Method, device, equipment and medium for counting the number of engineering vehicle transport trips

Abstract

The present invention provides a method, device, equipment and medium for counting the number of transport trips of engineering vehicles, including: the vehicle end obtains three-dimensional acceleration data in an idle state; the vehicle end performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment, and determines the real-time empty and heavy load state based on the power spectrum density; the vehicle end transmits the empty and heavy load state to a trip counting platform; wherein the empty and heavy load state is used to generate an empty and heavy load curve of the vehicle, and the empty and heavy load curve is used to count the number of transport trips of the vehicle, and the acceleration data is collected by a consumer-grade three-axis accelerometer. The present invention has strong adaptability to different engineering vehicles, simple structure, high accuracy, strong resistance to transportation road interference, low cost, strong robustness, durability and practicality, convenient integration and fusion judgment with other sensors, and easy installation and use.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering vehicle transportation, and in particular to a method, device, equipment and medium for counting the number of engineering vehicle transportation trips. Background Art

[0002] In the field of construction transportation, construction vehicles must load and unload cargo at specific locations, completing a complete transport process known as a trip. Trip counts are a key factor in cost settlements between construction contractors, transportation companies, and drivers, making driver trip statistics a crucial and error-prone process. For larger transport teams, with numerous drivers and vehicles, management costs are high, making trip counts more challenging. Therefore, a reliable and cost-effective method for counting trips is crucial.

[0003] In current technological inventions, most methods such as GPS positioning, pressure strain sensor and multi-sensor fusion are used to count the number of trips. Although they can achieve the expected goals, most of them have their own shortcomings.

[0004] For example, trip counting methods based on GPS positioning typically use a positioning module to collect the vehicle's real-time location trajectory and, based on specific algorithms, determine whether the vehicle has completed a trip. However, all GPS-based trip counting methods inevitably face issues such as poor GPS positioning signals, GPS signal loss, and GPS mispositioning. This presents challenges in remote areas, obstructed terrain, tunnels, and other challenging terrain. Furthermore, this approach cannot assess vehicle status based on load capacity, determining whether the driver's loading and unloading volume has changed, or whether speculators are completing trips without a load or unloading goods during transportation. This approach presents certain limitations.

[0005] The second approach relies on trip counts using stress or pressure sensors. By collecting information about the pressure or deformation of the vehicle's floor, this approach can determine whether a vehicle has completed a loading and unloading cycle. This design approach also presents challenges. Due to their specific placement requirements, stress and pressure sensors are difficult to install and are often exposed to certain locations on the vehicle body. These devices are susceptible to damage from impact, collisions, mud, sewage, and inclement weather during long-term operation. Furthermore, these devices often require differentiated calibration. Over time, the resulting deformation becomes irreversible, requiring dynamic calibration. This requires frequent on-site technician visits for commissioning and replacement, increasing maintenance costs for the project. The device itself is not competitively priced, and the cost of frequent damage and replacement is significant. Furthermore, construction sites often feature steep slopes, and the force decomposition of load cells on these slopes can lead to misjudgments. Addressing the slope decomposition issue requires the introduction of new sensors, further complicating the design and increasing costs.

[0006] The third approach relies on empty and loaded trip counting using a roof-mounted infrared camera. This camera captures images of the vehicle compartment and uses an intelligent algorithm to determine whether the compartment is loaded or unloaded, thereby determining whether a trip has been completed. This statistical approach can face issues with obstruction and lighting. Some construction vehicles have a curtain covering the rear compartment, which is necessary to prevent cargo from falling during urban transport. This prevents the camera from determining the vehicle's load status. Furthermore, obstruction caused by stains splashing onto the camera during transport can also prevent recognition. For many transporters, nighttime is peak transport time, and strong light shining on the camera can lead to misjudgments. This approach is also affected by installation location; significant variations in installation heighten the algorithm requirements. Furthermore, as with GPS solutions, it lacks accurate load information.

[0007] In addition, there is a need for a multi-sensor combination solution based on the above sensors, and a complete fusion algorithm is designed for comprehensive judgment. However, this obviously increases the equipment cost, the algorithm structure is complex, and it is not ideal.

[0008] Therefore, a reliable and low-cost trip counting method is an urgent issue that needs to be solved in the industry. Summary of the Invention

[0009] The present invention provides a method, device, equipment and medium for counting the number of engineering vehicle transport trips, which are used to solve the problem of number of trips counting in the prior art and overcome the problem of high cost of counting the number of engineering vehicle transport trips in the prior art.

[0010] The present invention provides a method for counting the number of engineering vehicle transport trips, comprising:

[0011] The vehicle side obtains three-dimensional acceleration data in the idle state;

[0012] The vehicle side performs frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment, and determines a real-time empty or loaded state based on the power spectrum density;

[0013] The vehicle terminal transmits the empty or loaded state to the trip number statistics platform;

[0014] The empty and loaded states are used to generate an empty and loaded curve of the vehicle, and the empty and loaded curve is used to count the number of transport trips of the vehicle. The acceleration data is collected by a consumer-grade three-axis accelerometer.

[0015] According to a method for counting the number of transport trips of an engineering vehicle provided by the present invention, the vehicle terminal performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment, including:

[0016] The vehicle side performs a fast Fourier transform on the azimuth acceleration in the acceleration data to obtain a frequency domain amplitude corresponding to the azimuth acceleration;

[0017] The vehicle side determines the power spectrum density corresponding to the celestial acceleration according to the frequency domain amplitude.

[0018] According to a method for counting the number of engineering vehicle transport trips provided by the present invention, the vehicle side determines the real-time empty or loaded state based on the power spectrum density, including:

[0019] The vehicle end determines the real-time empty and heavy load status based on the power spectrum density and a pre-calibrated empty and heavy load power spectrum density base value.

[0020] According to a method for counting the number of transport trips of engineering vehicles provided by the present invention, the vehicle side obtains acceleration data in three directions in an idle state, and further includes:

[0021] The vehicle terminal obtains the rotation speed information or vehicle speed information of the vehicle;

[0022] The vehicle side determines whether the vehicle is in an idle state based on the rotation speed information or the vehicle speed information;

[0023] The vehicle end obtains three-dimensional acceleration data when the vehicle is in an idling state.

[0024] According to a method for counting the number of transport trips of an engineering vehicle provided by the present invention, the vehicle terminal performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment, including:

[0025] The vehicle side determines the attitude of the consumer-grade three-axis accelerometer relative to a horizontal plane based on the acceleration data, wherein the attitude includes a roll angle and a pitch angle relative to the horizontal plane;

[0026] If the vehicle detects that the difference between the absolute value of the roll angle and the preset maximum roll angle does not exceed a first preset threshold and the difference between the absolute value of the pitch angle and the preset maximum pitch angle does not exceed a second preset threshold, the vehicle performs the following steps: performing frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment;

[0027] If the vehicle side detects that the difference between the absolute value of the roll angle and the preset maximum roll angle exceeds a first preset threshold or the difference between the absolute value of the pitch angle and the preset maximum pitch angle exceeds a second preset threshold, the acceleration is subjected to coordinate transformation processing, and then the steps are executed: the vehicle side performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idling moment.

[0028] The present invention also provides a method for counting the number of engineering vehicle transport trips, comprising:

[0029] The trip statistics platform receives real-time empty and heavy load status;

[0030] The trip number statistics platform generates an empty and loaded curve of the vehicle according to the empty and loaded state;

[0031] The trip counting platform counts the number of transport trips of the vehicle according to the empty and loaded curve;

[0032] Among them, the empty and heavy load states are obtained based on the power spectrum density of the vehicle, and the power spectrum density is obtained after frequency domain analysis of the vehicle's acceleration data. The acceleration data is the three-dimensional acceleration data of the vehicle when it is in an idle state, and the acceleration data is collected by a consumer-grade three-axis accelerometer.

[0033] The present invention also provides a device for counting the number of transport trips of engineering vehicles, comprising:

[0034] The acceleration detection module is used to obtain acceleration data in three directions in the idle state;

[0035] a data analysis module, configured to perform frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment, and determine a real-time idle or heavy load state based on the power spectrum density;

[0036] A data transmission module, used for transmitting the empty and heavy-load status to a trip number statistics platform;

[0037] The empty and loaded states are used to generate an empty and loaded curve of the vehicle, and the empty and loaded curve is used to count the number of transport trips of the vehicle. The acceleration data is collected by a consumer-grade three-axis accelerometer.

[0038] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for counting the number of engineering vehicle transport trips as described above is implemented.

[0039] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for counting the number of engineering vehicle transport trips as described above is implemented.

[0040] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for counting the number of engineering vehicle transport trips.

[0041] The method for counting construction vehicle transport trips provided by the present invention overcomes the high cost and other issues of prior art in counting construction vehicle transport trips by using a consumer-grade triaxial accelerometer. Combined with a vibration frequency domain analysis method, the method can calculate the energy of the fundamental frequency vibration of the vehicle engine from the vehicle acceleration when the vehicle is idling, estimate the vehicle's load state, and thus count the number of vehicle transport trips based on the vehicle's load state. The method has strong adaptability to different construction vehicles, a simple structure, high accuracy, strong resistance to transportation road interference, low cost, strong robustness, durability and practicality, and is easy to integrate with other sensors for fusion judgment and installation and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is one of the flow charts of the method for counting the number of engineering vehicle transport trips provided by the present invention;

[0044] Figure 2 This is a time domain analysis diagram of a typical engineering vehicle idling for 5 seconds without load;

[0045] Figure 3 This is a frequency domain analysis diagram of a typical engineering vehicle idling for 5 seconds without load;

[0046] Figure 4This is a time domain analysis chart of a typical engineering vehicle idling for 5 seconds with full load;

[0047] Figure 5 This is a frequency domain analysis diagram of a typical engineering vehicle idling for 5 seconds with full load;

[0048] Figure 6 This is a statistical diagram of the power spectrum density distribution of a typical engineering vehicle in the no-load state;

[0049] Figure 7 This is a statistical diagram of the power spectrum density distribution of a typical engineering vehicle under full load;

[0050] Figure 8 This is a box plot of the power spectrum density of a typical engineering vehicle with no load or load.

[0051] Figure 9 This is a schematic diagram of the installation structure of a three-axis accelerometer in the method for counting the number of engineering vehicle transport trips provided by the present invention;

[0052] Figure 10 This is the second flow chart of the method for counting the number of engineering vehicle transport trips provided by the present invention;

[0053] Figure 11 This is the third flow chart of the method for counting the number of engineering vehicle transport trips provided by the present invention;

[0054] Figure 12 This is a flow chart of data preprocessing of the method for counting the number of engineering vehicle transport trips of the present invention;

[0055] Figure 13 This is the fourth flow chart of the method for counting the number of engineering vehicle transport trips provided by the present invention;

[0056] Figure 14 This is a schematic diagram of the structure of the device for counting the number of transport trips of engineering vehicles provided by the present invention;

[0057] Figure 15 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0059] Figure 1 This is one of the flow charts of the method for counting the number of engineering vehicle transport trips provided in the embodiment of this application. Figure 1The present application provides a method for counting the number of construction vehicle transport trips, including:

[0060] Step 10: The vehicle obtains acceleration data in three directions in an idle state;

[0061] In this embodiment, the vehicle first detects whether it is idling via the CAN bus (Controller Area Network). If so, the vehicle's inertial measurement unit (IMU) acquires acceleration data from the idle state. The vehicle's IMU is a universal three-axis accelerometer that collects three-dimensional acceleration data in real time. The accelerometer's output frequency is no less than 100Hz. It is worth noting that the system described in this invention is still effective when using consumer-grade sensors, without significantly affecting accuracy and reliability.

[0062] Among them, the inertial measurement unit used to detect the acceleration of the vehicle of the present invention is a consumer-grade three-axis accelerometer, which is installed on the top of the engineering car, with the horizontal direction facing upward. It can also be installed on a smart device, such as integrated on an infrared camera to realize empty and heavy load judgment based on the fusion of vision and accelerometer. The three-axis accelerometer is used to collect the three-dimensional acceleration data of the vehicle, and the installation position of the three-axis accelerometer is horizontally facing upward. It should be noted that the three directions mentioned above include the x-axis direction, the y-axis direction and the z-axis direction. The x-axis direction can be a direction parallel to the forward direction of the vehicle body. When the x-axis direction is in the forward direction of the vehicle body, the y-axis points to the right side of the vehicle body, and the z-axis points to the sky.

[0063] Step 20: The vehicle performs frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment, and determines a real-time idle or loaded state based on the power spectrum density;

[0064] After acquiring the vehicle's acceleration data at idle, frequency domain analysis is performed on the data to obtain the vehicle's power spectrum density at the current idle moment. This power spectrum density can then be used to determine the vehicle's real-time empty or loaded state. The empty or loaded state can include either an empty or loaded state. Before analyzing the acceleration data, preprocessing is required. This preprocessing includes zero bias correction, outlier and noise removal, and attitude correction.

[0065] Step 30: The vehicle terminal transmits the empty or loaded state to a trip statistics platform;

[0066] The empty and loaded states are used to generate an empty and loaded curve of the vehicle, and the empty and loaded curve is used to count the number of transport trips of the vehicle. The acceleration data is collected by a consumer-grade three-axis accelerometer.

[0067] The vehicle's empty or loaded status is then transmitted to the trip statistics platform, which collects the empty and loaded status information transmitted by the vehicle to calculate the vehicle's transport trips. Specifically, the trip statistics platform collects the vehicle's real-time empty and loaded status information transmitted by the vehicle, uploads the collected empty and loaded status information, and generates an empty and loaded curve for the vehicle, thereby calculating the vehicle's transport trips based on the empty and loaded curve. For example, the trip statistics platform obtains the vehicle's real-time load information when idling. Based on the empty and loaded results, the platform can calculate the number of trips according to a specific logic, such as counting an empty trip for more than 30 minutes or a fully loaded trip for more than 30 minutes as one trip. The platform can then transmit the actual trip information to the target terminal.

[0068] Furthermore, when the trip statistics platform detects empty or loaded statuses transmitted by the vehicle, it accumulates and sums the real-time empty or loaded status values according to a preset weight to obtain a cumulative sum value. This cumulative sum value is then compared with a cumulative sum threshold. If the cumulative sum threshold is exceeded, the currently received empty or loaded status value is cleared, and the load status of the current construction vehicle is not switched. When the trip statistics platform determines the empty or loaded status of a vehicle, it can use a cumulative sum algorithm to eliminate the possibility of false recognition of empty or loaded status to a certain extent. This is based on the fact that the load status of construction vehicles does not switch frequently in a short period of time. Excessively rapid repeated switching is often a case of false recognition. Therefore, a cumulative sum threshold can be set so that only long-term load changes will cause the load status to switch, thereby avoiding the impact of accidental recognition on state switching.

[0069] In one possible embodiment, different linear weights are assigned to different states. The received empty and loaded states are then cumulatively summed based on the linear weights to obtain a summed value. When the summed value reaches a threshold (the threshold includes an upper or lower threshold for the loaded state), the state is switched. This allows the identified empty and loaded state of the vehicle to be less susceptible to misidentification than the original state judgment result. In this embodiment, by setting cumulative summation logic, the empty and loaded states after misidentification are calculated, which, to a certain extent, eliminates misidentification of empty and loaded state judgments and improves the accuracy of empty and loaded state judgments and trip count statistics.

[0070] It should be noted that the weight used for the cumulative summation in the above example is a linear function, so the sum of the empty and loaded states is a straight line with a certain slope. Different function weights can also be used for cumulative summation, such as exponential function weights (correspondingly, using exponential function weights has the effect of lowering the weight of older history and increasing the weight of more recent history). In addition, the trip count statistics platform can also reduce the impact of misidentification by distinguishing between the three states of "empty, loaded, and fuzzy."

[0071] Furthermore, in order to make the engineering vehicle transport trip statistics method of the present application more adaptable, reduce the impact of the calibration threshold in the trip statistics platform on different scenarios and reduce the labor cost of on-site calibration, the present invention provides a method for long-term training of empty and loaded data received from the vehicle end using the k-means dynamic clustering algorithm.

[0072] The trip statistics platform receives the power spectrum density of the vehicle in the idle state and stores the power spectrum density data in the data storage unit. It clusters the power spectrum density data through the k-means algorithm to obtain the adaptive empty and heavy load thresholds, and returns the empty and heavy load thresholds to the vehicle so that the vehicle can judge the empty and heavy load status based on the adaptive empty and heavy load thresholds and the current power spectrum density.

[0073] For ease of understanding, the theoretical basis of the present invention is explained. The theoretical basis of the present invention is:

[0074] (1) There are three basic links in the generation of vibration and the transmission of vibration energy, namely, the vibration source, the transmission path and the receptor. The vibration sources of the vehicle in motion are the engine, the road surface and the wind excitation. Since the vibrations of the three vibration sources with different frequencies are intertwined, it is difficult to judge the vehicle status. However, when the vehicle is idling, there is only a single vibration source, the engine, with simple vibration characteristics and a single frequency domain component, which is easy to distinguish. Figure 2 and Figure 3 They are respectively the time domain and frequency domain analysis diagrams of a 5-second no-load idling data. Figure 4 and Figure 5 They are respectively the time domain and frequency domain analysis diagrams of a 5-second data of full load idling. Figure 2-5 It can be seen that for the same vehicle, the vibration frequency and excitation force amplitude of the vibration source are fixed at idle speed under no-load and full-load conditions. Since the propagation process is completely rigid, the vibration frequency of the receptor will not change. The only thing that changes during the state change is the vibration amplitude. According to Newton's second law,

[0075] F=ma

[0076] It is known that this is related to the load mass. The greater the load mass, the smaller the amplitude. The analysis of power spectrum density will further amplify this difference, thereby distinguishing the empty and heavy loads of the vehicle with a certain degree of accuracy. It is worth noting that Figure 2-5 The shown example is not a special case. The empty and loaded conditions of the same vehicle still conform to the above rules. Different engine models may have some influence on the fundamental frequency. Vehicle model, tire pressure, installation position, road surface firmness at idle position, road surface slope at idle position, etc. have no influence on the frequency and only affect the amplitude.

[0077] (2) Power spectral density (PSD) refers to the density concept used to represent the distribution of signal power at each frequency point. It is a measure of the mean square value of a random variable and is the average power per unit frequency. In other words, the average power of the signal can be obtained by integrating the power spectrum in the frequency domain. For points with higher power spectral density, the signal power at that frequency is higher.

[0078] To solve the power spectral density, follow these steps:

[0079] First calculate the Fourier transform of x(t): X(jw);

[0080] Modulo: |X(jw)|, then square: |X(jw)| 2 , and then divided by the sample length: |X(jw)| 2 / L;

[0081] Get the power spectral density function of x(t): Gxx(w)=G xx =|X(jw)| 2 / L.

[0082] (3) Figure 6 and Figure 7 The power spectrum density distribution statistics of an engineering vehicle under empty and heavy load conditions during long-term operation are respectively drawn. The power spectrum density of each unit time in the process is statistically analyzed and drawn into a box plot (boxplot) as shown below: Figure 8 The results show that while there is some overlap in the power spectral density distribution between empty and loaded vehicles, overall there is a degree of differentiation. Similarly, the data from this engineering vehicle is not an isolated case; data collected from other vehicles using the aforementioned rules also exhibits the same properties. Therefore, based on the vehicle's power spectral density, it is possible to determine the vehicle's load condition and whether it is loaded or empty.

[0083] Further, please refer to Figure 9 The processor 10 of the three-axis accelerometer is mounted on the rubber ball 20. For the scenario in which the empty and loaded vehicle recognition is poor in the project, the present invention also provides a hardware solution. When integrating the processor of the three-axis accelerometer, the processor of the three-axis accelerometer is mounted on the rubber ball hook. The rubber ball and the processor of the three-axis accelerometer form a module with the same natural frequency as the vehicle, and the resonance principle is used to achieve the amplification of the idle amplitude. Where k is the stiffness of the resonant system, which is related to the material and shape of the rubber ball; m is the total mass of the forced vibration system; and f0 is the natural resonant frequency. Engineering practice has proven that the idling power spectrum density with amplified resonance amplitude provides greater discrimination and effectively reduces the false recognition rate.

[0084] The proposed method for counting construction vehicle transport trips uses a consumer-grade triaxial accelerometer, overcoming the high cost of existing methods. Combined with vibration frequency domain analysis, it can calculate the energy of the fundamental frequency vibration of the engine from vehicle acceleration while the vehicle is idling, estimating the vehicle's load state and thus counting the number of transport trips based on the vehicle's load state. The method boasts strong adaptability to different construction vehicles, a simple structure, high accuracy, strong resistance to transport road interference, low cost, strong robustness, durability, practicality, and ease of integration and fusion with other sensors, as well as ease of installation and use.

[0085] In one embodiment, please refer to Figure 10 Step 20: The vehicle performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment, including:

[0086] Step 201: The vehicle performs a fast Fourier transform on the azimuth acceleration in the acceleration data to obtain a frequency domain amplitude corresponding to the azimuth acceleration.

[0087] In step 202, the vehicle determines the power spectrum density corresponding to the azimuth acceleration based on the frequency domain amplitude.

[0088] In step 203, the vehicle determines a real-time empty and heavy load state based on the power spectrum density and a pre-calibrated empty and heavy load power spectrum density base value.

[0089] In this embodiment, the pre-processed acceleration is analyzed in the frequency domain. First, the data analysis unit packages the data according to a preset window length, such as Figure 2-5 The example shown has an inertial measurement unit sampling frequency of 100 Hz, a preset window length of 5 seconds for the data analysis unit, and an analysis is performed once per second. The analysis process is as follows:

[0090] (1) For the input celestial acceleration a z4 Perform fast Fourier transform and get a z4 The frequency domain amplitude of the signal Y = X(jw);

[0091] (2) a is obtained as follows z4 The discrete power spectral density psd of the signal:

[0092]

[0093] Among them, Y * represents the conjugate of Y, and L is the length of the Y signal.

[0094] (3) Retain the first half of the discrete power spectrum density psd and increase the amplitude by 1 to obtain the unilateral discrete power spectrum density psd2 (if P2 is an odd length, retain the first half and the middle point);

[0095] psd2=psd(1:floor(L / 2)+1)*2

[0096] Among them, the function floor(x) means rounding down.

[0097] (4) Expand the corresponding frequency of discrete PSD2 according to the sampling frequency to obtain the corresponding frequency f of PSD:

[0098] f=Fs*(0:floor(L / 2)) / L

[0099] Where Fs is the sampling frequency of the three-axis accelerometer of the inertial measurement unit.

[0100] (5) To collect a in idle state z,bias After data processing, the obtained z4,bias Perform the above spectrum analysis to find the frequency f0 corresponding to the maximum value point of psd2. f0 is the calibrated engine vibration fundamental frequency.

[0101] (6) For real-time a z4 According to the preset window length, the psd of the window is obtained once per second, the fundamental frequency f of the maximum point of psd2 is found, and the error between f and f0 is determined to be less than the preset value (first determine whether the frequency f is the fundamental frequency f0 of the engine). If not, the window is discarded, otherwise the maximum value Mpsd of psd2 is obtained.

[0102] (7) According to the pre-calibrated empty and heavy load corresponding power spectrum density base value, determine which load state the maximum value MPSD belongs to. If the psd2 calibration value is close to the empty load to a certain extent, the load state corresponding to the window data is judged to be empty load, otherwise it is judged to be heavy load.

[0103] When calculating the power spectral density (PSD) of the IMU (Infrared Unit) accelerometer (IMU), considering that in some scenarios the IMU's z-axis accelerometer may not fully reflect the vibration direction due to the influence of the device installation posture error, the present invention provides an angle search method to find the optimal angle (roll angle and pitch angle) reflecting the vehicle vibration direction, and perform appropriate rotation on the IMU coordinate system. The power spectral density (PSD) reflecting the optimal direction of vehicle vibration is calculated instead of directly using the original z-axis. The power spectral density (PSD) obtained in this way is more discriminative.

[0104] The specific method is as follows: during initialization, after obtaining a period of idle data, the power spectral density is calculated, a roll angle and pitch angle search range is set in advance (this range is usually not large, and the general installation error is small), and the peak value of the power spectral density is used as the objective function. The convex optimization method is used to find the optimal solution that maximizes the objective function value within the search range to obtain the optimal roll angle and the optimal pitch angle. The original IMU coordinate system is rotated twice around the two axes to obtain a new coordinate system, and the z-axis of the new coordinate system is used as the celestial direction. In subsequent calculations, the idle data corresponding to the z-axis of the new coordinate system is used to obtain the power spectral density.

[0105] In addition, the output mode of three states, empty, heavy and fuzzy, can also be designed, which is mainly for Figure 8 A scenario where the crossover between the two states is too high.

[0106] In general, the process of judging the empty or heavy load state of a vehicle based on acceleration data is as follows: perform fast Fourier transform on the azimuth acceleration in the acceleration data to obtain the frequency domain amplitude corresponding to the azimuth acceleration, and then calculate the discrete power spectrum density psd corresponding to the frequency domain amplitude according to the formula based on the frequency domain amplitude corresponding to the azimuth acceleration; then, retain the first half of the discrete power spectrum density psd and increase the amplitude by 1 times to obtain the unilateral discrete power spectrum density psd2, and then compare the maximum value Mpsd of psd2 with the empty or heavy load power spectrum density base value corresponding to the empty or heavy load that has been calibrated and selected in advance to determine which load state the maximum value Mpsd belongs to.

[0107] In one embodiment, please refer to Figure 11 Step 10: The vehicle side obtains acceleration data in three directions in an idle state, further comprising:

[0108] Step 11, the vehicle terminal obtains the rotation speed information or vehicle speed information of the vehicle;

[0109] Step 12: The vehicle determines whether the vehicle is in an idle state based on the rotation speed information or the vehicle speed information;

[0110] In step 13, the vehicle side obtains acceleration data in three directions when the vehicle is in an idling state.

[0111] In this embodiment, the vehicle's rotational speed or speed can be used to determine whether the vehicle is idling, and three-dimensional acceleration data can be obtained while the vehicle is idling. This embodiment uses the vehicle's speed or rotational speed to determine whether the vehicle is idling, further reducing the cost of trip counts. Furthermore, the vehicle's speed can be obtained via a speed detection unit, which is an external system outside the present invention. This information is optional but serves to assist in the determination. The speed detection unit can be the speed output by the vehicle's CAN or the speed output by the vehicle's onboard GPS.

[0112] It should be noted that, in the absence of a speed detection unit, the acceleration output by the inertial measurement unit can also be used to determine vehicle speed. Therefore, the data analysis unit itself also has the function of determining vehicle speed. When speed detection information is available, it relies on vehicle speed. In the absence of speed information, it determines whether the vehicle is idling based on the variance of the acceleration and the variance threshold. When the vehicle is idling, the variance of the acceleration is less than the variance threshold. Therefore, the variance of the acceleration and the variance threshold can be used to determine whether the vehicle is idling.

[0113] In one embodiment, step 20, the vehicle performs frequency domain analysis on the acceleration data to obtain a real-time empty or loaded state, including:

[0114] Step 221: The vehicle determines the attitude of the consumer-grade three-axis accelerometer relative to the horizontal plane based on the acceleration data, wherein the attitude includes a roll angle and a pitch angle relative to the horizontal plane;

[0115] Step 222: If the vehicle detects that the difference between the absolute value of the roll angle and the preset maximum roll angle does not exceed a first preset threshold and the difference between the absolute value of the pitch angle and the preset maximum pitch angle does not exceed a second preset threshold, the vehicle performs frequency domain analysis on the acceleration data to determine a real-time empty or loaded state.

[0116] In step 223, if the vehicle detects that the difference between the absolute value of the roll angle and the preset maximum roll angle exceeds a first preset threshold or the difference between the absolute value of the pitch angle and the preset maximum pitch angle exceeds a second preset threshold, the acceleration is subjected to coordinate conversion processing, and then the step is executed: the vehicle performs frequency domain analysis on the acceleration data to obtain a real-time empty or heavy load status.

[0117] Before analyzing the acceleration data, the acceleration data needs to be preprocessed, including zero bias correction, de-wiring and noise removal, and attitude correction. The data preprocessing unit performs a series of processing on the data. First, the three-dimensional acceleration ax, ay, and az of the vehicle in a static state for a few seconds are obtained, and the average value of each of them is obtained in the static state and set as the zero bias value a. x,bias 、a y,bias 、a z,bias The attitude of the inertial measurement unit relative to the horizontal plane is obtained according to the following formula:

[0118]

[0119]

[0120] Where roll is the roll angle and pitch is the pitch angle. The absolute values of the obtained roll angle and pitch angle are compared with the preset maximum roll angle and maximum pitch angle, respectively. If they do not exceed the threshold, the attitude error of the inertial measurement unit is considered negligible. Otherwise, the output of the inertial measurement unit needs to be converted into coordinates.

[0121] The output of the inertial measurement unit can be obtained by decomposing the roll angle and pitch angle into a x2 、a y2 、a z2 , and re-collect the static zero bias a of the inertial measurement unit after attitude correction x,bias 、a y,bias 、a z,bias , the subsequent acceleration output must be subtracted from the zero bias to obtain the acceleration data a after subtracting the zero bias x3 、a y3 、a z3 , the specific process is as follows Figure 12 The acceleration data after debiasing must be filtered to remove outliers and high-frequency noise. Outliers and noise refer to the abnormal values of the sensor data and the noise signals generated by the device and the process. The filters can adopt various filters commonly used in engineering, such as Kalman filter, wavelet filter, low-pass filter, etc., which can achieve the expected effect. Some filters can effectively remove noise and outliers at the same time. The cleaned acceleration data a x4 、a y4 、a z4 Output to the next module for frequency domain analysis of speed data.

[0122] It is worth noting that in actual use, installation posture errors should be avoided as much as possible, and reducing the intermediate links in data processing will better ensure reliable results.

[0123] Figure 13 This is one of the flow charts of the method for counting the number of engineering vehicle transport trips provided in the embodiment of this application. Figure 13 The present application provides a method for counting the number of construction vehicle transport trips, including:

[0124] Step 100: The trip statistics platform receives the real-time empty and heavy load status;

[0125] Step 200: The trip statistics platform generates an empty and loaded curve of the vehicle according to the empty and loaded state;

[0126] Step 300: the trip counting platform counts the number of transport trips of the vehicle according to the empty and loaded curve;

[0127] Among them, the empty and heavy load states are obtained based on the power spectrum density of the vehicle, and the power spectrum density is obtained after frequency domain analysis of the vehicle's acceleration data. The acceleration data is the three-dimensional acceleration data of the vehicle when it is in an idle state, and the acceleration data is collected by a consumer-grade three-axis accelerometer.

[0128] In this embodiment, the vehicle first detects whether it is idling. If so, the inertial measurement unit (IMU) acquires acceleration data from the current idle state. The vehicle's IMU is a universal three-axis accelerometer that collects three-dimensional acceleration data in real time. The accelerometer's output frequency is no less than 100Hz. It is worth noting that the system described in this invention is still effective when using consumer-grade sensors, without significantly affecting accuracy and reliability.

[0129] Among them, the inertial measurement unit used to detect the acceleration of the vehicle in the present invention is a consumer-grade three-axis accelerometer, which is installed on the top of the engineering car, with the horizontal direction facing upward. It can also be installed on a smart device, such as integrated on an infrared camera to realize empty and heavy load judgment based on the fusion of vision and accelerometer. The three-axis accelerometer is used to collect the three-dimensional acceleration data of the vehicle, and the installation position of the three-axis accelerometer is horizontally facing upward. It should be noted that the three directions mentioned above include the x-axis direction, the y-axis direction and the z-axis direction. The x-axis direction can be a direction parallel to the vehicle body. When the x-axis direction is a direction parallel to the vehicle body, the y-axis direction is a direction perpendicular to the vehicle body, and the z-axis is a vertical direction perpendicular to the horizontal plane.

[0130] After acquiring the vehicle's acceleration data at idle, frequency domain analysis is performed on the acceleration data to obtain the vehicle's power spectrum density at the current idle moment. The vehicle's real-time empty or loaded state is determined based on the power spectrum density, and the vehicle's current loaded or empty state is determined based on the power spectrum density analysis. The empty or loaded state includes either an empty or loaded state.

[0131] Before analyzing the acceleration data, the acceleration data needs to be preprocessed, which includes zero bias correction, outlier and noise removal, and attitude correction.

[0132] The empty and loaded states are used to generate an empty and loaded curve of the vehicle, and the empty and loaded curve is used to count the number of transport trips of the vehicle. The acceleration data is collected by a consumer-grade three-axis accelerometer.

[0133] The vehicle's empty or loaded status is then transmitted to the trip statistics platform, which collects the empty and loaded status information transmitted by the vehicle to calculate the vehicle's transport trips. Specifically, the trip statistics platform collects the vehicle's real-time empty and loaded status information transmitted by the vehicle, generates an empty and loaded curve based on the collected empty and loaded status information, and then calculates the vehicle's transport trips based on the empty and loaded curve. For example, the trip statistics platform obtains the vehicle's real-time load information when idling. The platform can calculate the number of trips based on the empty and loaded results according to a specific logic, such as counting an empty trip for more than 30 minutes or a fully loaded trip for more than 30 minutes as one trip. The platform can then transmit the actual trip information to the target terminal.

[0134] The proposed method for counting construction vehicle transport trips uses a consumer-grade triaxial accelerometer, overcoming the high cost of existing methods. Combined with vibration frequency domain analysis, it can calculate the energy of the fundamental frequency vibration of the engine from vehicle acceleration while the vehicle is idling, estimating the vehicle's load state and thus counting the number of transport trips based on the vehicle's load state. The method boasts strong adaptability to different construction vehicles, a simple structure, high accuracy, strong resistance to transport road interference, low cost, strong robustness, durability, practicality, and ease of integration and fusion with other sensors, as well as ease of installation and use.

[0135] The following describes the device for counting the number of engineering vehicle transport trips provided by the present invention. The device for counting the number of engineering vehicle transport trips described below and the method for counting the number of engineering vehicle transport trips described above can be referenced to each other.

[0136] Please refer to Figure 14 The device for counting the number of engineering vehicle transport trips proposed by the present invention comprises:

[0137] The acceleration detection module is used to obtain acceleration data in three directions in the idle state;

[0138] a data analysis module, configured to perform frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment, and determine a real-time idle or heavy load state based on the power spectrum density;

[0139] A data transmission module, used for transmitting the empty and heavy-load status to a trip number statistics platform;

[0140] The empty and loaded states are used to generate an empty and loaded curve of the vehicle, and the empty and loaded curve is used to count the number of transport trips of the vehicle. The acceleration data is collected by a consumer-grade three-axis accelerometer.

[0141] Furthermore, the data analysis module is further used to:

[0142] The vehicle side performs a fast Fourier transform on the azimuth acceleration in the acceleration data to obtain a frequency domain amplitude corresponding to the azimuth acceleration;

[0143] The vehicle side determines the power spectrum density corresponding to the celestial acceleration according to the frequency domain amplitude.

[0144] Furthermore, the data analysis module is further used to:

[0145] The vehicle end determines the real-time empty and heavy load status based on the power spectrum density and a pre-calibrated empty and heavy load power spectrum density base value.

[0146] Furthermore, the acceleration detection module is further used to:

[0147] The vehicle terminal obtains the rotation speed information or vehicle speed information of the vehicle;

[0148] The vehicle side determines whether the vehicle is in an idle state based on the rotation speed information or the vehicle speed information;

[0149] The vehicle end obtains three-dimensional acceleration data when the vehicle is in an idling state.

[0150] Furthermore, the data analysis module is further used to:

[0151] The vehicle side determines the attitude of the consumer-grade three-axis accelerometer relative to a horizontal plane based on the acceleration data, wherein the attitude includes a roll angle and a pitch angle relative to the horizontal plane;

[0152] If the vehicle detects that the difference between the absolute value of the roll angle and the preset maximum roll angle does not exceed a first preset threshold and the difference between the absolute value of the pitch angle and the preset maximum pitch angle does not exceed a second preset threshold, the vehicle performs the following steps: performing frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment;

[0153] If the vehicle side detects that the difference between the absolute value of the roll angle and the preset maximum roll angle exceeds a first preset threshold or the difference between the absolute value of the pitch angle and the preset maximum pitch angle exceeds a second preset threshold, the acceleration is subjected to coordinate transformation processing, and then the steps are executed: the vehicle side performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idling moment.

[0154] Figure 15 An example of a physical structure diagram of an electronic device is shown below. Figure 15As shown, the electronic device may include: a processor 1510, a communications interface 1520, a memory 1530, and a communications bus 1540, wherein the processor 1510, the communications interface 1520, and the memory 1530 communicate with each other via the communications bus 1540. The processor 1510 may call logic instructions in the memory 1530 to execute a method for counting the number of transport trips of an engineering vehicle, the method comprising: obtaining three-dimensional acceleration data in an idle state at a vehicle end; performing frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment, and determining a real-time empty and loaded state based on the power spectrum density; transmitting the empty and loaded state to a trip counting platform at the vehicle end; wherein the empty and loaded state is used to generate an empty and loaded curve for the vehicle, and the empty and loaded curve is used to count the number of transport trips of the vehicle, and the acceleration data is collected using a consumer-grade three-axis accelerometer.

[0155] In addition, the logic instructions in the above-mentioned memory 1530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0156] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for counting the number of engineering vehicle transport trips provided by the above methods, the method including: the vehicle end obtains three-dimensional acceleration data in an idle state; the vehicle end performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment, and determines the real-time empty and heavy load state based on the power spectrum density; the vehicle end transmits the empty and heavy load state to a trip count statistics platform; wherein, the empty and heavy load state is used to generate the empty and heavy load curve of the vehicle, and the empty and heavy load curve is used to count the number of transport trips of the vehicle, and the acceleration data is collected through a consumer-grade three-axis accelerometer.

[0157] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the method for counting the number of transport trips of engineering vehicles provided by the above-mentioned methods, the method comprising: the vehicle end obtains three-dimensional acceleration data in an idle state; the vehicle end performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment, and determines the real-time empty and heavy-load state based on the power spectrum density; the vehicle end transmits the empty and heavy-load state to a trip counting platform; wherein, the empty and heavy-load state is used to generate an empty and heavy-load curve of the vehicle, and the empty and heavy-load curve is used to count the number of transport trips of the vehicle, and the acceleration data is collected by a consumer-grade three-axis accelerometer.

[0158] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0159] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for counting the number of transport trips of engineering vehicles, characterized in that: include: The vehicle side obtains three-dimensional acceleration data in the idle state; The vehicle side performs frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment, and determines a real-time empty or loaded state based on the power spectrum density; The vehicle terminal transmits the empty or loaded state to the trip number statistics platform; The empty and loaded states are used to generate an empty and loaded curve of the vehicle, and the empty and loaded curve is used to count the number of transport trips of the vehicle. The acceleration data is collected by a consumer-grade three-axis accelerometer. The vehicle side performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment, including: The vehicle side determines the attitude of the consumer-grade three-axis accelerometer relative to a horizontal plane based on the acceleration data, wherein the attitude includes a roll angle and a pitch angle relative to the horizontal plane; If the vehicle detects that the difference between the absolute value of the roll angle and the preset maximum roll angle does not exceed a first preset threshold and the difference between the absolute value of the pitch angle and the preset maximum pitch angle does not exceed a second preset threshold, the vehicle performs the following steps: performing frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment; If the vehicle detects that the difference between the absolute value of the roll angle and a preset maximum roll angle exceeds a first preset threshold or the difference between the absolute value of the pitch angle and a preset maximum pitch angle exceeds a second preset threshold, coordinate conversion processing is performed on the acceleration, and then the steps are performed: the vehicle performs frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment; The vehicle side performs frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment, and determines a real-time idle or heavy load state based on the power spectrum density, including: The vehicle side performs a fast Fourier transform on the azimuth acceleration in the acceleration data to obtain a frequency domain amplitude Y corresponding to the azimuth acceleration; The vehicle side obtains the discrete power spectrum density (psd) of the celestial acceleration based on the following formula: ； in, represents the conjugate of Y, L is the length of Y; The vehicle side retains the first half of the discrete power spectrum density psd and increases the amplitude by 1 times to obtain a unilateral discrete power spectrum density psd2; the discrete power spectrum density psd2 is obtained based on the following formula: psd2=psd(1:floor(L / 2)+1) 2; Among them, the function floor(x) means rounding down; The vehicle side expands the corresponding frequency of the discrete power spectrum density psd2 according to the sampling frequency to obtain the corresponding frequency f of psd; the corresponding frequency f of psd is obtained based on the following formula: f=Fs (0:floor(L / 2)) / L; Where Fs is the sampling frequency of the three-axis accelerometer of the inertial measurement unit; The vehicle end collects the celestial acceleration in the idle state The celestial acceleration obtained after data processing Perform spectrum analysis and find the frequency f0 corresponding to the maximum value of psd2. f0 is the fundamental frequency of engine vibration after calibration. The vehicle obtains the psd of the window once per second for the real-time azimuth acceleration according to a preset window length, finds the fundamental frequency f of the psd2 maximum point, and determines whether the error between f and f0 is less than a preset value. If not, the window is discarded, otherwise the maximum value Mpsd of psd2 is obtained; The vehicle end determines which load state the maximum value MPSD belongs to based on the empty and heavy load power spectrum density base value corresponding to the empty and heavy load selected in advance. If the psd2 calibration value is close to the empty load to a certain extent, the load state corresponding to the window data is judged to be empty load, otherwise it is judged to be heavy load.

2. The method for counting the number of engineering vehicle transport trips according to claim 1, characterized in that: The vehicle side performs frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment, including: The vehicle side performs a fast Fourier transform on the azimuth acceleration in the acceleration data to obtain a frequency domain amplitude corresponding to the azimuth acceleration; The vehicle side determines the power spectrum density corresponding to the celestial acceleration according to the frequency domain amplitude.

3. The method for counting the number of engineering vehicle transport trips according to claim 1, characterized in that: The vehicle side determines a real-time empty or heavy load state based on the power spectrum density, including: The vehicle end determines the real-time empty and heavy load status based on the power spectrum density and a pre-calibrated empty and heavy load power spectrum density base value.

4. The method for counting the number of engineering vehicle transport trips according to claim 1, characterized in that: The vehicle acquires three-dimensional acceleration data in the idle state, including: The vehicle terminal obtains the rotation speed information or vehicle speed information of the vehicle; The vehicle side determines whether the vehicle is in an idle state based on the rotation speed information or the vehicle speed information; The vehicle end obtains three-dimensional acceleration data when the vehicle is in an idling state.

5. A method for counting the number of engineering vehicle transport trips according to any one of claims 1 to 4, characterized in that: include: The trip statistics platform receives real-time empty and heavy load status; The trip number statistics platform generates an empty and loaded curve of the vehicle according to the empty and loaded state; The trip counting platform counts the number of transport trips of the vehicle according to the empty and loaded curve; Among them, the empty and heavy load states are obtained based on the power spectrum density of the vehicle, and the power spectrum density is obtained after frequency domain analysis of the vehicle's acceleration data. The acceleration data is the three-dimensional acceleration data of the vehicle when it is in an idle state, and the acceleration data is collected by a consumer-grade three-axis accelerometer.

6. A device for counting the number of transport trips of engineering vehicles, characterized in that: include: The acceleration detection module is used to obtain acceleration data in three directions in the idle state; a data analysis module, configured to perform frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment, and determine a real-time idle or heavy load state based on the power spectrum density; A data transmission module, used for transmitting the empty and heavy-load status to a trip number statistics platform; The empty and loaded states are used to generate an empty and loaded curve of the vehicle, and the empty and loaded curve is used to count the number of transport trips of the vehicle. The acceleration data is collected by a consumer-grade three-axis accelerometer. The performing frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment includes: Determining, based on the acceleration data, a posture of the consumer-grade three-axis accelerometer relative to a horizontal plane, wherein the posture includes a roll angle and a pitch angle relative to the horizontal plane; If it is detected that the difference between the absolute value of the roll angle and the preset maximum roll angle does not exceed a first preset threshold and the difference between the absolute value of the pitch angle and the preset maximum pitch angle does not exceed a second preset threshold, the steps of: performing frequency domain analysis on the acceleration data on the vehicle side to obtain a power spectrum density at the current idle moment; If it is detected that the difference between the absolute value of the roll angle and the preset maximum roll angle exceeds a first preset threshold or the difference between the absolute value of the pitch angle and the preset maximum pitch angle exceeds a second preset threshold, coordinate conversion processing is performed on the acceleration, and then the steps are performed: the vehicle end performs frequency domain analysis on the acceleration data to obtain a power spectrum density at the current idle moment; The performing frequency domain analysis on the acceleration data to obtain the power spectrum density at the current idle moment, and determining the real-time idle and heavy load states based on the power spectrum density, includes: Performing a fast Fourier transform on the celestial acceleration in the acceleration data to obtain a frequency domain amplitude Y corresponding to the celestial acceleration; The discrete power spectral density (psd) of the celestial acceleration is obtained based on the following formula: ； in, represents the conjugate of Y, L is the length of Y; The first half of the discrete power spectrum density psd is retained and the amplitude is doubled to obtain the unilateral discrete power spectrum density psd2; the discrete power spectrum density psd2 is obtained based on the following formula: psd2=psd(1:floor(L / 2)+1) 2; Among them, the function floor(x) means rounding down; The corresponding frequency of the discrete power spectrum density psd2 is expanded according to the sampling frequency to obtain the corresponding frequency f of psd; the corresponding frequency f of psd is obtained based on the following formula: f=Fs (0:floor(L / 2)) / L; Where Fs is the sampling frequency of the three-axis accelerometer of the inertial measurement unit; To collect the celestial acceleration at idle state The celestial acceleration obtained after data processing Perform spectrum analysis and find the frequency f0 corresponding to the maximum value of psd2. f0 is the fundamental frequency of engine vibration after calibration. The real-time celestial acceleration is obtained once per second according to a preset window length. The fundamental frequency f of the maximum point of psd2 is found. The error between f and f0 is determined to be less than a preset value. If not, the window is discarded. Otherwise, the maximum value Mpsd of psd2 is obtained. The load state to which the maximum value MPSD belongs is determined based on the pre-calibrated empty and heavy load power spectrum density base value corresponding to the empty and heavy load. If the psd2 calibration value is close to the empty load to a certain extent, the load state corresponding to the window data is judged to be empty load, otherwise it is judged to be heavy load.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for counting the number of engineering vehicle transport trips as described in any one of claims 1 to 4 or claim 5 is implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for counting the number of engineering vehicle transport trips as described in any one of claims 1 to 4 or claim 5 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for counting the number of engineering vehicle transport trips as described in any one of claims 1 to 4 or claim 5 is implemented.

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