A Visual Tallying Method for Container Terminals Based on Big Data

Abstract

The present invention relates to the technical field of terminal tallying, and discloses a visualization tallying method for container terminals based on big data. Periodically detect the parameters of container tallying equipment based on a preset detection time and parameter acquisition equipment to obtain periodically feedback detection parameters, classify them to obtain a periodically feedback detection parameter sequence, generate an initialization mark for the container tallying equipment. When an unknown initialization mark is recognized, analyze the periodically feedback detection parameter sequence, calculate the periodically feedback detection parameter coefficient, perform curve fitting to obtain a periodically feedback detection parameter coefficient curve, calculate the tallying state evaluation factor, and determine whether to intervene in the tallying behavior of the container tallying equipment. If so, generate an intervention result based on the tallying state evaluation factor to achieve comprehensive monitoring and accurate evaluation, improve the equipment failure warning ability, improve the tallying efficiency and safety, effectively reduce the tallying deviation and safety risk, and provide a scientific basis for terminal operations.

Description

Technical Field

[0001] The present invention relates to the technical field of terminal tallying, and in particular to a visual tallying method for container terminals based on big data. Background Art

[0002] Tallying at the terminal is the work of counting, accepting, organizing storage and loading and unloading of import and export containers. It is a very important part of modern logistics and transportation. It specifically includes: receiving and counting goods, organizing storage and loading and unloading, recording and reporting, maintaining equipment and safety management, etc. With the continuous development of global trade and the continuous advancement of logistics technology, tallying at the terminal is also constantly innovating and improving.

[0003] In the prior art, the operating status evaluation of terminal tallying equipment mainly relies on manual visual inspection or single sensor detection. This traditional method has obvious limitations. Specifically, the current evaluation method can only perform a single analysis of basic parameters such as feedback voltage and feedback current during the operation of the equipment, and cannot achieve a comprehensive analysis of multi-dimensional data. This single judgment mode not only reduces the accuracy of equipment status evaluation, but also makes it difficult to establish a scientific risk warning mechanism and provide reliable data support for terminal tallying operations. When the tallying equipment is unstable in operation, due to the lack of effective real-time monitoring and early warning, serious consequences such as tallying deviation and cargo damage are very likely to occur. Especially in high-load operating environments, subtle changes in equipment performance may be ignored, ultimately leading to major economic losses or safety accidents. Summary of the invention

[0004] The embodiment of the present invention provides a visual tallying method for container terminals based on big data, which realizes comprehensive monitoring and accurate evaluation of the operating status of terminal tallying equipment, significantly improves the equipment operation reliability and fault warning capability, effectively reduces tallying deviations and safety risks, provides a scientific basis for terminal operations, and improves tallying efficiency and safety.

[0005] In order to achieve the above object, the present invention provides a container terminal visual tallying method based on big data, comprising:

[0006] Pre-deploy multiple parameter collection devices on the container tallying equipment, and perform periodic parameter detection on the container tallying equipment based on a preset detection time and the parameter collection devices to obtain multiple groups of periodic feedback detection parameters;

[0007] Classifying each group of periodic feedback detection parameters to obtain a plurality of periodic feedback detection parameter sequences, performing initialization judgment on the container tallying device based on the periodic feedback detection parameter sequences, and generating an initialization mark, wherein the initialization mark includes a risk initialization mark, a safety initialization mark, and an unknown initialization mark;

[0008] When the unknown initialization mark is identified, the periodic feedback detection parameter sequence is analyzed, and a periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence is calculated based on the analysis result;

[0009] Extracting the periodic feedback detection parameter coefficient of each periodic feedback detection parameter sequence, performing curve fitting to obtain a corresponding periodic feedback detection parameter coefficient curve, and calculating the tallying state evaluation factor of the container tallying equipment based on the periodic feedback detection parameter coefficient curve;

[0010] Based on the tallying state evaluation factor and the preset tallying state evaluation factor, it is determined whether to intervene in the tallying behavior of the container tallying device; if so, an intervention result of the container tallying device is generated based on the tallying state evaluation factor.

[0011] Furthermore, when the container tallying device is initialized based on the periodic feedback detection parameter sequence and an initialization mark is generated, it includes:

[0012] Obtaining a safety periodic feedback detection parameter corresponding to each periodic feedback detection parameter sequence;

[0013] Performing initialization judgment on the container tallying device based on the safety periodic feedback detection parameter, and generating the risk initialization mark for the container tallying device when the periodic feedback detection parameters in the periodic feedback detection parameter sequence are all greater than or equal to the safety periodic feedback detection parameter;

[0014] When the periodic feedback detection parameters in the periodic feedback detection parameter sequence are all less than the safety periodic feedback detection parameter, generating the safety initialization mark for the container tallying device;

[0015] When there is a periodic feedback detection parameter in the periodic feedback detection parameter sequence that is greater than or equal to the safety periodic feedback detection parameter, and there is a periodic feedback detection parameter that is less than the safety periodic feedback detection parameter, the unknown initialization mark is generated for the container tallying device.

[0016] Further, when analyzing the periodic feedback detection parameter sequence and calculating the periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence based on the analysis result, it includes:

[0017] Generating analysis sequence numbers for the periodic feedback detection parameters in the periodic feedback detection parameter sequence based on the acquisition time sequence;

[0018] Determine a first periodic feedback detection parameter corresponding to a first analysis sequence number, and determine first periodic feedback detection parameter differences between all remaining periodic feedback detection parameters and the first periodic feedback detection parameter;

[0019] Selecting a first maximum periodic feedback detection parameter difference from all first periodic feedback detection parameter differences, and generating a maximum difference identifier;

[0020] Determine a second periodic feedback detection parameter corresponding to a second analysis sequence number, and determine a second periodic feedback detection parameter difference between all remaining periodic feedback detection parameters and the second periodic feedback detection parameter corresponding to the second periodic feedback detection parameter;

[0021] Selecting a second maximum periodic feedback detection parameter difference from all second periodic feedback detection parameter differences, and generating a maximum difference identifier;

[0022] Analyze the periodic feedback detection parameters corresponding to the remaining analysis sequence numbers to determine a plurality of maximum difference identifiers;

[0023] Extracting the maximum periodic feedback detection parameter differences corresponding to all the maximum difference identifiers, and determining whether there are identical maximum periodic feedback detection parameter differences, and if so, calculating the same difference sum of all identical maximum periodic feedback detection parameter differences;

[0024] Calculate the different difference values ​​and values ​​of the remaining maximum periodic feedback detection parameter difference values ​​corresponding to the maximum difference value identifier;

[0025] Determine a ratio of the same difference sum value to the different difference sum value as a periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence;

[0026] If it does not exist, then select the maximum parameter difference and the minimum parameter difference from the maximum periodic feedback detection parameter difference corresponding to the maximum difference identifier;

[0027] Calculate the extreme difference and value of the maximum parameter difference and the minimum parameter difference;

[0028] Calculate the remaining difference and value of the remaining maximum periodic feedback detection parameter difference corresponding to the maximum difference identifier;

[0029] A ratio of the extreme difference value and the residual difference value is determined as a periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence.

[0030] Furthermore, when calculating the tallying state evaluation factor of the container tallying equipment based on the periodic feedback detection parameter coefficient curve, it includes:

[0031] Determine the coefficient slope corresponding to each periodic feedback detection parameter coefficient on the periodic feedback detection parameter coefficient curve, and construct the calculation coefficient e w , where e is a constant and w is the coefficient slope;

[0032] Determine a coefficient mean value corresponding to all periodic feedback detection parameter coefficients on the periodic feedback detection parameter coefficient curve;

[0033] Calculate the product of each calculation coefficient and the coefficient mean value respectively as the sub-tally status assessment factor;

[0034] The tallying state assessment factor of the container tallying equipment is calculated according to all the sub-tally tallying state assessment factors.

[0035] Furthermore, when calculating the tallying state evaluation factor of the container tallying equipment according to all the sub-tallying state evaluation factors, it includes:

[0036] The tallying state assessment factor of the container tallying equipment is calculated according to the following formula:

[0037] ;

[0038] ;

[0039] ;

[0040] Among them, q is the tally status evaluation factor of the container tally equipment, r1 is the first calculation factor, r2 is the second calculation factor, max is the maximum value symbol, p is the number of sub-tally status evaluation factors, y u is the u-th sub-tally status assessment factor, y1 is the variance of all sub-tally status assessment factors, y2 is the mean of all sub-tally status assessment factors, and y3 is the standard deviation of all sub-tally status assessment factors.

[0041] Further, when judging whether to intervene in the tallying behavior of the container tallying device based on the tallying state evaluation factor and the preset tallying state evaluation factor, it includes:

[0042] When the tallying state assessment factor is less than the preset tallying state assessment factor, it is determined not to intervene in the tallying behavior of the container tallying device, and a safety mark is generated for the container tallying device;

[0043] When the tallying state assessment factor is greater than or equal to the preset tallying state assessment factor, it is determined to intervene in the tallying behavior of the container tallying device, and a risk mark is generated for the container tallying device.

[0044] Furthermore, when the intervention result of the container tallying equipment is generated based on the tallying state evaluation factor, it includes:

[0045] Collecting historical tallying behaviors of the container tallying equipment within a preset time, analyzing the historical tallying behaviors, and dividing the historical tallying behaviors into historical deviation tallying behaviors and historical compliance tallying behaviors;

[0046] Determine the historical tally deviation value corresponding to each historical deviation tally behavior;

[0047] Counting the number of historical deviant tallying behaviors of the aforementioned historical deviant tallying behaviors, and counting the number of historical compliant tallying behaviors of the aforementioned historical compliant tallying behaviors;

[0048] Calculating the historical tallying behavior value of the container tallying device based on the historical tallying deviation value, the number of historical deviation tallying behaviors, and the number of historical compliance tallying behaviors;

[0049] The tallying state evaluation factor is adjusted according to the historical tallying behavior value to obtain an intervention tallying state evaluation factor of the container tallying device, and an intervention result of the container tallying device is generated based on the intervention tallying state evaluation factor.

[0050] Further, when the tallying state evaluation factor is adjusted according to the historical tallying behavior value to obtain the intervention tallying state evaluation factor of the container tallying device, it includes:

[0051] Preset multiple preset historical tally behavior values;

[0052] Presetting a plurality of preset evaluation factor adjustment values;

[0053] According to the historical tally behavior value and a plurality of preset historical tally behavior values, a corresponding preset evaluation factor adjustment value is selected, wherein the historical tally behavior value and the preset evaluation factor adjustment value are in a positive proportional relationship;

[0054] The product value of the selected preset assessment factor adjustment value and the tallying state assessment factor is calculated as the intervention tallying state assessment factor of the container tallying equipment.

[0055] Further, when generating the intervention result of the container tallying equipment based on the intervention tallying state evaluation factor, it includes:

[0056] Presetting a preset intervention tally state assessment factor, and when the intervention tally state assessment factor is less than the preset intervention tally state assessment factor, generating an online maintenance result for the container tally device;

[0057] When the intervention tallying state assessment factor is greater than or equal to the preset intervention tallying state assessment factor, a shutdown inspection result is generated for the container tallying equipment.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The present invention discloses a visualized tallying method for container terminals based on big data. The method performs periodic parameter detection on container tallying equipment based on preset detection time and parameter acquisition equipment, obtains periodic feedback detection parameters, classifies them, obtains periodic feedback detection parameter sequences, and generates initialization marks for container tallying equipment. When an unknown initialization mark is identified, the periodic feedback detection parameter sequence is analyzed, the periodic feedback detection parameter coefficients are calculated, curve fitting is performed, the periodic feedback detection parameter coefficient curve is obtained, and the tallying state evaluation factor is calculated. It is determined whether to intervene in the tallying behavior of the container tallying equipment. If so, the intervention result is generated based on the tallying state evaluation factor, so as to achieve comprehensive monitoring and accurate evaluation, enhance the equipment failure warning capability, improve the tallying efficiency and safety, effectively reduce the tallying deviation and safety risks, and provide a scientific basis for terminal operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0061] Figure 1 A flow chart of a method for visualized tallying of container terminals based on big data in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0062] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0063] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0064] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0065] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "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 a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0066] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0067] like Figure 1 As shown, an embodiment of the present invention discloses a container terminal visual tallying method based on big data, comprising:

[0068] S110: Pre-deploy multiple parameter collection devices on the container tallying equipment, and perform periodic parameter detection on the container tallying equipment based on a preset detection time and the parameter collection devices to obtain multiple groups of periodic feedback detection parameters;

[0069] In this embodiment, when tallying containers, the tallying equipment involved includes a mobile tallying gate, a container truck, a camera, etc. The mobile tallying gate adopts a gantry structure, and the camera efficiently collects and identifies information data such as the container truck number and the container box number. Photovoltaic solar panels + energy storage batteries are used to provide power to achieve green, environmentally friendly and sustainable use. Among them, the overall size of the mobile tallying gate is 6000×6000×4000mm, and it consists of a gantry, an equipment box, a camera, a wireless bridge, a photovoltaic panel, etc. The gantry frame structure is made of galvanized steel pipes, the columns and beams around are made of 100×100mm galvanized steel pipes, and the columns and beams inside the frame are made of 50×50mm galvanized steel pipes. The equipment box contains battery packs, reverse control all-in-one machines, power distribution modules, switches and identification all-in-one machines. There are 5 cameras, 3 at the top, front, middle and back, and 1 on each side. Using a new generation of spliced ​​cameras, the container surface information can be clear and complete, and the vehicle number and box number recognition accuracy is high. Four photovoltaic panels are installed on the top to provide green power. To achieve green environmental protection and sustainable use, the truck-loaded container passes through the mobile tally gate. During the process, the spliced ​​camera and the identification machine work and output the identification record, including the vehicle number, container number, and container surface photos. The photovoltaic panel generates electricity and transmits it to the energy storage battery, which outputs 220V electricity through the inverter integrated machine for use by cameras, equipment integrated machines and other equipment.

[0070] In this embodiment, the container tallying equipment is the above-mentioned container truck.

[0071] In this embodiment, the parameter acquisition device includes a voltage sensor, a current sensor, a vibration sensor, a motor speed sensor, etc., which are not shown one by one here.

[0072] In this embodiment, the preset detection time is pre-set, and preferably the 10th second, 20th second, 30th second, 40th second, 50th second, 60th second, 70th second, 80th second, 90th second and 100th second. Here, 10 preset detection times are set, and the specific time can be adjusted according to actual needs.

[0073] In this embodiment, a set of periodic feedback detection parameters can be collected at each preset detection time, that is, 10 sets of periodic feedback detection parameters can be collected.

[0074] In this embodiment, the periodic feedback detection parameters include voltage parameters, current parameters, vibration parameters, motor speed parameters, etc., which are not shown one by one here.

[0075] The beneficial effect of the above technical solution is that the mobile tally gate helps to realize the intelligence and automation of the port. By integrating technologies such as the Internet of Things, big data, and artificial intelligence, the mobile tally gate can realize functions such as automatic container confirmation, automatic information transmission, and remote monitoring, making port cargo loading and unloading more intelligent and automated. This will further improve the port's operating efficiency and service quality, enhance the port's competitiveness, and provide reliable data support for subsequent evaluations by collecting multiple sets of periodic feedback detection parameters to ensure comprehensive data collection.

[0076] S120: Classifying each group of periodic feedback detection parameters to obtain multiple periodic feedback detection parameter sequences, performing initialization judgment on the container tallying device based on the periodic feedback detection parameter sequences, and generating an initialization mark, wherein the initialization mark includes a risk initialization mark, a safety initialization mark, and an unknown initialization mark;

[0077] In this embodiment, classifying each group of periodic feedback detection parameters means classifying the parameters of the same type corresponding to each preset detection time. For example, as mentioned above, the voltage parameters at the 10th second, the voltage parameters at the 20th second, etc. are classified to obtain a periodic feedback detection parameter sequence containing only voltage parameters.

[0078] In some embodiments of the present application, when the container tallying device is initialized based on the periodic feedback detection parameter sequence and an initialization mark is generated, it includes:

[0079] Obtaining a safety periodic feedback detection parameter corresponding to each periodic feedback detection parameter sequence;

[0080] Performing initialization judgment on the container tallying device based on the safety periodic feedback detection parameter, and generating the risk initialization mark for the container tallying device when the periodic feedback detection parameters in the periodic feedback detection parameter sequence are all greater than or equal to the safety periodic feedback detection parameter;

[0081] When the periodic feedback detection parameters in the periodic feedback detection parameter sequence are all less than the safety periodic feedback detection parameter, generating the safety initialization mark for the container tallying device;

[0082] When there is a periodic feedback detection parameter in the periodic feedback detection parameter sequence that is greater than or equal to the safety periodic feedback detection parameter, and there is a periodic feedback detection parameter that is less than the safety periodic feedback detection parameter, the unknown initialization mark is generated for the container tallying device.

[0083] In this embodiment, the safety periodic feedback detection parameter is set in advance and corresponds to the periodic feedback detection parameter sequence one by one. For example, the safety voltage parameter is set to 15V, and the rest can be set according to actual conditions.

[0084] In this embodiment, when the risk initialization mark is detected, a shutdown maintenance reminder is directly issued to the container tallying equipment.

[0085] In this embodiment, the unknown initialization mark means that it is not possible to directly determine whether the container tallying equipment has an operation risk, and further determination is required.

[0086] The beneficial effect of the above technical solution is that the present invention performs initialization judgment on container tallying equipment based on safety periodic feedback detection parameters and generates an initialization mark, thereby realizing initialization judgment on container tallying equipment and avoiding the occurrence of container tallying equipment with higher risks.

[0087] S130: When the unknown initialization mark is identified, the periodic feedback detection parameter sequence is analyzed, and a periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence is calculated based on the analysis result;

[0088] In some embodiments of the present application, when analyzing the periodic feedback detection parameter sequence and calculating the periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence based on the analysis result, it includes:

[0089] Generating analysis sequence numbers for the periodic feedback detection parameters in the periodic feedback detection parameter sequence based on the acquisition time sequence;

[0090] Determine a first periodic feedback detection parameter corresponding to a first analysis sequence number, and determine first periodic feedback detection parameter differences between all remaining periodic feedback detection parameters and the first periodic feedback detection parameter;

[0091] Selecting a first maximum periodic feedback detection parameter difference from all first periodic feedback detection parameter differences, and generating a maximum difference identifier;

[0092] Determine a second periodic feedback detection parameter corresponding to a second analysis sequence number, and determine a second periodic feedback detection parameter difference between all remaining periodic feedback detection parameters and the second periodic feedback detection parameter corresponding to the second periodic feedback detection parameter;

[0093] Selecting a second maximum periodic feedback detection parameter difference from all second periodic feedback detection parameter differences, and generating a maximum difference identifier;

[0094] Analyze the periodic feedback detection parameters corresponding to the remaining analysis sequence numbers to determine a plurality of maximum difference identifiers;

[0095] Extracting the maximum periodic feedback detection parameter differences corresponding to all the maximum difference identifiers, and determining whether there are identical maximum periodic feedback detection parameter differences, and if so, calculating the same difference sum of all identical maximum periodic feedback detection parameter differences;

[0096] Calculate the different difference values ​​and values ​​of the remaining maximum periodic feedback detection parameter difference values ​​corresponding to the maximum difference value identifier;

[0097] Determine a ratio of the same difference sum value to the different difference sum value as a periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence;

[0098] If it does not exist, then select the maximum parameter difference and the minimum parameter difference from the maximum periodic feedback detection parameter difference corresponding to the maximum difference identifier;

[0099] Calculate the extreme difference and value of the maximum parameter difference and the minimum parameter difference;

[0100] Calculate the remaining difference and value of the remaining maximum periodic feedback detection parameter difference corresponding to the maximum difference identifier;

[0101] A ratio of the extreme difference value and the residual difference value is determined as a periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence.

[0102] In this embodiment, the analysis sequence numbers are S1, S2, S3, etc., and the specific setting number is consistent with the number of periodic feedback detection parameters.

[0103] In this embodiment, the first analysis sequence number is S1 mentioned above.

[0104] In this embodiment, the first periodic feedback detection parameter difference here refers to a value obtained by subtracting the first periodic feedback detection parameter from all remaining periodic feedback detection parameters.

[0105] In this embodiment, the second periodic feedback detection parameter difference here refers to a value obtained by subtracting the second periodic feedback detection parameter from all remaining periodic feedback detection parameters.

[0106] In this embodiment, the periodic feedback detection parameter differences corresponding to the remaining analysis sequence numbers are not illustrated one by one here.

[0107] In this embodiment, the maximum parameter difference refers to the maximum value among all maximum periodic feedback detection parameter differences, and the minimum parameter difference refers to the minimum value among all maximum periodic feedback detection parameter differences.

[0108] The beneficial effect of the above technical solution is that: the present invention determines the ratio of the same difference sum value and the different difference sum value as the periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence; or determines the ratio of the extreme difference sum value and the remaining difference sum value as the periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence. In the face of two different situations, two different calculation methods are provided to ensure the calculation accuracy of the periodic feedback detection parameter coefficient. The periodic feedback detection parameter coefficient can reflect the overall parameter change of the periodic feedback detection parameter sequence, and realizes comprehensive analysis.

[0109] S140: extracting the periodic feedback detection parameter coefficient of each periodic feedback detection parameter sequence, performing curve fitting to obtain a corresponding periodic feedback detection parameter coefficient curve, and calculating the tallying state evaluation factor of the container tallying equipment based on the periodic feedback detection parameter coefficient curve;

[0110] In this embodiment, a fixed value is used as the horizontal axis, where the fixed value is 1, 2, 3, 4, 5, 6, etc., which is an arithmetic progression. The specific setting number is consistent with the number of periodic feedback detection parameter coefficients, and all periodic feedback detection parameters are fitted based on the extraction order.

[0111] In some embodiments of the present application, when calculating the tallying state evaluation factor of the container tallying equipment based on the periodic feedback detection parameter coefficient curve, it includes:

[0112] Determine the coefficient slope corresponding to each periodic feedback detection parameter coefficient on the periodic feedback detection parameter coefficient curve, and construct the calculation coefficient e w , where e is a constant and w is the coefficient slope;

[0113] Determine a coefficient mean value corresponding to all periodic feedback detection parameter coefficients on the periodic feedback detection parameter coefficient curve;

[0114] Calculate the product of each calculation coefficient and the coefficient mean value respectively as the sub-tally status assessment factor;

[0115] The tallying state assessment factor of the container tallying equipment is calculated according to all the sub-tally tallying state assessment factors.

[0116] In this embodiment, the method for determining the slope will not be described in detail.

[0117] The beneficial effect of the above technical solution is that the present invention calculates the product value of each calculation coefficient and the coefficient mean value respectively as the sub-tallying state evaluation factor, which lays a foundation for calculating the tallying state evaluation factor. The tallying state evaluation factor of the container tallying equipment is calculated according to all the sub-tallying state evaluation factors, which not only ensures the calculation accuracy and efficiency of the tallying state evaluation factor, but also can reflect the operating state of the container tallying equipment, without manual participation in calculation and judgment, eliminating the subjectivity and error of the evaluation.

[0118] In some embodiments of the present application, when calculating the tallying state assessment factor of the container tallying device according to all the sub-tallying state assessment factors, it includes:

[0119] The tallying state assessment factor of the container tallying equipment is calculated according to the following formula:

[0120] ;

[0121] ;

[0122] ;

[0123] Among them, q is the tally status evaluation factor of the container tally equipment, r1 is the first calculation factor, r2 is the second calculation factor, max is the maximum value symbol, p is the number of sub-tally status evaluation factors, y u is the u-th sub-tally status assessment factor, y1 is the variance of all sub-tally status assessment factors, y2 is the mean of all sub-tally status assessment factors, and y3 is the standard deviation of all sub-tally status assessment factors.

[0124] S150: judging whether to intervene in the tallying behavior of the container tallying device based on the tallying state evaluation factor and a preset tallying state evaluation factor, and if so, generating an intervention result of the container tallying device based on the tallying state evaluation factor.

[0125] In some embodiments of the present application, when judging whether to intervene in the tallying behavior of the container tallying device based on the tallying state evaluation factor and the preset tallying state evaluation factor, it includes:

[0126] When the tallying state assessment factor is less than the preset tallying state assessment factor, it is determined not to intervene in the tallying behavior of the container tallying device, and a safety mark is generated for the container tallying device;

[0127] When the tallying state assessment factor is greater than or equal to the preset tallying state assessment factor, it is determined to intervene in the tallying behavior of the container tallying device, and a risk mark is generated for the container tallying device.

[0128] In this embodiment, the preset tally status assessment factor is set in advance, preferably 12 here, and can be adjusted according to actual conditions.

[0129] The beneficial effect of the above technical solution is: when the tallying state assessment factor is less than the preset tallying state assessment factor, the tallying behavior is not intervened and a safety mark is generated. At this time, it belongs to the normal fluctuation of the periodic feedback detection parameter and there is no operation risk. When the tallying state assessment factor is greater than or equal to the preset tallying state assessment factor, the tallying behavior is intervened and a risk mark is generated. At this time, the container tallying equipment is in risky operation and further measures need to be taken to avoid increased risks.

[0130] In some embodiments of the present application, when generating the intervention result of the container tallying equipment based on the tallying state evaluation factor, it includes:

[0131] Collecting historical tallying behaviors of the container tallying equipment within a preset time, analyzing the historical tallying behaviors, and dividing the historical tallying behaviors into historical deviation tallying behaviors and historical compliance tallying behaviors;

[0132] Determine the historical tally deviation value corresponding to each historical deviation tally behavior;

[0133] Counting the number of historical deviant tallying behaviors of the aforementioned historical deviant tallying behaviors, and counting the number of historical compliant tallying behaviors of the aforementioned historical compliant tallying behaviors;

[0134] Calculating the historical tallying behavior value of the container tallying device based on the historical tallying deviation value, the number of historical deviation tallying behaviors, and the number of historical compliance tallying behaviors;

[0135] The tallying state evaluation factor is adjusted according to the historical tallying behavior value to obtain an intervention tallying state evaluation factor of the container tallying device, and an intervention result of the container tallying device is generated based on the intervention tallying state evaluation factor.

[0136] In this embodiment, collecting the historical tallying behaviors of the container tallying equipment within the preset time refers to collecting the historical tallying behaviors of the container tallying equipment within the past day, from loading the container to placing the container in the standard placement position is one historical tallying behavior, and the preset time is the past day.

[0137] In this embodiment, when tallying and placing containers, if the actual placement position of the container does not match the standard placement position, the corresponding historical tallying behavior is judged to be a historical deviation tallying behavior; if they match, the corresponding historical tallying behavior is judged to be a historical compliance tallying behavior.

[0138] In this embodiment, when determining the historical tally deviation value corresponding to each historical deviation tally behavior, it can be determined according to the following method:

[0139] Obtaining a standard placement image and an actual placement image of a container;

[0140] Preprocessing the standard placement image and the actual placement image, including image denoising, grayscale conversion, and edge detection;

[0141] Extracting the position contour in the standard placement image as the reference contour;

[0142] Extracting the position contour in the actual placement image as the actual contour;

[0143] The reference contour is aligned with the actual contour, and the deviation between the two is calculated, and the deviation is used as the historical tally deviation value, wherein the deviation calculation includes at least one of translation deviation, rotation deviation and scaling deviation.

[0144] In this embodiment, the historical tallying behavior value of the container tallying equipment is calculated according to the following formula:

[0145] ;

[0146] Wherein, d is the historical tally behavior value of the container tally equipment, f1 is the first calculation coefficient, preferably 0.3, g1 is the number of historical deviation tally behaviors, g2 is the number of historical compliance tally behaviors, f2 is the second calculation coefficient, preferably 0.7, and h j is the jth historical tally deviation value, h max is the maximum historical tally deviation value.

[0147] The beneficial effect of the above technical solution is: the present invention calculates the historical tallying behavior value of the container tallying equipment based on the historical tallying deviation value, the number of historical deviation tallying behaviors and the number of historical compliant tallying behaviors. The historical tallying behavior value can reflect the historical tallying placement situation of the container tallying equipment. When the historical tallying behavior value is larger, it means that the historical tallying placement situation of the container tallying equipment is worse. Conversely, when the historical tallying behavior value is smaller, it means that the historical tallying placement situation of the container tallying equipment is better. By calculating the historical tallying behavior value, the generation accuracy of the intervention result can be guaranteed to avoid affecting the normal progress of the tallying work.

[0148] In some embodiments of the present application, when the tallying state evaluation factor is adjusted according to the historical tallying behavior value to obtain the intervention tallying state evaluation factor of the container tallying device, it includes:

[0149] Preset multiple preset historical tally behavior values;

[0150] Presetting a plurality of preset evaluation factor adjustment values;

[0151] According to the historical tally behavior value and a plurality of preset historical tally behavior values, a corresponding preset evaluation factor adjustment value is selected, wherein the historical tally behavior value and the preset evaluation factor adjustment value are in a positive proportional relationship;

[0152] The product value of the selected preset assessment factor adjustment value and the tallying state assessment factor is calculated as the intervention tallying state assessment factor of the container tallying equipment.

[0153] In this embodiment, the number of preset historical tally behavior values ​​is preferably three, including a first preset historical tally behavior value, a second preset historical tally behavior value and a third preset historical tally behavior value, wherein the first preset historical tally behavior value is preferably 4, the second preset historical tally behavior value is preferably 6, and the third preset historical tally behavior value is preferably 8, which can be adjusted according to actual conditions.

[0154] In this embodiment, the number of preset evaluation factor adjustment values ​​is preferably four, including a first preset evaluation factor adjustment value, a second preset evaluation factor adjustment value and a third preset evaluation factor adjustment value, wherein the first preset evaluation factor adjustment value is preferably 0.85, the second preset evaluation factor adjustment value is preferably 0.9, the third preset evaluation factor adjustment value is preferably 1.1, and the fourth preset evaluation factor adjustment value is preferably 1.15, and the specific value can also be adjusted according to actual conditions.

[0155] In this embodiment, when the historical tally behavior value is less than the first preset historical tally behavior value, the first product value of the first preset evaluation factor adjustment value and the tally state evaluation factor is calculated as the intervention tally state evaluation factor of the container tally device. When the historical tally behavior value is greater than or equal to the first preset historical tally behavior value and less than the second preset historical tally behavior value, the second product value of the second preset evaluation factor adjustment value and the tally state evaluation factor is calculated as the intervention tally state evaluation factor of the container tally device. When the historical tally behavior value is greater than or equal to the second preset historical tally behavior value and less than the third preset historical tally behavior value, the third product value of the third preset evaluation factor adjustment value and the tally state evaluation factor is calculated as the intervention tally state evaluation factor of the container tally device. When the historical tally behavior value is greater than or equal to the fourth preset historical tally behavior value, the fourth product value of the fourth preset evaluation factor adjustment value and the tally state evaluation factor is calculated as the intervention tally state evaluation factor of the container tally device.

[0156] The beneficial effect of the above technical solution is that: the present invention selects the corresponding preset evaluation factor adjustment value according to the historical tallying behavior value and multiple preset historical tallying behavior values, realizes the dynamic adjustment of the tallying state evaluation factor, comprehensively considers the tallying state evaluation factor and the historical tallying behavior value of the container tallying equipment, that is, considers the real-time operation state and the historical operation state of the container tallying equipment, ensures the dynamic adjustment accuracy, effectively improves the generation accuracy of the intervention result, and eliminates errors.

[0157] In some embodiments of the present application, when generating the intervention result of the container tallying device based on the intervention tallying state evaluation factor, it includes:

[0158] Presetting a preset intervention tally state assessment factor, and when the intervention tally state assessment factor is less than the preset intervention tally state assessment factor, generating an online maintenance result for the container tally device;

[0159] When the intervention tallying state assessment factor is greater than or equal to the preset intervention tallying state assessment factor, a shutdown inspection result is generated for the container tallying equipment.

[0160] In this embodiment, the preset intervention tally status assessment factor is preferably 16, and can be adjusted according to actual conditions.

[0161] The beneficial effects of the above technical solution are as follows: Online maintenance refers to small-scale inspections and repairs performed during the operation of the equipment to ensure the continuous and efficient operation of the equipment. Shutdown maintenance refers to large-scale inspections and repairs performed after the equipment stops running to solve potential or existing serious failures. Generating online maintenance results helps to timely discover and deal with minor equipment problems, avoid downtime affecting tallying work, and improve efficiency. Generating shutdown maintenance results can prevent serious equipment failures, ensure safe operation, and reduce losses.

Claims

1. A container terminal visual tallying method based on big data, characterized in that: include: Pre-deploy multiple parameter collection devices on the container tallying equipment, and perform periodic parameter detection on the container tallying equipment based on a preset detection time and the parameter collection devices to obtain multiple groups of periodic feedback detection parameters; Classifying each group of periodic feedback detection parameters to obtain a plurality of periodic feedback detection parameter sequences, performing initialization judgment on the container tallying device based on the periodic feedback detection parameter sequences, and generating an initialization mark, wherein the initialization mark includes a risk initialization mark, a safety initialization mark, and an unknown initialization mark; When the unknown initialization mark is identified, the periodic feedback detection parameter sequence is analyzed, and a periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence is calculated based on the analysis result; Extracting the periodic feedback detection parameter coefficient of each periodic feedback detection parameter sequence, performing curve fitting to obtain a corresponding periodic feedback detection parameter coefficient curve, and calculating the tallying state evaluation factor of the container tallying equipment based on the periodic feedback detection parameter coefficient curve; Determining whether to intervene in the tallying behavior of the container tallying device based on the tallying state evaluation factor and the preset tallying state evaluation factor, and if so, generating an intervention result of the container tallying device based on the tallying state evaluation factor; When analyzing the periodic feedback detection parameter sequence and calculating the periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence based on the analysis result, it includes: Generating analysis sequence numbers for the periodic feedback detection parameters in the periodic feedback detection parameter sequence based on the acquisition time sequence; Determine a first periodic feedback detection parameter corresponding to a first analysis sequence number, and determine first periodic feedback detection parameter differences between all remaining periodic feedback detection parameters and the first periodic feedback detection parameter; Selecting a first maximum periodic feedback detection parameter difference from all first periodic feedback detection parameter differences, and generating a maximum difference identifier; Determine a second periodic feedback detection parameter corresponding to a second analysis sequence number, and determine a second periodic feedback detection parameter difference between all remaining periodic feedback detection parameters and the second periodic feedback detection parameter corresponding to the second periodic feedback detection parameter; Selecting a second maximum periodic feedback detection parameter difference from all second periodic feedback detection parameter differences, and generating a maximum difference identifier; Analyze the periodic feedback detection parameters corresponding to the remaining analysis sequence numbers to determine a plurality of maximum difference identifiers; Extracting the maximum periodic feedback detection parameter differences corresponding to all the maximum difference identifiers, and determining whether there are identical maximum periodic feedback detection parameter differences, and if so, calculating the same difference sum of all identical maximum periodic feedback detection parameter differences; Calculate the different difference values ​​and values ​​of the remaining maximum periodic feedback detection parameter difference values ​​corresponding to the maximum difference value identifier; Determine a ratio of the same difference sum value to the different difference sum value as a periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence; If it does not exist, then select the maximum parameter difference and the minimum parameter difference from the maximum periodic feedback detection parameter difference corresponding to the maximum difference identifier; Calculate the extreme difference and value of the maximum parameter difference and the minimum parameter difference; Calculate the remaining difference and value of the remaining maximum periodic feedback detection parameter difference corresponding to the maximum difference identifier; A ratio of the extreme difference value and the residual difference value is determined as a periodic feedback detection parameter coefficient of the periodic feedback detection parameter sequence.

2. The method for visual tallying of container terminals based on big data according to claim 1 is characterized in that: When the container tallying device is initialized and judged based on the periodic feedback detection parameter sequence and an initialization mark is generated, it includes: Obtaining a safety periodic feedback detection parameter corresponding to each periodic feedback detection parameter sequence; Performing initialization judgment on the container tallying device based on the safety periodic feedback detection parameter, and generating the risk initialization mark for the container tallying device when the periodic feedback detection parameters in the periodic feedback detection parameter sequence are all greater than or equal to the safety periodic feedback detection parameter; When the periodic feedback detection parameters in the periodic feedback detection parameter sequence are all less than the safety periodic feedback detection parameter, generating the safety initialization mark for the container tallying device; When there is a periodic feedback detection parameter in the periodic feedback detection parameter sequence that is greater than or equal to the safety periodic feedback detection parameter, and there is a periodic feedback detection parameter that is less than the safety periodic feedback detection parameter, the unknown initialization mark is generated for the container tallying device.

3. The method for visual tallying of container terminals based on big data according to claim 1 is characterized in that: When calculating the tallying state evaluation factor of the container tallying equipment based on the periodic feedback detection parameter coefficient curve, it includes: Determine the coefficient slope corresponding to each periodic feedback detection parameter coefficient on the periodic feedback detection parameter coefficient curve, and construct the calculation coefficient e w , where e is a constant and w is the coefficient slope; Determine a coefficient mean value corresponding to all periodic feedback detection parameter coefficients on the periodic feedback detection parameter coefficient curve; Calculate the product of each calculation coefficient and the coefficient mean value respectively as the sub-tally status assessment factor; The tallying state assessment factor of the container tallying equipment is calculated according to all the sub-tally tallying state assessment factors.

4. The method for visual tallying of container terminals based on big data according to claim 3 is characterized in that: When calculating the tallying state assessment factor of the container tallying equipment according to all the sub-tally tallying state assessment factors, it includes: The tallying state assessment factor of the container tallying equipment is calculated according to the following formula: ； ； ； Among them, q is the tally status evaluation factor of the container tally equipment, r1 is the first calculation factor, r2 is the second calculation factor, max is the maximum value symbol, p is the number of sub-tally status evaluation factors, y u is the u-th sub-tally status assessment factor, y1 is the variance of all sub-tally status assessment factors, y2 is the mean of all sub-tally status assessment factors, and y3 is the standard deviation of all sub-tally status assessment factors.

5. The method for visual tallying of container terminals based on big data according to claim 1 is characterized in that: When judging whether to intervene in the tallying behavior of the container tallying device based on the tallying state evaluation factor and the preset tallying state evaluation factor, it includes: When the tallying state assessment factor is less than the preset tallying state assessment factor, it is determined not to intervene in the tallying behavior of the container tallying device, and a safety mark is generated for the container tallying device; When the tallying state assessment factor is greater than or equal to the preset tallying state assessment factor, it is determined to intervene in the tallying behavior of the container tallying device, and a risk mark is generated for the container tallying device.

6. The method for visual tallying of container terminals based on big data according to claim 1, characterized in that: When the intervention result of the container tallying equipment is generated based on the tallying state evaluation factor, it includes: Collecting historical tallying behaviors of the container tallying equipment within a preset time, analyzing the historical tallying behaviors, and dividing the historical tallying behaviors into historical deviation tallying behaviors and historical compliance tallying behaviors; Determine the historical tally deviation value corresponding to each historical deviation tally behavior; Counting the number of historical deviant tallying behaviors of the aforementioned historical deviant tallying behaviors, and counting the number of historical compliant tallying behaviors of the aforementioned historical compliant tallying behaviors; Calculating the historical tallying behavior value of the container tallying device based on the historical tallying deviation value, the number of historical deviation tallying behaviors, and the number of historical compliance tallying behaviors; The tallying state evaluation factor is adjusted according to the historical tallying behavior value to obtain an intervention tallying state evaluation factor of the container tallying device, and an intervention result of the container tallying device is generated based on the intervention tallying state evaluation factor.

7. The method for visual tallying of container terminals based on big data according to claim 6 is characterized in that: When the tallying state evaluation factor is adjusted according to the historical tallying behavior value to obtain the intervention tallying state evaluation factor of the container tallying device, it includes: Preset multiple preset historical tally behavior values; Presetting a plurality of preset evaluation factor adjustment values; According to the historical tally behavior value and a plurality of preset historical tally behavior values, a corresponding preset evaluation factor adjustment value is selected, wherein the historical tally behavior value and the preset evaluation factor adjustment value are in a positive proportional relationship; The product value of the selected preset assessment factor adjustment value and the tallying state assessment factor is calculated as the intervention tallying state assessment factor of the container tallying equipment.

8. The method for visual tallying of container terminals based on big data according to claim 6 is characterized in that: When generating the intervention result of the container tallying equipment based on the intervention tallying state evaluation factor, it includes: Presetting a preset intervention tally state assessment factor, and when the intervention tally state assessment factor is less than the preset intervention tally state assessment factor, generating an online maintenance result for the container tally device; When the intervention tallying state assessment factor is greater than or equal to the preset intervention tallying state assessment factor, a shutdown inspection result is generated for the container tallying equipment.

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