CN117763392A - Air conditioner detection method for railway passenger car - Google Patents
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Abstract
The invention discloses a detection method of an air conditioner of a railway passenger car, which comprises the following steps: firstly, building a database by a component, and storing historical data; secondly, information when the air conditioning system operates is collected; then, calculating an air conditioner power value and a noise value according to the acquired information; then, constructing a historical result data set, and matching the acquired information with the historical data to obtain the result data set; then, judging whether the result data set is empty or not; finally, judging and obtaining an air conditioner detection result according to the comprehensive evaluation index value; the invention relates to the technical field of detection of railway coaches, which aims to automatically obtain a detection result by comprehensively evaluating according to an air conditioner noise value, an air conditioner temperature and humidity value and an air conditioner power value, calculating to obtain a comprehensive evaluation index value and automatically obtaining the air conditioner detection result and solve the problem that the detection of the existing railway coaches is time-consuming and labor-consuming because the detection result is mainly obtained by manually analyzing detection data.
Description
Technical Field
The invention relates to the technical field of detection of railway coaches, in particular to a detection method of an air conditioner of a railway coach.
Background
A railway passenger car is a fixed rail transit system running inside or between cities, and is generally composed of subways, light rails, trams, and the like. The rail passenger car is a public transport means with centralized power supply, fixed rails and running according to a train schedule, and has the main advantages of being capable of transporting passengers in a large quantity, reducing traffic jams, improving transport efficiency, reducing environmental pollution and the like.
The air conditioner of the railway passenger car is an air conditioning system used in rail vehicles such as railway trains and subway trains, and the air conditioning system of the railway passenger car can provide proper temperature and humidity so as to ensure that passengers can enjoy comfortable air quality in traveling.
In order to ensure stable operation of the air conditioner of the railway carriage, the air conditioner of the railway carriage needs to be detected regularly, the detection result of the air conditioner of the railway carriage is mainly obtained by manually analyzing the detection data, and the detection is time-consuming and labor-consuming, so that the detection method of the air conditioner of the railway carriage is provided for solving the problems.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a detection method for an air conditioner of a railway passenger car, which has the advantages of automatically obtaining a detection result and the like, and solves the problems that the detection result of the existing air conditioner of the railway passenger car is mainly obtained by manually analyzing detection data, and the detection is time-consuming and labor-consuming.
(II) technical scheme
In order to achieve the purpose of automatically obtaining the detection result, the invention provides the following technical scheme:
a detection method of an air conditioner of a railway passenger car comprises the following steps:
s1, constructing a database, wherein the database stores historical temperature and humidity information, historical noise information, historical current information, a historical air conditioner noise value set, a historical air conditioner power value and a corresponding historical air conditioner detection result as historical data;
s2, acquiring temperature and humidity information, noise information and current information when the air conditioning system operates;
s3, calculating to obtain an air conditioner power value according to the temperature and humidity information and the current information, and calculating to obtain an air conditioner noise value according to the noise information;
s4, constructing a historical result data set, matching the collected temperature and humidity information, the air conditioner noise value and the current information with the historical data of the database, acquiring a matched historical air conditioner detection result, and adding the matched historical air conditioner detection result into the historical result data set;
s5, judging whether the historical result data set is empty, if not, outputting a historical air conditioner detection result in the historical result data set, and if so, carrying out comprehensive evaluation according to an air conditioner noise value set, an air conditioner temperature and humidity value and an air conditioner power value, and calculating to obtain a comprehensive evaluation index value;
s6, judging and obtaining an air conditioner detection result according to the comprehensive evaluation index value.
Preferably, the temperature and humidity information, the noise information and the current information acquired in the step S2 are different modes M of the air conditioning system of the ith compartment α Gear D β Lower corresponding acquisition T 0 A value of the time period; the temperature and humidity information is acquired by a temperature sensor and a humidity sensor, and comprises T 0 Arithmetic mean value TP of temperature over time period B Arithmetic mean value H with humidity j The method comprises the steps of carrying out a first treatment on the surface of the The current information is acquired by a current sensor, and the current information comprises T 0 Arithmetic mean value S of current in time period d 。
Preferably, in step S3, the air conditioner power value is obtained by calculating according to the temperature and humidity information and the current information, and the method includes the following sub-steps:
s3.1.1 according to the temperature value TP of the air conditioner B And a humidity value H j Current value S uploaded to current sensor d Correcting to obtain corrected current value I γ ；
In TP s The temperature value of the air conditioner running under the experimental condition; i max·LBT The maximum current value of the current gear operation under the experimental condition; h x The humidity value of the air conditioner operation under the experimental condition; μ is the humidity coefficient of the current;
s3.1.2 according to I γ Calculating gear D β Power PD of (2) β ；
PD β ＝I γ *U*e -λt ；
Wherein U is rated voltage; e is the base of natural logarithm, 2.71828; t represents the current age; λ represents an attenuation coefficient;
s3.1.3 calculating the mode power PM α ；
In DF δ Is gear D β Weights of (2);
s3.1.4, calculating the air conditioner power ZP;
preferably, the calculating in step S3 according to the noise information obtains the air conditioner noise value set, which includes the following sub-steps:
s3.2.1, collecting a sound signal by using a microphone, and converting the collected sound signal into a digital signal by using an analog-to-digital converter;
s3.2.2, noise reduction and filtering pretreatment are carried out on the digital signals;
s3.2.3, converting the preprocessed digital signals into frequency domain representation by using a Fourier transform algorithm, obtaining frequency spectrum information, and calculating energy values on different frequencies;
s3.2.4, calculating the dB according to the energy value, wherein the expression is as follows:
dB＝10*log(E/E0)；
wherein E is the energy value of the signal, E0 is the reference energy value;
s3.2.5, the dB values of the different frequencies are put into the noise value set a { }.
Preferably, the step S3.2.2 further comprises the sub-steps of:
s3.2.2.1, the digital signals are encoded by an encoder to obtain hidden layer representation, and then decoded and reconstructed by a decoder to reduce the environmental noise in the acquired signals;
s3.2.2.2 the digital signals of different frequencies are selectively passed or blocked using IIR high pass filters to extract the effective sound signal.
Preferably, the step S3.2.3 further comprises the sub-steps of:
s3.2.3.1, dividing the preprocessed digital signal into a plurality of discrete samples;
s3.2.3.2 converting the discrete signal into a frequency domain representation using a fourier transform algorithm, resulting in spectral information, including frequency and energy values;
s3.2.3.3, calculating the square of the amplitude corresponding to each frequency according to the obtained frequency domain representation, and performing normalization processing to obtain the energy value on each frequency.
Preferably, the step S4 further comprises the substeps of:
s4.1, setting a compensation parameter omega for each acquired humidity value, temperature value and current value to obtain a humidity value TP B Temperature value H j And a current value S d Is not limited by the compensation interval:
TP B -omega is less than or equal to humidity value compensation interval TP B +ω，
H j Omega is less than or equal to temperature value compensation interval H j +ω，
S d -omega is less than or equal to current value compensation interval S d +ω；
S4.2, sequentially taking out historical data in the database, judging whether the noise value set A { } belongs to the historical noise information set, and jumping to S4.3, or jumping to S4.6;
s4.3, judging whether the temperature value in the historical temperature and humidity information is in a temperature value compensation interval, if so, jumping to S4.4, otherwise, jumping to S4.6;
s4.4, judging whether the humidity value in the historical temperature and humidity information is in a humidity value compensation interval, if so, jumping to S4.5, otherwise, jumping to S4.6;
s4.5, judging whether the current value in the historical current information is in a current value compensation interval, if so, jumping to S4.7, otherwise, jumping to S4.6;
s4.6, setting the detection result of the historical air conditioner to be null;
and S4.7, adding the historical air conditioner detection result set matrix into a historical result data set, acquiring each historical detection result in the data set, and outputting the final historical air conditioner detection result with the highest same historical detection result.
Preferably, the step S5 performs comprehensive evaluation according to the air conditioner noise value set, the air conditioner temperature and humidity value and the air conditioner power value, and calculates a comprehensive evaluation index value, where the method further includes the following sub-steps:
s5.1, obtaining a power evaluation factor ZPE according to the air conditioner power ZP, wherein the expression is as follows:
s5.2, obtaining a noise evaluation factor dBE according to the noise value set A { }, wherein the expression is as follows:
s5.3, calculating to obtain a comprehensive evaluation index value CEV, wherein the expression is as follows:
CEV＝ZPF*ZPE+DBF*dBE；
wherein ZPF is a power evaluation factor weight value, and DBF is a noise evaluation factor weight value.
Preferably, when 1< CEV is less than or equal to 2, outputting 'good running condition of the ith carriage'; when 2< CEV is less than or equal to 3, outputting 'the running condition of the ith carriage is general'; when CEV is 3< and is less than or equal to 4, outputting 'poor running condition of the ith carriage'; when 4< CEV is less than or equal to 6, outputting 'the running condition of the ith carriage is poor'.
(III) beneficial effects
Compared with the prior art, the invention provides a method for detecting an air conditioner of a railway passenger car, which comprises the following steps of
The beneficial effects are that:
according to the method for detecting the air conditioner of the railway passenger car, comprehensive evaluation is carried out according to the air conditioner noise value, the air conditioner temperature and humidity value and the air conditioner power value, the comprehensive evaluation index value is obtained through calculation, the air conditioner detection result is automatically obtained, the purpose of automatically obtaining the detection result is achieved, and the problem that the detection of the existing air conditioner of the railway passenger car is time-consuming and labor-consuming due to the fact that the detection result is mainly obtained by manually analyzing the detection data is solved.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting an air conditioner of a railway passenger car;
fig. 2 is a schematic diagram of a flow chart of calculating and obtaining an air conditioner power value by using the method for detecting the air conditioner of the railway passenger car;
fig. 3 is a schematic diagram of a process for matching the collected temperature and humidity information, air conditioner noise value and current information with historical data of a database according to the method for detecting the air conditioner of the railway passenger car.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, a method for detecting an air conditioner of a railway passenger car includes the following steps:
s1, constructing a database, wherein the database stores historical temperature and humidity information, historical noise information, historical current information, a historical air conditioner noise value set, a historical air conditioner power value and a corresponding historical air conditioner detection result as historical data;
s2, acquiring temperature and humidity information, noise information and current information when the air conditioning system operates;
s3, calculating to obtain an air conditioner power value according to the temperature and humidity information and the current information, and calculating to obtain an air conditioner noise value according to the noise information;
s4, constructing a historical result data set, matching the collected temperature and humidity information, the air conditioner noise value and the current information with the historical data of the database, acquiring a matched historical air conditioner detection result, and adding the matched historical air conditioner detection result into the historical result data set;
s5, judging whether the historical result data set is empty, if not, outputting a historical air conditioner detection result in the historical result data set, and if so, carrying out comprehensive evaluation according to an air conditioner noise value set, an air conditioner temperature and humidity value and an air conditioner power value, and calculating to obtain a comprehensive evaluation index value;
s6, judging and obtaining an air conditioner detection result according to the comprehensive evaluation index value.
In this embodiment, the temperature and humidity information, the noise information and the current information acquired in step S2 are different modes M of the air conditioning system of the ith compartment α Gear D β Lower corresponding acquisition T 0 A value of the time period; the temperature and humidity information is acquired by a temperature sensor and a humidity sensor, and the temperature and humidity information comprises T 0 Arithmetic mean value TP of temperature over time period B Arithmetic mean value H with humidity j The method comprises the steps of carrying out a first treatment on the surface of the The current information is acquired by a current sensor, and the current information comprises T 0 Arithmetic mean value S of current in time period d 。
In this embodiment, in step S3, an air conditioner power value is obtained by calculating according to temperature and humidity information and current information, and the method includes the following sub-steps:
s3.1.1 according to the temperature value TP of the air conditioner B And a humidity value H j Current value S uploaded to current sensor d Correcting to obtain corrected current value I γ ；
In TP s The temperature value of the air conditioner running under the experimental condition; i max·LBT The maximum current value of the current gear operation under the experimental condition; h x The humidity value of the air conditioner operation under the experimental condition; μ is the humidity coefficient of the current;
s3.1.2 according to I γ Calculating gear D β Power PD of (2) β ；
PD β ＝I γ *U*e -λt ；
Wherein U is rated voltage; e is the base of natural logarithm, 2.71828; t represents the current age; λ represents an attenuation coefficient;
s3.1.3 calculating the mode power PM α ；
In DF δ Is gear D β Weights of (2);
s3.1.4, calculating the air conditioner power ZP;
in this embodiment, in step S3, an air conditioner noise value set is obtained by calculation according to noise information, which includes the following sub-steps:
s3.2.1, collecting a sound signal by using a microphone, and converting the collected sound signal into a digital signal by using an analog-to-digital converter;
s3.2.2, noise reduction and filtering pretreatment are carried out on the digital signals;
s3.2.3, converting the preprocessed digital signals into frequency domain representation by using a Fourier transform algorithm, obtaining frequency spectrum information, and calculating energy values on different frequencies;
s3.2.4, calculating dB values of different frequencies according to the energy values, wherein the dB values are expressed as follows:
dB＝10*log(E/E0)；
s3.2.5, the dB values of the different frequencies are put into the noise value set a { }.
Wherein E is the energy value of the signal, E0 is the reference energy value;
in this embodiment, the step S3.2.2 further includes the following sub-steps:
s3.2.2.1 coding the digital signal by an encoder to obtain a hidden layer representation, then decoding and reconstructing by a decoder to reduce noise in the acquired signal,
during training, the self-encoder learns a noise pattern in the signal by minimizing a reconstruction error between the input and the reconstruction output, thereby realizing noise reduction;
s3.2.2.2, selectively passing or blocking digital signals of different frequencies using an IIR high pass filter to extract an effective sound signal, wherein the IIR high pass filter is calculated as:
initializing an input signal sequence and an output signal sequence;
for each sampling point k, calculating an output signal y [ k ];
y[k]＝b0*x[k]+b1*x[k-1]+b2*x[k-2]+...+am*y[k-m]
where bn and am are the coefficients of the filter, n is the forward coefficient order of the filter, m is the feedback coefficient order of the filter, and z is the unit delay;
storing the calculated output signal, and updating the input signal and the output signal sequence;
repeating the steps until all the sampling points are processed.
In this embodiment, the step S3.2.3 further includes the following sub-steps:
s3.2.3.1, dividing the preprocessed digital signal into a plurality of discrete samples;
s3.2.3.2 converting the discrete signal into a frequency domain representation using a fourier transform algorithm, resulting in spectral information, including frequency and energy values;
s3.2.3.3, calculating the square of the amplitude corresponding to each frequency according to the obtained frequency domain representation, and performing normalization processing to obtain the energy value on each frequency.
In this embodiment, step S4 further includes the following sub-steps:
s4.1, setting a compensation parameter omega for each acquired humidity value, temperature value and current value to obtain a humidity value TP B Temperature value H j And a current value S d Is not limited by the compensation interval:
TP B -omega is less than or equal to humidity value compensation interval TP B +ω，
H j Omega is less than or equal to temperature value compensation interval H j +ω，
S d -omega is less than or equal to current value compensation interval S d +ω；
S4.2, sequentially taking out historical data in the database, judging whether the noise value in the historical noise information is in a noise value compensation interval, and jumping to S4.3, or else jumping to S4.6;
s4.3, judging whether the temperature value in the historical temperature and humidity information is in a temperature value compensation interval, if so, jumping to S4.4, otherwise, jumping to S4.6;
s4.4, judging whether the humidity value in the historical temperature and humidity information is in a humidity value compensation interval, if so, jumping to S4.5, otherwise, jumping to S4.6;
s4.5, judging whether the current value in the historical current information is in a current value compensation interval, if so, jumping to S4.7, otherwise, jumping to S4.6;
s4.6, setting the detection result of the historical air conditioner to be null;
and S4.7, adding the historical air conditioner detection result set matrix into a historical result data set, acquiring each historical detection result in the data set, and outputting the final historical air conditioner detection result with the highest same historical detection result.
In this embodiment, step S5 performs comprehensive evaluation according to the air conditioner noise value, the air conditioner temperature and humidity value and the air conditioner power value, and calculates to obtain a comprehensive evaluation index value, where the method further includes the following sub-steps:
s5.1, obtaining a power evaluation factor ZPE according to the air conditioner power ZP, wherein the expression is as follows:
s5.2, obtaining a noise evaluation factor dBE according to the noise value set A { }, wherein the expression is as follows:
s5.3, calculating to obtain a comprehensive evaluation index value CEV, wherein the expression is as follows:
CEV＝ZPF*ZPE+DBF*dBE；
wherein ZPF is a power evaluation factor weight value, and DBF is a noise evaluation factor weight value;
when the power and noise values of the air conditioner have extreme values, a certain value is larger, and the other value is smaller, the situation of inaccurate judgment exists, in the embodiment, the set power evaluation factor weight value and the noise evaluation factor weight value are regulated and controlled, when the certain value is larger, the evaluation factor weight of the changed value is also larger, namely, the evaluation factor is in direct proportion to the evaluation factor weight, and the situation of inaccurate judgment can be avoided.
In this embodiment, step S6 determines, according to the comprehensive evaluation index value, to obtain an air conditioner detection result, where the method further includes: when CEV is 1< and is less than or equal to 2, outputting 'good running condition of the ith carriage'; when 2< CEV is less than or equal to 3, outputting 'the running condition of the ith carriage is general'; when CEV is 3< and is less than or equal to 4, outputting 'poor running condition of the ith carriage'; when 4< CEV is less than or equal to 6, outputting 'the running condition of the ith carriage is poor'.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. The method for detecting the air conditioner of the railway passenger car is characterized by comprising the following steps of:
s1, constructing a database, wherein the database stores historical temperature and humidity information, historical noise information, historical current information, a historical air conditioner noise value set, a historical air conditioner power value and a corresponding historical air conditioner detection result as historical data;
s2, acquiring temperature and humidity information, noise information and current information when the air conditioning system operates;
s3, calculating to obtain an air conditioner power value according to the temperature and humidity information and the current information, and calculating to obtain an air conditioner noise value according to the noise information;
s4, constructing a historical result data set, matching the collected temperature and humidity information, the air conditioner noise value and the current information with the historical data of the database, acquiring a matched historical air conditioner detection result, and adding the matched historical air conditioner detection result into the historical result data set;
s5, judging whether the historical result data set is empty, if not, outputting a historical air conditioner detection result in the historical result data set, and if so, carrying out comprehensive evaluation according to an air conditioner noise value set, an air conditioner temperature and humidity value and an air conditioner power value, and calculating to obtain a comprehensive evaluation index value;
s6, judging and obtaining an air conditioner detection result according to the comprehensive evaluation index value.
2. The method for detecting an air conditioner of a railway carriage according to claim 1, wherein the temperature and humidity information, the noise information and the current information collected in the step S2 are different modes M of an air conditioning system of an ith carriage α Gear D β Lower corresponding acquisition T 0 A value of the time period; the temperature and humidity information is acquired by a temperature sensor and a humidity sensor, and comprises T 0 Arithmetic mean value TP of temperature over time period B Arithmetic mean value H with humidity j The method comprises the steps of carrying out a first treatment on the surface of the The current information is acquired by a current sensor, and the current information comprises T 0 Arithmetic mean value S of current in time period d 。
3. The method for detecting the air conditioner of the railway passenger car according to claim 2, wherein in the step S3, the air conditioner power value is obtained by calculating according to the temperature and humidity information and the current information, and the method comprises the following sub-steps:
s3.1.1 according to the temperature value TP of the air conditioner B And a humidity value H j Current value S uploaded to current sensor d Correcting to obtain corrected current value I γ ；
In TP s The temperature value of the air conditioner running under the experimental condition; i max·LBT The maximum current value of the current gear operation under the experimental condition; h x The humidity value of the air conditioner operation under the experimental condition; μ is the humidity coefficient of the current;
s3.1.2 according to I γ Calculating gear D β Power PD of (2) β ；
PD β ＝I γ *U*e -λt ；
Wherein U is rated voltage; e is the base of natural logarithm, 2.71828; t represents the current age; λ represents an attenuation coefficient;
s3.1.3 calculating the mode power PM α ；
In DF δ Is gear D β Weights of (2);
s3.1.4, calculating the air conditioner power ZP;
4. a method for detecting an air conditioner of a railway carriage according to claim 3, wherein the step S3 of calculating an air conditioner noise value set according to noise information includes the following sub-steps:
s3.2.1, collecting a sound signal by using a microphone, and converting the collected sound signal into a digital signal by using an analog-to-digital converter;
s3.2.2, noise reduction and filtering pretreatment are carried out on the digital signals;
s3.2.3, converting the preprocessed digital signals into frequency domain representation by using a Fourier transform algorithm, obtaining frequency spectrum information, and calculating energy values on different frequencies;
s3.2.4, calculating the dB according to the energy value, wherein the expression is as follows:
dB＝10*log(E/E0)；
wherein E is the energy value of the signal, E0 is the reference energy value;
s3.2.5, the dB values of the different frequencies are put into the noise value set a { }.
5. The method of claim 4, wherein the step S3.2.2 further comprises the sub-steps of:
s3.2.2.1, the digital signals are encoded by an encoder to obtain hidden layer representation, and then decoded and reconstructed by a decoder to reduce the environmental noise in the acquired signals;
s3.2.2.2 the digital signals of different frequencies are selectively passed or blocked using IIR high pass filters to extract the effective sound signal.
6. The method of claim 4, wherein the step S3.2.3 further comprises the sub-steps of:
s3.2.3.1, dividing the preprocessed digital signal into a plurality of discrete samples;
s3.2.3.2 converting the discrete signal into a frequency domain representation using a fourier transform algorithm, resulting in spectral information, including frequency and energy values;
s3.2.3.3, calculating the square of the amplitude corresponding to each frequency according to the obtained frequency domain representation, and performing normalization processing to obtain the energy value on each frequency.
7. The method for detecting the air conditioner of the railway carriage according to claim 4, wherein the step S4 further comprises the substeps of:
s4.1, setting a compensation parameter omega for each acquired humidity value, temperature value and current value to obtain a humidity value TP B Temperature value H j And a current value S d Is not limited by the compensation interval:
TP B -omega is less than or equal to humidity value compensation interval TP B +ω，
H j Omega is less than or equal to temperature value compensation interval H j +ω，
S d -omega is less than or equal to current value compensation interval S d +ω；
S4.2, sequentially taking out historical data in the database, judging whether the noise value set A { } belongs to the historical noise information set, and jumping to S4.3, or jumping to S4.6;
s4.3, judging whether the temperature value in the historical temperature and humidity information is in a temperature value compensation interval, if so, jumping to S4.4, otherwise, jumping to S4.6;
s4.4, judging whether the humidity value in the historical temperature and humidity information is in a humidity value compensation interval, if so, jumping to S4.5, otherwise, jumping to S4.6;
s4.5, judging whether the current value in the historical current information is in a current value compensation interval, if so, jumping to S4.7, otherwise, jumping to S4.6;
s4.6, setting the detection result of the historical air conditioner to be null;
and S4.7, adding the historical air conditioner detection result set matrix into a historical result data set, acquiring each historical detection result in the data set, and outputting the final historical air conditioner detection result with the highest same historical detection result.
8. The method for detecting the air conditioner of the railway passenger car according to claim 4, wherein the step S5 is to perform comprehensive evaluation according to the set of air conditioner noise values, the air conditioner temperature and humidity values and the air conditioner power values, and calculate a comprehensive evaluation index value, and the method further comprises the following sub-steps:
s5.1, obtaining a power evaluation factor ZPE according to the air conditioner power ZP, wherein the expression is as follows:
s5.2, obtaining a noise evaluation factor dBE according to the noise value set A { }, wherein the expression is as follows:
s5.3, calculating to obtain a comprehensive evaluation index value CEV, wherein the expression is as follows:
CEV＝ZPF*ZPE+DBF*dBE；
wherein ZPF is a power evaluation factor weight value, and DBF is a noise evaluation factor weight value.
9. The method for detecting the air conditioner of the railway passenger car according to claim 8, wherein the step S6 is to judge and obtain the detection result of the air conditioner according to the comprehensive evaluation index value, and further comprises: when CEV is 1< and is less than or equal to 2, outputting 'good running condition of the ith carriage'; when 2< CEV is less than or equal to 3, output
"the running condition of the ith carriage is general"; when CEV is 3< and is less than or equal to 4, outputting 'poor running condition of the ith carriage'; when 4< CEV is less than or equal to 6, outputting 'the running condition of the ith carriage is poor'.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311776971.0A CN117763392B (en) | 2023-12-22 | 2023-12-22 | Air conditioner detection method for railway passenger car |
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