Method and system for evaluating working fluency of container terminal

Abstract

The invention provides a container terminal work fluency evaluation method and system, and belongs to the technical field of container scheduling, and the method comprises the following steps: obtaining a grid base map of a container terminal and positioning data of each device, carrying out the coordinate matching of the positioning data and the grid base map, obtaining the dynamic space coordinate information of each device, and carrying out the calculation of the dynamic space coordinate information; marshalling is carried out; extracting position information of each device at the t moment and the t + t moment, and generating a device position distribution map at the t moment and the t + t moment by combining the grid base map; correlating corresponding points in the equipment position distribution diagrams at the t moment and the t + t moment by adopting correlation analysis, and generating an equipment situation vector field; vorticity field analysis is conducted on the equipment situation vector field, and the wharf work fluency at the t + t moment is evaluated according to the analysis result. According to the method, the fluency of the wharf operation can be accurately evaluated, and the scheduling efficiency of the container wharf is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of container scheduling, and particularly relates to a method and system for evaluating the working fluency of a container terminal. Background Art

[0002] At present, equipment such as quay cranes, gantry cranes, and container trucks in areas such as ship loading and unloading, container yards, etc. at the terminal are often allocated according to on-site dispatchers, and the dispatchers rely on the terminal production system and equipment dispatch system to adjust control instructions. However, since the dispatchers may not be able to pay attention to every location at all times, therefore, changes in a certain area may be missed, resulting in delays in the adjustment of equipment operation instructions or conflicts between front and back instructions, making the container terminal unable to schedule in real time and accurately, thus affecting the working fluency of the container terminal.

[0003] In summary, the existing container terminal scheduling method is guided by on-site dispatchers based on actual experience, and conflicts may occur between front and back dispatch instructions during manual dispatching, thus affecting the operation efficiency. Summary of the Invention

[0004] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method for evaluating the working fluency of a container terminal, including the following steps:

[0005] Obtain the grid base map of the container terminal and the positioning data of each device, perform coordinate matching on the positioning data and the grid base map to obtain the dynamic spatial coordinate information of each device, and group the data according to the dynamic spatial coordinate information to obtain the position numbers and working status numbers of each device at different times;

[0006] Extract the position information of each device at time t and time t+△t according to the position numbers and working status numbers of each device at different times, and generate the device position distribution maps at time t and time t+△t in combination with the grid base map;

[0007] Use correlation analysis to associate the corresponding points in the device position distribution maps at time t and time t+△t to generate the device situation vector field from time t to time t+△t;

[0008] Perform vorticity field analysis on the device situation vector field, and evaluate the working fluency of the terminal at time t+△t according to the analysis result, and the evaluation result is used to adjust the scheduling strategy.

[0009] Preferably, the obtaining of the grid base map of the container terminal is specifically as follows:

[0010] Obtain the terminal design drawing, the terminal GPS boundary positioning map, and the terminal yard bay area map;

[0011] Grid the dock design drawing, the dock GPS boundary positioning map, and the dock yard bay area map respectively;

[0012] Fuse and overlay the gridded dock design drawing, the dock GPS boundary positioning map, and the dock yard bay area map to obtain a grid base map of the container terminal containing the dock plane coordinates and the yard bay data.

[0013] Preferably, the method of using correlation analysis to associate the corresponding points in the equipment position distribution maps at time t and time t+Δt to generate the equipment situation vector field from t to t+Δt includes the following steps:

[0014] Select a corresponding mass point in the equipment position distribution maps at time t and time t+Δt, and obtain the mass point image functions of the mass point at time t and time t+Δt;

[0015] Calculate the cross-correlation function of the mass point image functions of the mass point at time t and time t+Δt according to the two-dimensional cross-correlation function;

[0016] Transform the cross-correlation function according to the autocorrelation function to obtain the equipment situation vector field from t to t+Δt.

[0017] Preferably, obtain the positioning data of each device in the container terminal. The positioning data includes GPS data, Beidou data, or magnetic nail activation data of each device in the container terminal. Specifically: read the GPS data and magnetic nail activation data of each device in the container terminal through multiple sensors; identify the sensor number, device type, and device motion state of the GPS data, and extract the valid data; classify the extracted valid data according to categories or working attributes; the devices include gantry cranes, quay cranes, and container trucks.

[0018] Preferably, before matching the coordinates of the positioning data and the grid base map, it also includes denoising and data cleaning of the positioning data to obtain positioning data of interference-free data.

[0019] Preferably, after matching the coordinates of the positioning data and the grid base map, it also includes correcting the matching of the positioning data and the grid base map. Specifically: correct the positioning data deviating from the grid based on the nearest grid and motion continuity, and supplement the missing data by interpolation based on the velocity trajectory.

[0020] Preferably, the position information of each device at time t and time t+Δt is extracted from the information obtained by grouping, and the device position distribution maps at time t and time t+Δt are generated in combination with the grid base map. Specifically: the position information is longitude and latitude data, the coordinate transformation is performed on the radian distances corresponding to the longitude and latitude data of the device at time t and time t+Δt to obtain the device movement distance, and the trajectory of the device is analyzed according to the device movement distance to obtain the device distribution data of the device on the grid at each moment.

[0021] Preferably, the vorticity field analysis of the device state vector field is performed through the following formula:

[0022]

[0023]

[0024] In the formula, Ω represents vorticity, which is defined as the curl of velocity, v represents velocity, is the Hamiltonian operator, v x and v y respectively represent the components of velocity v on the x and y axes, and i, j, and k are the unit vectors on the x, y, and z axes respectively.

[0025] Preferably, the work fluency of the wharf at time t+Δt is evaluated according to the analysis result. Specifically: if the fluency is greater than the set critical value, the fluency meets the requirements; if the fluency is less than the set critical value, the process of obtaining the device position distribution map is repeated, the problematic areas are located and diagnosed, and the location and diagnosis results are used to adjust the scheduling strategy.

[0026] The present invention also provides a container terminal operation efficiency evaluation system, including:

[0027] A data acquisition module for acquiring the grid base map of the container terminal and the positioning data of each device, performing coordinate matching on the positioning data and the grid base map to obtain the dynamic space coordinate information of each device, and performing data grouping according to the dynamic space coordinate information to obtain the position number and working state number of each device at different times;

[0028] A device position distribution map generation module for extracting the position information of each device at time t and time t+Δt according to the position numbers and working state numbers of each device at different times, and generating the device position distribution maps at time t and time t+Δt in combination with the grid base map;

[0029] A device state vector field acquisition module for associating the corresponding points in the device position distribution maps at time t and time t+Δt by using correlation analysis to generate the device state vector field from time t to time t+Δt;

[0030] A fluency evaluation module is used to perform vorticity field analysis on the equipment situation vector field, and evaluate the fluency of the terminal operation at time t+△t according to the analysis results. The evaluation results are used to adjust the scheduling strategy.

[0031] The container terminal operation fluency evaluation method and system provided by the present invention have the following beneficial effects:

[0032] By matching the positioning data of each device in the container terminal with the grid base map of the container terminal, accurate positioning data of each device moving between grids can be obtained; by grouping the dynamic spatial coordinate information of each device, statistical analysis of the obtained accurate positioning data can be performed moment by moment, the real-time position and status data of each device can be obtained, and the device position distribution map of each device at different times can be obtained; by performing correlation analysis on the corresponding points at different times in the device position distribution map, an equipment situation vector field can be generated; by performing vorticity field analysis on the equipment situation vector field, the fluency of the terminal operation can be accurately evaluated, thereby inferring whether the working efficiency and status of each device are reasonable, and providing a judgment basis for on-site dispatching personnel, avoiding the situation where there may be conflicts between front and back dispatching instructions due to manual dispatching, thereby improving the accuracy and real-time performance of terminal equipment dispatching and the dispatching efficiency of the container terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention and its design solutions, the accompanying drawings required for this embodiment will be briefly introduced below. The accompanying drawings in the following description are only partial embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is a flowchart of the container terminal operation fluency evaluation method according to the embodiment of the present invention;

[0035] Figure 2 It is a flowchart of another representation of the present invention;

[0036] Figure 3 It is a matching diagram of positioning data and the grid base map;

[0037] Figure 4 It is a flowchart of the method for generating the situation field;

[0038] Figure 5 It is a schematic diagram of cross-correlation analysis;

[0039] Figure 6 It is a schematic diagram of the speed vector field result;

[0040] Figure 7 It is the actual operation diagram of the situation vector field of terminal equipment;

[0041] Figure 8 It is the analysis diagram of the situation vector field of terminal equipment;

[0042] Figure 9 It is Figure 8 the decomposition diagram of, where Figure 9 the (a), (b), and (c) of are the equipment situation fields of the quay crane, gantry crane, and container truck respectively. Specific implementation mode

[0043] In order to enable those skilled in the art to better understand the technical solution of the present invention and be able to implement it, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and cannot be used to limit the protection scope of the present invention.

[0044] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the technical solution of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0045] In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that unless otherwise clearly specified or limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In the description of the present invention, unless otherwise stated, the meaning of "plurality" is two or more, which will not be elaborated here.

[0046] Embodiment

[0047] The present invention provides a method for evaluating the working fluency of a container terminal, specifically as Figures 1 - 2 shown, including the following steps:

[0048] Step 1: Obtain the grid base map of the container terminal and the positioning data of each device, match the positioning data with the grid base map to obtain the dynamic spatial coordinate information of each device, and group the data according to the dynamic spatial coordinate information to obtain the position numbers and working status numbers of each device at different times.

[0049] (1) The specific process of obtaining the grid base map of the container terminal is as follows:

[0050] Obtain the terminal design drawing, the terminal GPS boundary positioning map, and the terminal yard bay area map;

[0051] Grid the terminal design drawing, the terminal GPS boundary positioning map, and the terminal yard bay area map respectively;

[0052] Fuse and overlay the gridded terminal design drawing, the terminal GPS boundary positioning map, and the terminal yard bay area map to obtain the grid base map of the container terminal containing the terminal plane coordinates and yard bay data.

[0053] (2) Data collection: Obtain the positioning data of each device in the container terminal. The positioning data includes the GPS data, Beidou data, or magnetic nail activation data of each device in the container terminal and their combinations. Specifically: Read the positioning data of each device in the container terminal through multiple sensors. Among them, the GPS data and Beidou data come from the positioning devices installed on the container trucks. By identifying the sensor numbers, device types, and device motion states of the GPS data and Beidou data, five columns of data including the device name, time, longitude and latitude, and the grid where it is located can be directly returned, so as to extract the valid data; The magnetic nail activation data comes from the magnetic nails arranged at specific positions in the yard by the container trucks. When the container truck passes through the magnetic nail (the longitude and latitude are fixed), the magnetic nail is activated and feeds back the longitude and latitude position of the container truck at this time; Read the GPS data, terminal yard bay data, terminal plane coordinates, and magnetic nail activation data of each device (container trucks, quay cranes, gantry cranes, etc.) through each sensor to realize the multiple determination of the positions of each device, which can improve the positioning accuracy and reduce the error.

[0054] Data identification: Identify the grid base map of the container terminal and the collected data. Since the total system has a large amount of data input and output and related data updates every moment, it is necessary to identify and save the collected data signals and identify and eliminate the noise data. The valid data signals include but are not limited to device types and motion states. The identification methods include but are not limited to identifying according to the signal channel number, and can also be identified according to the data characteristics.

[0055] Data Classification: Classify the extracted valid data according to categories or working attributes. Specifically: Classify into different mechanical equipment such as external container trucks, internal container trucks, gantry cranes, quay cranes, reach stackers, and straddle carriers according to categories; conduct multi-level classification according to working attributes, divided into two major categories: loading and unloading category and horizontal transportation category, and further subdivide the loading and unloading category into four major categories: stacking area, quay area, fixed track category, and mobile category, and the horizontal transportation category is divided into two categories: loading and unloading category and inward and outward category.

[0056] Data Cleaning: Denoise and clean the classified positioning data to obtain positioning data of interference-free data.

[0057] Data Statistics: Conduct data statistics on the positioning data of interference-free data, such as the quantity, spatial coordinates (GPS position, bay position, etc.) of equipment such as quay cranes, gantry cranes, and container trucks. In the present invention, it also includes equipment PLC data (data obtained by a programmable logic controller) for judging whether the equipment is working normally.

[0058] Coordinate Matching and Correction: Match the positioning data with the grid base map to obtain the dynamic spatial coordinate information of each equipment, correct the positioning data deviating from the grid based on the nearest grid and motion continuity, and supplement the missing data by interpolation based on the speed trajectory.

[0059] The coordinate matching between the positioning data of quay cranes, gantry cranes, and container trucks, which are mobile equipment in the terminal, and the grid base map is as Figure 3 shown, Figure 3 The green dots in it are the original positioning data (specifically longitude and latitude data), and the blue boxes (varying in density) are the terminal grid information. By matching the positioning data and the grid base map, accurate positioning data (red dots) of mobile equipment moving between grids can be obtained.

[0060] Data Grouping: Re-group the statistically processed data, and combine the split-pole signals sent by the terminal operating system instruction TOS instruction to group the equipment and its data related to each pole. The split-pole signal is a signal system used to command and control terminal operations, mainly used to improve the safety and efficiency of lifting operations. In this embodiment, the grouping of the present invention is divided into several categories: 1. Grouping according to task status: 1.a During transportation operations (when the equipment is moving), 1.b Idle, 1.c Waiting for loading and unloading (when the equipment is not moving but at the same position as the gantry crane or quay crane); 2. According to equipment type: 2.a Container truck, 2.b Gantry crane, 2.c Quay crane; 3. According to the location of the equipment: 3.a On the terminal surface, 3.b On the main road of the yard, 3.c Inside the yard operation bay. According to the above grouping, each moving point constituting the velocity field can be marked for subsequent analysis.

[0061] Step 2: Extract the position information of each device at time t and t+Δt according to the position numbers and working status numbers of each device at different times, and generate the device position distribution maps at time t and t+Δt in combination with the grid base map. The specific steps are as follows:

[0062] Extraction of device position information at time t: Extract the position information of each device at time t, which needs to combine GPS data and bay data.

[0063] Generation of device position distribution map at time t: Combine the extracted position information at each time and the grid base map to generate the device position distribution map at time t.

[0064] The method for obtaining the device position distribution map at t+Δt is the same as above.

[0065] The position information is longitude and latitude data. Perform coordinate transformation on the radian distances corresponding to the longitude and latitude data of the device at time t and t+Δt to obtain the device movement distance. Analyze the trajectory of the device according to the device movement distance to obtain the device distribution data of the device on the grid at each moment.

[0066] Step 3: Use correlation analysis to associate the corresponding points in the device position distribution maps at time t and t+Δt to generate the velocity vector field of the device situation vector field from t to t+Δt. The specific process is as Figure 4 and Figure 5 shown.

[0067] Taking the images of two consecutive exposures as an example, let the time of the first exposure (the first frame image) be t 1 , and the time of the second exposure (the second frame image) be t 2 (see Figure 5 ), the time interval Δt = t 2 -t 1 , and the displacements of a certain particle in the two directions of the horizontal plane Cartesian coordinate system XOY in this image area are (Δx, Δy). The particle image at time t 1 is expressed as p(x, y) = I(x, y) + n 1 (x, y). Based on the premise that Δt is small enough and the particle movement is not violent, the particle image at time t 2 can be expressed as q(x, y) = I(x + Δx, y + Δy) + n 2 (x, y), where I(x, y) represents the spatial position coordinates of the particle at the current time t 1 , and n 1 (x, y) and n 2 (x, y) are random noises in the image system.

[0068] Formula (1) is the two-dimensional cross-correlation principle function of the binary functions f(x, y) and g(x, y):

[0069] r fg (m,n) = ∫∫f(x,y)g * (x - m,y - n)dxdy (1);

[0070] where g * (x,y) is the complex conjugate function of g(x,y), x and y are two independent variables, and m and n are the offsets corresponding to x and y.

[0071] In the present invention, the cross-correlation function r pq (τ x ,τ y ) of p(x,y) and q(x,y) is calculated, and it is assumed that the noise and the effective image function are uncorrelated in the statistical sense, as shown in formula (2):

[0072]

[0073] In the formula, τ x ,τ y is the time shift variable.

[0074] According to the definition of the autocorrelation function (when the two functions in the cross-correlation principle formula are equal, it is the autocorrelation function), let f(x,y) = g(x,y) in formula (1), and the autocorrelation function of I(x,y) is:[[]]

[0075] r(τ x ,τ y ) = ∫∫I(x,y)I(x + τ x ,y + τ y )dxdy (3);

[0076] Combining formulas (2) and (3), the cross-correlation function of p(x,y) and q(x,y) can be transformed into:[[]]

[0077] r pq (τ x ,τ y ) = r(τ x +Δx,τ y +Δy) (4);

[0078] After obtaining the cross-correlation function of p(x,y) and q(x,y), since the autocorrelation function is an even function and reaches the maximum value at the origin, and the maximum value is the relative displacement of the particle within Δt, the velocity vector field characterizing the device state vector field can be obtained by calculating the cross-correlation function. Specifically, as Figure 6 shown.

[0079] However, in the process of calculating the cross-correlation function, due to the huge amount of calculation in the time domain, in the specific implementation process, the cross-correlation function is converted to the frequency domain for calculation based on the Wiener-Khinchin cross-correlation theorem. By using the frequency domain properties of the cross-correlation function and two-dimensional FFT, two forward transforms and one inverse transform can be performed to obtain the cross-correlation function r pq (τ x ,τ y ). The specific process is as follows.

[0080] P(u, v) = ∫∫p(x, y)e -j(ux+vy) dxdy (5);

[0081] Q(u, v) = ∫∫q(x, y)e -j(ux+vy) dxdy (6);

[0082] R(u, v) = ∫∫r pq (x, y)e -j(ux+vy) dxdy (7);

[0083] Among them, P(u, v), Q(u, v), and R(u, v) are the two-dimensional Fourier transforms of p(x, y), q(x, y), and r pq (x, y) respectively, According to the Wiener-Khinchin cross-correlation theorem, we have:

[0084] R(u, v) = P * (u, v) · Q(u, v) (8);

[0085] Among them, P * (u, v) is the complex conjugate form of P(u, v).

[0086] Step 4: Perform vorticity field analysis on the equipment situation vector field to obtain the curl vector field characterizing the equipment situation vector field (as shown in Figures 7 - 9 , where Figure 9 (a), (b), and (c) of

[0087] are the equipment situation fields of the gantry crane, quay crane, and container truck respectively), and evaluate the smoothness of the terminal operation at time t + △t based on the velocity vector field and the curl vector field.

[0088]

[0089]

[0090]

[0091] In the formula, Ω represents vorticity, which is defined as the curl of velocity, v represents velocity, is the Hamiltonian operator, v x and v y respectively represent the components of velocity v on the x and y axes, i, j, and k are the unit vectors on the x, y, and z axes, α is a coefficient, and its specific value depends on the actual situation of the wharf. The meaning expressed by this formula is that the smoothness is inversely proportional to the vorticity Ω.

[0092] (1) Judge the moment of t + △t. If the moment of t + △t is the last moment of the system operation, then this loop jumps out and the operation process ends; if the moment of t + △t is not the last moment of the system operation, then the moment of t + △t will be updated to the moment of t and the process of vorticity field analysis will be repeated to form a loop.

[0093] (2) Cumulative smoothness evaluation Analyze the working smoothness of the wharf, and combine it with the vorticity field analysis to quantitatively evaluate the smoothness

[0094] (3) Judgment of cumulative smoothness : Select the smoothness critical value N for the actual situation of the wharf. If then the smoothness at this moment meets the requirements, and return to the loop process; if then the smoothness at the moment of t + △t does not meet the requirements and further analysis is needed.

[0095] (4) Abnormality location: If it is judged that the smoothness at this moment does not meet the smoothness requirements. Therefore, in combination with the equipment situation distribution, locate the possible problematic places. For example: When the container truck and the crane repeatedly move between two adjacent grids within a period of time, it is identified as a positioning fault; when the mobile equipment has a displacement of more than 9m within adjacent moments [within 1 second], it is identified as a positioning fault. Here, it is assumed that all mobile equipment strictly complies with the wharf speed limit requirement of 30km / h; when the container truck and the crane that are paired with each other [this pairing information can be read from the instruction list in the wharf operation system] have non-simultaneous longitude and latitude data changes of 0 for more than 5s, that is, they stop at a certain position; calculate the traveling speed of the container truck through the displacement of adjacent points, calculate the speed uniformity of the whole process, and evaluate whether the driver's driving behavior is stable and judge the fuel consumption level.

[0096] (5) Diagnostic analysis: Analyze and diagnose the abnormal location found, and analyze the reasons for the problems in combination with the instruction conditions of the equipment at this location.

[0097] ​(6) Provide auxiliary decision-making: Feed back the possible reasons for poor fluency obtained from the analysis to the dispatcher to provide a basis for judgment in subsequent dispatching.

[0098] TOS instruction: Instruction issuance and integration.

[0099] The present invention also provides a container terminal operation efficiency evaluation system, including:

[0100] A data acquisition module, configured to acquire the grid base map of the container terminal and the positioning data of each device, perform coordinate matching on the positioning data and the grid base map to obtain the dynamic spatial coordinate information of each device, and perform data grouping according to the dynamic spatial coordinate information to obtain the position numbers and working status numbers of each device at different times;

[0101] A device position distribution map generation module, configured to extract the position information of each device at time t and time t + Δt according to the position numbers and working status numbers of each device at different times, and generate a device position distribution map at time t and time t + Δt in combination with the grid base map;

[0102] A device situation vector field acquisition module, configured to perform correlation analysis on the corresponding points in the device position distribution maps at time t and time t + Δt to generate a device situation vector field from time t to time t + Δt;

[0103] A fluency evaluation module, configured to perform vorticity field analysis on the device situation vector field and evaluate the working fluency of the terminal at time t + Δt according to the analysis result, and the evaluation result is used to adjust the dispatching strategy.

[0104] The above embodiments are only preferred specific embodiments of the present invention, and the protection scope of the present invention is not limited thereto. Any simple changes or equivalent replacements of technical solutions that can be obviously obtained by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for evaluating the working fluency of a container terminal, characterized in that: The steps include: Obtain the grid base map of the container terminal and the positioning data of each device, perform coordinate matching on the positioning data and the grid base map to obtain the dynamic spatial coordinate information of each device, group data according to the dynamic spatial coordinate information, and obtain the position number and working status number of each device at different times; Extract the location information of each device at time t and time t+△t according to the location number and working status number of each device at different times, and generate the device location distribution map at time t and time t+△t in combination with the grid base map; Correlation analysis is used to associate the corresponding points in the equipment position distribution diagram at time t and time t+△t, and the equipment situation vector field at time t~t+△t is generated; The vortex field analysis is performed on the equipment situation vector field, and the terminal operation smoothness at time t+△t is evaluated based on the analysis results, and the evaluation results are used to adjust the scheduling strategy.

2. The method for evaluating the working fluency of a container terminal according to claim 1, characterized in that: The grid base map of the container terminal is obtained as follows: Obtain terminal design drawings, terminal GPS boundary positioning maps and terminal yard bay area maps; Grid the wharf design drawing, wharf GPS boundary positioning map and wharf yard bay area map respectively; The gridded terminal design drawing, terminal GPS boundary positioning map and terminal yard bay area map are fused and superimposed to obtain a grid base map of the container terminal containing the terminal plane coordinates and yard bay location data.

3. The method for evaluating the working fluency of a container terminal according to claim 1, characterized in that: The method of using correlation analysis to associate corresponding points in the device position distribution diagram at time t and time t+△t to generate the device situation vector field at time t to time t+△t includes the following steps: Select a corresponding particle in the device position distribution diagram at time t and time t+△t, and obtain the particle image function of the particle at time t and time t+△t; Calculate the cross-correlation function of the particle image function of a particle at time t and time t+△t according to the two-dimensional cross-correlation function; The cross-correlation function is transformed according to the autocorrelation function to obtain the device situation vector field at time t to t+Δt.

4. The method for evaluating the working fluency of a container terminal according to claim 1, characterized in that: The positioning data of each device at the container terminal is obtained, and the positioning data includes GPS data, Beidou data or magnetic nail activation data of each device at the container terminal. Specifically, the positioning data of each device at the container terminal is read by multiple sensors; the sensor number, device type and device motion status of the GPS data are identified to extract valid data; and the extracted valid data is classified according to category or work attribute.

5. The method for evaluating the working fluency of a container terminal according to claim 1, characterized in that: Before the coordinate matching of the positioning data and the grid base map is performed, the positioning data is subjected to noise reduction and data cleaning to obtain positioning data without interference data.

6. The method for evaluating the working fluency of a container terminal according to claim 1, characterized in that: After the coordinate matching of the positioning data and the grid base map is performed, the matching of the positioning data and the grid base map is also corrected, specifically: the positioning data deviating from the grid is corrected based on the nearest grid and motion continuity, and the missing data is supplemented by interpolation based on the velocity trajectory.

7. The method for evaluating the working fluency of a container terminal according to claim 1, characterized in that: The position information of each device at time t and time t+△t is extracted from the information obtained from the grouping, and the device position distribution map at time t and time t+△t is generated in combination with the grid base map. Specifically, the position information is longitude and latitude data, and the arc distance corresponding to the longitude and latitude data of the device at time t and time t+△t is transformed into coordinates to obtain the device movement distance, and the trajectory of the device is analyzed according to the device movement distance to obtain the device distribution data of the device on the grid at each moment.

8. The method for evaluating the working fluency of a container terminal according to claim 1, characterized in that: The vortex field analysis of the equipment situation vector field is performed by the following formula: Where Ω represents vorticity, which is defined as the curl of velocity, v represents velocity, is the Hamiltonian operator, v x and v y They represent the components of velocity v on the x and y axes respectively, and i, j and k are the unit vectors on the x, y and z axes respectively.

9. The method for evaluating the working fluency of a container terminal according to claim 1, characterized in that: The terminal operation fluency at time t+△t is evaluated based on the analysis results. Specifically, if the fluency is greater than a set critical value, the fluency meets the requirements; if the fluency is less than the set critical value, the equipment location distribution map acquisition process is repeated to locate and diagnose the problematic areas. The positioning and diagnosis results are used to adjust the scheduling strategy.

10. A container terminal operation efficiency evaluation system, characterized in that: include: A data acquisition module is used to acquire the grid base map of the container terminal and the positioning data of each device, perform coordinate matching on the positioning data and the grid base map, obtain the dynamic spatial coordinate information of each device, group data according to the dynamic spatial coordinate information, and obtain the position number and working status number of each device at different times; The device location distribution map generation module is used to extract the location information of each device at time t and time t+△t according to the location number and working status number of each device at different times, and generate the device location distribution map at time t and time t+△t in combination with the grid base map; The equipment situation vector field acquisition module is used to associate the corresponding points in the equipment position distribution diagram at time t and time t+△t by using correlation analysis to generate the equipment situation vector field at time t to time t+△t; The fluency evaluation module is used to perform vortex field analysis on the equipment situation vector field, and evaluate the terminal operation fluency at time t+△t based on the analysis results. The evaluation results are used to adjust the scheduling strategy.

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