Early warning method and system applied to blockage of sewage suction pipe of sewage suction truck and medium

Abstract

The invention relates to an early warning method and system for blockage of a sewage suction pipe applied to a sewage suction truck and a medium, and the method comprises the steps: M1, enabling the sewage suction truck to run to a target area, lowering the sewage suction pipe, and obtaining the temperature data information of a sewage material in real time based on a temperature sensor on the sewage suction pipe, acquiring shear rate data information of the sewage in real time based on a flow sensor on the sewage suction pipe, and acquiring shear stress data information of the sewage in real time based on a pressure sensor on the sewage suction pipe; and M2, on the basis of the temperature data information of the dirty material, the shear speed data information of the dirty material and the shear stress data information of the dirty material, constructing a viscosity prediction model of the dirty material based on a particle swarm optimization BP neural network. According to the invention, early warning can be carried out in advance in the sewage suction process of the sewage suction truck according to the viscosity change of the sewage, the damage of the sewage suction pump or the sewage suction pipe is prevented, the system can adapt to different sewage suction environments, accurate early warning is carried out, and the sewage suction efficiency of the sewage suction truck is improved.

Description

Technical Field

[0001] The invention relates to the technical field of sewage suction vehicles, and in particular to an early warning method, system and medium for clogging of a sewage suction pipe of a sewage suction vehicle. Background Art

[0002] With the rapid development of the economy, people's living standards are constantly improving, and domestic wastewater in daily life will continue to increase. Domestic wastewater enters the sewage pool through the sewer. The sewage pool requires a sewage suction truck to regularly suck sewage. During the treatment process, the sewage may contain impurities such as stones, cotton yarn, and leaves that will clog the suction pipe. If the suction continues, it will not only damage the suction pipe, but also the suction pump will be damaged due to overheating. Therefore, how to prevent the suction pipe or suction pump from being damaged during the suction process has become a problem that we urgently need to solve. Summary of the invention

[0003] In view of the above problems, the present invention provides an early warning method, system and medium for the blockage of the sewage suction pipe of the sewage suction truck. It can not only provide early warning of the sewage suction process of the sewage suction truck according to the change of the viscosity of the sewage to prevent damage to the sewage suction pump or the sewage suction pipe, but also can adapt to different sewage suction environments, provide accurate early warning and improve the sewage suction efficiency of the sewage suction truck.

[0004] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows: an early warning method for blockage of a sewage suction pipe of a sewage suction truck, the method comprising:

[0005] M1. The sewage suction truck drives to the target area and lowers the sewage suction pipe. The temperature data information of the sewage is obtained in real time based on the temperature sensor on the sewage suction pipe, the shear rate data information of the sewage is obtained in real time based on the flow sensor on the sewage suction pipe, and the shear stress data information of the sewage is obtained in real time based on the pressure sensor on the sewage suction pipe;

[0006] M2. Based on the temperature data information of the waste, the shear velocity data information of the waste and the shear stress data information of the waste, a waste viscosity prediction model based on a particle swarm optimization BP neural network is constructed to predict the viscosity of the waste and obtain the predicted viscosity data information of the waste;

[0007] M3. Based on the predicted viscosity data information of the waste, the viscosity of the waste is optimized by using a bowerbird optimization algorithm based on the golden sine to obtain the optimized viscosity data information of the waste;

[0008] M4. Based on the optimized sewage viscosity data information, establish a warning function F for sewage suction pipe blockage, calculate the warning value of sewage blockage, and obtain sewage blockage warning value data information.

[0009] Furthermore, the method further comprises:

[0010] M5. Based on the warning value data information of the sewage blockage, a preset threshold is set. If the warning value of the sewage blockage is greater than the preset threshold, a warning message is issued and the sewage suction pipe is closed. If the warning value of the sewage blockage is less than the preset threshold, the sewage suction pipe works normally.

[0011] Furthermore, in step M2, the construction of a waste viscosity prediction model based on a particle swarm optimization BP neural network to predict the viscosity of the waste includes:

[0012] M21. Based on the temperature data information of the waste material, the shear velocity data information of the waste material and the shear stress data information of the waste material, construct a BP neural network kernel function G i ,

[0013]

[0014] Among them, (α i ,β i ,λ i ) is the parameter of the BP neural network kernel function, x is the temperature data information of the waste material, y is the shear velocity data information of the waste material, z is the shear stress data information of the waste material, and the parameters of the BP neural network function are calculated to obtain the parameter data information of the BP neural network kernel function of the waste material;

[0015] M22. Based on the parameter data information of the BP neural network kernel function of the waste material, the particle population is initialized and the fitness function H of the individual particle population is constructed.

[0016]

[0017] Among them, g is the parameter data information of the BP neural network kernel function of the pollutant, σ1, σ2 and σ3 are the fitness factors of the individuals in the particle population, and the fitness values ​​of the individuals in the particle population are calculated to obtain the fitness value data information of the individuals in the particle population;

[0018] M23. Based on the fitness value data information of the individual particle population, establish a population target optimization function W,

[0019]

[0020] Among them, h is the fitness value data information of the individual particle population, η1 and η2 are the optimization factors of the particle population, and the parameters of the BP neural network kernel function of the waste are optimized to obtain the parameter data information of the BP neural network kernel function of the optimized waste;

[0021] M24. Based on the parameter data information of the optimized BP neural network kernel function of the waste, a viscosity prediction function Q of the waste is established.

[0022]

[0023] Among them, r is the parameter data information of the BP neural network kernel function of the optimized sewage, μ1, μ2 and μ3 are the viscosity prediction factors of the sewage, and the viscosity of the sewage is predicted to obtain the predicted viscosity data information of the sewage.

[0024] Furthermore, the constraints of the viscosity prediction factors μ1, μ2 and μ3 of the sewage are:

[0025]

[0026] The constraints of the fitness factors σ1, σ2 and σ3 of the individuals in the particle population are:

[0027]

[0028] Furthermore, the constraint function f of the optimization factors η1 and η2 of the particle population is,

[0029]

[0030] Among them, the value range of the constraint function f is,

[0031]

[0032] Furthermore, in step M3, the optimization of the viscosity of the waste material by using the golden sine-based bowerbird optimization algorithm includes:

[0033] M31. Based on the predicted viscosity data information of the sewage, the initial position, search step and maximum number of iterations of the bowerbird are set to initialize the bowerbird population to obtain the initialized bowerbird population data information;

[0034] M32. Based on the initialized gardener bird population data information, construct the fitness function S of the gardener bird individual,

[0035]

[0036] Wherein, a is the initialized data information of the gardener bird population, ρ1, ρ2 and ρ3 are fitness factors, and the fitness values ​​of individuals in the gardener bird population are calculated to obtain the fitness value data information of individuals in the gardener bird population;

[0037] M33. Based on the fitness value data information of individuals in the gardener bird population, a target optimization function P based on the golden sine is established.

[0038]

[0039] Among them, b is the fitness value data information of individuals in the bowerbird population, δ1, δ2 and δ3 are the golden sine factors of individuals in the bowerbird population, and the viscosity of the sewage is optimized to obtain the optimized viscosity data information of the sewage.

[0040] Furthermore, the golden sine factors δ1, δ2 and δ3 of the individuals in the bowerbird population are,

[0041]

[0042] Among them, b is the fitness value data information of individuals in the bowerbird population.

[0043] Furthermore, the warning function F of the blockage of the sewage suction pipe is:

[0044]

[0045] Among them, c is the viscosity data information of the optimized sewage, and θ1 and θ2 are the early warning determining factors of the sewage suction pipe of the sewage suction truck.

[0046] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides an early warning system for blockage of a sewage suction pipe of a sewage suction truck, including a computer device, which is programmed or configured to execute any one of the steps of the early warning method for blockage of a sewage suction pipe of a sewage suction truck.

[0047] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the early warning methods for blockage of a sewage suction pipe of a sewage suction truck.

[0048] The present invention has the following positive effects:

[0049] 1. The present invention predicts the viscosity of the sewage by constructing a sewage viscosity prediction model based on a particle swarm optimization BP neural network, and optimizes the viscosity of the sewage by combining a bowerbird optimization algorithm based on the golden sine. It can not only provide early warning of the sewage suction process of the sewage suction truck according to the viscosity change of the sewage to prevent damage to the sewage suction pump or the sewage suction pipe, but also perform real-time detection of the smoothness of the sewage suction process, thereby improving the efficiency of sewage suction and reducing the cost of sewage suction.

[0050] 2. The present invention establishes an early warning function F for sewage suction pipe blockage, calculates the early warning value of sewage blockage, and obtains early warning value data information of sewage blockage. It can not only adapt to different sewage suction environments, give accurate early warnings, and improve the sewage suction efficiency of sewage suction vehicles, but also is widely applicable to the suction, transportation and discharge of sludge, sewage or silt in petrochemical sewage pools collected in urban rainwater wells, inspection wells and various ditches. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0052] Figure 2 The present invention is a sewage viscosity prediction model based on particle swarm optimization BP neural network, and a schematic diagram of a flow chart for predicting the viscosity of sewage;

[0053] Figure 3 It is a flow chart of the bowerbird optimization algorithm based on the golden sine of the present invention;

[0054] Figure 4 It is a structural schematic diagram of the present invention;

[0055] Figure 5 It is a schematic diagram of the sewage suction process of the present invention;

[0056] Figure 6 It is a schematic diagram of the sewage discharge process of the present invention.

[0057] Explanation of the numbers in the figure: 1-sewage suction tank, 2-sewage suction pipe, 3-sewage suction truck, 4-sewage suction pump, 5-anti-overflow valve, 6-filter tee. DETAILED DESCRIPTION

[0058] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0059] Example 1: Figure 1 or Figure 4 As shown, an early warning method for blockage of a sewage suction pipe of a sewage suction truck is provided, the method comprising:

[0060] M1. The sewage suction truck 3 drives to the target area and lowers the sewage suction pipe 2. The sewage suction pump 4 arranged under the sewage suction tank 1 is started. The temperature data information of the sewage is obtained in real time based on the temperature sensor on the sewage suction pipe 2. The shear rate data information of the sewage is obtained in real time based on the flow sensor on the sewage suction pipe 2. The shear stress data information of the sewage is obtained in real time based on the pressure sensor on the sewage suction pipe;

[0061] M2. Based on the temperature data information of the waste, the shear velocity data information of the waste and the shear stress data information of the waste, a waste viscosity prediction model based on a particle swarm optimization BP neural network is constructed to predict the viscosity of the waste and obtain the predicted viscosity data information of the waste;

[0062] M3. Based on the predicted viscosity data information of the waste, the viscosity of the waste is optimized by using a bowerbird optimization algorithm based on the golden sine to obtain the optimized viscosity data information of the waste;

[0063] M4. Based on the optimized sewage viscosity data information, establish a warning function F for sewage suction pipe blockage, calculate the warning value of sewage blockage, and obtain sewage blockage warning value data information.

[0064] In this embodiment, the method further includes:

[0065] M5. Based on the warning value data information of the sewage blockage, a preset threshold is set. If the warning value of the sewage blockage is greater than the preset threshold, a warning message is issued and the sewage suction pipe is closed. If the warning value of the sewage blockage is less than the preset threshold, the sewage suction pipe works normally.

[0066] In this embodiment, if Figure 2 As shown, in step M2, the construction of a waste viscosity prediction model based on a particle swarm optimization BP neural network and the prediction of the waste viscosity include:

[0067] M21. Based on the temperature data information of the waste material, the shear velocity data information of the waste material and the shear stress data information of the waste material, construct a BP neural network kernel function G i ,

[0068]

[0069] Among them, (α i ,β i ,λ i ) is the parameter of the BP neural network kernel function, x is the temperature data information of the waste material, y is the shear velocity data information of the waste material, z is the shear stress data information of the waste material, and the parameters of the BP neural network function are calculated to obtain the parameter data information of the BP neural network kernel function of the waste material;

[0070] M22. Based on the parameter data information of the BP neural network kernel function of the waste material, the particle population is initialized and the fitness function H of the individual particle population is constructed.

[0071]

[0072] Among them, g is the parameter data information of the BP neural network kernel function of the pollutant, σ1, σ2 and σ3 are the fitness factors of the individuals in the particle population, and the fitness values ​​of the individuals in the particle population are calculated to obtain the fitness value data information of the individuals in the particle population;

[0073] M23. Based on the fitness value data information of the individual particle population, establish a population target optimization function W,

[0074]

[0075] Among them, h is the fitness value data information of the individual particle population, η1 and η2 are the optimization factors of the particle population, and the parameters of the BP neural network kernel function of the waste are optimized to obtain the parameter data information of the BP neural network kernel function of the optimized waste;

[0076] M24. Based on the parameter data information of the optimized BP neural network kernel function of the waste, a viscosity prediction function Q of the waste is established.

[0077]

[0078] Among them, r is the parameter data information of the BP neural network kernel function of the optimized sewage, μ1, μ2 and μ3 are the viscosity prediction factors of the sewage, and the viscosity of the sewage is predicted to obtain the predicted viscosity data information of the sewage.

[0079] In this embodiment, the constraints of the viscosity prediction factors μ1, μ2 and μ3 of the waste are:

[0080]

[0081] The constraints of the fitness factors σ1, σ2 and σ3 of the individuals in the particle population are:

[0082]

[0083] In this embodiment, the constraint function f of the optimization factors η1 and η2 of the particle population is,

[0084]

[0085] Among them, the value range of the constraint function f is,

[0086]

[0087] In this embodiment, if Figure 5 or Figure 6 As shown, a sewage suction pipe 2 is provided at the side of the sewage suction tank body 1, and an anti-overflow valve 5 is provided at the top of the sewage suction tank body 1. The anti-overflow valve 5 is connected to the filter tee 6 through a pipeline, and the filter tee 6 is connected to the sewage suction pump 4. During the sewage suction process, the sewage suction pump 4 draws out the air in the sewage suction tank body 1, and a negative pressure is formed inside the sewage suction tank body 1. The sewage suction pipe 2 is placed in the sewage suction pool, and the sewage is sucked into the sewage suction tank body 1 due to the difference in internal and external pressures. During the sewage discharge process, the sewage suction pump 4 draws air into the sewage suction tank body 1 and presses the sewage out of the sewage suction tank body 1, thereby completing the sewage suction or sewage discharge work.

[0088] Embodiment 2: Based on the early warning method for blockage of a sewage suction pipe of a sewage suction truck in Embodiment 1, the present invention is further illustrated and described below.

[0089] like Figure 1 or Figure 4 As shown, an early warning method for blockage of a sewage suction pipe of a sewage suction truck is provided, the method comprising:

[0090] M1. The sewage suction truck 3 drives to the target area and lowers the sewage suction pipe 2. The sewage suction pump 4 arranged under the sewage suction tank 1 is started. The temperature data information of the sewage is obtained in real time based on the temperature sensor on the sewage suction pipe 2. The shear rate data information of the sewage is obtained in real time based on the flow sensor on the sewage suction pipe 2. The shear stress data information of the sewage is obtained in real time based on the pressure sensor on the sewage suction pipe;

[0091] M2. Based on the temperature data information of the waste, the shear velocity data information of the waste and the shear stress data information of the waste, a waste viscosity prediction model based on a particle swarm optimization BP neural network is constructed to predict the viscosity of the waste and obtain the predicted viscosity data information of the waste;

[0092] M3. Based on the predicted viscosity data information of the waste, the viscosity of the waste is optimized by using a bowerbird optimization algorithm based on the golden sine to obtain the optimized viscosity data information of the waste;

[0093] M4. Based on the optimized sewage viscosity data information, establish a warning function F for sewage suction pipe blockage, calculate the warning value of sewage blockage, and obtain sewage blockage warning value data information.

[0094] In this embodiment, if Figure 3 As shown, in step M3, the optimization of the viscosity of the waste material using the golden sine-based bowerbird optimization algorithm includes:

[0095] M31. Based on the predicted viscosity data information of the sewage, the initial position, search step and maximum number of iterations of the bowerbird are set to initialize the bowerbird population to obtain the initialized bowerbird population data information;

[0096] M32. Based on the initialized gardener bird population data information, construct the fitness function S of the gardener bird individual,

[0097]

[0098] Wherein, a is the initialized data information of the gardener bird population, ρ1, ρ2 and ρ3 are fitness factors, and the fitness values ​​of individuals in the gardener bird population are calculated to obtain the fitness value data information of individuals in the gardener bird population;

[0099] M33. Based on the fitness value data information of individuals in the gardener bird population, a target optimization function P based on the golden sine is established.

[0100]

[0101] Among them, b is the fitness value data information of individuals in the bowerbird population, δ1, δ2 and δ3 are the golden sine factors of individuals in the bowerbird population, and the viscosity of the sewage is optimized to obtain the optimized viscosity data information of the sewage.

[0102] In this embodiment, the golden sine factors δ1, δ2 and δ3 of the individuals in the bowerbird population are,

[0103]

[0104] Among them, b is the fitness value data information of individuals in the bowerbird population.

[0105] In this embodiment, the warning function F of the blockage of the sewage suction pipe is:

[0106]

[0107] Among them, c is the viscosity data information of the optimized sewage, and θ1 and θ2 are the early warning determining factors of the sewage suction pipe of the sewage suction truck.

[0108] In this embodiment, the present invention provides an early warning system for blockage of a sewage suction pipe of a sewage suction truck, including a computer device programmed or configured to execute any one of the steps of the early warning method for blockage of a sewage suction pipe of a sewage suction truck.

[0109] In this embodiment, the present invention further provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the above-described early warning methods for blockage of a sewage suction pipe of a sewage suction truck.

[0110] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0111] In summary, the present invention can not only provide early warning for the sewage suction process of the sewage suction truck according to the change in the viscosity of the sewage to prevent damage to the sewage suction pump or the sewage suction pipe, but also can adapt to different sewage suction environments, provide accurate early warning, and improve the sewage suction efficiency of the sewage suction truck.

[0112] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. An early warning method for blockage of a sewage suction pipe of a sewage suction truck, characterized in that: The method comprises: M1. The sewage suction truck drives to the target area and lowers the sewage suction pipe. The temperature data information of the sewage is obtained in real time based on the temperature sensor on the sewage suction pipe, the shear rate data information of the sewage is obtained in real time based on the flow sensor on the sewage suction pipe, and the shear stress data information of the sewage is obtained in real time based on the pressure sensor on the sewage suction pipe; M2. Based on the temperature data information of the waste, the shear velocity data information of the waste and the shear stress data information of the waste, a waste viscosity prediction model based on a particle swarm optimization BP neural network is constructed to predict the viscosity of the waste and obtain the predicted viscosity data information of the waste; M3. Based on the predicted viscosity data information of the waste, the viscosity of the waste is optimized by using a bowerbird optimization algorithm based on the golden sine to obtain the optimized viscosity data information of the waste; M4. Based on the optimized sewage viscosity data information, establish a warning function F for sewage suction pipe blockage, calculate the warning value of sewage blockage, and obtain sewage blockage warning value data information.

2. The early warning method for blockage of a sewage suction pipe of a sewage suction truck according to claim 1 is characterized in that: The method further comprises: M5. Based on the warning value data information of the sewage blockage, a preset threshold is set. If the warning value of the sewage blockage is greater than the preset threshold, a warning message is issued and the sewage suction pipe is closed. If the warning value of the sewage blockage is less than the preset threshold, the sewage suction pipe works normally.

3. The early warning method for blockage of a sewage suction pipe of a sewage suction truck according to claim 1 is characterized in that: In step M2, the construction of a waste viscosity prediction model based on a particle swarm optimization BP neural network to predict the viscosity of the waste includes: M21. Based on the temperature data information of the waste material, the shear velocity data information of the waste material and the shear stress data information of the waste material, construct a BP neural network kernel function G i , Among them, (α i ,β i ,λ i ) is the parameter of the BP neural network kernel function, x is the temperature data information of the waste material, y is the shear velocity data information of the waste material, z is the shear stress data information of the waste material, and the parameters of the BP neural network function are calculated to obtain the parameter data information of the BP neural network kernel function of the waste material; M22. Based on the parameter data information of the BP neural network kernel function of the waste material, the particle population is initialized and the fitness function H of the individual particle population is constructed. Among them, g is the parameter data information of the BP neural network kernel function of the pollutant, σ1, σ2 and σ3 are the fitness factors of the individuals in the particle population, and the fitness values ​​of the individuals in the particle population are calculated to obtain the fitness value data information of the individuals in the particle population; M23. Based on the fitness value data information of the individual particle population, establish a population target optimization function W, Among them, h is the fitness value data information of the individual particle population, η1 and η2 are the optimization factors of the particle population, and the parameters of the BP neural network kernel function of the waste are optimized to obtain the parameter data information of the BP neural network kernel function of the optimized waste; M24. Based on the parameter data information of the optimized BP neural network kernel function of the waste, a viscosity prediction function Q of the waste is established. Among them, r is the parameter data information of the BP neural network kernel function of the optimized sewage, μ1, μ2 and μ3 are the viscosity prediction factors of the sewage, and the viscosity of the sewage is predicted to obtain the predicted viscosity data information of the sewage.

4. The early warning method for blockage of a sewage suction pipe of a sewage suction truck according to claim 3 is characterized in that: The constraints of the viscosity prediction factors μ1, μ2 and μ3 of the pollutant are: The constraints of the fitness factors σ1, σ2 and σ3 of the individuals in the particle population are:

5. The early warning method for blockage of the sewage suction pipe of a sewage suction truck according to claim 3 is characterized in that: The constraint function f of the optimization factors η1 and η2 of the particle population is, Among them, the value range of the constraint function f is, 6. The early warning method for blockage of a sewage suction pipe of a sewage suction truck according to claim 1, characterized in that: In step M3, the optimization of the viscosity of the waste material using the golden sine-based bowerbird optimization algorithm includes: M31. Based on the predicted viscosity data information of the sewage, the initial position, search step and maximum number of iterations of the bowerbird are set to initialize the bowerbird population to obtain the initialized bowerbird population data information; M32. Based on the initialized gardener bird population data information, construct the fitness function S of the gardener bird individual, Wherein, a is the initialized data information of the gardener bird population, ρ1, ρ2 and ρ3 are fitness factors, and the fitness values ​​of individuals in the gardener bird population are calculated to obtain the fitness value data information of individuals in the gardener bird population; M33. Based on the fitness value data information of individuals in the gardener bird population, a target optimization function P based on the golden sine is established. Among them, b is the fitness value data information of individuals in the bowerbird population, δ1, δ2 and δ3 are the golden sine factors of individuals in the bowerbird population, and the viscosity of the sewage is optimized to obtain the optimized viscosity data information of the sewage.

7. The early warning method for blockage of a sewage suction pipe of a sewage suction truck according to claim 6 is characterized in that: The golden sine factors δ1, δ2 and δ3 of the individuals in the bowerbird population are, Among them, b is the fitness value data information of individuals in the bowerbird population.

8. The early warning method for blockage of a sewage suction pipe of a sewage suction truck according to claim 1 is characterized in that: The early warning function F of the blockage of the sewage suction pipe is: Among them, c is the viscosity data information of the optimized sewage, and θ1 and θ2 are the early warning determining factors of the sewage suction pipe of the sewage suction truck.

9. An early warning system for blockage of a sewage suction pipe of a sewage suction truck, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the early warning method for blockage of the sewage suction pipe of a sewage suction truck as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that is programmed or configured to execute the early warning method for blockage of a sewage suction pipe of a sewage suction truck as described in any one of claims 1 to 8.

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