CN114722596B - River fish shoal abundance identification method and monitoring system - Google Patents
River fish shoal abundance identification method and monitoring system Download PDFInfo
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Abstract
The invention discloses a method for identifying the richness of river fish shoal and a monitoring system, wherein the method comprises the following steps: acquiring the fish school passing data of a river main stream and a river branch in a preset period of a fish school passing monitoring system in real time; calculating the average queuing length of the river main stream fish school passing through the monitoring system according to the pre-constructed M/M/n model and the main stream fish school passing data; calculating the average queuing length of the river tributary fish school passing through the monitoring system according to the pre-constructed G/M/i model and the tributary fish school passing data; carrying out weighted summation on the main stream fish shoal queuing length and the tributary fish shoal queuing length to obtain the average queuing length of the river fish shoal weighted passing monitoring system; determining the richness level of the river fish school through the average queuing length of the monitoring system based on the fish school weighting. The method is simple and efficient in process, and can provide support for making decisions for relevant departments or provide real-time effective basis for regional fish catching.
Description
Technical Field
The invention relates to an information technology, in particular to a river fish shoal richness identification method and a river fish shoal richness monitoring system.
Background
At present, uncertainty of river fish resources and targeted fish protection methods lack reasonable measurement systems and evaluation standards, so that people have certain randomness in fishing. The existing fishing method divides the fishing period and the fishing prohibition period according to the months, has no real-time property and scientificity, and the local area often damages local river fishes because the time is not adjusted according to local conditions.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a river fish shoal abundance identification method and a river fish shoal abundance monitoring system, so that support is provided for decision making of relevant departments or real-time effective basis is provided for regional fish catching, local fish catching is sustainable and environment-friendly, and ecological development and local fish protection are facilitated.
The technical scheme is as follows: the invention relates to a method for identifying the richness of river fish schools, which comprises the following steps:
s1, acquiring fish school passing data of a river main stream and a river branch in a preset period of a fish school passing monitoring system in real time;
s2, calculating the average queuing length of the river main stream fish school passing through the monitoring system according to a pre-constructed M/M/n model and the main stream fish school passing data; the M/M/n model is a continuous time model in an operational research queuing theory, the arrival time interval of a first M-finger trunk flow fish school and the service time of a monitoring system in the model are both subjected to exponential distribution, and the total number of monitoring channels of the n-finger trunk flow fish school monitoring system is n;
s3, calculating the average queuing length of the river branch fish shoal passing through the monitoring system according to the pre-constructed G/M/i model and the branch fish shoal passing data; the G/M/i model is a continuous time model in an operation research queuing theory, the arrival time interval of a tributary fish school in the model is subjected to general distribution, the service time of a monitoring system is subjected to exponential distribution, and i refers to the total number i of monitoring channels of a tributary fish school secondary monitoring system;
s4, carrying out weighted summation on the average queuing length of the main stream fish shoals and the average queuing length of the tributary fish shoals to obtain the weighted queuing length of the river fish shoals passing through a monitoring system;
and S5, determining the richness level of the river fish school through the weighted queuing length of the monitoring system based on the fish school weighting.
The traffic data of the fish shoal of the main stream and the tributary of the river in the step S1 comprise the number of monitoring channels of the main stream monitoring system, the total number of the fish shoals passing through the main stream monitoring system in unit time, the number of monitoring channels of the tributary monitoring system and the total number of the fish shoals passing through the tributary monitoring system in unit time.
The step S2 specifically comprises the following steps:
s2.1, acquiring historical data of a main-flow fish school monitoring system in a preset time period, calculating the expectation of the number of fish passing through a monitoring channel in unit time based on the historical data of the main-flow fish school monitoring system, and determining the expectation of the number of fish passing through the monitoring channel in unit time as the service rate mu of a single monitoring channel;
s2.2, determining the total number of the fish passing through all monitoring channels of the main flow in unit time as a service rate r of a main flow system;
s2.3, calculating the service intensity S of the monitoring system according to the total number n of the monitoring channels of the main flow fish school monitoring system, the service rate r of the main flow system and the service rate mu of a single monitoring channel;
s2.4, initializing a pre-constructed M/M/n model, and binding the total number of monitoring channels, the service rate of a single monitoring channel and the service strength of the monitoring system with the M/M/n model to generate the M/M/n model of binding parameters;
s2.5, calculating the probability that the number of the fishes in the monitoring system is k after the main flow monitoring system is balanced according to the total number of monitoring channels of the monitoring system, the service intensity of the monitoring system and the M/M/n model of the binding parameters;
and S2.6, calculating the average queue length of the main flow fish school according to the service rate of the main flow system and the M/M/n model of the binding parameter.
The step S2.6 specifically comprises the following steps:
average queuing length E [ X ] of the main streaming fish shoal q ]Summing the service rate r of the main stream to generate a shoal arrival rate lambda;
wherein, the calculation formula of the probability that the number of the fishes in the system is k is as follows:
wherein, P k The probability that the number of fish in the main flow fish school monitoring system is k, n is the total number of monitoring channels of the main flow fish school monitoring system, and s is the service intensity of the monitoring system, namely
Wherein, the average queuing length calculation formula of the M/M/n model is as follows:
wherein s is the monitoring system service strength, P n The probability that the number of the fishes in the main flowing fish shoal monitoring system is n is E [ X q ]Is thatAnd the average queuing length of the main flow fish school.
The step S3 specifically comprises the following steps:
s3.1, acquiring historical data of all tributary fish swarm monitoring systems in a preset time period, calculating the expectation of the number of fish passing through a monitoring channel in unit time based on the historical data of the tributary fish swarm monitoring systems, and determining the expectation of the number of fish passing through the unit time as the service rate mu of a secondary single monitoring channel;
s3.2, determining the total number of the fishes passing through all monitoring channels of the tributary in unit time as a tributary system service rate r;
s3.3, calculating the service intensity S of the secondary monitoring system according to the total number i of the monitoring channels of all the tributary fish shoal secondary monitoring systems, the tributary system service rate (r) and the secondary single monitoring channel service rate mu;
s3.4, initializing a pre-constructed G/M/i model, and binding the total number of monitoring channels, the service rate of a secondary single monitoring channel and the service strength of the secondary monitoring system with the G/M/i model to generate a G/M/i model of a binding parameter;
s3.5, calculating the service time standard deviation of the secondary monitoring system after the tributary monitoring system is balanced according to the total number of the monitoring channels of the secondary monitoring system, the service rate of the single secondary monitoring channel and the G/M/i model of the binding parameter;
and S3.6, calculating the average queuing length of the tributary fish school according to the service time standard deviation of the secondary monitoring system and the G/M/i model of the binding parameter.
The step S3.6 is specifically as follows:
average queuing length E [ X ] of branch fish shoal q ]Summing the service rate r of the tributary system to generate a tributary fish school arrival rate lambda;
the secondary monitoring system service intensity calculation formula is as follows:
wherein χ is the arrival rate of the branch shoal, δ is the service rate of the secondary single monitoring channel, and i is the total number of monitoring channels of the secondary monitoring system;
the service time standard deviation calculation formula of the secondary monitoring system after the tributary monitoring system is balanced is as follows:
σ＝A * (iδ-iδσ)；
wherein, σ is the standard deviation of the service time of the secondary monitoring system, i is the total number of monitoring channels of the secondary monitoring system, δ is the service rate of a single monitoring channel of the secondary, and A × Q is the LST of the branch shoal queuing time;
the tributary fish school average queuing length calculation formula, namely the average queuing length of the G/M/i model, is as follows:
wherein, E (X) qc ) The average queuing length of all the tributaries, namely the fish school of the c tributaries is obtained;
the river fish shoal weighted queuing length calculation formula is as follows:
wherein ε is the total number of fish passing through the monitoring system per unit time interval of the river stream, η i The total number of fish passing through the monitoring system per unit time interval for the ith tributary; e (X) qc ) I.e. the average queue length of the c tributary fish schools, E (X) q ) Namely the average queuing length of the main flow fish school.
The step S5 specifically comprises the following steps:
s5.1, loading a river fish shoal richness grade division table;
s5.2, identifying a weighted queuing length interval where the weighted queuing length of the river fish shoal is located from the river fish shoal rich level division table;
and S5.2, determining the fish shoal enrichment evaluation grade corresponding to the weighted queuing length interval as the river fish shoal enrichment grade.
A river fish shoal richness identification system adopts the river fish shoal richness identification method, and comprises the following modules:
the fish school passing data acquisition module: the system is used for acquiring monitoring data of a river branch flow fish school passing system and monitoring data of the river branch flow fish school passing system in real time within a preset time interval;
the average queuing length calculation module of the river main stream fish shoal: the system comprises a pre-constructed M/M/n model, a river main stream fish shoal monitoring data acquisition unit, a river main stream fish shoal average queuing length calculation unit and a river main stream fish shoal monitoring data acquisition unit, wherein the pre-constructed M/M/n model is used for acquiring river main stream fish shoal monitoring data;
the river branch fish shoal average queuing length calculation module: the method is used for calculating the average queuing length of the river tributary fish school according to a pre-constructed G/M/i model and the river tributary fish school monitoring data;
the river fish shoal weighted queuing length calculation module: the system is used for carrying out weighted summation on the received average queue length of the fish shoal of the main stream of the river and the average queue length of the fish shoal of the tributary of the river to obtain the weighted queue length of the fish shoal of the river;
the fish school enrichment schedule loading module: the system is used for adjusting the richness of the river fish shoals corresponding to the weighted queuing length of the river fish shoals in real time;
the river fish shoal abundance discrimination module: the method is used for determining the richness of the river fish shoal according to the weighted queuing length of the river fish shoal.
A computer storage medium on which a computer program is stored which, when executed by a processor, implements a method of identifying richness of a river fish school as described above.
A computer device comprises a storage, a processor and a computer program which is stored on the storage and can be operated on the reprocessor, and the processor executes the computer program to realize the method for identifying the richness of the river fish shoal.
Has the advantages that: compared with the prior art, the invention has the following advantages: the invention adopts the M/M/n model and the G/M/i model which are constructed based on the queuing theory to respectively calculate the average queuing length of the river main flow fish school and the river branch flow fish school to determine the survival condition grade of the fish school, so that the overall survival condition of the river fish school can be identified in real time only by acquiring simple fish school data through a monitoring system, the process is simple and efficient, and simultaneously, the invention can provide support for making decisions for related departments or provide real-time effective basis for regional fish catching.
Drawings
FIG. 1 is a schematic diagram of a method for identifying richness of river fish shoal;
FIG. 2 is a schematic diagram of a river fish shoal monitoring system;
fig. 3 is a schematic diagram of a terminal structure of a river fish shoal monitoring system.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
As shown in fig. 1, a method for identifying the richness of river fish schools includes the following steps:
s1, acquiring fish school passing data of a river main stream and a river branch in a preset period of a fish school passing monitoring system in real time.
The river fish shoal monitoring system is a system which is placed on a river section and is composed of a plurality of monitoring channels and used for recording and monitoring the traffic data of river fish shoals in real time. Because of the specificity of the river, a main stream and c branch streams are generally available, and c is any positive integer greater than or equal to 1, so that the model is established, and the method has universal applicability. A plurality of information acquisition devices are arranged in each monitoring channel, and the traffic data of the river main stream fish shoal and the traffic data of the river branch fish shoal in a preset period are acquired in real time through the plurality of information acquisition devices.
Generally, when a dry flow fish school arrives at a monitoring channel, the time interval of successive arrival obeys Poisson distribution, n monitoring channels are arranged in a dry flow river channel in total, the service time of the monitoring channels obeys exponential distribution, the parameter lambda of the Poisson distribution of the arrival rate of the dry flow fish school is easily obtained through real-time monitoring data, the parameter mu of the service time exponential distribution of a monitoring system is obtained, and the average queuing length of the dry flow fish school can be obtained after the parameter lambda is combined with the M/M/n model of the binding parameter; when the tributary fish school arrives at the monitoring channel, when the tributary fish school arrives at the secondary monitoring channel, the time intervals of successive arrival obey general distribution, i secondary monitoring channels are arranged in the tributary river channel in total, the service time of the monitoring channel obeys exponential distribution, the parameter chi of general distribution of the arrival rate of the tributary fish school, the parameter delta of the service time of the monitoring system, the standard difference sigma of the service time of the secondary monitoring system and the G/M/i model of the binding parameter are combined to obtain the average queuing length of the tributary fish school. When the fish arrives, if the fish has an idle monitoring point, the fish is served immediately, otherwise, the fish is arranged into a queue to wait, and the waiting time is infinite.
In one possible implementation, the following fish passage parameters can be obtained in real time by a plurality of information collecting devices: the total number of fish per unit time in the main and side streams of the river through all the monitoring channels. Specifically, the number of monitoring points of the river main stream at least comprises the number of all fishes passing through the main stream monitoring point in unit time. Specifically, the tributary monitoring points include at least the number of fish that are monitored per unit time by all tributaries at the monitoring points. In a possible implementation mode, the respective fish swarm traffic volume of a main flow monitoring channel and a branch flow monitoring channel in unit time can be obtained in real time through a plurality of information acquisition devices, and the number i of the main flow monitoring channels and the number m of the branch flow monitoring channels can be determined according to the types of the monitoring channels of a river fish swarm monitoring system.
Specifically, the traffic data of the fish school of the main stream and the branch stream of the river at least comprises the number of monitoring channels of a main stream monitoring system, the total number of the fish school passing through the main stream monitoring system in unit time, the number of monitoring channels of a branch monitoring system and the total number of the fish school passing through the branch monitoring system in unit time.
S2, calculating the average queuing length of the river main stream fish school passing through the monitoring system according to a pre-constructed M/M/n model and the main stream fish school passing data; the step S2 specifically includes:
s2.1, acquiring historical data of a main flow fish school monitoring system in a preset time period, calculating the expectation of the number of fish passing through a monitoring channel in unit time based on the historical data of the main flow fish school monitoring system, and determining the expectation of the number of fish passing through the unit time as the service rate mu of a single monitoring channel;
s2.2, determining the total number of the fish passing through all monitoring channels of the main flow in unit time as a service rate r of a main flow system;
s2.3, calculating the service intensity S of the monitoring system according to the total number n of the monitoring channels of the main flow fish school monitoring system, the service rate r of the main flow system and the service rate mu of a single monitoring channel;
s2.4, initializing a pre-constructed M/M/n model, and binding the total number of monitoring channels, the service rate of a single monitoring channel and the service strength of the monitoring system with the M/M/n model to generate the M/M/n model of binding parameters;
s2.5, calculating the probability that the number of the fishes in the monitoring system is k after the main flow monitoring system is balanced according to the total number of monitoring channels of the monitoring system, the service intensity of the monitoring system and the M/M/n model of the binding parameters;
wherein the M/M/n model and the G/M/i model are created based on queuing theory. And (4) indirectly researching the richness of the river fish school by adopting a queuing theory. The queuing system consists of an input process and arrival rule, a queuing rule, a service mechanism structure, service time and service planning. The queuing model is divided into a plurality of categories according to the difference of input processes, queuing rules and service organizations, and Kendell notation is represented as A/B/C/D/E/F. A represents the distribution of time intervals in which customers arrive successively; b represents the distribution of service time of the service system; c represents the number of service desks of the service system; d represents the system capacity of the service system; e represents the system state of the service system; f denotes the service rule of the system. Namely: number of service stations/system capacity/system status/service rules for input/output/parallel.
The prior research has wide application of a queuing theory to the information field and the traffic field, and because of the real-time property and the high efficiency of the queuing theory, the invention reasonably selects and uses the combination of an M/M/n model in the queuing theory and the real-time monitoring data of the mainstream fish swarm, combines a G/M/i model in the queuing theory and the real-time monitoring data of the tributary fish swarm, and uses the weighted queuing length of the weighted river fish swarm to reflect the richness of the river fish swarm, thereby being intuitive and high-efficiency. The model of the river main stream fish school is set as an M/M/n model, M represents that the arrival interval time distribution of the fish in the river accords with Poisson distribution, M means that the service time distribution of the monitoring channels accords with exponential distribution, and n monitoring channels are provided; the model of the river branch fish school is set as a G/M/i model, G represents that the arrival interval time distribution of the fish in the river accords with general distribution, M means that the service time distribution of the monitoring channels accords with exponential distribution, and i monitoring channels are provided; wherein n and i are any undetermined positive integers, and the method has stronger universality due to the river width and the requirement of the tributary. Through verification of a large amount of river real data, the judgment of the richness of the fish shoal obtained by the method accords with the actual situation, the actual application effect is good, and the richness of the river shoal can be recognized through simple information input.
And S2.6, calculating the average queuing length of the main flow fish school according to the service rate of the main flow system and the M/M/n model of the binding parameter. The step S2.6 specifically comprises the following steps:
average queuing length E [ X ] of the main flow fish school q ]Summing the service rate r of the main stream to generate a shoal arrival rate lambda;
wherein, the calculation formula of the probability that the number of the fishes in the system is k is as follows:
wherein, P k The probability that the number of fish in the main flow fish school monitoring system is k, n is the total number of monitoring channels of the main flow fish school monitoring system, and s is the service intensity of the monitoring system, namely
Wherein, the average queuing length calculation formula of the M/M/n model is as follows:
wherein s is the monitoring system service strength, P n The probability that the number of the fishes in the main flowing fish shoal monitoring system is n is E [ X q ]Namely the average queuing length of the main flow fish school.
S3, calculating the average queuing length of the river branch fish shoal passing through the monitoring system according to the pre-constructed G/M/i model and the branch fish shoal passing data; the step S3 specifically comprises the following steps:
s3.1, acquiring historical data of all tributary fish swarm monitoring systems in a preset time period, calculating the expectation of the number of fish passing through a monitoring channel in unit time based on the historical data of the tributary fish swarm monitoring systems, and determining the expectation of the number of fish passing through the unit time as the service rate mu of a secondary single monitoring channel;
s3.2, determining the total number of the fish passing through all monitoring channels of the tributary in unit time as a tributary system service rate r;
s3.3, calculating the service intensity S of the secondary monitoring system according to the total number i of the monitoring channels of all the tributary fish shoal secondary monitoring systems, the tributary system service rate (r) and the secondary single monitoring channel service rate mu;
s3.4, initializing a pre-constructed G/M/i model, and binding the total number of monitoring channels, the service rate of a secondary single monitoring channel and the service strength of the secondary monitoring system with the G/M/i model to generate a G/M/i model of a binding parameter;
s3.5, calculating the service time standard deviation of the secondary monitoring system after the tributary monitoring system is balanced according to the total number of the monitoring channels of the secondary monitoring system, the service rate of the single secondary monitoring channel and the G/M/i model of the binding parameter;
and S3.6, calculating the average queuing length of the tributary fish school according to the service time standard deviation of the secondary monitoring system and the G/M/i model of the binding parameter. The step S3.6 is specifically as follows:
average queuing length E [ X ] of branch fish shoal q ]Calculating the service rate r of tributary systemGenerating a branch shoal arrival rate lambda;
the secondary monitoring system service intensity calculation formula is as follows:
wherein χ is the arrival rate of the branch shoal, δ is the service rate of the secondary single monitoring channel, and i is the total number of monitoring channels of the secondary monitoring system;
the service time standard deviation calculation formula of the secondary monitoring system after the tributary monitoring system is balanced is as follows:
σ＝A * (iδ-iδσ)；
wherein, σ is the standard deviation of the service time of the secondary monitoring system, i is the total number of monitoring channels of the secondary monitoring system, δ is the service rate of a single monitoring channel of the secondary, and A × Q is the LST of the branch shoal queuing time;
the tributary fish school average queuing length calculation formula, namely the average queuing length of the G/M/i model, is as follows:
wherein, E (X) qc ) The average queue length of the fish school of all the tributaries, namely the c tributaries is obtained;
the weighted average queuing length calculation formula of the fish school of the river is as follows:
wherein ε is the total number of fish passing through the monitoring system per unit time interval of the river stream, η i The total number of fish passing through the monitoring system in the unit time interval of the ith branch; e (X) qc ) I.e. the average queue length of the c tributary fish schools, E (X) q ) Namely the average queuing length of the main flow fish school.
For the present invention to provide a preferred solution, the preset time of the monitoring data may be set to the monitoring system data of the past 90 minutes. It should be noted that the preset time period of the monitoring data should be adjusted and set according to actual situations, and details are not described herein.
Specifically, taking a certain river in Jiangsu as an example, the number of branches is 2, the statistical fish population is pseudorasbora parva, the average queuing length of the main stream of the river fish population is 2.41, the average queuing length of the branch fish population is 0.72, the total number of fish passing through the monitoring system in the unit time interval of the main stream of the river is 23.3, and the total number of fish passing through the monitoring system in the unit time interval of the branch is 1.1 and 4.2 respectively, as shown in Table 1:
table 1 specific fish shoal monitoring data and average queuing length index table
S4, carrying out weighted summation on the average queuing length of the main stream fish shoals and the average queuing length of the tributary fish shoals to obtain the weighted queuing length of the river fish shoals passing through a monitoring system;
and S5, determining the richness level of the river fish school through the weighted queuing length of the monitoring system based on the fish school weighting. The step S5 specifically comprises the following steps:
s5.1, loading a river fish school abundance grade division table;
s5.2, identifying a weighted queuing length interval where the weighted queuing length of the river fish shoal is located from the river fish shoal rich level division table;
s5.2, determining the fish shoal enrichment evaluation level corresponding to the weighted queuing length interval as the river fish shoal enrichment level, wherein a river fish shoal enrichment degree table is shown in a table 2:
TABLE 2 river and fish school richness degree table
| River fish shoal weighted queuing length | Abundance of river fish school | Suggesting local prospective fishing behavior |
| <1 | Lack of supply | Period of no fishing |
| <4 | In general | Time-limited fishing period |
| >4 | Rich | During the fishing period |
According to the schematic diagram of the river fish shoal abundance recognition method provided by the embodiment of the application, firstly, river flow main flow fish shoal monitoring data and branch flow fish shoal monitoring data in a preset period of a monitoring system are obtained in real time, then relevant parameters corresponding to an M/M/n model and a G/M/i model of main flow and branch flow research are specified by calculating data information in the preset period, the average queuing length of the main flow fish shoal is calculated according to the pre-constructed M/M/n model and the fish shoal data, and the average queuing length of the branch flow fish shoal is calculated according to the pre-constructed G/M/i model and the fish shoal data. And then weighting and summing the average queuing lengths of the main stream and the branch streams to obtain the river weighted fish shoal queuing length, and finally determining the richness of the fish shoal based on the river weighted fish shoal queuing length.
It should be noted that initialization of the M/n model and the G/M/i model is to construct a model in advance, change all parameters of the model into initial states, obtain parameters related to the model according to the collected river fish school monitoring data, such as the number of monitoring channels, mathematical expectation of channel service time, service intensity of main stream fish school monitoring channels, expectation of the number of fish passing through the monitoring channels in the tributary monitoring channels in unit time, the total number of fish passing through all the monitoring channels in the tributary in unit time and the total number of monitoring channels of all the tributary fish school secondary monitoring systems in unit time, and then bind the obtained parameters in the present application with the corresponding model.
In the embodiment of the application, the M/M/n model and the G/M/i model which are constructed based on the queuing theory are adopted to calculate the average queuing lengths of the river main-flow fish swarm and the river branch-flow fish swarm respectively to determine the richness index of the river fish swarm, so that the richness of the river fish swarm can be identified in real time only by acquiring simple fish swarm data passing through a monitoring system, the process is simple and efficient, and meanwhile, a decision can be made for relevant departments or a real-time effective basis is provided for regional fish catching.
As shown in fig. 2, a river fish shoal richness identification system, which adopts the above-mentioned river fish shoal richness identification method, includes the following modules:
the fish school passing data acquisition module: the system is used for acquiring monitoring data of a river branch flow fish school passing system and monitoring data of the river branch flow fish school passing system in real time within a preset time interval;
the average queuing length calculation module of the river main stream fish shoal: the system is used for calculating the average queuing length of the fish school of the main stream of the river according to a pre-constructed M/M/n model and the monitoring data of the fish school of the main stream of the river;
the average queuing length calculation module of the river tributary fish school: the method is used for calculating the average queuing length of the river tributary fish school according to a pre-constructed G/M/i model and the river tributary fish school monitoring data;
the river fish shoal weighted queuing length calculation module: the system is used for carrying out weighted summation on the received average queue length of the fish shoal of the main stream of the river and the average queue length of the fish shoal of the tributary of the river to obtain the weighted queue length of the fish shoal of the river;
the fish school enrichment schedule loading module: the system is used for adjusting the richness of the river fish shoals corresponding to the weighted queuing length of the river fish shoals in real time;
the river fish shoal abundance discrimination module: the method is used for determining the richness of the river fish shoal according to the weighted queuing length of the river fish shoal.
As shown in fig. 3, a computer storage medium has a computer program stored thereon, and the computer program is executed by a processor to implement the above-mentioned method for identifying richness of river fish.
A computer device comprises a storage, a processor and a computer program which is stored on the storage and can be operated on the reprocessor, and the processor executes the computer program to realize the method for identifying the richness of the river fish shoal.
Claims (10)
1. A method for identifying the richness of river fish shoal is characterized by comprising the following steps:
s1, acquiring fish school passing data of a river main stream and a river branch in a preset period of a fish school passing monitoring system in real time;
s2, calculating the average queuing length of the river main stream fish school passing through the monitoring system according to a pre-constructed M/M/n model and the main stream fish school passing data; the M/M/n model is a continuous time model in an operational research queuing theory, the arrival time interval of a first M-finger trunk flow fish school and the service time of a monitoring system in the model are both subjected to exponential distribution, and the total number of monitoring channels of the n-finger trunk flow fish school monitoring system is n;
s3, calculating the average queuing length of the branch fish school of the river passing through the monitoring system according to a pre-constructed G/M/i model and the branch fish school passing data; the G/M/i model is a continuous time model in an operational research queuing theory, the arrival time interval of the branch fish shoal in the model is subjected to general distribution, the service time of the monitoring system is subjected to index distribution, and i refers to the total number i of monitoring channels of the branch fish shoal secondary monitoring system;
s4, carrying out weighted summation on the average queuing length of the main stream fish shoals and the average queuing length of the tributary fish shoals to obtain the weighted queuing length of the river fish shoals passing through a monitoring system;
and S5, determining the richness level of the river fish school through the weighted queuing length of the monitoring system based on the fish school weighting.
2. The method as claimed in claim 1, wherein the traffic data of the fish shoal of the main stream and the tributary of the river in step S1 includes the number of monitoring channels of the main stream monitoring system, the total number of fish shoals passing through the main stream monitoring system per unit time, the number of monitoring channels of the tributary monitoring system, and the total number of fish shoals passing through the tributary monitoring system per unit time.
3. The method for identifying the richness of the river fish shoal according to claim 1, wherein the step S2 specifically comprises:
s2.1, acquiring historical data of a main flow fish school monitoring system in a preset time period, calculating the expectation of the number of fish passing through a monitoring channel in unit time based on the historical data of the main flow fish school monitoring system, and determining the expectation of the number of fish passing through the unit time as the service rate mu of a single monitoring channel;
s2.2, determining the total number of the fish passing through all monitoring channels of the main flow in unit time as a service rate r of a main flow system;
s2.3, calculating the service intensity S of the monitoring system according to the total number n of the monitoring channels of the main flow fish school monitoring system, the service rate r of the main flow system and the service rate mu of a single monitoring channel;
s2.4, initializing a pre-constructed M/M/n model, and binding the total number of monitoring channels, the service rate of a single monitoring channel and the service strength of the monitoring system with the M/M/n model to generate the M/M/n model of binding parameters;
s2.5, calculating the probability that the number of the fishes in the monitoring system is k after the main flow monitoring system is balanced according to the total number of monitoring channels of the monitoring system, the service intensity of the monitoring system and the M/M/n model of the binding parameters;
and S2.6, calculating the average queuing length of the main flow fish school according to the service rate of the main flow system and the M/M/n model of the binding parameter.
4. The method for identifying the richness of the river fish shoal according to claim 3, wherein the step S2.6 is specifically as follows:
average queuing length E [ X ] of the main flow fish school q ]Summing the service rate r of the main stream to generate a shoal arrival rate lambda;
wherein, the calculation formula of the probability that the number of the fishes in the system is k is as follows:
wherein, P k The probability that the number of fish in the main flow fish school monitoring system is k, n is the total number of monitoring channels of the main flow fish school monitoring system, and s is the service intensity of the monitoring system, namely
Wherein, the average queuing length calculation formula of the M/M/n model is as follows:
wherein s is the monitoring system service intensity, P n The probability that the number of the fishes in the main flowing fish shoal monitoring system is n is E [ X q ]Namely the average queuing length of the main flow fish school.
5. The method for identifying the richness of the river fish shoal according to claim 1, wherein the step S3 specifically comprises:
s3.1, acquiring historical data of all tributary fish swarm monitoring systems in a preset time period, calculating the expectation of the number of fish passing through a monitoring channel in unit time based on the historical data of the tributary fish swarm monitoring systems, and determining the expectation of the number of fish passing through the unit time as the service rate mu of a secondary single monitoring channel;
s3.2, determining the total number of the fishes passing through all monitoring channels of the tributary in unit time as a tributary system service rate r;
s3.3, calculating the service intensity S of the secondary monitoring system according to the total number i of the monitoring channels of all the tributary fish shoal secondary monitoring systems, the tributary system service rate (r) and the secondary single monitoring channel service rate mu;
s3.4, initializing a pre-constructed G/M/i model, and binding the total number of monitoring channels, the service rate of a secondary single monitoring channel and the service strength of the secondary monitoring system with the G/M/i model to generate a G/M/i model of a binding parameter;
s3.5, calculating the service time standard deviation of the secondary monitoring system after the tributary monitoring system is balanced according to the total number of the monitoring channels of the secondary monitoring system, the service rate of the single secondary monitoring channel and the G/M/i model of the binding parameter;
and S3.6, calculating the average queuing length of the tributary fish school according to the service time standard deviation of the secondary monitoring system and the G/M/i model of the binding parameter.
6. The method for identifying the richness of the river fish shoal according to claim 5, wherein the step S3.6 is specifically as follows:
average queuing length E [ X ] of tributary fish shoal q ]Summing the service rate r of the tributary system to generate a tributary fish school arrival rate lambda;
the secondary monitoring system service intensity calculation formula is as follows:
wherein χ is the arrival rate of the branch shoal, δ is the service rate of the secondary single monitoring channel, and i is the total number of monitoring channels of the secondary monitoring system;
the service time standard deviation calculation formula of the secondary monitoring system after the tributary monitoring system is balanced is as follows:
σ＝A * (iδ-iδσ)；
wherein, σ is the standard deviation of the service time of the secondary monitoring system, i is the total number of monitoring channels of the secondary monitoring system, δ is the service rate of a single monitoring channel of the secondary, and A × Q is the LST of the branch shoal queuing time;
the tributary fish school average queuing length calculation formula, namely the average queuing length of the G/M/i model, is as follows:
wherein, E (X) qc ) The average queuing length of all the tributaries, namely the fish school of the c tributaries is obtained;
the river fish shoal weighted queuing length calculation formula is as follows:
wherein ε is the total number of fish passing through the monitoring system per unit time interval of the river stream, η i The total number of fish passing through the monitoring system per unit time interval for the ith tributary; e (X) qc ) I.e. the average queue length of the c tributary fish schools, E (X) q ) Namely the average queue length of the main flowing fish school.
7. The method for identifying the richness of the river fish shoal according to claim 1, wherein the step S5 specifically comprises:
s5.1, loading a river fish shoal richness grade division table;
s5.2, identifying a queuing length interval where the river fish shoal weighted queuing length is located from the river fish shoal rich level division table;
and S5.2, determining the fish shoal enrichment evaluation grade corresponding to the weighted queuing length interval as the river fish shoal enrichment grade.
8. A system for identifying richness of a fish school of a river, the system using a method for identifying richness of a fish school of a river as claimed in any one of claims 1 to 7, the system comprising the following modules:
the fish school passing data acquisition module: the system is used for acquiring monitoring data of a river branch flow fish school passing system and monitoring data of the river branch flow fish school passing system in real time within a preset time interval;
the average queuing length calculation module of the river main stream fish shoal: the system comprises a pre-constructed M/M/n model, a river main stream fish shoal monitoring data acquisition unit, a river main stream fish shoal average queuing length calculation unit and a river main stream fish shoal monitoring data acquisition unit, wherein the pre-constructed M/M/n model is used for acquiring river main stream fish shoal monitoring data;
the average queuing length calculation module of the river tributary fish school: the method is used for calculating the average queuing length of the river tributary fish school according to a pre-constructed G/M/i model and the river tributary fish school monitoring data;
the river fish shoal weighted queuing length calculation module: the system is used for carrying out weighted summation on the received average queuing length of the fish shoals of the river main stream and the average queuing length of the fish shoals of the river tributaries to obtain the weighted queuing length of the fish shoals of the river;
the fish school enrichment schedule loading module: the system is used for adjusting the richness of the river fish shoals corresponding to the weighted queuing length of the river fish shoals in real time;
the river fish shoal abundance discrimination module: the method is used for determining the richness of the river fish shoal according to the weighted queuing length of the river fish shoal.
9. A computer storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing a method for identifying richness of a river shoal as claimed in any one of claims 1 to 7.
10. A computer device comprising a storage, a processor and a computer program stored in the storage and executable on the processor, wherein the processor executes the computer program to implement a method for identifying richness of a river fish school according to any one of claims 1 to 7.
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