State analysis method and system for vehicle weighing

Abstract

The invention provides a state analysis method and system for vehicle weighing, and relates to the technical field of wagon balance weighing, and the method comprises the steps: obtaining the current weight information in real time based on the triggering of an acquisition event; obtaining current optimization information corresponding to the current weight information according to a first sampling threshold value; performing trend analysis on the current optimization information, and identifying a current state corresponding to the current optimization information; matching a state event for the current optimization information in combination with the current state; and on the basis of the ending of the acquisition event, summarizing all optimization information, and constructing a weight change curve with a state event mark, so as to completely present the full-period weight evolution from the moment when the vehicle gets on the scale to the moment when the vehicle gets off the scale, provide a high-accuracy result for single weighing, and improve the data reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of weighbridge weighing, in particular to a state analysis method and system for vehicle weighing, an electronic device, a computer readable storage medium and a computer program product. BACKGROUND

[0002] The weighbridge, also known as a truck scale, is a large scale set on the ground, usually used to weigh the load of a truck. It is the main weighing device for bulk cargo measurement. The weighbridge is provided with a sensor, which can detect, record and transmit real-time weight information on the weighbridge.

[0003] The current weighbridge is based on a strain type weighing sensor. The pressure signal exerted by the vehicle gravity is collected in real time, converted into an electrical signal and transmitted to the instrument system. Low-pass filters and other noise reduction processes are used to obtain a smooth signal curve, and the weighing value is locked and output.

[0004] These traditional technology applications can only filter high-frequency noise such as engine vibration, wind disturbance, etc. They are relatively low-level and basic, and have no processing logic for low-frequency human interference (such as drivers getting on and off the vehicle, rolling and sliding of goods or liquid), resulting in limited accuracy of the weighing result. The traditional technology application mainly focuses on the weight stable point at a certain moment, and cannot identify the continuous dynamic changes in the whole weighing cycle.

[0005] Therefore, a state analysis method and system for vehicle weighing, an electronic device, a computer readable storage medium and a computer program product are needed. SUMMARY

[0006] The present application provides a state analysis method and system for vehicle weighing, an electronic device, a computer readable storage medium and a computer program product for identifying continuous dynamic changes in the whole weighing cycle to improve the accuracy of the weighing result.

[0007] The state analysis method for vehicle weighing provided by the present application adopts the following technical solution, comprising: Based on the trigger of the collection event, the current weight information is acquired in real time; According to the first sampling threshold, the current optimization information corresponding to the current weight information is obtained; The current optimization information is analyzed to identify the current state corresponding to the current optimization information; The current state is matched with the state event for the current optimization information; Based on the end of the collection event, all optimization information is summarized to construct a weight change curve with a state event marker.

[0008] Optionally, the obtaining the current optimization information corresponding to the current weight information according to the first sampling threshold comprises: combining the current weight information and the first sampling threshold, constructing a current optimization set; wherein, according to the current weight information and the first sampling threshold, searching for initial weight information; and according to the initial weight information and the current weight information, collecting weight information to obtain the current optimization set; combining the correction factor and the preset correction condition, judging whether the current optimization set needs to be updated; if yes, re-construction of the current optimization set; otherwise, according to the current optimization set, optimizing the current weight information to obtain the current optimization information.

[0009] Optionally, the obtaining the current optimization information corresponding to the current weight information according to the first sampling threshold comprises: calculating the weight of each weight information in the current optimization set; combining each weight information in the current optimization set and the corresponding weight, updating the current weight information to obtain the current optimization information.

[0010] Optionally, the trend analysis of the current optimization information to identify the current state corresponding to the current optimization information comprises: according to the correlation sequence, performing correlation judgment on the current optimization information; if the current optimization information is valid, calculating the trend change rate of the current optimization information; based on the trend change rate and the state judgment condition, marking the current state corresponding to the current optimization information.

[0011] Optionally, the matching of the current state to the current optimization information comprises: according to the change difference of the current optimization information and the current state, judging the type of the state event; identifying whether the current optimization information is interference data, if yes, removing the current optimization information.

[0012] Optionally, the based on the end of the collection event, collecting all optimization information to construct a weight change curve with state event marking comprises: based on the driving-off operation of the target object, generating a weight change curve according to the weight information set; according to the state event of the weight information and the collection time, segmenting and optimizing the weight change curve; associating the event mark with the optimized weight change curve to obtain the weight change curve with event mark.

[0013] Optionally, the segmenting optimization of the weight change curve according to the state event and the collection time of the collection information comprises: grouping the weight information in the weight information set according to the state event and the collection time, to obtain a plurality of state event groups; determining a stability result according to the size relationship between the variance of the state event group and the stability threshold value; performing smoothing processing on the weight change curve based on the stability result, to obtain an optimized weight change curve; wherein, for a stable state event group, first smoothing processing is performed; and for an unstable state event group, second smoothing processing is performed.

[0014] The state analysis system for vehicle weighing provided in the application adopts the technical scheme as follows, comprising: a collection module, configured to obtain current weight information in real time based on triggering of a collection event; an optimization module, configured to obtain current optimization information corresponding to the current weight information according to a first sampling threshold value; a state analysis module, configured to perform trend analysis on the current optimization information, and identify a current state corresponding to the current optimization information; an event analysis and matching module, configured to match a state event for the current optimization information in combination with the current state; a summary module, configured to summarize all optimization information based on ending of the collection event, and construct a weight change curve with a state event marker.

[0015] Optionally, the optimization module comprises: a first optimization submodule, configured to construct a current optimization set in combination with the current weight information and the first sampling threshold value; wherein, initial weight information is found according to the current weight information and the first sampling threshold value; and weight information between the initial weight information and the current weight information is summarized to obtain the current optimization set; a second optimization submodule, configured to determine whether the current optimization set needs to be updated in combination with a correction factor and a preset correction condition; a third optimization submodule, configured to reconstruct the current optimization set if the current optimization set needs to be updated; a fourth optimization submodule, configured to optimize the current weight information according to the current optimization set to obtain the current optimization information if the current optimization set does not need to be updated.

[0016] Optionally, the fourth optimization submodule comprises: a weight calculation unit, configured to calculate the weight of each weight information in the current optimization set; An updating unit is configured to update the current weight information by combining each weight information and the corresponding weight in the current optimization set to obtain current optimization information.

[0017] Optionally, the state analysis module comprises: A relevance judgment submodule is configured to judge the relevance of the current optimization information according to the correlation sequence. A trend analysis submodule is configured to calculate a trend change rate of the current optimization information if the current optimization information is valid. A state marking submodule is configured to mark the current state corresponding to the current optimization information based on the trend change rate and the state judgment condition.

[0018] Optionally, the event analysis and matching module comprises: A first matching submodule is configured to judge the type of the state event according to the change difference of the current optimization information and the current state. A second matching submodule is configured to identify whether the current optimization information is interference data, and if so, to isolate the current optimization information.

[0019] Optionally, the summary module comprises: A curve generation submodule is configured to generate a weight change curve according to the weight information set based on the driving-off operation of the target object. A segmented optimization submodule is configured to segmentally optimize the weight change curve according to the state event of the weight information and the collection time. An association submodule is configured to associate the event mark with the optimized weight change curve to obtain a weight change curve with an event mark.

[0020] Optionally, the segmented optimization submodule comprises: A grouping unit is configured to group the weight information in the weight information set according to the state event and the collection time to obtain a plurality of state event groups. A stability judgment unit is configured to determine a stability result according to the size relationship between the variance of the state event group and the stability threshold. A curve optimization unit is configured to perform smoothing processing on the weight change curve based on the stability result to obtain an optimized weight change curve, wherein a first smoothing processing is performed on the stable state event group, and a second smoothing processing is performed on the unstable state event group.

[0021] The specification also provides an electronic device, wherein the electronic device comprises: a processor; and a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the above methods.

[0022] The specification also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs which, when executed by a processor, implement any of the above methods.

[0023] The specification also provides a computer program product, wherein the computer program product comprises computer programs / instructions which, when executed by a processor, implement any of the above methods.

[0024] In the present application, the current weight information is acquired in real time through triggering based on a collection event; the current optimization information corresponding to the current weight information is obtained according to a first sampling threshold; the current optimization information is analyzed for a trend to identify a current state corresponding to the current optimization information; the current state is combined to match a state event for the current optimization information; based on the end of the collection event, all optimization information is summarized to construct a weight change curve with a state event marker to completely present the full-cycle weight evolution from a vehicle scale to a scale to provide a high-accuracy result for single weighing and improve data reliability. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 A principle diagram of a state analysis method for vehicle weighing provided by an embodiment of the specification; Figure 2 A structure diagram of a state analysis system for vehicle weighing provided by an embodiment of the specification; Figure 3 A structure diagram of an electronic device provided by an embodiment of the specification; Figure 4 A principle diagram of a computer-readable medium provided by an embodiment of the specification. DETAILED DESCRIPTION

[0026] The following description is provided to disclose the present application so that those skilled in the art can implement the present application. The preferred embodiments in the following description are only examples, and other obvious modifications can be made by those skilled in the art. The basic principles of the present application defined in the following description can be applied to other embodiments, modifications, improvements, equivalents and other technical solutions without departing from the spirit and scope of the present application.

[0027] Exemplary embodiments of the present application will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, can be implemented in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the inventive concept to those skilled in the art. Like reference numerals refer to like elements throughout the specification. Repetitive descriptions of like elements will be omitted for sake of brevity.

[0028] In the case of a certain embodiment described in accordance with the technical concept of the present application, the features, structures, characteristics or other details described do not exclude that they can be combined in a suitable manner in one or more other embodiments.

[0029] In the description of the specific embodiments, the features, structures, characteristics or other details described are for the purpose of making the embodiments sufficiently understood by those skilled in the art. However, it does not exclude that one or more of the specific features, structures, characteristics or other details can not be practiced by those skilled in the art without the specific features, structures, characteristics or other details.

[0030] The flowcharts shown in the drawings are only exemplary illustrations, and do not necessarily include all the contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to the actual situation.

[0031] The block diagrams shown in the drawings are only functional entities, and do not necessarily have to correspond to physically independent entities. That is, these functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] The term "and / or" or "and / or" includes all combinations of any one or more of the associated listed items.

[0033] Figure 1 A schematic diagram of a state analysis method for vehicle weighing is provided for the embodiments of the present specification, which method comprises: S1, based on the triggering of the acquisition event, acquiring current weight information in real time; S2, according to a first sampling threshold, obtaining current optimization information corresponding to the current weight information; S3, performing trend analysis on the current optimization information to identify a current state corresponding to the current optimization information; S4, matching a state event to the current optimization information in combination with the current state; S5 based on the end of the collection event, all the optimization information is summarized, and the weight change curve with state event mark is constructed.

[0034] In the field of vehicle weighing, when a truck is weighed by a weighbridge, the common operation includes: after the front gate of the weighbridge is opened, the truck starts to slowly drive into the weighbridge, and as the truck gradually enters, the real-time weight of the weighbridge gradually increases until the truck completely drives into the weighbridge, at which time the weight value of the weighbridge reaches a peak.

[0035] During this process, due to the influence of vehicle driving and ground conditions, the reading of the weighbridge may fluctuate slightly, but overall it shows a steady upward trend. During the process of the truck completely stopping, the driver getting off and leaving the weighbridge area, the weight value of the weighbridge decreases after a small fluctuation and then tends to be stable, reflecting only the weight of the truck. After the weighing is completed, the driver gets on the truck again. As the weight of the truck increases, the weight value of the weighbridge increases again and finally remains stable. At this time, the gate of the weighbridge is opened, the truck starts to drive away from the weighbridge, and the weight value of the weighbridge gradually decreases as the truck drives away until it finally returns to zero, completing the entire weighing process and obtaining the weight change curve.

[0036] During the entire weighing process described above, a stable weight value after the driver gets off and leaves the weighbridge area is generally selected as the weight of the truck. However, due to vehicle / environmental dynamic interference, stable state misjudgment, etc., the accuracy of the weighing result is not high. Based on this, the present specification discloses a state analysis method for vehicle weighing, which specifically includes: S1 based on the triggering of the collection event, real-time acquisition of current weight information; S11 based on the driving-in operation of the target object, triggering the collection event; The target object refers to a vehicle that needs to be weighed.

[0037] The driving-in operation of the target object refers to the case where the target object drives towards the weighbridge without being weighed.

[0038] In an embodiment of the present specification, a camera is arranged at the driving-in edge of the weighbridge; when the camera recognizes the target object during the process of the target object driving towards the weighbridge, the collection event is triggered.

[0039] In another embodiment of the present specification, the vehicle information is read based on RFID identification technology, and when the vehicle identity is confirmed, the collection event is triggered.

[0040] In still another embodiment of the present specification, a radar sensor can also be arranged on the weighbridge; when it is monitored that the distance between the target object and the radar sensor is ≤ a preset monitoring threshold, the collection event is triggered.

[0041] In other embodiments of the present specification, a barrier gate can also be provided, and the collection event is triggered when the barrier gate is opened.

[0042] Based on the above operation, the target object triggers the collection event before being weighed.

[0043] If the above sensor cannot trigger, the collection is directly triggered by the real-time obtained weight increase signal of the weighbridge.

[0044] The collection event includes real-time and continuous weight information collection by the weighbridge. In another embodiment of the present specification, the collection event also includes constructing an initial complete information set .

[0045] S12 real-time acquisition of current weight information; After triggering the collection event, the current weight information is obtained according to the preset sampling interval. The weight information obtained at the current time is taken as the current weight information .

[0046] As preferred, the weight information is a weight signal with a time stamp.

[0047] S2 obtains current optimization information corresponding to the current weight information according to the first sampling threshold; S21 obtains the first sampling threshold; The first sampling threshold is used to determine the number of weight information in the current optimization set.

[0048] The first sampling threshold is initialized. In order to facilitate the optimization of weight information in the early stage of information collection, a smaller .

[0049] In subsequent operations, the first sampling threshold will be dynamically updated according to the size of the correction factor.

[0050] S22 constructs a current optimization set in combination with the current weight information and the first sampling threshold; The current optimization set includes a plurality of weight information . In the current optimization set, the weight information is arranged in the collection order.

[0051] S221 finds the initial weight information according to the current weight information ; Wherein, , is a positive integer. .

[0052] S222 aggregates the weight information between the initial weight information and the current weight information to obtain a current optimization set; the current optimization set ; for any weight information in the current optimization set , .

[0053] S23 optimizes the current optimization set; S231 calculates a correction factor of the current optimization set; In an embodiment of the present disclosure, the correction factor is a variance of the current optimization set.

[0054] Specifically, the correction factor . Wherein, the arithmetic mean of all weight information in the current optimization set, that is, .

[0055] S232 combines the correction factor with a preset correction condition to determine whether the current optimization set needs to be updated; if yes, step S22 is executed to rebuild the current optimization set; otherwise, step S24 is executed; According to the correction factor and the preset correction condition, it is possible to update the first sampling threshold; when the first sampling threshold changes, the range of weight information in the current optimization set will be affected. Therefore, when the first sampling threshold changes, the current optimization set needs to be updated, that is, S22 is executed again.

[0056] The preset correction condition ; Wherein, the greater the data fluctuation, the smaller the first sampling threshold, and the smaller the data fluctuation, the greater the first sampling threshold.

[0057] To be specific, if the correction factor is greater than an upper threshold , it means that the data fluctuation is too large, and in order to improve the sensitivity to recent changes, the first sampling threshold needs to be reduced, specifically, the minimum sampling threshold is taken as the new first sampling threshold, that is, .

[0058] If the correction factor is less than a lower threshold , it means that the data fluctuation is too small, and in order to increase the smoothing effect, the first sampling threshold needs to be increased, specifically, the maximum sampling threshold is taken as the new first sampling threshold, that is, .

[0059] If the correction factor is between the upper threshold and the lower threshold , that is, , it indicates that the fluctuation is moderate, and in order to balance the smoothness and sensitivity, the first sampling threshold may be dynamically adjusted, and the new first sampling threshold .

[0060] S24 optimizes the current weight information according to the current optimization set to obtain current optimization information.

[0061] S241 calculates the weight of each weight information in the current optimization set ; For the i-th weight information in the current optimization set , the corresponding weight , wherein is a smoothing coefficient.

[0062] S242 updates the current weight information by combining each weight information in the current optimization set and the corresponding weight to obtain the current optimization information.

[0063] The current optimization information .

[0064] In an embodiment of the present specification, the trend analysis is performed according to the improved adaptive weighted moving average (AWMA).

[0065] The present application dynamically adjusts the range of the current optimization set according to the correction factor; and processes the current weight information by combining the weight to generate the current optimization information, so that the curve corresponding to the data is smoother, the granularity is expanded, and the trend analysis in step S3 is facilitated.

[0066] In an embodiment of the present specification, the weight information and the corresponding optimization information can be stored at the same time, so as to facilitate the comparison of the difference between the weight information and the optimization information in the later period.

[0067] S3 performs trend analysis on the current optimization information to identify the current state corresponding to the current optimization information. S31 performs correlation judgment on the current optimization information according to the correlation sequence. S311 obtains the correlation sequence. The correlation sequence includes: the continuous optimization information before the current optimization information; and each optimization information is arranged according to the collection time.

[0068] S312 combines the current optimization information and the correlation sequence to calculate the correlation degree of the current optimization information; correlation degree .

[0069] If the correlation degree is less than or equal to the similarity threshold, it is determined that the correlation degree of the current optimization information and the historical optimization information is high, the correlation of the current optimization information is valid, and the current optimization information is transmitted to step S32 for processing.

[0070] If the correlation degree is greater than the similarity threshold, it is determined that the correlation degree of the current optimization information and the historical optimization information is low, the correlation of the current optimization information is invalid, and the current optimization information is not used for subsequent analysis.

[0071] The present specification continuously acquires newly generated dynamic weight data, and performs forward matching with previously collected data, compares the new data with historical data within a window, and verifies the validity of the new data by judging the correlation. By ensuring the continuity with the historical data, the reliability of the overall trend analysis and state detection is improved.

[0072] The current optimization information is added to the end of the correlation sequence, and the oldest optimization information is removed, to complete the update of the correlation sequence.

[0073] S313 adds the current optimization information to the first complete information set ; In order to facilitate the generation of the subsequent weight curve, the current optimization information is added to the first complete information set .

[0074] The first complete information set refers to an ordered set of optimization information acquired by the target object between triggering real-time collection events and acquiring the current optimization information , .

[0075] S32, if the current optimization information is valid, calculates the trend change rate of the current optimization information; S321 constructs a current analysis set according to the current optimization information and a second sampling threshold; The current analysis set includes a plurality of optimization information In the current analysis set, the optimization information is arranged in the collection order.

[0076] S321-1 finds the initial optimization information according to the current optimization information and the second sampling threshold ; The second sampling threshold is used to determine the number of optimization information in the current analysis set.

[0077] wherein, . .

[0078] S321-2 aggregates the optimization information between the initial optimization information and the current optimization information to obtain a current analysis set; the current analysis set .

[0079] For any optimization information in the current analysis set , .

[0080] S322 calculates trend information of the current optimization information according to the current analysis set; For the optimization information in the current analysis set, a weighted trend value is calculated to obtain current trend information; the trend information ; wherein, is a decay function, and . . is an index variable for summing, used to traverse the historical weight signal in the current analysis set, .

[0081] Therefore, the current trend information .

[0082] S323 obtains a trend change rate based on the difference between the current trend information and the previous trend information; S323-1 retrieves the previous trend information ; ; S323-2 takes the difference between the current trend information and the previous trend information as the trend change rate; that is, .

[0083] S33 marks a current state corresponding to the current optimization information based on the trend change rate and a state judgment condition.

[0084] The current state includes: a current fluctuation state, a current change trend, and a current mutation case.

[0085] S331 defines a strength threshold and a direction threshold ; S332 judges the current fluctuation state based on the size relationship between the absolute value of the trend change rate and the strength threshold . Specifically, if , the current fluctuation state is marked as "stable state"; If , the current fluctuation state is marked as "change state".

[0086] S333 determines the current change trend based on the size relationship between the trend change rate and the direction threshold value . Specifically, if , the current change trend is marked as "upward trend"; If , the current change trend is marked as "downward trend"; If , the current change trend is marked as "stable fluctuation".

[0087] S334 determines the current mutation situation based on the change dimension value of the current optimization information; The change difference value of the optimization information is the difference between the optimization information and its previous optimization information , that is = . The change absolute value of the optimization information

[0088] is the absolute value of the change difference value of the optimization information , that is, the change absolute value of the optimization information is .

[0089] The change dimension value of the optimization information includes the change absolute value of the last Φ optimization information ; includes the change absolute value of the optimization information , the change absolute value of the optimization information , the change absolute value of the optimization information ,..., and the change absolute value of the optimization information .

[0090] If the change dimension value of the current optimization information is greater than the burst risk factor , the current mutation situation of the current optimization information is a sudden change, and the current optimization information is marked as a sudden change; otherwise, the current mutation situation of the current optimization information is no sudden change.

[0091] In an embodiment of the present specification, the change dimension value of the optimization information includes the last three optimization information ​The absolute value of the change. The change dimension values ​​of the current optimization information include: 、 、 .like ,and ,and , then mark the weight burst; if ,or ,or , the current emergency situation is that no sudden changes are found.

[0092] This manual analyzes the trend change rate to obtain the current fluctuation status, current change trend and current emergency situation.

[0093] S4 combines the current state with the current optimization information matching state event; S41 based on current optimization information The difference in change and the current state, determine the type of state event; Types of status events include, but are not limited to: vehicle entering, vehicle leaving, vehicle stationary, vehicle position adjustment, driver getting on and off the vehicle, etc.

[0094] Among them, the change trend of the optimization information is continuous increase and then stable, and the corresponding state events are: vehicle entering (weighing); The trend of the optimization information is a continuous decrease followed by a steady state, and the corresponding status events are: vehicle leaves (unloading); The changing trends of the optimization information are continuous increase, stability, and continuous decrease, and the state event corresponding to the stable changing trend is: the vehicle is stationary.

[0095] The change trend of the optimization information is rapid rise / fall and then stabilizes, and the corresponding status event is: vehicle position adjustment.

[0096] Small fluctuations are caused by drivers getting on and off the vehicle.

[0097] In one embodiment of the present specification, a static threshold is defined as .

[0098] If continuous If the difference in changes meets the first change condition, the current optimization information and the previous The state event of the optimization information is marked as: vehicle entering. The first change condition includes: the current fluctuation state is "changing state", the current change trend is "upward trend", and the current emergency situation is "no emergency change found".

[0099] If continuous If the difference in changes meets the second change condition, the current optimization information and the previous The state event of the optimal information is marked as: vehicle leaving. The second change condition includes: the current fluctuation state is "change state", the current change trend is "downward trend", and the current sudden change is "no sudden change found".

[0100] If the continuous change difference values all meet the third change condition, the state event of the current optimal information and the previous optimal information is marked as: vehicle position adjustment. Wherein, The third change condition includes: the current fluctuation state is "change state", the current change trend is "upward trend", and the current sudden change is "no sudden change found".

[0101] If the continuous change difference values all meet the fourth change condition, the state event of the current optimal information and the previous optimal information is marked as: vehicle position adjustment. Wherein, The fourth change condition includes: the current fluctuation state is "change state", the current change trend is "downward trend", and the current sudden change is "no sudden change found".

[0102] If the continuous change difference values all meet the fifth change condition, the state event of the current optimal information and the previous optimal information is marked as: vehicle stationary. The fifth change condition includes: the continuous change difference values all The stationary threshold , the current optimal information ≠ 0, and the current fluctuation state is "stable state", the current change trend is "smooth fluctuation", and the current sudden change is "no sudden change found".

[0103] Wherein, the continuous change difference values are all less than the stationary threshold It means: for all the optimal information between the current optimal information and the optimal information , the change difference value of each optimal information is less than .

[0104] If the continuous change difference values all meet the sixth change condition, the state event of the current optimal information and the previous optimal information is marked as: driver getting on and off. The sixth change condition includes: the continuous change difference values all The stationary threshold , and the current fluctuation state is "stable state", the current change trend is "smooth fluctuation", and the current sudden change is "no sudden change found".

[0105] After the marking is completed, the review can also be performed based on the definition of each state event.

[0106] S42 identifies whether the current optimization information is interference data, and if so, the current optimization information is isolated; In the trend analysis of step S3, there may be a sudden change result, such as a short and small downward trend in a continuous upward trend, or a short and small fluctuation (during the period when the front and rear wheels are weighed), which is not a sawtooth detail in step S2, and the related optimization information cannot be event marked, therefore, the optimization information of the above situation needs to be isolated from interference.

[0107] S5 based on the end of the collection event, all optimization information is summarized to construct a weight change curve with state event marking.

[0108] S51 based on the driving-off operation of the target object, a weight change curve is generated according to the optimization information set; The driving-off operation of the target object refers to the case that the target object drives off the weighbridge and has already weighed off.

[0109] In an embodiment of the present specification, a third sensor is arranged at the driving-off edge of the weighbridge; during the process that the target object drives off the weighbridge, when the third sensor cannot identify the target object, it is determined that the target object has completed the driving-off operation.

[0110] In another embodiment of the present specification, if there is no third sensor or the sensor is in an offline state, the signal that the weight of the weighbridge is reduced to zero is obtained in real time as the end.

[0111] At this time, the optimization information set includes all optimization information of the target object from the driving-in operation to the driving-off operation. According to all the optimization information, a weight change curve of the target object is constructed.

[0112] S52 segments and optimizes the weight change curve according to the state event and collection time of the optimization information; S521 groups the optimization information in the optimization information set according to the state event and collection time to obtain a plurality of state event groups; S522 determines the stability result according to the size relationship between the variance of the state event group and the stability threshold value; If the variance of the state event group is < the stability threshold value, it is determined that the stability result of the state event group is stable; If the variance of the state event group is ≥ the stability threshold value, it is determined that the stability result of the state event group is unstable.

[0113] S523 smoothing the weight change curve based on the stability result to obtain an optimized weight change curve; For the stable state event group, first smoothing is performed; specifically, simple average or linear interpolation method is used for smoothing. For the unstable state event group, second smoothing is performed; specifically, more complex filtering algorithm, such as Kalman filtering, is used to eliminate noise and interference.

[0114] S53 associates the event mark with the optimized weight change curve to obtain a weight change curve with event mark.

[0115] Meanwhile, the corresponding smoothed event mark is output, and the event mark is associated with the smoothed weight dynamic curve, so that the user can clearly understand the events and weight changes in the vehicle weighing process.

[0116] S54 obtains a weight change curve segment of the vehicle at rest, and determines the weighing result of the vehicle from the weight change curve segment.

[0117] According to the events, the present application can obtain the changes in the whole vehicle weighing process, such as the time when the weighing starts, the time of the whole process, the highest value in the adjustment process, whether the personnel get on or off the scale after stopping, the time, the stable weight of the vehicle with or without personnel, etc.

[0118] The unattended weighing process of the ground scale can be truly achieved, and the required event point corresponding photos and videos are automatically captured / intercepted as archives.

[0119] The present application obtains the weight information in real time through step S1, performs average filtering on the weight information of S1 through step S2, so that the set maintains fine granularity; through step S3, the filtered data is analyzed for change trend to generate a state, so that the time interval granularity of the set is larger; through step S4, the state logic is separated from interference matching events, and the set granularity is further increased; through step S5, the events are further smoothed to obtain a segmented result. Based on the above multi-level data processing (filtering→trend analysis→logic matching), noise suppression and detail retention are balanced, the whole process curve of weighing is completely restored, and errors and misjudgments are reduced.

[0120] Figure 2 A structural diagram of a state analysis system for vehicle weighing is provided for the embodiments of the present application, and the system comprises: The acquisition module 210 is used for acquiring current weight information in real time based on the triggering of the acquisition event; The optimization module 220 is used for obtaining current optimization information corresponding to the current weight information according to a first sampling threshold; The state analysis module 230 is configured to perform trend analysis on the current optimization information, and identify a current state corresponding to the current optimization information. The event analysis matching module 240 is configured to match a state event for the current optimization information in combination with the current state. The summary module 250 is configured to summarize all the optimization information based on an end of the collection event, and construct a weight change curve with a state event label.

[0121] Optionally, the optimization module 220 comprises: The first optimization submodule is configured to construct a current optimization set in combination with the current weight information and the first sampling threshold; wherein initial weight information is found according to the current weight information and the first sampling threshold; and weight information between the initial weight information and the current weight information is summarized to obtain the current optimization set. The second optimization submodule is configured to determine whether the current optimization set needs to be updated in combination with a correction factor and a preset correction condition. The third optimization submodule is configured to reconstruct the current optimization set if the current optimization set needs to be updated. The fourth optimization submodule is configured to optimize the current weight information according to the current optimization set to obtain the current optimization information if the current optimization set does not need to be updated.

[0122] Optionally, the fourth optimization submodule comprises: The weight calculation unit is configured to calculate a weight of each weight information in the current optimization set. The update unit is configured to update the current weight information in combination with each weight information in the current optimization set and a corresponding weight to obtain the current optimization information.

[0123] Optionally, the state analysis module 230 comprises: The correlation judgment submodule is configured to perform correlation judgment on the current optimization information according to a correlation sequence. The trend analysis submodule is configured to calculate a trend change rate of the current optimization information if the current optimization information is valid. The state labeling submodule is configured to label a current state corresponding to the current optimization information based on the trend change rate and a state judgment condition.

[0124] Optionally, the event analysis matching module 240 comprises: The first matching submodule is configured to determine a type of the state event according to a change difference of the current optimization information and the current state. The second matching sub-module is configured to identify whether the current optimization information is interference data, and if so, to isolate the current optimization information.

[0125] Optionally, the aggregation module 250 comprises: The curve generation sub-module is configured to generate a weight change curve based on the weight information set according to the departure operation of the target object. The segmented optimization sub-module is configured to perform segmented optimization on the weight change curve according to the state events and the collection times of the weight information. The correlation sub-module is configured to correlate the event markers with the optimized weight change curve to obtain the weight change curve with event markers.

[0126] Optionally, the segmented optimization sub-module comprises: The grouping unit is configured to group the weight information in the weight information set according to the state events and the collection times to obtain a plurality of state event groups. The stability judgment unit is configured to determine a stability result according to the size relationship between the variance of the state event group and a stability threshold. The curve optimization unit is configured to perform smoothing processing on the weight change curve based on the stability result to obtain an optimized weight change curve, wherein a first smoothing processing is performed on a stable state event group, and a second smoothing processing is performed on an unstable state event group.

[0127] The functions of the system of the embodiments of the present application have been described in the above method embodiments, and thus the descriptions of the present embodiments are not detailed, and the relevant descriptions in the foregoing embodiments can be referred to, and will not be repeated here.

[0128] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product in the form of one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0129] The electronic device embodiments of the present application are described below, which can be regarded as the physical form of the embodiments of the above-mentioned method and device embodiments of the present application. The details described in the electronic device embodiments of the present application should be regarded as a supplement to the above-mentioned method or device embodiments; the details not disclosed in the electronic device embodiments of the present application can be realized by referring to the above-mentioned method or device embodiments.

[0130] Figure 3A structural schematic diagram of an electronic device is provided for an embodiment of the present specification. Figure 3 The displayed computer device is merely an example and should not bring any limitation to the function and use range of the embodiments of the present application.

[0131] As shown in Figure 3 The computer device 300 of the example embodiment is in the form of a general-purpose data processing device. The components of the computer device 300 can 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, and the like.

[0132] The memory 320 stores a computer readable program, which can be a source program or a code only readable program. The program can be executed by the processor 310, so that the processor 310 performs the steps of various embodiments of the present application. For example, the processor 310 can perform the steps as shown in Figure 1

[0133] The memory 320 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) and / or a cache memory, and can further include a read only memory (ROM). The memory 320 can also include a program / utility having a set (at least one) of program modules that include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or a combination thereof can include implementation of a network environment.

[0134] Also included are buses (not shown), which can be one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or local bus using any of a variety of bus structures, and the like.

[0135] ​The computer device 300 can also communicate with one or more external devices such as a keyboard, a pointing device, a display, a network device, a Bluetooth device, etc. through a communication interface 330. The communication interface 330 enables communication over the communication network 340 with other data processing systems or devices. The communication interface 330 can also include a modem, a network adapter, a Bluetooth device, an infrared device, a wireless device, or a cable, and can be implemented with one or more communication buses or communication channels, such as an Ethernelt, a Token Ring, and / or a Fiber Channel, using a variety of communications protocols as appropriate. The communication interface 330 can be used to enable the computer device 300 to communicate with one or more other data processing systems or devices, such as a router, a modem, a server, a bridge, a switch, or other communication device using an appropriate communications protocol. Such communication can occur across a network, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, through a communication adapter.

[0136] Figure 4 is a schematic diagram of a computer readable medium embodiment of the present application. As shown in Figure 4 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 electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. The computer readable medium enables the above-described method of the present application when the computer program is executed by one or more data processing devices.

[0137] From the above description of the embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present application can be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a number of instructions to enable a data processing device (which can be a personal computer, a server, or a network device, etc.) to perform the above-described method according to the present application.

[0138] The computer readable storage medium can include data signals on a carrier wave modulated or otherwise propagated on a baseband or a carrier frequency with a transmitter of a computing system and captured, detected or otherwise interpreted by an interpreter of a computing system. As used herein a computer readable storage medium or computer readable medium includes any medium that provides (i.e., stores and / or transmits) data or instructions for use by or in connection with the instruction execution system, apparatus, or device. The computer readable storage medium or computer readable medium can be any available medium or means as a non-exhaustive list of which include a storage device and / or a propagation medium. The computer readable storage medium or computer readable medium can be a computer readable storage medium or computer readable medium that is non-transitory. A non-transitory computer readable storage medium or computer readable medium excludes propagated signals per se.

[0139] Program code embodied on a computer readable storage medium can be transmitted using any apparatus adapted to transmit such a program code, including a transmitter, etc. As used herein, programs can be realized in (a) source code, (b) object code, or (c) an intermediate form of the programs. The programs, of this application can be implemented in a standardized or proprietary language, including C, C++, Java, assembly language, etc. Even though described in connection with one or more embodiments, as will become clear from the discussion, many modifications, enhancements, alternatives, improvements, and tool extensions of, or to, the programs can occur to those skilled in the art from the description herein.

[0140] In view of the above, the present application can be implemented in a method, apparatus, electronic device, or computer readable medium for performing computer programs. Some or all of the functions of the present application can be implemented in practice using a general-purpose data processing device such as a microprocessor or a digital signal processor (DSP).

[0141] If the technical solution of the present application involves personal information, the product applying the technical solution of the present application has been explicitly informed of the personal information processing rules before processing the personal information, and has obtained the personal independent consent. If the technical solution of the present application involves sensitive personal information, the product applying the technical solution of the present application has obtained the personal independent consent before processing the sensitive personal information, and at the same time meets the requirement of "explicit consent". For example, at the personal information collection device such as camera, a clear and prominent mark is set to inform that it has entered the personal information collection range and will collect personal information. If the individual voluntarily enters the collection range, it is considered to agree to collect personal information. Or on the device for processing personal information, through the pop-up information or by asking the individual to upload his personal information, the individual's authorization is obtained under the condition of using obvious mark / information to inform the individual of the personal information processing rules. The personal information processing rules can include personal information processor, personal information processing purpose, processing method and personal information type, etc.

[0142] The above specific embodiments further illustrate the purpose, technical solution and beneficial effects of the present application. It should be understood that the present application is not inherently related to any specific computer, virtual device or electronic equipment, and various general-purpose devices can also implement the present application. The above is only a specific embodiment of the present application and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A state analysis method for vehicle weighing, characterized by, The method comprises the following steps: Real-time acquisition of current weight information based on a collection event trigger; According to a first sampling threshold, obtain current optimization information corresponding to the current weight information; Trend analysis of the current optimization information to identify the current state corresponding to the current optimization information; Matching state events to 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.

2. The method of claim 1, wherein, According to a first sampling threshold, obtain current optimization information corresponding to the current weight information, comprising: Based on the current weight information and the first sampling threshold, a current optimization set is constructed; wherein, the initial weight information is found according to 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; Combined with the correction factor and the preset correction condition, it is judged whether the current optimization set needs to be updated; If yes, the current optimization set is reconstructed; Otherwise, the current weight information is optimized according to the current optimization set to obtain the current optimization information.

3. The method of claim 2, wherein, According to the current optimization set, the current weight information is optimized to obtain the current optimization information, comprising: Calculate the weight of each weight information in the current optimization set; Combined with each weight information and the corresponding weight in the current optimization set, the current weight information is updated to obtain the current optimization information.

4. The method of claim 1, wherein, The trend analysis of the current optimization information to identify the current state corresponding to the current optimization information, comprising: According to the correlation sequence, the correlation of the current optimization information is judged; If the current optimization information is valid, the trend change rate of the current optimization information is calculated; Based on the trend change rate and the state judgment condition, the current state corresponding to the current optimization information is marked.

5. The method of claim 1, wherein, The matching state events to the current optimization information based on the current state, comprising: According to the change difference of the current optimization information and the current state, the type of state event is judged; Identify whether the current optimization information is interference data, if yes, the current optimization information is isolated.

6. The method of claim 1, wherein, Based on the end of the collection event, all optimization information is summarized to construct a weight change curve with state event markers, comprising: Based on the driving off operation of the target object, a weight change curve is generated according to the weight information set; According to the state event of the weight information and the collection time, the weight change curve is segmented and optimized; The event markers are associated with the optimized weight change curve to obtain the weight change curve with event markers.

7. The method of claim 6, wherein, According to the state event of the collection information and the collection time, the weight change curve is segmented and optimized, comprising: According to the state event and the collection time, the weight information in the weight information set is grouped to obtain a plurality of state event groups; According to the size relationship between the variance of the state event group and the stability threshold, the stability result is determined; Based on the stability result, the weight change curve is smoothed to obtain an optimized weight change curve; wherein, for a stable state event group, first smoothing processing is performed; and for an unstable state event group, second smoothing processing is performed.

8. A condition analysis system for vehicle weighing, characterized by The method comprises: The acquisition module is configured to acquire current weight information in real time based on a trigger of an acquisition event; The optimization module is configured to obtain current optimization information corresponding to the current weight information according to a first sampling threshold; The state analysis module is configured to perform trend analysis on the current optimization information to identify a current state corresponding to the current optimization information; The event analysis and matching module is configured to match a state event for the current optimization information in combination with the current state; The summary module is configured to summarize all optimization information based on an end of the acquisition event to construct a weight change curve with a state event label.

9. An electronic device, comprising: The electronic device comprises: a processor; and a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of 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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