weak risk element interval is a numerical value interval of the value of the water level difference of the vertical shaft between Ex +2En and Ex +3 En;
and the risk element interval is a numerical interval in which the vertical shaft water head value is greater than Ex +3 En.
Specifically, determining a shaft water head risk early warning threshold value according to the shaft water head risk interval includes:
taking the numerical upper and lower limits of the weak risk element interval as the upper and lower limits of the early warning threshold value of the shaft water level difference weak risk state;
and taking the numerical value lower limit of the risk element interval as the lower limit of the early warning threshold value of the water level difference risk state of the vertical shaft.
In another aspect, there is provided a risk assessment device for a water delivery system of a hydraulic ship lift, the device comprising:
the vertical shaft water level difference monitoring data acquisition unit is configured to acquire vertical shaft water level difference monitoring data of a water delivery system of the hydraulic ship lift;
the digital characteristic value acquisition unit is configured to call a cloud model reverse cloud generator according to the vertical shaft water level difference monitoring data to acquire digital characteristic values Ex, En and He of the vertical shaft water level difference monitoring data, wherein Ex is an expected value, En is an entropy value, and He is a super-entropy value;
the water level difference cloud droplet group acquisition unit is configured to call a cloud model forward cloud generator according to the digital characteristic values Ex, En and He to generate a vertical shaft water level difference data cloud droplet group;
the water head risk interval confirming unit is configured to determine the contribution rate of the shaft water head value of any interval to the shaft water head risk based on the shaft water head data cloud drop group, and confirm a plurality of shaft water head risk intervals according to the contribution rate;
and the risk early warning threshold confirming unit is configured to determine a shaft water head risk early warning threshold according to the shaft water head risk interval.
Preferably, the device further comprises an early warning unit configured to perform a shaft water head risk early warning when the monitored value of the shaft water head is within a shaft water head risk early warning threshold value.
In a third aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method of the first aspect.
In a fourth aspect, a computing device is provided, which includes a memory and a processor, wherein the memory stores executable code, and the processor executes the executable code to implement the method of the first aspect.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a risk assessment method for a water delivery system of a hydraulic ship lift according to an embodiment of the present invention.
Fig. 2 is a structural diagram of a risk assessment device for a water delivery system of a hydraulic ship lift according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of torque generated by a synchronizing shaft caused by water head difference between shafts of a water delivery system of the hydraulic ship lift.
Fig. 4 is a schematic diagram of risk division of unsynchronization of water level in a shaft of a water delivery system of a hydraulic ship lift.
Fig. 5 is a cloud view of a water level difference of a vertical shaft of a water delivery system of a flood-scene hydraulic ship lift according to an embodiment of the invention.
Fig. 6 is a cloud view of water level differences of a vertical shaft of a water delivery system of a hydraulic ship lift at different lifting heights according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As described above, most of the existing methods for evaluating the operation risk state of the ship lift are based on the fuzzy comprehensive evaluation theory, and in the evaluation process, the influence of human factors is difficult to eliminate, so that the problem of one-sidedness exists. In the research of the technical problems, the inventor provides a risk assessment method for a water delivery system of a hydraulic ship lift based on a cloud model. The following summarizes the concepts and principles of the present inventive arrangements.
The water delivery system is a power lifting system of a hydraulic ship lift, the arrangement scheme of the water delivery system directly influences the running safety of the ship lift, the hydraulic ship lift drives the ship chamber to lift through a hydraulic drive balance weight, and the synchronous lifting of all balance weights, namely the synchronous lifting of the water level in each vertical well, must be ensured.
In the water delivery process, a water level difference phenomenon exists between the left vertical shaft, the right vertical shaft and the same vertical shaft of the water delivery system, except that the flow velocity of water flow and turbulence generate water power to act on the floating drum counterweight, the hydrodynamic risk of the water delivery system is mainly expressed in that the maximum vertical shaft water level difference exceeds an allowable value. The water level difference of the vertical shaft of the water delivery system can directly cause the increase of the torque of the synchronizing shaft of the mechanical system, as shown in fig. 3, the water level difference of the vertical shaft causes the inconsistent submerging depth of the balance weights of the floating drums in each vertical shaft, the different tensile forces borne by the steel wire ropes connected with the floating drums are caused, the larger the water level difference of the vertical shaft is, the larger the difference of the tensile forces of the steel wire ropes at the adjacent balance weight sides is, the torque of the synchronizing shaft between the winding drums is directly increased, the risk potential hazard that the synchronizing shaft is broken due to the fact that the torque of the synchronizing shaft exceeds the standard is brought, and the operation safety of the ship lift is threatened.
The cloud model theory is a theory for establishing an uncertain conversion model between a qualitative concept and quantitative description, can be applied to research on uncertainty of risks, and can reflect the fuzziness and randomness of the risks. The method includes the following steps that based on a cloud model theory, the concept of asynchronous risk of the water level of the vertical shaft is provided for the first time in the specification, a water delivery system operation risk assessment system is constructed, the cognition of the operation risk of the water delivery system is improved from a primary qualitative concept to a high-level quantitative description level, and the main ideas of the scheme provided in the specification are as follows:
firstly, acquiring the risk of the asynchronous water level of a vertical shaft of a water delivery system of the hydraulic ship lift, and defining the risk on a cloud model. Specifically, it is assumed that U is a quantitative discourse domain corresponding to an effect quantity of a risk object (asynchronous shaft water level), C is a qualitative concept (shaft water level difference phenomenon) on U, if a quantitative value x belongs to U, x is defined as a shaft water level difference value, x is a random realization of the shaft water level difference phenomenon C, and a membership degree μ (x) of x to C belongs to [0,1] is a random distribution rule with a stable tendency:
the above formula represents: the distribution of the water head x over the shaft water level de-synchronization U is called cloud, and x is called a cloud droplet. Cloud droplets are the basic unit of a cloud model, and a cloud is a group of many cloud droplets distributed over C, represented by C (x, μ). In the cloud model, the characteristics of the research object are reflected by adopting expectation, entropy and super-entropy concepts. The expectation represents the center of the qualitative concept, is the mean value of cloud droplets in the qualitative concept, reflects the average change degree of the water level difference data of the vertical shaft, and is expressed by Ex. The entropy can reflect the size of the uncertain degree of the change of the water level difference of the vertical shaft, and is expressed by En. The uncertainty of the entropy is measured by the super entropy and is expressed by He, the dispersion degree of the uncertainty is reflected, the variation uncertainty degree of the water level difference of the vertical shaft becomes more and more discrete along with the increase of the super entropy, and otherwise, the variation uncertainty degree of the water level difference of the vertical shaft is expressed as a stable characteristic.
Secondly, determining the risk change degree of the vertical shaft water level difference through a cloud model based on the definition of the vertical shaft water level asynchronous risk and the monitoring data of the vertical shaft water level difference; and then determining the contribution rate of the shaft water head values of different intervals to the shaft water level asynchronization according to the shaft water head risk change degree, and specifically dividing the risk intervals of the shaft water head according to the contribution rate. And finally, determining a shaft water level difference risk early warning index threshold according to the asynchronous risk state of the shaft water level.
The risk assessment method for the water delivery system of the hydraulic ship lift provided by the invention is further explained by combining the embodiment. Fig. 1 is a flowchart of a risk assessment method for a water delivery system of a hydraulic ship lift according to an embodiment of the present invention, as shown in fig. 1, the process of the method at least includes the following steps:
and 11, acquiring monitoring data of the water level difference of the vertical shaft of the water delivery system of the hydraulic ship lift.
As mentioned above, the solution of the present invention measures the risk condition of the water delivery system according to the shaft water head, and therefore, in this step, shaft water head monitoring data is obtained. In one embodiment, the monitoring data may be water head monitoring data over a period of time.
And step 12, calling a cloud model reverse cloud generator according to the vertical shaft water head monitoring data to obtain digital characteristic values Ex-expected value, En-entropy value and He-super-entropy value of the vertical shaft water head monitoring data.
In the step, according to the vertical well water head difference monitoring data, a cloud model reverse cloud generator is used, so that the digital characteristic values of the vertical well water head difference monitoring data, namely the Ex-expected value, the En-entropy value and the He-super-entropy value, can be obtained, and the specific meanings of the Ex-expected value, the En-entropy value and the He-super-entropy value are explained in the scheme thought, and are not repeated here.
And step 13, calling a cloud model forward cloud generator according to the digital characteristic values Ex, En and He to generate a vertical shaft water level difference data cloud droplet group.
In the step, a cloud model forward cloud generator is called to obtain the cloud droplet group of the water level difference data of the vertical shaft according to the digital characteristic values Ex, En and He by utilizing a normal distribution rule, and the reason that the normal distribution rule can be utilized is that a large amount of statistical data of the water level difference of the vertical shaft conform to the normal distribution rule.
In one embodiment, the cloud model forward cloud generator may be invoked according to the numerical characteristic values Ex, En, He to generate a mathematical representation of the cloud droplet population of the shaft water level difference data, including:
generating a normal distribution random function E 'n according to En and He, wherein the mathematical expression of the E' n is
The mathematical expression for generating normal random numbers xi, xi according to Ex and He is
Calculating the membership degree of xi according to the formula
Wherein x is cloud drop quantitative data reflecting the risk of the vertical shaft water head, and mu (x) is cloud drop membership data for calculating the vertical shaft water head monitoring data x.
In the step, quantitative → qualitative → quantitative conversion of the vertical shaft water head monitoring data is carried out through the vertical shaft water head monitoring data and the positive and reverse cloud generators in the cloud model, and three digital characteristic values (Ex, En and He) are selected to measure the change degree of the vertical shaft water head risk.
It is to be understood that, in different embodiments, the cloud droplet group for obtaining the shaft water level difference data may also be based on different membership functions, which are not described herein.
And step 14, determining the contribution rate of the shaft water head value of any interval to the shaft water head risk based on the shaft water head data cloud drop group, and confirming a plurality of shaft water head risk intervals according to the contribution rate.
In this step, based on the cloud droplet group of the shaft water level difference data obtained in step 13, the contribution rate of the shaft water level difference of each interval to the shaft water level difference risk needs to be determined, and then a plurality of shaft water level difference risk intervals can be determined according to the contribution rate.
According to one embodiment, the contribution Δ C of the shaft water head value Δ x in any interval to the shaft water head phenomenon is expressed as,
ΔC≈μ(x)·Δx/2πEn
the formula for the total contribution C is such that,
due to the fact that
Therefore, the water head difference value with larger contribution rate mostly falls in the interval [ Ex-3En, Ex +3En]Within this range, the value contribution of the shaft water head outside this interval is only 0.26%. On the basis of this, the method is suitable for the production,
in one embodiment, the contribution rate of the shaft water head value to the shaft water head risk may be measured by calculating the percentage of the shaft water head value in different interval ranges to all the quantitative values, and specifically, the shaft water head may be divided into 4 risk intervals (as shown in fig. 4), where:
the "safety element" interval: indicating that the value of the shaft water head falls below the cloud droplet population (in this example, the total contribution of the cloud droplet population to the shaft water head phenomenon is 84.13%). When the hydraulic ship lift is in full-flow operation, the monitoring value of the water level difference of the vertical shaft is concentrated in the safety interval.
The "safer elements" interval: represents the cloud droplet group whose value of the shaft water head falls within the interval [ Ex + En, Ex +2En ] (in this embodiment, the total contribution rate of the cloud droplet group to the shaft water head phenomenon is 13.59%).
In a specific example, when the hydraulic ship lift is in full-flow operation, if the monitoring value of the shaft water head is concentrated in the interval, the operator should pay attention to and track the subsequent change of the monitoring value of the shaft water head.
The interval of 'weak risk elements': represents cloud droplet clusters whose values of the shaft water head fall within the interval [ Ex +2En, Ex +3En ] (in this example, the total contribution to the shaft water head phenomenon is 2.15%).
In a specific example, when the hydraulic ship lift is in full-flow operation, if the monitoring value of the water level difference of the vertical shaft is concentrated in the interval, an operator should report the 'weak risk' state of the water delivery system.
The "risk elements" interval: cloud droplet clusters representing a value of the shaft water head exceeding (Ex +3En) (in this example, the total contribution rate to the shaft water head phenomenon is 0.13%), and thus the shaft water head phenomenon is considered to represent abnormal information.
In one specific example, when the hydraulic ship lift is in full-flow operation, if the monitoring value of the shaft water head difference is concentrated in the interval, the operator should report the 'risk' state of the water delivery system.
And step 15, determining a shaft water head risk early warning threshold value according to the shaft water head risk interval.
As can be seen from the foregoing, when the monitored shaft water head is in a certain state, the water delivery system is at risk, and at this time, a risk early warning needs to be performed, and according to the shaft water head risk interval obtained in step 14, a shaft water head risk early warning threshold value can be determined.
In one embodiment, the upper limit and the lower limit of the numerical value of the weak risk element interval can be used as the upper limit and the lower limit of the early warning threshold value of the shaft water level difference weak risk state; and taking the numerical value lower limit of the risk element interval as the lower limit of the early warning threshold value of the water level difference risk state of the vertical shaft.
In a more specific example, a risk early warning indicator threshold for unsynchronized water level in a shaft of a hydraulic ship lift water delivery system may be set as shown in table 1 below:
TABLE 1
According to one embodiment, the shaft water head risk early warning threshold value can be further determined according to the shaft lifting height level.
According to another embodiment, the risk early warning of the shaft water head can be carried out when the monitored numerical value of the shaft water head is within the risk early warning threshold value of the shaft water head.
The invention is further illustrated below by means of an embodiment in a practical scenario.
Scenario example 1 this example performed risk assessment on the water delivery system of an existing project "scenic water hydraulic ship lift" using the assessment method described in the present invention.
The hydraulic ship lift for the scenic flood is a navigation facility with the largest scale of laneway-Mega river basin, and has the tonnage of passing through the ship of 300 tons, the maximum lifting height of 66.86 meters and the maximum lifting weight of 3140 tons. The water delivery system of the landscape and flood force type ship lift adopts an equal inertia arrangement mode, and the equal inertia water delivery system can theoretically realize the synchronization of the water level of each vertical shaft, but in the actual engineering, because of the structural particularity and the mounting construction error, the difference of the hydrodynamic force conditions of the water delivery system generates the water level difference between the vertical shafts in the water delivery process, namely the hydrodynamic force risk problem of the water delivery system caused by the asynchronous water level in each vertical shaft.
A risk assessment method for a water delivery system of a hydraulic ship lift based on a cloud model comprises the following specific steps:
1. defining the shaft water level difference value is one-time random realization of the asynchronous risk of the shaft water level, and reflecting the characteristic of the asynchronous risk of the shaft water level by adopting expectation, entropy and super-entropy concepts.
2. Analyzing the shaft water head monitoring data, and realizing quantitative-qualitative-quantitative conversion of the shaft water head monitoring data through a forward cloud generator and a backward cloud generator in a cloud model to obtain three numerical characteristic values (Ex ═ 0.0149, En ═ 0.1199, and He ═ 0.0015) of the shaft water head monitoring data, and generating a cloud drop graph (as shown in fig. 5).
3. According to the vertical shaft water head monitoring data cloud picture, the vertical shaft water head is divided into a safety element, a safer element, a weak risk element and a risk element. The value of the safety element of the vertical shaft water head falls in the cloud droplet group smaller than (Ex + En), and the monitoring value of the vertical shaft water head is concentrated in the safety interval; if the monitoring value of the vertical shaft water head is concentrated in the interval, an operator needs to pay attention to and track the subsequent change condition of the monitoring value of the vertical shaft water head; if the monitoring value of the vertical shaft water head is concentrated in the interval, an operator reports the 'weak risk' state of the water delivery system; if the monitoring value of the water level difference of the vertical shaft is concentrated in the interval, an operator needs to report the risk state of the water delivery system.
4. According to the asynchronous risk state of the shaft water level, determining the risk early warning index threshold value of the shaft water level difference of the landscape flood hydraulic ship lift, as shown in table 2:
TABLE 2
Scene embodiment 2 this embodiment performs risk assessment on the phenomenon of unsynchronized water level of the shaft of the hydraulic ship lift water delivery system with a hundred-meter-level lifting height by using the assessment method of the present invention.
In the face of the development trend of 'ultra-high lift and ultra-large lifting weight' of a future hydraulic ship lift, under the condition of the size of a buoy balance weight and a mechanical synchronizing shaft of the landscape flood hydraulic ship lift, numerical value calculation is carried out to obtain water level difference data of a water delivery system shaft under lifting heights of 80m, 100m and 120m, and a water level difference cloud picture of the water delivery system shaft is obtained according to the cloud drop number N of 3000 (as shown in fig. 6). The water level difference mean values of the water conveying system vertical shaft under the lifting heights of 80m, 100m and 120m are respectively 0.2m, 0.22m and 0.24m, the turbulent fluctuation strength of the water flow of the water conveying system vertical shaft is increased along with the increase of the lifting height, and the uniformity of flow distribution and the synchronism of the water level of the vertical shaft are poor. According to the asynchronous risk state of the shaft water level, determining the asynchronous risk early warning index threshold value of the shaft water level of the hydraulic ship lift water delivery system at different lifting heights, as shown in table 3:
TABLE 3
According to another embodiment of the invention, a risk assessment device for a water delivery system of a hydraulic ship lift is provided, and the device can be deployed in any equipment, platform or equipment cluster with computing and processing capabilities. Fig. 2 is a block diagram of a risk assessment device for a water delivery system of a hydraulic ship lift according to an embodiment of the present invention, and as shown in fig. 2, the device 200 includes:
a shaft water head monitoring data acquisition unit 21 configured to acquire shaft water head monitoring data of the water delivery system of the hydraulic ship lift;
the digital characteristic value obtaining unit 22 is configured to call a cloud model reverse cloud generator according to the vertical well water level difference monitoring data to obtain digital characteristic values Ex, En and He of the vertical well water level difference monitoring data, wherein Ex is an expected value, En is an entropy value, and He is a super-entropy value;
the water level difference cloud droplet group acquisition unit 23 is configured to call a cloud model forward cloud generator according to the digital characteristic values Ex, En and He to generate a vertical shaft water level difference data cloud droplet group;
the water head risk interval confirming unit 24 is configured to determine the contribution rate of the shaft water head value of any interval to the shaft water head risk based on the shaft water head data cloud drop group, and confirm a plurality of shaft water head risk intervals according to the contribution rate;
and the risk early warning threshold confirming unit 25 is configured to determine a shaft water head risk early warning threshold according to the shaft water head risk interval.
In one embodiment, the apparatus 200 further comprises an early warning unit 26 configured to perform a shaft water head risk early warning when the monitored value of the shaft water head is within a shaft water head risk early warning threshold.
According to an embodiment of a further aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method described in fig. 1.
According to an embodiment of a further aspect of the present invention, there is provided a computing device comprising a memory and a processor, wherein the memory stores executable code, and the processor executes the executable code to implement the method described in fig. 1.
It can be seen from the above embodiments that, by using the method and the device for evaluating the risk of the water delivery system of the hydraulic ship lift based on the cloud model provided by the embodiments of the present invention, the cloud model reverse cloud generator is called according to the vertical shaft water head monitoring data to obtain the digital characteristic values Ex, En, He of the vertical shaft water head monitoring data; calling a forward cloud generator to generate a vertical shaft water level difference data cloud drop group according to the digital characteristic values Ex, En and He, then determining the contribution rate of the vertical shaft water level difference value of any interval to the vertical shaft water level difference risk, confirming a plurality of vertical shaft water level difference risk intervals according to the contribution rate, and then determining a vertical shaft water level difference risk early warning threshold value according to the vertical shaft water level difference risk intervals. The method can be applied to early warning of the operation risk of the hydraulic ship lift, and solves the problems of randomness and ambiguity of the risk of the asynchronous water level of the vertical shaft of the water delivery system of the hydraulic ship lift; qualitative to quantitative analysis of asynchronous risks of the water level of the vertical shaft is realized, and the influence of uncertainty on a risk evaluation result is reduced; the risk early warning indexes of the water delivery system shaft water level asynchronization are established, and the risk index threshold value of the water delivery system shaft water level asynchronization of the hectometer-level hydraulic ship lift can be predicted. The risk state index result obtained by the method accords with the actual situation, can provide decision support for operation safety risk prevention and control of the hydraulic ship lift, and has the advantages of good universality and high practical value.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for risk assessment of a water delivery system of a hydraulic ship lift, the method comprising:
acquiring vertical shaft water level difference monitoring data of a water delivery system of the hydraulic ship lift;
calling a cloud model reverse cloud generator according to the vertical shaft water head monitoring data to obtain digital characteristic values Ex, En and He of the vertical shaft water head monitoring data, wherein Ex is an expected value, En is an entropy value, and He is an ultra-entropy value;
calling a cloud model forward cloud generator according to the digital characteristic values Ex, En and He to generate a vertical shaft water level difference data cloud droplet group;
determining the contribution rate of the shaft water head value of any interval to the shaft water head risk based on the shaft water head data cloud drop group, and confirming a plurality of shaft water head risk intervals according to the contribution rate;
and determining a shaft water head risk early warning threshold value according to the shaft water head risk interval.
2. The method of claim 1, further comprising determining a shaft water head risk pre-warning threshold based on a shaft hoist height level.
3. The method of claim 1, further comprising performing a shaft water head risk warning when the value of the monitored shaft water head is within a shaft water head risk warning threshold.
4. The method of claim 1, wherein invoking a cloud model forward cloud generator based on the numerical eigenvalues Ex, En, He, generating a mathematical representation of the cloud droplet population of shaft water level difference data comprises:
generating a normal distribution random function E 'n according to En and He, wherein the mathematical expression of the E' n is
The mathematical expression for generating normal random numbers xi, xi according to Ex and He is
Calculating the membership degree of xi according to the formula
Wherein x is cloud drop quantitative data reflecting the risk of the vertical well water head, mu (x) is cloud drop membership data for calculating the monitoring data x of the vertical well water head, and NORM () is a random normal distribution function.
5. The method of claim 1, wherein the shaft water head risk zone comprises:
the safety element interval is a numerical value interval in which the value of the water level difference of the vertical shaft is smaller than Ex + En;
the safer element interval is a numerical value interval of the value of the water level difference of the vertical shaft between Ex + En and Ex +2 En;
the weak risk element interval is a numerical value interval of the value of the water level difference of the vertical shaft between Ex +2En and Ex +3 En;
and the risk element interval is a numerical interval in which the vertical shaft water head value is greater than Ex +3 En.
6. The method of claim 5, wherein determining a shaft water head risk pre-warning threshold based on the shaft water head risk interval comprises:
taking the numerical upper and lower limits of the weak risk element interval as the upper and lower limits of the early warning threshold value of the shaft water level difference weak risk state;
and taking the numerical value lower limit of the risk element interval as the lower limit of the early warning threshold value of the water level difference risk state of the vertical shaft.
7. A hydraulic ship lift water delivery system risk assessment device, the device comprising:
the vertical shaft water level difference monitoring data acquisition unit is configured to acquire vertical shaft water level difference monitoring data of a water delivery system of the hydraulic ship lift;
the digital characteristic value acquisition unit is configured to call a cloud model reverse cloud generator according to the vertical shaft water level difference monitoring data to acquire digital characteristic values Ex, En and He of the vertical shaft water level difference monitoring data, wherein Ex is an expected value, En is an entropy value, and He is a super-entropy value;
the water level difference cloud droplet group acquisition unit is configured to call a cloud model forward cloud generator according to the digital characteristic values Ex, En and He to generate a vertical shaft water level difference data cloud droplet group;
the water head risk interval confirming unit is configured to determine the contribution rate of the shaft water head value of any interval to the shaft water head risk based on the shaft water head data cloud drop group, and confirm a plurality of shaft water head risk intervals according to the contribution rate;
and the risk early warning threshold confirming unit is configured to determine a shaft water head risk early warning threshold according to the shaft water head risk interval.
8. The apparatus of claim 7, further comprising an early warning unit configured to perform a shaft water head risk early warning when the monitored value of the shaft water head is within a shaft water head risk early warning threshold.
9. A computer-readable storage medium, on which a computer program is stored which, when executed in a computer, causes the computer to carry out the method of any one of claims 1-6.
10. A computing device comprising a memory and a processor, wherein the memory has stored therein executable code that, when executed by the processor, implements the method of any of claims 1-6.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010873985 | 2020-08-26 | ||
| CN2020108739854 | 2020-08-26 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111984933A true CN111984933A (en) | 2020-11-24 |
Family
ID=73440099
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010878759.5A Pending CN111984933A (en) | 2020-08-26 | 2020-08-27 | Risk assessment method and device for water delivery system of hydraulic ship lift based on cloud model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111984933A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112966225A (en) * | 2021-03-22 | 2021-06-15 | 南昌航空大学 | Steel wire rope safety life assessment method based on normal distribution |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104850727A (en) * | 2015-01-27 | 2015-08-19 | 厦门大学 | Distributed big data system risk evaluation method based on cloud barycenter theory |
| CN106780117A (en) * | 2016-12-02 | 2017-05-31 | 国网浙江省电力公司经济技术研究院 | A kind of power distribution network project financial leasing methods of risk assessment |
-
2020
- 2020-08-27 CN CN202010878759.5A patent/CN111984933A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104850727A (en) * | 2015-01-27 | 2015-08-19 | 厦门大学 | Distributed big data system risk evaluation method based on cloud barycenter theory |
| CN106780117A (en) * | 2016-12-02 | 2017-05-31 | 国网浙江省电力公司经济技术研究院 | A kind of power distribution network project financial leasing methods of risk assessment |
Non-Patent Citations (3)
| Title |
|---|
| 刘精凯: "《基于云模型的水力式升船机输水系统运行风险分析》", 《水运工程》, no. 9, pages 14 - 18 * |
| 张志君: "《基于可拓云模型的干旱区水资源安全评价-以石河子垦区为例》", 《干旱区研究》, vol. 37, no. 4, pages 847 - 856 * |
| 肖磊;万智勇;黄耀英;刘钰;袁斌;: "基于最大熵和云模型的RCC坝变形监控指标拟定", 水利水运工程学报, no. 04, pages 24 - 29 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112966225A (en) * | 2021-03-22 | 2021-06-15 | 南昌航空大学 | Steel wire rope safety life assessment method based on normal distribution |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103544389B (en) | Autocrane method for diagnosing faults based on fault tree and fuzzy neural network | |
| CN111984933A (en) | Risk assessment method and device for water delivery system of hydraulic ship lift based on cloud model | |
| CN102530666A (en) | Elevator equipment | |
| CN103939507B (en) | Method and equipment for detecting braking capacity of brake | |
| CN104931364B (en) | Reinforced concrete structure method for testing fatigue and device based on piezomagnetism | |
| CN104118781A (en) | Method for determining balance coefficient | |
| CN116339214A (en) | Construction site safety monitoring system based on data analysis | |
| CN114548806B (en) | Hydraulic engineering supervisory systems based on big data and VR technique | |
| CN114323153B (en) | Offshore wind power foundation scouring and structural state monitoring and safety early warning method and device | |
| CN102963819B (en) | A kind of chaufeur method for supervising of building crane and system thereof | |
| CN106379787A (en) | Elevator pre-warning rescue car | |
| CN116292146B (en) | Tower barrel safety diagnosis system and method using inclination angle sensor | |
| CN105384074B (en) | A kind of falling proof device for hoisting machinery lifting mechanism | |
| CN110451412A (en) | A kind of derrick crane hydraulic lifting safety monitoring method and system | |
| CN104828665B (en) | A kind of double load capacity scene electrical inspection methods of friction type winder system | |
| CN110329908A (en) | A kind of heavy lift carrier master hangs connection and hangs test method | |
| CN105460789A (en) | Crane rate diagnosis method, system and crane | |
| CN115576239A (en) | Alternating current-direct current power supply intelligent control system based on artificial intelligence | |
| CN115235542A (en) | Ship reception chamber online monitoring method and monitoring system for ship lift | |
| Ye et al. | Research on bridge cranes Energy-saving index reduction based on rough set theory | |
| CN105460719A (en) | Elevator device and earthquake temporary operation restoring device of same | |
| CN115907565B (en) | Diversion tunnel structure safety evaluation method and device, electronic equipment and storage medium | |
| Xie et al. | Failure calculating and risk assessment method of crane parts | |
| CN111912463A (en) | Real-time online health state monitoring system for fixed winch hoist | |
| CN216041034U (en) | Online monitoring management system for runner maintenance platform of hydropower station unit |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20201124 |