Intelligent wagon balance management method and system

Abstract

The invention relates to the technical field of intelligent wagon balances, in particular to an intelligent wagon balance management method and system. Comprising the following steps: setting a plurality of monitoring points and a plurality of positioning points according to wagon balance equipment parameters; according to the wagon balance measurement data and the feedback data of all the positioning points, weighing parameters of the incoming vehicle are generated; generating a violation risk value of the incoming vehicle according to the wagon balance operation parameters and the monitoring data of each monitoring point, and judging whether to generate an alarm instruction or not according to the violation risk value; real-time position parameters of each incoming vehicle during weighing are monitored in real time, an initial weighing value is corrected according to a preset position analysis model and the real-time position parameters, measurement errors caused by position deviation during vehicle weighing are eliminated, the weighing precision of the weight of the weighing vehicle is improved, multi-angle image data are collected, and the weighing accuracy of the weighing vehicle is improved. The weighing process is dynamically monitored in combination with the operation parameters of the wagon balance, and early warning is carried out on potential violation behaviors in time, so that the weighing parameter distortion caused by fraud behaviors is avoided.

Description

Technical Field

[0001] This application relates to the technical field of intelligent weighbridges, and particularly to an intelligent weighbridge management method and system. Background Art

[0002] Weighing vehicles entering and leaving a power plant using a weighbridge is an important process. This helps record the weight information of goods, which is of great significance for managing the material flow, preventing overloading, monitoring inventory, and calculating taxes. However, in existing weighbridge systems, most processes rely on manual operations, which not only have low efficiency, but also cause the vehicle to not fully drive onto the weighbridge during the weighing process, resulting in a gross weight that is less than the actual mass; or illegal vehicles drive onto the weighbridge before / after weighing, causing the gross weight or tare weight to be greater than the actual mass, leading to an inability to guarantee the weighing accuracy.

[0003] When a vehicle violates the weighbridge regulations, it is generally alarmed on the spot (using an LED or speaker). The gate staff stops the relevant vehicle according to the alarm prompt and notifies the responsible person to carry out subsequent processing. The above verification process has a low degree of electronic intelligence, low efficiency, and it is difficult to avoid the cheating phenomenon of "cooperation" between the gate staff and the driver.

[0004] Content of the Application The purpose of this application is: To solve the above technical problems, this application provides an intelligent weighbridge management method and system, aiming to improve the weighing accuracy of the weighbridge and improve the management efficiency of fuel in the power plant.

[0005] In some embodiments of this application, by establishing multiple positioning points, the real-time position parameters of each vehicle entering the plant during weighing are monitored in real time, and the initial weighing value is corrected according to the preset position analysis model and the real-time position parameters, eliminating the measurement error caused by position deviation during vehicle weighing, and improving the weighing accuracy of the weight of the weighed vehicle. Achieve precise management of fuel.

[0006] In some embodiments of this application, by establishing multiple monitoring points, when a vehicle is being weighed, image data from multiple angles is collected, and the weighing process is dynamically monitored in combination with the operating parameters of the weighbridge, and potential violations are warned in a timely manner, avoiding the distortion of weighing parameters due to fraud behavior, and ensuring the safe and reliable operation of the weighbridge system.

[0007] In some embodiments of this application, an intelligent weighbridge management method is provided, including: Setting multiple monitoring points and multiple positioning points according to the weighbridge equipment parameters; Generating weighing parameters for vehicles entering the plant based on the weighbridge measurement data and the feedback data of all positioning points; Generating a violation risk value for vehicles entering the plant based on the weighbridge operating parameters and the monitoring data of each monitoring point, and determining whether to generate an alarm instruction according to the violation risk value; Among them, when setting multiple positioning points, it includes: Establish a sequence of positioning points A, A = (a 1 , a 2 … a i … a n ), where a i is the i-th positioning point; n is the number of positioning points.

[0008] In some embodiments of the present application, generating the weighing parameters of the incoming vehicle includes: Generating an initial weighing value of the incoming vehicle based on the weighbridge measurement data; Obtaining the feedback data of each positioning point; Generating the distance values between the incoming vehicle and each positioning point; Establish a sequence of distance values B, B = (b 1 , b 2 … b i … b n ), where bi is the distance value between the incoming vehicle and the i-th positioning point; Generating a position deviation value f of the incoming vehicle according to the preset position analysis model and the sequence of distance values B; Setting a compensation coefficient according to the position deviation value f; Correcting the initial weighing value according to the compensation coefficient and generating a primary weighing value of the incoming vehicle according to the correction result.

[0009] In some embodiments of the present application, the preset position analysis model includes: Traversing the historical parameters and establishing a sequence of vehicle categories C, C = (c 1 , c 2 … c i … c m ), where c i is the i-th vehicle category; m is the number of vehicle categories; Sequentially setting c i as the target vehicle category; Generating the standard distance values of each positioning point in the target vehicle category; Generating a comparison sub-model of the target vehicle category according to all the standard distance values; Sequentially generating the comparison sub-models of each vehicle category; Establish a sequence of comparison sub-models D, D = (d 1 , d 2 … d i … d m ), where d i is the comparison sub-model of the i-th vehicle category; Generating a position analysis model according to the sequence of comparison sub-models D.

[0010] In some embodiments of the present application, setting a compensation coefficient according to a position deviation value f includes: Setting a target comparison sub-model according to the incoming vehicle; Generating a position deviation value f according to the target comparison sub-model; f = (d i - d' i ) 2 ; wherein, d' i is the standard distance value from the target comparison sub-model generated to the i-th positioning point; Presetting a first position deviation value threshold F1; If f > F1, generating a first-level scheduling instruction; If f < F1, generating the centroid offset of the incoming vehicle according to the distance value sequence B and the target comparison sub-model; Setting the compensation coefficient according to the centroid offset.

[0011] In some embodiments of the present application, generating a violation risk value of the incoming vehicle includes: Obtaining the weighbridge operation parameters during the weighing period of the incoming vehicle; Establishing multiple time intervals based on the weighing period; Generating a slope value sequence T within each time interval according to the weighbridge operation parameters; T = (t 1 , t 2 …t i …t θ ), wherein, t i is the slope value of the i-th time interval; θ is the number of time intervals; Generating a first-level violation reference value K1 according to the slope value sequence T; Obtaining the monitoring data of each monitoring point during the weighing period of the incoming vehicle; Generating the reference value of each monitoring index according to all the monitoring data; Generating a second-level violation reference value K2; K2 = µ i * j i ; wherein, r is the number of monitoring indexes; µ i is the influence factor of the i-th monitoring index; j i is the reference index of the i-th monitoring index; Generating a violation risk value h according to the first-level violation evaluation value K1 and the second-level violation evaluation value K2.

[0012] In some embodiments of the present application, judging whether to generate an alarm instruction according to the violation risk value includes: Obtain the violation evaluation value h; h = e1 * K1 + e2 * K2; Wherein, e1 is a preset first weight coefficient, and e2 is a preset second weight coefficient; Preset the violation evaluation value threshold H1; If h > H1, generate a first-level alarm instruction; If h < H1, do not generate an alarm instruction.

[0013] In some embodiments of the present application, generating a first-level violation reference value K1 according to the slope value sequence T includes: K1 = e3 * Q3 * (t i - ∆t) 2 + e4 * Q4 * (t i - t') 2 ; Wherein, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; ∆t is the average value of all data in the slope value sequence T; t' is a standard slope value set based on the vehicle type of the incoming vehicle.

[0014] In some embodiments of the present application, an intelligent weighbridge management system is provided, including: A central control unit for setting a plurality of monitoring points and a plurality of positioning points according to the weighbridge device parameters; A positioning unit including a plurality of positioning sub-modules, and the positioning sub-modules are arranged at each positioning point; A monitoring unit including a plurality of monitoring sub-modules, and the monitoring sub-modules are arranged at each monitoring point; The central control unit includes: A first processing module for establishing a positioning point sequence A, A = (a 1 , a 2 … a i … a n ), wherein, a i is the i-th positioning point; n is the number of positioning points; A second processing module for generating the weighing parameters of the incoming vehicle according to the weighbridge measurement data and the feedback data of all positioning points; A third processing module for generating the violation risk value of the incoming vehicle according to the weighbridge operation parameters and the monitoring data of each monitoring point, and judging whether to generate an alarm instruction according to the violation risk value.

[0015] In some embodiments of the present application, the second processing module is further configured to: Generate an initial weighing value of the incoming vehicle according to the weighbridge measurement data; Obtain the feedback data of each positioning point; Generate the distance values between the incoming vehicle and each positioning point; Establish a distance value sequence B, B = (b 1 , b 2 …b i …b n ), where bi is the distance value between the incoming vehicle and the i-th positioning point; Generate the position deviation value f of the incoming vehicle according to the preset position analysis model and the distance value sequence B; Set the compensation coefficient according to the position deviation value f; Correct the initial weighing value according to the compensation coefficient and generate the primary weighing value of the incoming vehicle according to the correction result; Among them, the preset position analysis model includes: Traverse the historical parameters and establish a vehicle category sequence C, C = (c 1 , c 2 …c i …c m ), where c i is the i-th vehicle category; m is the number of vehicle categories; Set c i as the target vehicle category in sequence; Generate the standard distance values of each positioning point in the target vehicle category; Generate a comparison sub-model for the target vehicle category according to all the standard distance values; Generate the comparison sub-models of each vehicle category in sequence; Establish a comparison sub-model sequence D, D = (d 1 , d 2 …d i …d m ), where d i is the comparison sub-model of the i-th vehicle category; Generate a position analysis model according to the comparison sub-model sequence D; Setting the compensation coefficient according to the position deviation value f includes: Set the target comparison sub-model according to the incoming vehicle; Generate the position deviation value f according to the target comparison sub-model; f = (d i - d' i ) 2 ; Among them, d' i is the standard distance value generated based on the target comparison sub-model and corresponding to the i-th positioning point; Preset the first position deviation value threshold F1; If f > F1, generate a first-level scheduling instruction; If f < F1, generate the centroid offset of the incoming vehicle according to the distance value sequence B and the target comparison sub-model; Set the compensation coefficient according to the centroid offset.

[0016] In some embodiments of the present application, the third processing module is further configured to: Obtain the weighbridge operation parameters during the weighing period of the incoming vehicle; Establish multiple time intervals based on the weighing period; Generate a sequence of slope values T within each time interval according to the weighbridge operation parameters; T = (t1, t2…ti…tθ), where ti is the slope value of the i-th time interval; θ is the number of time intervals; Generate a first-level violation reference value K1 according to the sequence of slope values T; K1 = e3 * Q3 * (t i - ∆t) 2 + e4 * Q4 * (t i - t') 2 ; Where e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; ∆t is the average value of all data in the sequence of slope values T; t' is a standard slope value set based on the vehicle type of the incoming vehicle; Obtain the monitoring data of each monitoring point during the weighing period of the incoming vehicle; Generate a reference value for each monitoring index according to all the monitoring data; Generate a second-level violation reference value K2; K2 = µ i * j i ; Where r is the number of monitoring indexes; µ i is the influence factor of the i-th monitoring index; j i is the reference index of the i-th monitoring index; Generate a violation risk value h according to the first-level violation evaluation value K1 and the second-level violation evaluation value K2; h = e1 * K1 + e2 * K2; Where e1 is a preset first weight coefficient and e2 is a preset second weight coefficient; Preset a violation evaluation value threshold H1; If h > H1, generate a first-level alarm instruction; If h < H1, do not generate an alarm instruction.

[0017] Compared with the prior art, the intelligent weighbridge management method and system in the embodiments of the present application have the following beneficial effects: By establishing multiple positioning points, the real-time position parameters of each incoming vehicle when weighing are monitored in real time, and the initial weighing value is corrected according to the preset position analysis model and real-time position parameters, so as to eliminate the measurement error caused by position deviation when the vehicle is weighed, and improve the weighing accuracy of the weight of the weighed vehicle. Precise management of fuel is realized.

[0018] By establishing multiple monitoring points, when the vehicle is weighed, image data from multiple angles are collected, and the weighing process is dynamically monitored in combination with the operating parameters of the weighbridge, and potential violations are warned in a timely manner, so as to avoid the distortion of weighing parameters due to fraud behavior and ensure the safe and reliable operation of the weighbridge system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic flowchart of an intelligent weighbridge management method in a preferred embodiment of the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following will further describe in detail the specific embodiments of the present application in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0021] In the description of the present application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application 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 should not be construed as limiting the present application.

[0022] The terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0023] In the description of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" 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 an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0024] As Figure 1 shown, a preferred embodiment of an intelligent weighbridge management method according to an embodiment of the present application includes: S101: Set a plurality of monitoring points and a plurality of positioning points according to the weighbridge device parameters; S102: Generate the weighing parameters of the incoming vehicle according to the weighbridge measurement data and the feedback data of all positioning points; S103: Generate a violation risk value of the incoming vehicle according to the weighbridge operation parameters and the monitoring data of each monitoring point, and determine whether to generate an alarm instruction according to the violation risk value; Among them, when setting a plurality of positioning points, it includes: Establish a positioning point sequence A, A = (a 1 , a 2 … a i … a n ), where a i is the i-th positioning point; n is the number of positioning points.

[0025] Specifically, set a plurality of positioning points according to the weighbridge device parameters, and set an infrared positioning device at each positioning point. By collecting the distance parameters between the incoming vehicle and each infrared positioning device, generate the real-time position parameters of the incoming vehicle.

[0026] Specifically, set a plurality of monitoring points according to the weighbridge device parameters, and set a camera device at each monitoring point to collect image data in all directions, and determine whether there is a violation behavior by analyzing the image data.

[0027] In a preferred embodiment of the embodiment of the present application, generating the weighing parameters of the incoming vehicle includes: Generate an initial weighing value of the incoming vehicle according to the weighbridge measurement data; Obtain the feedback data of each positioning point; Generate the distance value between the incoming vehicle and each positioning point; Establish a distance value sequence B, B = (b 1 , b 2 … b i … b n ), where bi is the distance value between the incoming vehicle and the i-th positioning point; Generate the position deviation value f of the incoming vehicle according to the preset position analysis model and the distance value sequence B; Set the compensation coefficient according to the position deviation value f; Correct the initial weighing value according to the compensation coefficient, and generate the primary weighing value of the incoming vehicle according to the correction result.

[0028] Specifically, after the incoming vehicle gets on the weighbridge, collect the feedback data of each positioning point, calculate the distance values between the vehicle body of the incoming vehicle and each positioning point, and determine the position parameters of the incoming vehicle through all the distance values.

[0029] Specifically, during the weighing process of the vehicle, different positions of its center of gravity will have different disturbing effects on the final weighing result. By positioning the incoming vehicle, determine the corresponding center of gravity offset, and set the corresponding compensation coefficient to correct the initial weighing value, so that the weighing value of the incoming vehicle is more accurate.

[0030] Specifically, the preset position analysis model includes: Traverse the historical parameters, establish the vehicle category sequence C, C = (c 1 , c 2 … c i … c m ), where c i is the i-th vehicle category; m is the number of vehicle categories; Set c i as the target vehicle category in turn; Generate the standard distance values of each positioning point in the target vehicle category; Generate the comparison sub-model of the target vehicle category according to all the standard distance values; Generate the comparison sub-models of each vehicle category in turn; Establish the comparison sub-model sequence D, D = (d 1 , d 2 … d i … d m ), where d i is the comparison sub-model of the i-th vehicle category; Generate the position analysis model according to the comparison sub-model sequence D.

[0031] Specifically, traverse the historical parameters of all incoming vehicles, screen out multiple vehicle categories, and generate the standard parking positions after weighing according to the body sizes and vehicle equipment parameters of each vehicle category. Thus, generate the standard distance values between the vehicle of this type and each positioning point during weighing.

[0032] Specifically, setting the compensation coefficient according to the position deviation value f includes: Set the target comparison sub-model according to the incoming vehicle; Generate a position deviation value f according to the target comparison sub-model; f = (d i - d' i ) 2 ; where d' i is the standard distance value from the target comparison sub-model to the i-th positioning point; Preset the first position deviation value threshold F1; If f > F1, generate a first-level scheduling instruction; If f < F1, generate the centroid offset of the incoming vehicle according to the distance value sequence B and the target comparison sub-model; Set the compensation coefficient according to the centroid offset.

[0033] Specifically, the first-level scheduling instruction means that the current incoming vehicle is not fully on the weighbridge and needs to be adjusted.

[0034] Specifically, by screening historical weighing parameters, corresponding training data is generated, thereby establishing the mapping relationship between the centroid offset of each type of vehicle and the compensation coefficient. By analyzing the position of the incoming vehicle during the weighing process, the disturbance to the weighing result based on the position deviation is corrected, and the weighing accuracy of the incoming vehicle is improved.

[0035] It can be understood that in the above embodiments, by establishing multiple positioning points, the real-time position parameters of each incoming vehicle during weighing are monitored in real time, and the initial weighing value is corrected according to the preset position analysis model and the real-time position parameters, so as to eliminate the measurement error caused by the position deviation during vehicle weighing, improve the weighing accuracy of the vehicle during weighing. At the same time, the vehicle does not need to repeatedly adjust its position during the weighing process, improving the weighing efficiency of the vehicle and realizing the precise management of fuel.

[0036] In the preferred embodiment of this application, generating the violation risk value of the incoming vehicle includes: Obtain the weighbridge operation parameters during the weighing period of the incoming vehicle; Based on the weighing period, establish multiple time intervals; Generate a sequence of slope values T within each time interval according to the weighbridge operation parameters; T = (t 1 , t 2 … t i … t θ ), where t i is the slope value of the i-th time interval; θ is the number of time intervals; Generate a first-level violation reference value K1 according to the sequence of slope values T; Obtain the monitoring data of each monitoring point during the weighing period of the incoming vehicle; Generate reference values for each monitoring indicator based on all monitoring data; Generate a secondary violation reference value K2; K2 = µ i *j i ; where r is the number of monitoring indicators; µ i is the influence factor of the i-th monitoring indicator; j i is the reference indicator of the i-th monitoring indicator; Generate a violation risk value h based on the primary violation evaluation value K1 and the secondary violation evaluation value K2.

[0037] Specifically, the monitoring indicators are various types of characteristic parameters extracted based on historical violation data, including but not limited to, whether the empty car weight of the incoming vehicle is abnormal, whether there are people on the vehicle during the weighing process, whether there are sundries around the weighing body, and other parameters.

[0038] Specifically, the larger the secondary violation evaluation value, the more violations exist in the current weighing process based on video analysis.

[0039] Specifically, the slope value is determined by whether the data sent by the weighbridge instrument serial port (i.e., the change value of voltage) is a continuous linear curve. The system checks the instrument value once every 0.1 s and automatically calculates the slope value of the value on the mass curve (the value of the latter point - the value of the former point).

[0040] Specifically, determine whether to generate an alarm instruction based on the violation risk value, including: Obtain the violation evaluation value h; h = e1 * K1 + e2 * K2; where e1 is a preset first weight coefficient and e2 is a preset second weight coefficient; Preset a violation evaluation value threshold H1; If h > H1, generate a primary alarm instruction; If h < H1, do not generate an alarm instruction.

[0041] Specifically, the primary alarm instruction means that there are violations in the current incoming vehicle during the weighing process, resulting in distorted weighing data, so it is necessary to re-weigh to avoid affecting the management efficiency of the power plant due to data distortion.

[0042] Specifically, the primary violation reference value K1 includes: K1 = e3 * Q3 * (t i - ∆t) 2 + e4 * Q4 * (t i - t')2 ; wherein, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; ∆t is the average value of all data in the slope value sequence T; t' is a standard slope value set based on the vehicle category of the incoming vehicle.

[0043] Specifically, all parameters in the model are normalized by the preset third fixed coefficient and fourth fixed coefficient, so that each parameter is within the same value range.

[0044] Specifically, the standard slope values of each vehicle category are generated by analyzing historical data.

[0045] Specifically, the larger the first violation evaluation value is, the greater the possibility that the current weighbridge is operating abnormally.

[0046] It can be understood that in the above embodiments, by establishing multiple monitoring points, when the vehicle passes the weighbridge, image data from multiple angles is collected, and the weighing process is dynamically monitored in combination with the operating parameters of the weighbridge, and potential violations are warned in a timely manner, avoiding the distortion of weighing parameters due to fraud behavior, and ensuring the safe and reliable operation of the weighbridge system.

[0047] Based on another preferred implementation of an intelligent weighbridge management method in any of the above preferred embodiments, a preferred embodiment of the present invention provides an intelligent weighbridge management system, including: A central control unit, configured to set multiple monitoring points and multiple positioning points according to the weighbridge device parameters; A positioning unit, including multiple positioning sub-modules, and the positioning sub-modules are arranged at each positioning point; A monitoring unit, including multiple monitoring sub-modules, and the monitoring sub-modules are arranged at each monitoring point; The central control unit includes: A first processing module, configured to establish a positioning point sequence A, A = (a 1 , a 2 …a i …a n ), wherein, a i is the i-th positioning point; n is the number of positioning points; A second processing module, configured to generate weighing parameters of the incoming vehicle according to the weighbridge measurement data and the feedback data of all positioning points; A third processing module, configured to generate a violation risk value of the incoming vehicle according to the weighbridge operating parameters and the monitoring data of each monitoring point, and determine whether to generate an alarm instruction according to the violation risk value.

[0048] Specifically, the positioning sub-module is preferably an infrared positioning device, which generates real-time position parameters of the incoming vehicle by collecting distance parameters between the incoming vehicle and each infrared positioning device.

[0049] Specifically, the monitoring sub-module is preferably a camera device, which is used to collect image data from all directions and determine whether there are any violations by analyzing the image data.

[0050] In the preferred embodiment of this application, the second processing module is further configured to: Generate an initial weighing value of the incoming vehicle based on the weighbridge measurement data; Obtain feedback data of each positioning point; Generate distance values between the incoming vehicle and each positioning point; Establish a distance value sequence B, B = (b 1 , b 2 … b i … b n ), where bi is the distance value between the incoming vehicle and the i-th positioning point; Generate a position deviation value f of the incoming vehicle according to the preset position analysis model and the distance value sequence B; Set a compensation coefficient according to the position deviation value f; Correct the initial weighing value according to the compensation coefficient and generate a primary weighing value of the incoming vehicle according to the correction result; Among them, the preset position analysis model includes: Traverse historical parameters to establish a vehicle category sequence C, C = (c 1 , c 2 … c i … c m ), where c i is the i-th vehicle category; m is the number of vehicle categories; Successively set c i as the target vehicle category; Generate standard distance values of each positioning point in the target vehicle category; Generate a comparison sub-model of the target vehicle category according to all the standard distance values; Successively generate comparison sub-models of each vehicle category; Establish a comparison sub-model sequence D, D = (d 1 , d 2 … d i … d m ), where d i is the comparison sub-model of the i-th vehicle category; Generate a position analysis model according to the comparison sub-model sequence D; Setting a compensation coefficient according to the position deviation value f includes: Set a target comparison sub-model according to the incoming vehicle; Generate a position deviation value f according to the target comparison sub-model; f = (d i - d' i ) 2 ; Among them, d' i is the standard distance value from the target comparison sub-model to the i-th positioning point; Preset the first position deviation value threshold F1; If f > F1, generate a first-level scheduling instruction; If f < F1, generate the center-of-gravity offset of the incoming vehicle according to the distance value sequence B and the target comparison sub-model; Set the compensation coefficient according to the center-of-gravity offset.

[0051] In the preferred embodiment of the present application, the third processing module is further configured to: Obtain the weighbridge operation parameters during the weighing period of the incoming vehicle; Establish multiple time intervals based on the weighing period; Generate a slope value sequence T within each time interval according to the weighbridge operation parameters; T = (t1, t2…ti…tθ), where ti is the slope value of the i-th time interval; θ is the number of time intervals; Generate a first-level violation reference value K1 according to the slope value sequence T; K1 = e3 * Q3 * (t i - ∆t) 2 + e4 * Q4 * (t i - t') 2 ; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; ∆t is the average value of all data in the slope value sequence T; t' is the standard slope value set according to the vehicle type of the incoming vehicle; Obtain the monitoring data of each monitoring point during the weighing period of the incoming vehicle; Generate the reference value of each monitoring index according to all the monitoring data; Generate a second-level violation reference value K2; K2 = µ i * j i ; Among them, r is the number of monitoring indicators; µ i is the influence factor of the i-th monitoring index; j iis the reference index for the i-th monitoring index; Generate a violation risk value h based on the first-level violation evaluation value K1 and the second-level violation evaluation value K2; h = e1 * K1 + e2 * K2; where e1 is a preset first weight coefficient and e2 is a preset second weight coefficient; Preset a violation evaluation value threshold H1; If h > H1, generate a first-level alarm instruction; If h < H1, do not generate an alarm instruction.

[0052] According to the first concept of the present application, by establishing multiple positioning points, the real-time position parameters of each incoming vehicle when weighing are monitored in real time, and the initial weighing value is corrected according to the preset position analysis model and real-time position parameters, so as to eliminate the measurement error caused by position deviation when the vehicle is weighed, and improve the weighing accuracy of the weight of the weighed vehicle. Achieve precise management of fuel.

[0053] According to the second concept of the present application, by establishing multiple monitoring points, when the vehicle is weighed, image data from multiple angles is collected, and the weighing process is dynamically monitored in combination with the operating parameters of the weighbridge, and potential violations are warned in a timely manner, so as to avoid the distortion of weighing parameters due to fraud behavior and ensure the safe and reliable operation of the weighbridge system.

[0054] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and replacements can still be made, and these improvements and replacements should also be regarded as the protection scope of the present application.

Claims

1. A smart weighbridge management method, characterized in that: Including: Setting multiple monitoring points and multiple positioning points according to the weighbridge equipment parameters; Generating the weighing parameters of the incoming vehicles according to the weighbridge measurement data and the feedback data of all positioning points; Generating the violation risk value of the incoming vehicles according to the weighbridge operation parameters and the monitoring data of each monitoring point, and judging whether to generate an alarm instruction according to the violation risk value; Among them, when setting multiple positioning points, including: Establish a sequence of positioning points A, A=(a1, a2…a i …a n ), where a i is the i-th positioning point; n is the number of positioning points.

2. The intelligent weighbridge management method according to claim 1, characterized in that: Generating the weighing parameters of the incoming vehicles, including: Generating the initial weighing value of the incoming vehicles according to the weighbridge measurement data; Obtaining the feedback data of each positioning point; Generating the distance values between the incoming vehicles and each positioning point; Establish a distance value sequence B, B=(b1, b2…b i …b n ), where bi is the distance between the vehicle entering the factory and the i-th positioning point; Generating the position deviation value f of the incoming vehicles according to the preset position analysis model and the distance value sequence B; Setting the compensation coefficient according to the position deviation value f; Correcting the initial weighing value according to the compensation coefficient, and generating the first-level weighing value of the incoming vehicles according to the correction result.

3. The intelligent weighbridge management method according to claim 2, characterized in that: The preset position analysis model includes: Traverse the historical parameters and establish the vehicle category sequence C, C=(c1,c2…c i …c m ), where c i is the i-th vehicle category; m is the number of vehicle categories; Set c in sequence i is the target vehicle category; Generating the standard distance values of each positioning point in the target vehicle category; Generating the comparison sub-model of the target vehicle category according to all the standard distance values; Generating the comparison sub-models of each vehicle category in turn; Establish the comparison sub-model series D, D = (d1, d2…d i …d m ), where d i is the comparison sub-model of the i-th vehicle category; Generating the position analysis model according to the comparison sub-model sequence D.

4. The intelligent weighbridge management method according to claim 3, characterized in that: Setting the compensation coefficient according to the position deviation value f, including: Setting the target comparison sub-model according to the incoming vehicles; Generating the position deviation value f according to the target comparison sub-model; f=[ (d i -d' i ) 2 ]; Among them, d' i is the standard distance value to the i-th positioning point generated based on the target comparison sub-model; Presetting the first position deviation value threshold F1; If f > F1, generating a first-level scheduling instruction; If f < F1, generating the center of gravity offset of the incoming vehicles according to the distance value sequence B and the target comparison sub-model; Setting the compensation coefficient according to the center of gravity offset.

5. The intelligent weighbridge management method according to claim 2, characterized in that: Generating the violation risk value of the incoming vehicles, including: Obtaining the weighbridge operation parameters during the weighing period of the incoming vehicles; Establishing multiple time intervals based on the weighing period; Generating the slope value sequence T within each time interval according to the weighbridge operation parameters; T = (t1, t2…t i …t θ ), where t i is the slope value of the i-th time interval; θ is the number of time intervals; Generating the first-level violation reference value K1 according to the slope value sequence T; Obtaining the monitoring data of each monitoring point during the weighing period of the incoming vehicles; Generating the reference values of each monitoring index according to all the monitoring data; Generating the second-level violation reference value K2; K2=[ µ i *j i ]; Among them, r is the number of monitoring indicators; µ i is the influencing factor of the i-th monitoring indicator; j i is the reference indicator of the i-th monitoring indicator; Generating the violation risk value h according to the first-level violation evaluation value K1 and the second-level violation evaluation value K2.

6. The intelligent weighbridge management method according to claim 5, characterized in that: Judging whether to generate an alarm instruction according to the violation risk value, including: Obtaining the violation evaluation value h; h = e1 * K1 + e2 * K2; Among them, e1 is the preset first weight coefficient, and e2 is the preset second weight coefficient; Presetting the violation evaluation value threshold H1; If h > H1, generating a first-level alarm instruction; If h < H1, not generating an alarm instruction.

7. The intelligent weighbridge management method according to claim 5, characterized in that: Generating the first-level violation reference value K1 according to the slope value sequence T, including: K1=e3*Q3* (t i -∆t) 2 ]+e4*Q4* (t i -t') 2 ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; ∆t is the average value of all the data in the slope value sequence T; t' is the standard slope value set based on the vehicle category of the incoming vehicles.

8. An intelligent weighbridge management system, using the intelligent weighbridge management method according to any one of claims 1 to 7, characterized in that: Including: The central control unit is used to set multiple monitoring points and multiple positioning points according to the weighbridge equipment parameters; The positioning unit includes multiple positioning sub-modules, and the positioning sub-modules are arranged at each positioning point; The monitoring unit includes multiple monitoring sub-modules, and the monitoring sub-modules are arranged at each monitoring point; The central control unit includes: The first processing module is used to establish a positioning point sequence A, A=(a1, a2…a i …a n ), where a i is the i-th positioning point; n is the number of positioning points; The second processing module is used to generate the weighing parameters of the incoming vehicles based on the weighbridge measurement data and the feedback data of all positioning points; The third processing module is used to generate the violation risk value of the incoming vehicles based on the weighbridge operation parameters and the monitoring data of each monitoring point, and determine whether to generate an alarm instruction according to the violation risk value.

9. The intelligent weighbridge management system according to claim 8, characterized in that: The second processing module is further used to: Generate the initial weighing value of the incoming vehicles based on the weighbridge measurement data; Obtain the feedback data of each positioning point; Generate the distance values between the incoming vehicles and each positioning point; Establish a distance value sequence B, B=(b1, b2…b i …b n ), where bi is the distance between the vehicle entering the factory and the i-th positioning point; Generate the position deviation value f of the incoming vehicles according to the preset position analysis model and the distance value sequence B; Set the compensation coefficient according to the position deviation value f; Correct the initial weighing value according to the compensation coefficient, and generate the first-level weighing value of the incoming vehicles according to the correction result; Among them, the preset position analysis model includes: Traverse the historical parameters and establish the vehicle category sequence C, C=(c1,c2…c i …c m ), where c i is the i-th vehicle category; m is the number of vehicle categories; Set c in sequence i is the target vehicle category; Generate the standard distance values of each positioning point in the target vehicle category; Generate the comparison sub-model of the target vehicle category according to all the standard distance values; Generate the comparison sub-models of each vehicle category in turn; Establish the comparison sub-model series D, D = (d1, d2…d i …d m ), where d i is the comparison sub-model of the i-th vehicle category; Generate the position analysis model according to the comparison sub-model sequence D; Setting the compensation coefficient according to the position deviation value f includes: Set the target comparison sub-model according to the incoming vehicles; Generate the position deviation value f according to the target comparison sub-model; f=[ (d i -d' i ) 2 ]; Among them, d' i is the standard distance value to the i-th positioning point generated based on the target comparison sub-model; Preset the first position deviation value threshold F1; If f > F1, generate the first-level scheduling instruction; If f < F1, generate the center-of-gravity offset of the incoming vehicles according to the distance value sequence B and the target comparison sub-model; Set the compensation coefficient according to the center-of-gravity offset.

10. The intelligent weighbridge management system according to claim 9, characterized in that: The third processing module is further used to: Obtain the weighbridge operation parameters during the weighing period of the incoming vehicles; Establish multiple time intervals based on the weighing period; Generate the slope value sequence T within each time interval according to the weighbridge operation parameters; T = (t1, t2…ti…tθ), where ti is the slope value of the i-th time interval; θ is the number of time intervals; Generate the first-level violation reference value K1 according to the slope value sequence T; K1=e3*Q3* (t i -∆t) 2 ]+e4*Q4* (t i -t') 2 ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; ∆t is the average value of all data in the slope value sequence T; t' is the standard slope value set based on the vehicle category of the incoming vehicles; Obtain the monitoring data of each monitoring point during the weighing period of the incoming vehicles; Generate the reference values of each monitoring index according to all the monitoring data; Generate the second-level violation reference value K2; K2=[ µ i *j i ]; Among them, r is the number of monitoring indicators; µ i is the influencing factor of the i-th monitoring indicator; j i is the reference indicator of the i-th monitoring indicator; Generate the violation risk value h according to the first-level violation evaluation value K1 and the second-level violation evaluation value K2; h = e1*K1 + e2*K2; Among them, e1 is the preset first weight coefficient, and e2 is the preset second weight coefficient; Preset the violation evaluation value threshold H1; If h > H1, generate the first-level alarm instruction; If h < H1, do not generate the alarm instruction.

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