A state analysis method and system for vehicle weighing

Abstract

The application provides a state analysis method and system for vehicle weighing, and relates to the technical field of weighbridge weighing, which comprises the following steps: acquiring current weight information in real time based on the triggering of a collection event; obtaining current optimization information corresponding to the current weight information according to a first sampling threshold; performing trend analysis on the current optimization information to identify a current state corresponding to the current optimization information; matching a state event for the current optimization information in combination with the current state; based on the end of the collection event, summarizing all optimization information, and constructing a weight change curve with a state event marker to completely present the whole-cycle weight evolution from the vehicle on the scale to the vehicle off the scale, so as to provide a high-accuracy result for single weighing and improve data reliability.

Description

Technical Field

[0001] This invention relates to the field of weighbridge technology, and more particularly to a method, system, electronic device, computer-readable storage medium, and computer program product for vehicle weighing condition analysis. Background Technology

[0002] A weighbridge, also known as a truck scale, is a large scale installed on the ground, typically used to weigh the tonnage of truck cargo. It is a primary weighing device for measuring bulk goods. Weighbridges are equipped with sensors that can detect, record, and transmit real-time weight information.

[0003] Current weighbridges are based on strain gauge load cells. They collect the pressure signal applied by the vehicle's weight in real time, convert it into an electrical signal and transmit it to the instrument system. Noise reduction is performed using low-pass filters to obtain a smooth signal curve, and the weighing value is locked for output.

[0004] These traditional technologies can only filter high-frequency noise, such as car engine vibration and wind disturbance. Compared to the basic level, they lack processing logic for low-frequency human interference (such as drivers getting in and out of the vehicle, and the rolling and sliding of goods or liquids), resulting in limited accuracy of their weighing results. Furthermore, traditional technologies mainly focus on the weight stability point at a certain moment and cannot identify continuous dynamic changes throughout the weighing cycle.

[0005] Therefore, there is a need to propose a condition analysis method, system, electronic device, computer-readable storage medium, and computer program product for vehicle weighing. Summary of the Invention

[0006] This specification provides a method, system, electronic device, computer-readable storage medium, and computer program product for analyzing the state of vehicle weighing, used to identify continuous dynamic changes throughout the weighing cycle to improve the accuracy of weighing results.

[0007] The condition analysis method for vehicle weighing provided in this application adopts the following technical solution, including:

[0008] Based on the triggering of the acquisition event, the current weight information is obtained in real time;

[0009] Based on the first sampling threshold, the current optimization information corresponding to the current weight information is obtained;

[0010] Perform trend analysis on the current optimization information to identify the current state corresponding to the current optimization information;

[0011] The current state is combined with the current optimization information matching state event;

[0012] Based on the end of the acquisition event, all optimization information is summarized, and a weight change curve with state event markers is constructed.

[0013] Optionally, obtaining the current optimization information corresponding to the current weight information based on the first sampling threshold includes:

[0014] Combining the current weight information and the first sampling threshold, a current optimization set is constructed; wherein, the initial weight information is found based on the current weight information and the first sampling threshold; the weight information between the initial weight information and the current weight information is summarized to obtain the current optimization set;

[0015] Based on the correction factor and preset correction conditions, determine whether the current optimization set needs to be updated;

[0016] If so, then rebuild the current optimization set;

[0017] Otherwise, the current weight information is optimized based on the current optimization set to obtain the current optimization information.

[0018] Optionally, optimizing the current weight information based on the current optimization set to obtain current optimization information includes:

[0019] Calculate the weight of each piece of weight information in the current optimization set;

[0020] By combining the weight information and corresponding weight of each item in the current optimization set, the current weight information is updated to obtain the current optimization information.

[0021] Optionally, the step of performing trend analysis on the current optimization information and identifying the current state corresponding to the current optimization information includes:

[0022] Based on the associated sequence, the current optimization information is relevance determined;

[0023] If the current optimization information is valid, then calculate the trend change rate of the current optimization information;

[0024] Based on the trend change rate and state judgment conditions, mark the current state corresponding to the current optimization information.

[0025] Optionally, the step of combining the current state with the current optimization information matching state event includes:

[0026] Based on the change difference in the current optimization information and the current state, determine the type of the state event;

[0027] Identify whether the current optimization information is interfering data; if so, remove the current optimization information.

[0028] Optionally, based on the end of the acquisition event, summarizing all optimization information and constructing a weight change curve with state event markers includes:

[0029] Based on the departure operation of the target object, a weight change curve is generated according to the weight information set;

[0030] The weight change curve is segmented and optimized based on the status events and acquisition time of the weight information.

[0031] The event markers are associated with the optimized weight change curve to obtain the weight change curve with event markers.

[0032] Optionally, the step of segmenting and optimizing the weight change curve based on the status events and acquisition time of the collected information includes:

[0033] The weight information in the weight information set is grouped by combining the state events and the acquisition time to obtain several state event groups;

[0034] The stability result is determined based on the relationship between the variance of the state event group and the stability threshold.

[0035] Based on the stability results, the weight change curve is smoothed to obtain an optimized weight change curve; wherein, a first smoothing process is performed on stable state event groups, and a second smoothing process is performed on unstable state event groups.

[0036] The condition analysis system for vehicle weighing provided in this application adopts the following technical solution, including:

[0037] The data acquisition module is used to obtain the current weight information in real time based on the triggering of acquisition events;

[0038] The optimization module is used to obtain current optimization information corresponding to the current weight information based on the first sampling threshold.

[0039] The state analysis module is used to perform trend analysis on the current optimization information and identify the current state corresponding to the current optimization information;

[0040] The event analysis and matching module is used to match the current state with the current optimization information and state events.

[0041] The aggregation module is used to aggregate all optimization information based on the end of the acquisition event and construct a weight change curve with status event markers.

[0042] Optionally, the optimization module includes:

[0043] The first optimization submodule is used to construct a current optimization set by combining the current weight information and the first sampling threshold; wherein, the initial weight information is found according to the current weight information and the first sampling threshold; and the weight information between the initial weight information and the current weight information is summarized to obtain the current optimization set;

[0044] The second optimization submodule is used to determine whether the current optimization set needs to be updated by combining the correction factor and the preset correction conditions.

[0045] The third optimization submodule is used to rebuild the current optimization set if an update to the current optimization set is required.

[0046] The fourth optimization submodule is used to optimize the current weight information based on the current optimization set if no update is needed, thereby obtaining the current optimization information.

[0047] Optionally, the fourth optimization submodule includes:

[0048] The weight calculation unit is used to calculate the weight of each weight information in the current optimization set.

[0049] The update unit is used to update the current weight information by combining each weight information and its corresponding weight in the current optimization set, so as to obtain the current optimization information.

[0050] Optionally, the state analysis module includes:

[0051] The correlation determination submodule is used to determine the correlation of the current optimization information based on the correlation sequence.

[0052] The trend analysis submodule is used to calculate the trend change rate of the current optimization information if the current optimization information is valid.

[0053] The state marking submodule is used to mark the current state corresponding to the current optimization information based on the trend change rate and the state judgment condition.

[0054] Optionally, the event analysis and matching module includes:

[0055] The first matching submodule is used to determine the type of state event based on the change difference of the current optimization information and the current state;

[0056] The second matching submodule is used to identify whether the current optimization information is interference data. If so, the current optimization information is extracted.

[0057] Optionally, the aggregation module includes:

[0058] The curve generation submodule is used to generate a weight change curve based on the weight information set for the departure operation of the target object.

[0059] The segmented optimization submodule is used to segment and optimize the weight change curve based on the status events and acquisition time of the weight information.

[0060] The association submodule is used to associate the event markers with the optimized weight change curve to obtain the weight change curve with event markers.

[0061] Optional segmented optimization submodules include:

[0062] A grouping unit is used to group the weight information in the weight information set by combining the state event and the acquisition time to obtain several state event groups;

[0063] The stability determination unit is used to determine the stability result based on the relationship between the variance of the state event group and the stability threshold.

[0064] The curve optimization unit is used to smooth the weight change curve based on the stability results to obtain an optimized weight change curve; wherein, a first smoothing process is performed on stable state event groups, and a second smoothing process is performed on unstable state event groups.

[0065] This specification also provides an electronic device, wherein the electronic device includes:

[0066] Processor; and,

[0067] A memory that stores computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0068] This specification also provides a computer-readable storage medium that stores one or more programs that, when executed by a processor, implement any of the methods described above.

[0069] This specification also provides a computer program product, wherein the computer program product includes: a computer program / instruction, which, when executed by a processor, implements any of the methods described above.

[0070] In this application, current weight information is acquired in real time based on the triggering of a collection event; current optimization information corresponding to the current weight information is obtained according to a first sampling threshold; trend analysis is performed on the current optimization information to identify the current state corresponding to the current optimization information; a state event is matched for the current optimization information based on the current state; based on the end of the collection event, all optimization information is summarized to construct a weight change curve with state event markers, so as to fully present the weight evolution of the entire cycle from the vehicle being weighed to leaving the weighbridge, providing a high-accuracy result for a single weighing and improving data reliability. Attached Figure Description

[0071] Figure 1 A schematic diagram illustrating the principle of a condition analysis method for vehicle weighing provided in the embodiments of this specification;

[0072] Figure 2 This specification provides a schematic diagram of the structure of a condition analysis system for vehicle weighing, as illustrated in an embodiment.

[0073] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification;

[0074] Figure 4 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification. Detailed Implementation

[0075] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0076] Exemplary embodiments of the invention will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limiting the invention to the embodiments set forth herein. Rather, these exemplary embodiments are provided to make the invention more comprehensive and complete, and to facilitate a full communication of the inventive concept to those skilled in the art. The same reference numerals in the drawings denote the same or similar elements, components, or parts, and therefore repeated descriptions of them will be omitted.

[0077] Subject to the technical concept of this invention, the features, structures, characteristics or other details described in a particular embodiment may be combined in one or more other embodiments in a suitable manner.

[0078] In the description of specific embodiments, the features, structures, characteristics, or other details described in this invention are intended to enable those skilled in the art to fully understand the embodiments. However, it is not excluded that those skilled in the art can practice the technical solutions of this invention without one or more of the specific features, structures, characteristics, or other details.

[0079] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0080] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0081] The terms “and / or” or “and / or” include all combinations of any one or more of the listed items.

[0082] Figure 1 This specification provides a schematic diagram of a condition analysis method for vehicle weighing, which includes:

[0083] S1 acquires current weight information in real time based on the triggering of acquisition events;

[0084] S2 obtains the current optimization information corresponding to the current weight information based on the first sampling threshold;

[0085] S3 performs trend analysis on the current optimization information to identify the current state corresponding to the current optimization information;

[0086] S4 combines the current state to form the current optimization information matching state event;

[0087] S5, based on the end of the acquisition event, summarizes all the optimization information and constructs a weight change curve with state event markers.

[0088] In the field of vehicle weighing, the common operation of weighing vehicles using a weighbridge includes: after the front brake of the weighbridge is opened, the vehicle begins to slowly drive into the weighbridge. As the vehicle gradually enters, the real-time weight on the weighbridge gradually increases until the vehicle is completely inside the weighbridge, at which point the weight value on the weighbridge reaches its peak.

[0089] During this process, the weighbridge reading may fluctuate slightly due to vehicle movement and ground conditions, but overall it shows a steady upward trend. As the vehicle comes to a complete stop and the driver exits and leaves the weighbridge area, the weighbridge weight reading will decrease after a slight fluctuation, then stabilize, reflecting the weight of only the vehicle remaining. After weighing, the driver re-enters the vehicle. As the vehicle's weight increases, the weighbridge weight reading rises again and eventually stabilizes. At this point, the exit gate opens, the vehicle begins to leave the weighbridge, and the weighbridge weight reading gradually decreases as the vehicle departs until it finally reaches zero, completing the entire weighing process and yielding the weight change curve.

[0090] In the entire weighing process described above, a stable weight value is generally selected after the driver gets out of the vehicle and leaves the weighbridge area as the vehicle weight. However, due to dynamic interference from the vehicle / environment and misjudgment of stable states, the accuracy of the weighing results is not high. Therefore, this specification discloses a state analysis method for vehicle weighing, which specifically includes:

[0091] S1 acquires current weight information in real time based on the triggering of acquisition events;

[0092] S11 triggers a data collection event based on the entry operation of the target object;

[0093] The target object refers to the vehicle that needs to be weighed.

[0094] The entry operation based on the target object refers to the situation where the target object drives towards the weighbridge but does not enter the weighbridge.

[0095] In one embodiment of this specification, a camera is installed at the edge of the weighbridge; when the camera recognizes the target object as it drives toward the weighbridge, a data acquisition event is triggered.

[0096] In another embodiment of this specification, vehicle information is read based on RFID identification technology, and a collection event is triggered once the vehicle identity is confirmed.

[0097] In another embodiment of this specification, a radar sensor can also be installed on the weighbridge; when the distance of the target object from the radar sensor is ≤ a preset monitoring threshold, a data acquisition event is triggered.

[0098] In other embodiments of this specification, a barrier gate can also be set up, which triggers a data collection event when the current barrier gate is opened.

[0099] Based on the above operations, the data collection event is triggered before the target object is weighed.

[0100] If no sensor is available to trigger the acquisition, the acquisition will be directly triggered by the real-time increase in the weight of the weighbridge.

[0101] The data acquisition events include: real-time, continuous collection of weight information via a weighbridge. In another embodiment of this specification, the data acquisition events also include: constructing an initial complete information set. .

[0102] S12 obtains current weight information in real time;

[0103] After the data acquisition event is triggered, the current weight information is acquired according to the preset sampling interval. The weight information acquired at the current moment is used as the current weight information. .

[0104] Preferably, the weight information is a weight signal with a timestamp.

[0105] S2 obtains the current optimization information corresponding to the current weight information based on the first sampling threshold;

[0106] S21 obtains the first sampling threshold;

[0107] First sampling threshold Used to determine the amount of weight information in the current optimization set.

[0108] Initialize the first sampling threshold To facilitate optimization of weight information from the initial stages of data collection, a smaller value can be set based on the actual situation. .

[0109] In subsequent operations, the first sampling threshold will be dynamically updated according to the magnitude of the correction factor.

[0110] S22 combines the current weight information and the first sampling threshold to construct the current optimization set;

[0111] The current optimization set includes: several weight information items. In the current optimization set, weight information Arranged in the order of collection.

[0112] S221 based on current weight information Find the initial weight information using the first sampling threshold. ;

[0113] in, , It is a positive integer. .

[0114] S222 will use the initial weight information With current weight information The weight information between them is summarized to obtain the current optimization set;

[0115] Current optimization set For any weight information in the current optimization set , .

[0116] S23 optimizes the current optimization set;

[0117] S231 calculates the correction factor for the current optimization set;

[0118] In one embodiment of this specification, the correction factor Let be the variance of the current optimization set.

[0119] Specifically, correction factor .in, The arithmetic mean of all weight information in the current optimization set, i.e. .

[0120] S232 combines the correction factor and the preset correction conditions to determine whether the current optimization set needs to be updated; if so, proceed to step S22 to rebuild the current optimization set; otherwise, proceed to step S24.

[0121] Based on the correction factor and preset correction conditions, the first sampling threshold may be updated; when the first sampling threshold changes, it will affect the range of weight information in the current optimization set. Therefore, when the first sampling threshold changes, the current optimization set needs to be updated, that is, S22 needs to be re-executed.

[0122] Preset calibration conditions ;

[0123] Among them, the greater the data fluctuation, the smaller the first sampling threshold; the smaller the data fluctuation, the larger the first sampling threshold.

[0124] To elaborate, if the correction factor > Upper limit threshold This indicates that the data fluctuations are too large. To improve sensitivity to recent changes, the first sampling threshold needs to be reduced. Specifically, the minimum sampling threshold As the new first sampling threshold, that is, .

[0125] If correction factor <lower threshold This indicates that the data fluctuation is too small. To improve the smoothing effect, the first sampling threshold needs to be increased. Specifically, the maximum sampling threshold As the new first sampling threshold, that is, .

[0126] If correction factor At the upper limit threshold With lower threshold Between, that is, This indicates that the fluctuation is moderate. To balance smoothness and sensitivity, the first sampling threshold can be dynamically adjusted. Specifically, the new first sampling threshold .

[0127] S24 optimizes the current weight information based on the current optimization set to obtain the current optimization information.

[0128] S241 Calculates the weight of each weight information in the current optimization set. ;

[0129] For the current optimization set, the th Weight information The corresponding weight ,in, This is the smoothing coefficient.

[0130] S242 combines the weight information and corresponding weight of each weight in the current optimization set to update the current weight information and obtain the current optimization information.

[0131] Current optimization information .

[0132] In one embodiment of this specification, trend analysis is performed based on an improved adaptive weighted moving average (AWMA).

[0133] This invention dynamically adjusts the range of the current optimization set based on the correction factor; it processes the current weight information in combination with the weight to generate the current optimization information, so as to make the curve corresponding to the data smoother, expand the granularity, and facilitate the trend analysis in step S3.

[0134] In one embodiment of this specification, weight information and corresponding optimization information can be stored simultaneously to facilitate later comparison of the differences between the weight information and the optimization information.

[0135] S3 performs trend analysis on the current optimization information to identify the current state corresponding to the current optimization information;

[0136] S31. Based on the association sequence, the current optimization information is judged for its correlation.

[0137] S311 retrieves the associated sequence;

[0138] The associated sequence includes: consecutive information preceding the current optimization information. Each piece of optimization information is arranged according to its collection time.

[0139] S312 calculates the correlation degree of the current optimization information by combining the current optimization information and the associated sequence;

[0140] correlation .

[0141] If the correlation degree is less than or equal to the similarity threshold, then the correlation degree between the current optimization information and the historical optimization information is considered high, and the correlation of the current optimization information is considered valid, and it is passed to step S32 for processing.

[0142] If the correlation is greater than the similarity threshold, the correlation between the current optimization information and the historical optimization information is considered low, and the correlation of the current optimization information is invalid and will not be used for subsequent analysis.

[0143] This manual continuously acquires newly generated dynamic weight data and performs forward matching with previously collected data. It also compares the new data with historical data within the window, verifying the validity of the new data by determining its correlation. By ensuring consistency with historical data, the reliability of overall trend analysis and status detection is improved.

[0144] The current optimization information is added to the end of the associated sequence, the oldest optimization information is removed, and the update of the associated sequence is completed.

[0145] S313 will display the current optimization information. Added to the first complete information set ;

[0146] To facilitate the generation of subsequent weight curves, the current optimization information is added to the first complete information set. .

[0147] First complete information set It refers to the ordered set of optimization information acquired by the target object between the triggering of a real-time acquisition event and the acquisition of current optimization information. , .

[0148] S32 If the current optimization information is valid, then calculate the trend change rate of the current optimization information;

[0149] S321 constructs the current analysis set based on the current optimization information and the second sampling threshold;

[0150] The current analysis set includes: several optimization information items. In the current analysis set, optimization information... Arranged in the order of collection.

[0151] S321-1 Based on current optimization information Second sampling threshold Find initial optimization information ;

[0152] Second sampling threshold Used to determine the amount of optimization information in the current analysis set.

[0153] in, . .

[0154] S321-2 will use the initial optimization information With current optimization information The optimization information between them is summarized to obtain the current analysis set;

[0155] Current analysis set .

[0156] For any optimization information in the current analysis set , .

[0157] S322 calculates the trend information of the current optimization information based on the current analysis set;

[0158] Calculate the weighted trend value for the optimization information in the current analysis set to obtain the current trend information;

[0159] Trend Information ;

[0160] in, It is a decay function, and . . The index variable for summation is used to iterate through the historical weight signals in the current analysis set. .

[0161] Therefore, current trend information .

[0162] S323 calculates the rate of change of the trend based on the difference between the current trend information and the previous trend information;

[0163] S323-1 Retrieve previous trend information ;

[0164] ;

[0165] S323-2 uses the difference between the current trend information and the previous trend information as the trend change rate;

[0166] Right now, .

[0167] S33 marks the current state corresponding to the current optimization information based on the trend change rate and the state judgment condition.

[0168] The current status includes: the current fluctuation status, the current trend of change, and the current sudden change.

[0169] S331 defines the intensity threshold. and direction threshold ;

[0170] S332 is based on the absolute value of the trend change rate and the intensity threshold. The magnitude relationship is used to determine the current fluctuation state;

[0171] Specifically, if Mark the current fluctuation state as "stable state";

[0172] like The current fluctuation state is marked as "change state".

[0173] S333 is based on trend change rate and direction threshold. Based on the size relationship, determine the current trend of change;

[0174] Specifically, if Mark the current trend as "upward trend";

[0175] like Mark the current trend as "downward trend";

[0176] like The current trend is marked as "stable fluctuation".

[0177] S334 determines the current mutation situation based on the change dimension value of the current optimization information;

[0178] Optimize information Difference in change To optimize information Compared to the previous optimization information The difference, that is = .

[0179] Optimize information The absolute value of the change is optimization information The absolute value of the change difference, i.e., the optimization information. The absolute value of the change .

[0180] Optimize information The change dimension values ​​include: recent 𝜑 Optimization information The absolute value of the change; including: optimization information absolute value of change, optimization information absolute value of change, optimization information absolute value of change, ..., optimization information The absolute value of the change.

[0181] If the changes in all dimensions of the current optimization information are greater than the sudden risk factor If the current mutation status of the current optimization information is "a sudden change has occurred", then the current optimization information is marked as a sudden change by weight; otherwise, the current sudden status of the current optimization information is "no sudden change has been found".

[0182] In one embodiment of this specification, optimization information The change dimension values ​​include: information from the three most recent optimizations. The absolute value of the change. The dimensions of change in the current optimization information include: , , .like ,and ,and Then mark the weight burst; if ,or ,or If no sudden change has been found, then the current emergency situation is as follows.

[0183] This manual analyzes the trend change rate to obtain the current fluctuation status, current trend, and current emergency situation.

[0184] S4 combines the current state to form the current optimization information matching state event;

[0185] S41 based on current optimization information Difference in change Based on the current state, determine the type of state event;

[0186] The types of status events include, but are not limited to: vehicle entering, vehicle leaving, vehicle stationary, vehicle position adjustment, driver getting on or off the vehicle, etc.

[0187] Among them, the trend of the optimization information is a continuous rise followed by stabilization, and the corresponding state event is: vehicle enters (on the weighbridge).

[0188] The trend of the optimized information is a continuous decline followed by stabilization, and the corresponding state event is: vehicle leaves (unloaded from the weighbridge).

[0189] If the trend of the optimized information changes in sequence as continuous increase, stable, and continuous decrease, then the state event corresponding to its stable trend is: the vehicle is stationary.

[0190] The trend of the optimized information is a rapid rise / fall followed by a stabilization, and the corresponding state event is: vehicle position adjustment.

[0191] The minor fluctuations are due to the driver getting on and off the vehicle.

[0192] In one embodiment of this specification, a rest threshold is defined. .

[0193] If continuous If all the changes in the difference satisfy the first change condition, then the current optimization information and the previous ones will be combined. The state event of the optimization information is marked as: vehicle enters. The first change conditions include: the current fluctuation state is "changing state", the current change trend is "upward trend", and the current emergency situation is "no emergency change found".

[0194] If continuous If all the changes in the difference satisfy the second change condition, then the current optimization information and the previous ones will be combined. The state event of the optimization information is marked as: vehicle departs. The second change condition includes: the current fluctuation state is "changing state", the current change trend is "downward trend", and the current emergency situation is "no emergency change found".

[0195] If continuous If all the changes in the difference satisfy the third change condition, then the current optimization information and the previous ones will be combined. The status event for each optimization information is marked as: vehicle position adjustment. Among them, The third set of conditions for change includes: the current fluctuation state is "changing state", the current trend is "upward trend", and the current emergency situation is "no sudden change found".

[0196] If continuous If all the changes in the difference satisfy the fourth change condition, then the current optimization information and the previous ones will be combined. The status event for each optimization information is marked as: vehicle position adjustment. Among them, The fourth condition for change includes: the current fluctuation state is "changing state", the current trend is "downward trend", and the current emergency situation is "no sudden change found".

[0197] If continuous If all the changes in the difference satisfy the fifth change condition, then the current optimization information and the previous ones will be combined. The state event for the optimization information is marked as: vehicle stationary. The fifth change condition includes: continuous. Each change difference rest threshold The current optimization information is not equal to 0, and the current fluctuation state is "stable state", the current change trend is "smooth fluctuation", and the current emergency situation is "no sudden change found".

[0198] Among them, continuous Each change difference is less than the resting threshold. This refers to: for the current optimization information To optimize information All optimization information between them, the difference in change of each optimization information is less than .

[0199] If continuous If all the changes in the difference satisfy the sixth change condition, then the current optimization information and the previous ones will be combined. The state event for the optimization information is marked as: driver getting on and off the vehicle. The sixth change condition includes: continuous... Each change difference rest threshold Furthermore, the current fluctuation state is "stable state", the current trend is "smooth fluctuation", and the current emergency situation is "no sudden change found".

[0200] After the marking is completed, a review can be performed based on the definition of each state event.

[0201] S42 identifies whether the current optimization information is interference data; if so, it removes the current optimization information.

[0202] In the trend analysis of step S3, there may be sudden changes, such as a brief slight drop in a continuous upward trend, or a brief and continuous small fluctuation (during the time difference between the front and rear wheels on the pound). These sudden changes are not the sawtooth details in step S2, and the relevant optimization information cannot be marked as events. Therefore, it is necessary to remove the interference from the optimization information in the above situations.

[0203] S5, based on the end of the acquisition event, summarizes all the optimization information and constructs a weight change curve with state event markers.

[0204] S51 generates a weight change curve based on the target object's departure operation and the optimized information set.

[0205] The departure operation based on the target object refers to the situation where the target object leaves the weighbridge and has been unloaded.

[0206] In one embodiment of this specification, a third sensor is installed at the departure edge of the weighbridge; when the third sensor cannot identify the target object during the departure process of the target object from the weighbridge, it is determined that the target object has completed the departure operation.

[0207] In another embodiment of this specification, if no third sensor is set or the sensor is offline, the signal that the weighbridge weight has decreased to zero in real time is used as the end.

[0208] At this point, the optimization information set includes all optimization information for the target object from the entry operation to the departure operation. Based on all the optimization information, a weight change curve for the target object is constructed.

[0209] S52 performs segmented optimization on the weight change curve based on the status events and acquisition time of the optimization information;

[0210] S521 combines the state events and the acquisition time to group the optimization information in the optimization information set, resulting in several state event groups;

[0211] S522 determines the stability result based on the relationship between the variance of the state event group and the stability threshold;

[0212] If the variance of a state event group is less than the stability threshold, the stability result of the state event group is considered stable.

[0213] If the variance of a state event group is greater than or equal to the stability threshold, the stability result of the state event group is considered unstable.

[0214] Based on the stability results, S523 smooths the weight change curve to obtain an optimized weight change curve;

[0215] For stable state event groups, a first smoothing process is performed; specifically, a simple averaging or linear interpolation method is used for smoothing.

[0216] For unstable state event groups, a second smoothing process is performed. Specifically, more complex filtering algorithms, such as Kalman filtering, are used to eliminate noise and interference.

[0217] S53 associates the event markers with the optimized weight change curve to obtain a weight change curve with event markers.

[0218] At the same time, the corresponding smoothed event markers are output and associated with the smoothed weight dynamic curve so that users can clearly understand the events and weight changes during the vehicle weighing process.

[0219] S54 acquires the weight change curve segment of the vehicle at rest, and determines the vehicle's weighing result from it.

[0220] This invention can determine the changes throughout the entire vehicle weighing process based on events, such as the start time of weighing, the total time taken, the highest value during adjustment, whether personnel get on or off the weighing after the vehicle stops, the time at which the vehicle contains or does not contain personnel, etc.

[0221] It can truly achieve unattended operation of the weighbridge weighing process and automatically capture / extract photos and videos corresponding to the required event points for archiving.

[0222] This invention acquires weight information in real time through step S1, performs average filtering on the weight information from S1 through step S2 to maintain fine granularity in the data set, analyzes the trend of the filtered data through step S3 to generate states, increasing the time interval granularity of the data set, removes interfering matching events from the state logic through step S4 to further increase the granularity of the data set, and further smooths the events through step S5 to obtain segmented results. Based on the above multi-level data processing (filtering → trend analysis → logic matching), noise suppression and detail preservation are balanced, completely restoring the entire weighing process curve and reducing errors and misjudgments.

[0223] Figure 2 This specification provides a schematic diagram of a condition analysis system for vehicle weighing, which includes:

[0224] The acquisition module 210 is used to acquire the current weight information in real time based on the triggering of acquisition events;

[0225] Optimization module 220 is used to obtain current optimization information corresponding to the current weight information based on the first sampling threshold;

[0226] The state analysis module 230 is used to perform trend analysis on the current optimization information and identify the current state corresponding to the current optimization information;

[0227] Event analysis and matching module 240 is used to match state events with the current state for the current optimization information;

[0228] The aggregation module 250 is used to aggregate all optimization information based on the end of the acquisition event and construct a weight change curve with status event markers.

[0229] Optionally, the optimization module 220 includes:

[0230] The first optimization submodule is used to construct a current optimization set by combining the current weight information and the first sampling threshold; wherein, the initial weight information is found according to the current weight information and the first sampling threshold; and the weight information between the initial weight information and the current weight information is summarized to obtain the current optimization set;

[0231] The second optimization submodule is used to determine whether the current optimization set needs to be updated by combining the correction factor and the preset correction conditions.

[0232] The third optimization submodule is used to rebuild the current optimization set if an update to the current optimization set is required.

[0233] The fourth optimization submodule is used to optimize the current weight information based on the current optimization set if no update is needed, thereby obtaining the current optimization information.

[0234] Optionally, the fourth optimization submodule includes:

[0235] The weight calculation unit is used to calculate the weight of each weight information in the current optimization set.

[0236] The update unit is used to update the current weight information by combining each weight information and its corresponding weight in the current optimization set, so as to obtain the current optimization information.

[0237] Optionally, the state analysis module 230 includes:

[0238] The correlation determination submodule is used to determine the correlation of the current optimization information based on the correlation sequence.

[0239] The trend analysis submodule is used to calculate the trend change rate of the current optimization information if the current optimization information is valid.

[0240] The state marking submodule is used to mark the current state corresponding to the current optimization information based on the trend change rate and the state judgment condition.

[0241] Optionally, the event analysis and matching module 240 includes:

[0242] The first matching submodule is used to determine the type of state event based on the change difference of the current optimization information and the current state;

[0243] The second matching submodule is used to identify whether the current optimization information is interference data. If so, the current optimization information is extracted.

[0244] Optionally, the summarizing module 250 includes:

[0245] The curve generation submodule is used to generate a weight change curve based on the weight information set for the departure operation of the target object.

[0246] The segmented optimization submodule is used to segment and optimize the weight change curve based on the status events and acquisition time of the weight information.

[0247] The association submodule is used to associate the event markers with the optimized weight change curve to obtain the weight change curve with event markers.

[0248] Optional segmented optimization submodules include:

[0249] A grouping unit is used to group the weight information in the weight information set by combining the state event and the acquisition time to obtain several state event groups;

[0250] The stability determination unit is used to determine the stability result based on the relationship between the variance of the state event group and the stability threshold.

[0251] The curve optimization unit is used to smooth the weight change curve based on the stability results to obtain an optimized weight change curve; wherein, a first smoothing process is performed on stable state event groups, and a second smoothing process is performed on unstable state event groups.

[0252] The functions of the system in this embodiment have been described in the above method embodiments. Therefore, for any parts not detailed in this embodiment, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0253] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0254] The following describes embodiments of the electronic device of the present invention, which can be considered as implementations of the physical form of the methods and apparatus embodiments of the present invention described above. Details described in the embodiments of the electronic device of the present invention should be considered as supplements to the methods or apparatus embodiments described above; details not disclosed in the embodiments of the electronic device of the present invention can be implemented with reference to the methods or apparatus embodiments described above.

[0255] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. Figure 3 The computer device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0256] like Figure 3 As shown, the computer device 300 of this exemplary embodiment is manifested in the form of a general-purpose data processing device. The components of the computer device 300 may include, but are not limited to, at least one processor 310, at least one memory 320, a network interface 330, a display unit 340, an input component 350, etc.

[0257] The memory 320 stores a computer-readable program, which may be source code or read-only program code. The program can be executed by the processor 310, causing the processor 310 to perform the steps of various embodiments of the present invention. For example, the processor 310 can perform actions such as... Figure 1 The steps are shown.

[0258] The memory 320 may include a readable medium in the form of volatile memory cells, such as random access memory (RAM) and / or cache memory cells, and may further include read-only memory (ROM). The memory 320 may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0259] It also includes a bus (not shown) that can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0260] Computer device 300 can also communicate with one or more external devices (e.g., keyboard, monitor, network device, Bluetooth device, etc.), enabling users to interact with computer device 300 via these external devices, and / or enabling computer device 300 to communicate with one or more other data processing devices (e.g., router, modem, etc.). This communication can be made via network interface 330, or via a network adapter with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet). The network adapter can communicate with other modules of computer device 300 via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in computer device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0261] Figure 4 This is a schematic diagram of a computer-readable medium embodiment of the present invention. Figure 4As shown, the computer program can be stored on one or more computer-readable media. The computer-readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. When the computer program is executed by one or more data processing devices, the computer-readable medium enables the implementation of the methods described above in this invention.

[0262] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described in this invention can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a data processing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to this invention.

[0263] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0264] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0265] In summary, the present invention can be implemented by methods, apparatus, electronic devices, or computer-readable media that execute computer programs. In practice, some or all of the functions of the present invention can be implemented using general-purpose data processing devices such as microprocessors or digital signal processors (DSPs).

[0266] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0267] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A condition analysis method for vehicle weighing, characterized in that, include: Based on the triggering of the acquisition event, the current weight information is obtained in real time; Based on the first sampling threshold, the current optimization information corresponding to the current weight information is obtained; Specifically, the current optimization set is constructed by combining the current weight information and the first sampling threshold; The range of the current optimization set is dynamically adjusted based on the correction factor; the current weight information is processed in conjunction with the weights to generate the current optimization information; where the correction factor is the variance of the current optimization set. Trend analysis is performed on the current optimization information to identify the current state corresponding to the current optimization information. The current state includes: current fluctuation state, current change trend, and current abrupt change. Specifically, correlation judgment is performed on the current optimization information. If the correlation of the current optimization information is valid, a current analysis set is constructed based on the current optimization information and a second sampling threshold. The trend information of the current optimization information is calculated. The difference between the current trend information and the previous trend information is taken as the trend change rate. The current fluctuation state is determined based on the relationship between the absolute value of the trend change rate and the intensity threshold. The current change trend is determined based on the relationship between the trend change rate and the direction threshold. The current abrupt change is determined based on the change dimension value of the current optimization information. The current state is combined with the current optimization information matching state event; Based on the end of the acquisition event, all optimization information is summarized, and a weight change curve with state event markers is constructed.

2. The method as described in claim 1, characterized in that, The step of obtaining the current optimization information corresponding to the current weight information based on the first sampling threshold includes: Combining the current weight information and the first sampling threshold, a current optimization set is constructed; wherein, the initial weight information is found based on the current weight information and the first sampling threshold; the weight information between the initial weight information and the current weight information is summarized to obtain the current optimization set; Based on the correction factor and preset correction conditions, determine whether the current optimization set needs to be updated; If so, then rebuild the current optimization set; Otherwise, the current weight information is optimized based on the current optimization set to obtain the current optimization information.

3. The method as described in claim 2, characterized in that, The step of optimizing the current weight information based on the current optimization set to obtain current optimization information includes: Calculate the weight of each piece of weight information in the current optimization set; By combining the weight information and corresponding weight of each item in the current optimization set, the current weight information is updated to obtain the current optimization information.

4. The method as described in claim 1, characterized in that, The step of performing trend analysis on the current optimization information and identifying the current state corresponding to the current optimization information includes: Based on the associated sequence, the current optimization information is relevance determined; If the current optimization information is valid, then calculate the trend change rate of the current optimization information; Based on the trend change rate and state judgment conditions, mark the current state corresponding to the current optimization information.

5. The method as described in claim 1, characterized in that, The event that combines the current state with the current optimization information matching state includes: Based on the change difference in the current optimization information and the current state, determine the type of the state event; Identify whether the current optimization information is interfering data; if so, remove the current optimization information.

6. The method as described in claim 1, characterized in that, Based on the end of the acquisition event, all optimization information is summarized, and a weight change curve with state event markers is constructed, including: Based on the departure operation of the target object, a weight change curve is generated according to the weight information set; The weight change curve is segmented and optimized based on the status events and acquisition time of the weight information. The event markers are associated with the optimized weight change curve to obtain the weight change curve with event markers.

7. The method as described in claim 6, characterized in that, The step of segmenting and optimizing the weight change curve based on the state events and acquisition time of the weight information includes: The weight information in the weight information set is grouped by combining the state events and the acquisition time to obtain several state event groups; The stability result is determined based on the relationship between the variance of the state event group and the stability threshold. Based on the stability results, the weight change curve is smoothed to obtain an optimized weight change curve; wherein, a first smoothing process is performed on stable state event groups, and a second smoothing process is performed on unstable state event groups.

8. A condition analysis system for vehicle weighing, characterized in that, include: The data acquisition module is used to obtain the current weight information in real time based on the triggering of acquisition events; The optimization module is used to obtain current optimization information corresponding to the current weight information based on the first sampling threshold. Specifically, the current optimization set is constructed by combining the current weight information and the first sampling threshold; The range of the current optimization set is dynamically adjusted based on the correction factor; the current weight information is processed in conjunction with the weights to generate the current optimization information; where the correction factor is the variance of the current optimization set. The state analysis module is used to perform trend analysis on the current optimization information and identify the current state corresponding to the current optimization information. The current state includes: current fluctuation state, current change trend, and current abrupt change. Specifically, it performs correlation judgment on the current optimization information. If the correlation of the current optimization information is valid, it constructs a current analysis set based on the current optimization information and a second sampling threshold. It calculates the trend information of the current optimization information. The difference between the current trend information and the previous trend information is used as the trend change rate. Based on the relationship between the absolute value of the trend change rate and the intensity threshold, it judges the current fluctuation state. Based on the relationship between the trend change rate and the direction threshold, it judges the current change trend. Based on the change dimension value of the current optimization information, it judges the current abrupt change. The event analysis and matching module is used to match the current state with the current optimization information and state events. The aggregation module is used to aggregate all optimization information based on the end of the acquisition event and construct a weight change curve with status event markers.

9. An electronic device, wherein, The electronic device includes: Processor; and, A memory storing computer-executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein, The computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method of any one of claims 1-7.

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