State detection method and device of weighing equipment, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a state detection method and device of weighing equipment, a storage medium and electronic equipment, and the method comprises the steps: carrying out the following state detection operation on a first weighing equipment combination, the first weighing device combination comprises a first weighing device and a second weighing device; the first vehicle weight, detected by the first weighing device, of each target vehicle in the group of target vehicles and the second vehicle weight, detected by the second weighing device, of each target vehicle are obtained; performing normal distribution fitting on the target weight difference set to obtain a fitted normal distribution parameter value; and performing state detection on the first weighing equipment combination by comparing the fitted normal distribution parameter value with the target normal distribution parameter value to obtain a state detection result, thereby realizing real-time detection of the state of the weighing equipment.

Description

Technical Field

[0001] The present application relates to the technical field of dynamic weighing, and in particular, to a method and device for detecting the state of a weighing device, a storage medium, and an electronic device. Background Art

[0002] Dynamic weighing technology is now widely used in overloading control at entrances, detection at fixed overloading control stations, source supervision, and non-site law enforcement. Among them, the dynamic vehicle scale used for weighing is a key device.

[0003] A dynamic vehicle scale can only be used after passing metrological verification, and the metrological verification period is generally half a year or one year; moreover, the vehicle weight values actually obtained by the dynamic vehicle scale are all measurement values output by an automatic weighing instrument, and there is no true value of the vehicle weight for comparison. Therefore, due to the lack of reference standard data, it is impossible to accurately, effectively, and timely monitor the state of the weighing device, which will lead to untimely discovery and handling of problems with the weighing device, resulting in inaccurate vehicle weighing.

[0004] It can be seen that the state detection method of the weighing device in the related art has the problem of untimely state detection of the weighing device due to the lack of reference standard data. Summary of the Invention

[0005] The embodiments of the present application provide a method and device for detecting the state of a weighing device, a storage medium, and an electronic device, so as to at least solve the problem of poor real-time performance of the state detection of the weighing device in the state detection method of the weighing device in the related art due to the lack of reference standard data.

[0006] According to one aspect of the embodiments of the present application, a method for detecting the state of a weighing device is provided, including: performing the following state detection operations on a first weighing device combination, where the first weighing device combination includes a first weighing device and a second weighing device: obtaining the first vehicle weight of each target vehicle in a group of target vehicles detected by the first weighing device, and the second vehicle weight of each target vehicle detected by the second weighing device; performing a normal distribution fitting on a set of target weight differences to obtain the fitted normal distribution parameter values, where each target weight difference in the set of target weight differences is the difference between the first vehicle weight and the second vehicle weight of the same target vehicle in the group of target vehicles; performing a state detection on the first weighing device combination by comparing the fitted normal distribution parameter values with target normal distribution parameter values to obtain a state detection result, where the target normal distribution parameter values are preset normal distribution parameter values obtained by performing a normal distribution fitting on a set of reference weight differences, and each reference weight difference in the set of reference weight differences is the difference between the first vehicle weight and the second vehicle weight of the same reference vehicle in a group of reference vehicles detected by the first weighing device and the second weighing device.

[0007] According to another aspect of the embodiments of the present application, a device for detecting the state of a weighing device is further provided, including: an execution unit, configured to perform the following state detection operations on a first weighing device combination, where the first weighing device combination includes a first weighing device and a second weighing device: obtaining the first vehicle weight of each target vehicle in a group of target vehicles detected by the first weighing device, and the second vehicle weight of each target vehicle detected by the second weighing device; performing a normal distribution fitting on a set of target weight differences to obtain the fitted normal distribution parameter values, where each target weight difference in the set of target weight differences is the difference between the first vehicle weight and the second vehicle weight of the same target vehicle in the group of target vehicles; performing a state detection on the first weighing device combination by comparing the fitted normal distribution parameter values with target normal distribution parameter values to obtain a state detection result, where the target normal distribution parameter values are preset normal distribution parameter values obtained by performing a normal distribution fitting on a set of reference weight differences, and each reference weight difference in the set of reference weight differences is the difference between the first vehicle weight and the second vehicle weight of the same reference vehicle in a group of reference vehicles detected by the first weighing device and the second weighing device.

[0008] According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored. Wherein, the computer program is configured to execute the above-mentioned information detection method when running.

[0009] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. Wherein, the above-mentioned processor executes the above-mentioned information detection method through the computer program.

[0010] In the embodiments of the present application, the first vehicle weight and the second vehicle weight of each target vehicle in the same group of target vehicles detected by the first weighing device and the second weighing device are obtained respectively. Thus, the vehicle weight detection results of different weighing devices for the same group of target vehicles can be obtained; the target weight difference set of the differences between the first vehicle weight and the second vehicle weight of each target vehicle in a group of target vehicles is subjected to normal distribution fitting to obtain the fitted normal distribution parameter values. Thus, by subtracting the first vehicle weight from the second vehicle weight, the true weight value of each target vehicle can be removed, and only the difference of the measurement error is retained; the normal distribution parameter values of the target weight difference set are compared with the target normal distribution parameter values of the reference weight difference set to determine the state detection result of the first weighing device combination. Thus, according to the first weighing device combination, the corresponding target normal distribution parameter values for reference are provided, realizing the real-time detection of the state of the weighing device, and solving the problem that the state detection method of the weighing device in the related art is not timely due to the lack of reference standard data. Description of the Drawings

[0011] The drawings here are incorporated into the specification and constitute a part of this specification, showing the embodiments in line with the present application, and are used together with the specification to explain the principles of the present application.

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 It is a flowchart of a method for detecting the state of a weighing device according to an embodiment of the present application;

[0014] Figure 2 It is a flowchart of another method for detecting the state of a weighing device according to an embodiment of the present application;

[0015] Figure 3It is a schematic flowchart of another method for detecting the state of a weighing device according to an embodiment of the present application;

[0016] Figure 4 It is a schematic flowchart of another method for detecting the state of a weighing device according to an embodiment of the present application;

[0017] Figure 5 It is a schematic flowchart of another method for detecting the state of a weighing device according to an embodiment of the present application;

[0018] Figure 6 It is a schematic flowchart of another method for detecting the state of a weighing device according to an embodiment of the present application;

[0019] Figure 7 It is a structural block diagram of an optional state detection device for a weighing device according to an embodiment of the present application;

[0020] Figure 8 It is a structural block diagram of a computer system of an optional electronic device according to an embodiment of the present application. Detailed implementation manners

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] Dynamic weighing technology is now widely used in overloading control at entrances, detection at fixed overloading control stations, source supervision, and non-site law enforcement. Among them, the dynamic vehicle scale used for weighing is a key device. The dynamic vehicle scale can only be used after passing the metrological verification, and the metrological verification cycle is generally half a year or one year. However, there is no good monitoring method to judge the usage status of the dynamic vehicle scale during two adjacent verification cycles. Especially now, the number of unmanned overloading control stations is increasing continuously. Without an effective real-time monitoring method, problems may be discovered and processed in a timely manner, resulting in inaccurate vehicle weighing.

[0024] In related technologies, although the dynamic vehicle scale has regular metrological verification, there is no method for detecting the status of a weighing device during the verification cycle that can automatically and timely discover problems. And since the vehicle weight values actually obtained by the weighing device are all measurement values output by the automatic scale, there is no true value of the vehicle weight for comparison. Therefore, there is no reference standard data currently, resulting in the inability to accurately, effectively, and timely monitor the real-time status of the weighing device.

[0025] In recent years, due to the strong promotion of overloading control capacity building, the number of overloading control stations has been increasing continuously, and data networking and sharing have been achieved. The embodiment of this application can timely warn of problematic weighing devices (such as dynamic vehicle scales) through the method of comparative analysis of networked data at stations.

[0026] To solve at least part of the above problems, the embodiment of this application matches through time and license plate based on networked data, finds the passing vehicle data of the same target vehicle passing through two overloading control stations (i.e., weighing devices), and forms a data set; and introduces the concepts of standard deviation and mean, and uses the standard deviation and mean of the difference in weighing errors of the matching data at the two stations for a period of time after verification as a reference basis (here, the true weight of the vehicle that is difficult to measure is removed through the weighing difference); calculates the changes in the standard deviation and mean of the weighing differences of the matching data at the two stations at different times in real time and compares them with the reference values. When a deviation occurs, problems can be discovered and warned.

[0027] The embodiment of this application provides a method and device for detecting the status of a weighing device, a storage medium, and an electronic device, realizing the real-time detection of the status of the weighing device. The following describes the exemplary application of the electronic device provided by the embodiment of this application. Optionally, the above method for detecting the status of the weighing device can be executed alone by a processing device (such as a terminal or a server), or jointly executed by the weighing device and the processing device, or executed by other devices other than the weighing device and the processing device.

[0028] As an optional implementation manner, taking the processing device executing the method for detecting the status of the weighing device in this embodiment as an example. As Figure 1As shown, the process of the state detection method for the above weighing device may include the following steps.

[0029] In step S102, perform the following state detection operations on the first weighing device combination, where the first weighing device combination includes a first weighing device and a second weighing device:

[0030] Obtain the first vehicle weight of each target vehicle in a group of target vehicles detected by the first weighing device, and the second vehicle weight of each target vehicle detected by the second weighing device;

[0031] Perform a normal distribution fitting on the set of target weight differences to obtain the fitted normal distribution parameter values, where each target weight difference in the set of target weight differences is the difference between the first vehicle weight and the second vehicle weight of a target vehicle in a group of target vehicles;

[0032] Perform a state detection on the first weighing device combination by comparing the fitted normal distribution parameter values with the target normal distribution parameter values to obtain a state detection result, where the target normal distribution parameter values are preset normal distribution parameter values obtained by performing a normal distribution fitting on a set of reference weight differences, and each reference weight difference in the set of reference weight differences is the difference between the first vehicle weight of a reference vehicle in a group of reference vehicles detected by the first weighing device and the second vehicle weight of the same reference vehicle detected by the second weighing device.

[0033] In an actual application scenario, since it is difficult to accurately measure the true vehicle weight of the target vehicle, in the embodiments of the present application, by subtracting the true vehicle weight of the target vehicle, the difference between the first vehicle weight and the second vehicle weight of each target vehicle in a group of target vehicles detected by the first weighing device and the second weighing device in the first weighing device combination is calculated to obtain the target weight difference.

[0034] Here, the target weight difference is the error difference between the first weighing device and the second weighing device when detecting the same target vehicle.

[0035] In the related art, the difficulty of the state detection method for weighing devices lies in that the true weight of the vehicle cannot be obtained in real time, that is, the weighing device (for example, a dynamic vehicle scale) outputs a measured weight, as shown in reference formula (1):

[0036] er(i) = y(i) - h(i) × x(i) (1)

[0037] Among them, er(i) represents the measurement error of the i-th weighing device for detecting the target vehicle, y(i) represents the measured weight output by the i-th weighing device, h(i)×x(i) represents the true weight of the target vehicle, h(i) represents the gain of the weighing device (weighing sensor), and x(i) represents the ideal weighing signal corresponding to the i-th weighing device.

[0038] Since the true weight of the same weighed object (i.e., the target vehicle) matched by two weighing devices is the same, it can be canceled by subtraction; in the embodiment of the present application, the first vehicle weight and the second vehicle weight of the same target vehicle are subtracted to obtain the error difference of the same vehicle (i.e., the target weight difference), referring to formulas (2)-(3):

[0039] er(i,j) = er(i) - er(j) = (y(i) - h(i)×x(i)) - (y(j) - h(j)×x(j)) (2)

[0040] er(i,j) = y(i) - y(j) (3)

[0041] Among them, er(i,j) represents the error difference (i.e., the target weight difference) of the i-th weighing device and the j-th weighing device for detecting the same target vehicle, er(i) represents the measurement error of the i-th weighing device for detecting the target vehicle, er(j) represents the measurement error of the j-th weighing device for detecting the target vehicle, y(i) represents the measured weight output by the i-th weighing device of the target vehicle, and y(j) represents the measured weight output by the j-th weighing device of the target vehicle; it can be seen that the true weight of the target vehicle has been canceled out after subtraction, thus eliminating the dependence on the true weight of the target vehicle, enabling the state of the weighing sensor to be detected based on the measured weight.

[0042] At this time, the target weight difference of the same target vehicle is the subtraction of the measured weight output by the i-th weighing device and the measurement error of the j-th weighing device for detecting the target vehicle, which is a value that can be obtained in real time. In this way, the effect of real-time monitoring can be achieved by comparing whether the normal distribution of a set of target weight differences of a group of target vehicles meets the requirements.

[0043] That is, the normal distribution parameter values obtained by fitting the normal distribution of the target weight difference set are compared with the target normal distribution parameter values preset by fitting the normal distribution of the reference weight difference set to determine the state detection result of the first weighing device combination.

[0044] Here, the target weight difference set means that each target weight difference in the target weight difference set is the difference between the first vehicle weight of a target vehicle in a group of target vehicles and the second vehicle weight of the same target vehicle.

[0045] Among them, the status detection result is used to indicate that the first weighing device combination (including the first weighing device and the second weighing device) is in a normal state or an abnormal state;

[0046] For example, when the first weighing device combination is in a normal state, it means that both the first weighing device and the second weighing device can accurately measure the weight of the passing vehicles (the measurement error is less than the preset threshold); when the first weighing device combination is in an abnormal state, it means that there is an abnormal device among the first weighing device and the second weighing device that cannot accurately measure the weight of the passing vehicles (the measurement error is greater than the preset threshold).

[0047] Through the embodiments of the present application, the difference in the measured weight data of each target vehicle in a group of target vehicles measured by two weighing devices is fitted with a normal distribution, ensuring that the data can be obtained and applied in real time, and avoiding the problem of being unable to obtain the true weight of the vehicle.

[0048] Through the above steps of the embodiments of the present application, in the embodiments of the present application, the first vehicle weight and the second vehicle weight of each target vehicle in the same group of target vehicles detected by the first weighing device and the second weighing device are obtained respectively. Thus, the vehicle weight detection results of different weighing devices for a group of the same target vehicles can be obtained; the target weight difference set of the difference between the first vehicle weight and the second vehicle weight of each target vehicle in a group of target vehicles is fitted with a normal distribution to obtain the fitted normal distribution parameter values. Thus, by subtracting the first vehicle weight from the second vehicle weight, the true weight value of each target vehicle can be removed, and only the difference in the measurement error is retained; the normal distribution parameter values of the target weight difference set are compared with the target normal distribution parameter values of the reference weight difference set to determine the status detection result of the first weighing device combination. Thus, according to the first weighing device combination, the corresponding target normal distribution parameter values for reference are provided, realizing the real-time detection of the status of the weighing device and solving the problem of poor real-time performance of the status detection method of the weighing device in the related art due to the lack of reference standard data.

[0049] In an exemplary embodiment, the above method further includes:

[0050] S11. When the first weighing device and the second weighing device are in a normal state, obtain the first vehicle weight of each reference vehicle in the group of reference vehicles detected by the first weighing device, and obtain the second vehicle weight of each reference vehicle detected by the second weighing device to obtain the reference weight difference set;

[0051] S12. Fit the obtained reference weight difference set with a normal distribution to obtain the target normal distribution parameter values.

[0052] For a weighing device (e.g., a dynamic truck scale at a weight control site), the actual weight of the passing vehicle obtained contains errors. Refer to formula (4):

[0053] y = h*(x + Δx) = h*x + h*Δx = h*x + er (4)

[0054] Where y represents the measured weight of the vehicle, h represents the gain of the weighing device (load cell), x represents the ideal weighing signal, Δx represents the actual interference signal, er represents the weight error caused by the interference signal, and h*x represents the true weight of the vehicle.

[0055] Here, in electronics, the gain is usually the ratio of the signal output to the signal input of a system; the gain corresponding to the sensor combination is the amplification factor of the original signal output of the sensor combination.

[0056] After the dynamic truck scale (a weighing device for measuring the weight of a vehicle) is installed at a fixed location, its state is fixed relative to that location. Therefore, its error influence factor is fixed relative to that location. According to the probability distribution principle and the analysis of the detection data, it can be determined that the error distribution between the measured weight and the true weight at the fixed location conforms to the normal distribution law, that is, refer to formula (5):

[0057]

[0058] Where i represents the i-th weighing device, er(i) represents the measurement error of the i-th weighing device for the measured target vehicle (i.e., the error between the measured weight and the true weight), u i represents the mean value of the error between the measured weight and the true weight, represents the square of the standard deviation of the error between the measured weight and the true weight.

[0059] The difference between two errors that conform to the normal distribution also conforms to the normal distribution, that is, refer to formula (6):

[0060]

[0061] Where i and j represent two weighing devices with matching data (different measurement data of the same target vehicle); er(i,j) represents the difference between er(i) and er(j), u ij represents u i and u j The mean value of the difference, represents u i and u j The square of the standard deviation of the difference.

[0062] It should be noted that during the initial use stage after the weighing device is calibrated, the vehicle weight measured is the weight under normal conditions of the weighing device. Therefore, under normal conditions after the first weighing device and the second weighing device are calibrated, the first vehicle weight of each reference vehicle in a group of reference vehicles detected by the first weighing device is obtained, and the second vehicle weight of each reference vehicle detected by the second weighing device is obtained, to obtain a reference weight difference set; the obtained reference weight difference set is subjected to normal distribution fitting to obtain the target normal distribution parameter value; at this time, the target normal distribution parameter value can be used as a reference standard to be compared with the normal distribution parameter value actually obtained by the subsequent weighing device during application.

[0063] In some embodiments, after the dynamic vehicle scale is calibrated, the dynamic vehicle scale is stable in the initial stage of use. At this time, vehicle weight data that can be matched and is connected to the network for a certain period of time in the initial stage is collected (that is, different measurement data of the same target vehicle detected by different weighing devices).

[0064] Through the embodiments of the present application, when the first weighing device and the second weighing device are in a normal state after calibration, the target normal distribution parameter value is determined according to a group of target reference vehicles as a reference standard, and can be compared with the actual normal distribution parameter value of the subsequent first weighing device and the second weighing device during application to judge the real-time state detection result of the weighing device.

[0065] In an exemplary embodiment, refer to Figure 2 , obtaining the first vehicle weight of each target vehicle in a group of target vehicles detected by the first weighing device and the second vehicle weight of each target vehicle detected by the second weighing device includes:

[0066] S21, obtaining the vehicle weight of each first vehicle in the first vehicle set detected by the first weighing device within a specified time period, and the reference vehicle information of each first vehicle detected by the detection component associated with the first weighing device, where the reference vehicle information is the vehicle information used for vehicle weight matching;

[0067] S22, matching the first vehicle set and the second vehicle set based on the reference vehicle information, where the vehicle weight of each second vehicle in the second vehicle set is detected by the second weighing device, and the reference vehicle information of each second vehicle is detected by the detection component associated with the second weighing device;

[0068] S23, respectively determining the first vehicles in the first vehicle set that have matching second vehicles as a target vehicle, to obtain the first vehicle weight of each target vehicle and the second vehicle weight of each target vehicle.

[0069] While the first weighing device measures the weight of each first vehicle in the first vehicle set, it is also necessary to determine the reference information of the first vehicle through the detection components associated with the first weighing device, so as to determine the complete information of the first vehicle and match the corresponding vehicle weight data according to the reference information of the first vehicle in the follow-up.

[0070] Here, the detection components associated with the first weighing device can be cameras, speedometers, timers, etc., for determining the license plate number, vehicle characteristics (e.g., number of axles, vehicle type), passing time points, etc. of each first vehicle in the first vehicle set.

[0071] The second weighing device, the detection components associated with the second weighing device, the reference information of the second vehicle, etc. are the same as the above content and will not be elaborated here.

[0072] For example, in the overloading control scenario, data matching is performed between any two overloading control stations. The passing interval time between the two stations is calculated based on the distance between the two matched stations and the vehicle running speed specified by the road. Then, the vehicle data is screened according to the interval time, license plate, and other conditions that can be considered (such as weight, number of axles, vehicle type) to obtain the matching data of the two stations (i.e., the vehicle weights measured for the same group of target vehicles).

[0073] In some embodiments, when the verification between the i-th station (weighing device) and the j-th station (weighing device) is in a normal state, based on the first vehicle weight and the second vehicle weight of each vehicle in the matching data between the i-th station and the j-th station, a weight difference calculation is performed and a normal distribution fitting is carried out to obtain u ij and and take u ij and as the standard reference value u oij and At the same time, set the threshold θ u and θ δ , and developers in this field can set the threshold according to the performance of the specific product, and the form can be a fixed value, a proportional value, or others.

[0074] Through the embodiments of the present application, while matching the associated data of two weighing devices, factors such as vehicle speed and duration are also considered to avoid changes in the true weight (load) of the vehicle caused by factors such as vehicle unloading or passengers getting on and off the vehicle.

[0075] In an exemplary embodiment, referring to Figure 3 , based on the reference vehicle information, the first vehicle set and the second vehicle set are matched, including:

[0076] S31. Use the first vehicle as the vehicle to be matched respectively to perform the following vehicle matching operations. Here, the passing time of the vehicle to be matched is the time when the vehicle to be matched passes the first weighing device, and the passing time of each second vehicle is the time when each second vehicle passes the second weighing device:

[0077] S311. Screen out multiple candidate vehicles from the set of second vehicles whose time difference between the passing time and the passing time of the vehicle to be matched is within the preset time difference range.

[0078] Here, the preset time difference range is set according to the distance between the first weighing device and the second weighing device and the speed limit of the road section from the first weighing device to the second weighing device;

[0079] S312. Determine whether there is a candidate vehicle that matches the vehicle to be matched among the multiple candidate vehicles.

[0080] Here, the candidate vehicle that matches the vehicle to be matched is the candidate vehicle among the multiple candidate vehicles whose reference vehicle information is the same as that of the vehicle to be matched.

[0081] Use each first vehicle in the set of first vehicles as the vehicle to be matched respectively to perform the vehicle matching operation. Determine whether there is a candidate vehicle that matches the vehicle to be matched in the set of second vehicles according to the time difference between the passing times and the reference vehicle information, so as to facilitate subsequent obtaining of the vehicle data (i.e., the vehicle weights detected by multiple identical vehicles at different weighing devices) in the set of first vehicles and the set of second vehicles for normal distribution fitting.

[0082] In some embodiments, the passing time of the vehicle to be matched is the time when the vehicle to be matched passes the first weighing device. For example, it is the time point when the first vehicle weight of the vehicle to be matched is detected by the first weighing device, or the time point when the vehicle to be matched enters the first weighing device detected by the first weighing device; or the time point detected by the associated device of the first weighing device when the vehicle to be matched passes the first weighing device, etc. This application does not make any limitations here.

[0083] In the actual application scenario, the weighing device can be implemented as a dynamic truck scale at a vehicle weight detection site, etc. The first weighing device and the second weighing device can be adjacent vehicle weight detection sites (dynamic truck scales) on a section of the road, or non - adjacent vehicle weight detection sites. It can be understood that when the first weighing device and the second weighing device are adjacent (or the distance is relatively close), there is more vehicle data that matches in the set of first vehicles and the set of second vehicles.

[0084] To avoid changes in the load of the same target vehicle when passing through the first weighing device and the second weighing device (for example, passengers getting on or off or loading and unloading goods), it is necessary to set a preset time difference range to avoid different load weights of the same target vehicle at different time periods.

[0085] Here, the preset time difference range is set according to the distance between the first weighing device and the second weighing device and the speed limit of the road section from the first weighing device to the second weighing device; it can also be set in advance according to the actual situation, which is not limited in this application.

[0086] In some embodiments, candidate vehicles whose reference vehicle information is consistent with that of the vehicle to be matched are determined according to the unique identifier in the reference vehicle information. For example, the license plate number in the reference vehicle information, that is, if the license plate number of the vehicle to be matched is the same as that of the candidate vehicle, the current vehicle to be matched and the candidate vehicle are determined as matching vehicles (i.e., the same vehicle).

[0087] Through the embodiments of this application, candidate vehicles of the vehicle to be matched are determined according to the preset time difference range, and then it is judged whether there are matching vehicles among the candidate vehicles according to the reference information of the vehicle to be matched, so as to accurately judge the matching vehicle data within a reasonable time range.

[0088] In an exemplary embodiment, refer to Figure 4 , after performing the following state detection operations on the first weighing device combination, the above method further includes:

[0089] S41, when the state detection result is used to indicate that the state of the first weighing device combination is normal, both the first weighing device and the second weighing device are marked as weighing devices with normal state;

[0090] S42, when the state detection result is used to indicate that the state of the first weighing device combination is abnormal, determine the weighing device to be marked in the first weighing device combination;

[0091] Among them, the first weighing device combination does not include a weighing device marked as having an abnormal state, and the weighing device to be marked is a weighing device that is not marked as having a normal state or an abnormal state;

[0092] S43, when the number of determined weighing devices to be marked is one, mark the determined weighing device to be marked as a weighing device with an abnormal state;

[0093] S44, when the number of determined weighing devices to be marked is multiple, mark the determined multiple weighing devices to be marked as state-undetermined weighing devices with a state to be determined.

[0094] After detecting the state of the first weighing device combination by comparing the fitted normal distribution parameter values with the target normal distribution parameter values, the state detection result of the first weighing device combination is determined, where the state detection result includes normal state and abnormal state.

[0095] For example, when the first weighing device combination is in a normal state, it indicates that both the first weighing device and the second weighing device can accurately measure the weight of the passing vehicle (the measurement error is less than the preset threshold); when the first weighing device combination is in an abnormal state, it indicates that there is an abnormal device among the first weighing device and the second weighing device that cannot accurately measure the weight of the passing vehicle (the measurement error is greater than the preset threshold).

[0096] After obtaining the state detection result of the first weighing device combination, in response to the first weighing device combination being in a normal state, mark and record the first weighing device and the second weighing device in the current first weighing device combination as weighing devices in a normal state.

[0097] After obtaining the state detection result of the first weighing device combination, in response to the first weighing device combination being in an abnormal state, determine the weighing device to be marked in the current first weighing device combination (the first weighing device combination does not include a weighing device that has been marked as abnormal); here, the weighing device to be marked is a weighing device whose detection state has not been determined (that is, the state of the weighing device to be marked can be normal or abnormal); when the number of weighing devices to be marked is one, determine the weighing device to be marked as an abnormal weighing device, and when the number of weighing devices to be marked is multiple, mark the determined multiple weighing devices to be marked as weighing devices with undetermined status.

[0098] In an exemplary embodiment, referring to Figure 5 , after performing the following state detection operations on the first weighing device combination, the above method further includes:

[0099] S51, when there is at least one weighing device to be marked among multiple weighing devices, select a second weighing device combination from multiple weighing device combinations, where at least one of the two weighing devices in the second weighing device combination is not a weighing device with undetermined status;

[0100] S52, continue to perform state detection operations on the second weighing device combination until all multiple weighing devices have been marked as weighing devices in a normal state or an abnormal state.

[0101] Among them, the first weighing device combination belongs to multiple weighing device combinations. The multiple weighing device combinations include two weighing devices among the multiple weighing devices. Different weighing device combinations include at least partially different weighing devices. The weighing device combinations that include a weighing device with an abnormal state and the weighing device combinations that include two weighing devices with normal states among the multiple weighing device combinations are removed.

[0102] In the case where there is a weighing device marked as having an abnormal state among the multiple weighing devices, remove the weighing device combinations that include the weighing device with the abnormal state among the multiple weighing devices to obtain an updated multiple weighing device combination, and select a second weighing device combination from the updated multiple weighing device combination.

[0103] In practical applications, when the weighing device is implemented as a vehicle weight measurement site (dynamic truck scale), when the detection result of the current weighing device combination is abnormal, mark the current weighing device combination as a warning site combination; when the detection result of the current weighing device combination is normal, mark the current weighing device combination as a normal site combination;

[0104] According to the correspondence relationship between the warning site combination and the normal site combination, the specific abnormal site can be accurately located. For example, by statistically analyzing three sites A, B, and C, the error normal distribution fitting of three site combinations AB, AC, and BC can be obtained. If the error distributions of AB and BC exceed the set threshold and AC is normal, it indicates that there is a problem with the weighing scale (dynamic truck scale or weighing device) at site B.

[0105] In some embodiments, for multiple weighing device combinations, select a group of target vehicles that meet the target conditions for status detection operations to obtain status detection results, where the target conditions include at least one of the following: target interval (step) duration, within a target time period, greater than (or less than or equal to) a target weight threshold, and the number of vehicle axles.

[0106] For example, after the verification of multiple sites (weighing devices) is completed, select the data of two connected sites A and B for 1 month, select trucks with a weight greater than 7 tons as comparison data, match the data according to the passing interval time, license plate, and weight, and respectively obtain the matching data sets AB and AB, and calculate the mean value of the matching weight difference and the standard deviation u of the weight difference OAB and as a reference. Then, use a 1-month time period as a sliding window with a step size of half a month, that is, every half month later, statistically analyze the matching data with a length of 1 month. Calculate the mean value of the matching weight difference and the standard deviation u of the weight difference at the mth window period mAB and If |u mAB / u OAB |>θ uOr It indicates that there is a problem with one of the weighbridges at Site A and Site B. Re-check and calibrate Sites A and B.

[0107] For example, after the calibration of multiple sites is completed, select the data of three connected sites A, B, and C one month after calibration. Select trucks with a weight greater than 7 tons as comparison data. Match the data according to the passing interval time, license plate, and weight, and obtain the matching data sets AB, AC, and BC respectively. Calculate the mean of the weight difference and the standard deviation of the weight difference u OAB And u OAC And u OBC And As a reference. Then, take a one-month time period as a sliding window with a step size of half a month, that is, every half month later, count the matching data with a one-month length. Calculate the mean of the weight difference and the standard deviation of the weight difference for pairwise matching as u mAB And u mAC And u mBC And If |u mAB / u OAB | > θ u Or |u mAC / u OAC | > θ u Or And u mBC And Exceeds the threshold, indicating that there is a problem with the weighbridge at Site A. Alarm for Site A. This embodiment can evaluate the overall effect of the site weighbridge.

[0108] For example, after the calibration of multiple sites (weighing equipment) is completed, select the data of three connected sites A, B, and C one month after calibration. Select trucks with a weight greater than 7 tons as comparison data and classify them according to 2-axle, 3-axle, 4-axle, 5-axle, and 6-axle. Match the data according to the passing interval time, license plate, weight, and axle number, and obtain the matching data sets AB1-AB5, AC1-AC5, and BC1-BC5 respectively. 1-5 correspond to 2-6 axle trucks. Calculate the mean of the weight difference and the standard deviation of the weight difference u OAB1 And ~u OAB5 And u OAC1 And ~u OAC5 And u OBC1 And ~u OBC5 And As a reference. Then, a one-month time period is used as a sliding window with a step size of half a month, that is, every half month later, the matching data of a one-month length is statistically analyzed. In the mth window period, the mean value and standard deviation of the weight difference of pairwise matching are calculated as u mAB1 and ~u mAB5 and u mAC1 and ~u mAC5 and u mBC1 and ~u mBC5 and If |u mAB3 / u OAB3 |>θ u or |u mAC3 / u OAC3 |>θ u or and u mBC and does not exceed the threshold, indicating that the weighbridge at Site A has a measurement problem for 4-axle vehicles. Alarm Site A, and it is necessary to correct the 4-axle vehicle type.

[0109] Here, based on the networked data, matching is performed through time and license plate to find the passing vehicle data (including the measured vehicle weight, vehicle characteristics, etc.) of the same vehicle passing through two sites (for example, overloading control sites or other vehicle weight measurement sites), and a data set is formed.

[0110] Through the embodiments of the present application, automatic real-time monitoring of the device status is realized, making up for the deficiency that cannot be monitored within the cycle; obtaining automatic monitoring data in real time through the network ensures the reliability of the data; through the combined comparison of pairwise sites, the problem site can be automatically and accurately located and timely warned, achieving the technical effects of reducing labor costs and improving repair efficiency.

[0111] In an exemplary embodiment, referring to Figure 6 , by comparing the fitted normal distribution parameter values and the target normal distribution parameter values, the status of the first weighing device combination is detected, and the status detection result is obtained, including:

[0112] S61, determining the difference between the parameter values of the same normal distribution parameters in the fitted normal distribution parameter values and the target normal distribution parameter values to obtain a set of parameter differences, where each parameter difference in the set of parameter differences corresponds to a parameter difference threshold respectively;

[0113] S62, based on each parameter difference and the parameter difference threshold corresponding to each parameter difference, determining the status detection result;

[0114] Wherein, when there is at least one parameter difference greater than or equal to the corresponding parameter difference threshold in a group of parameter differences, the status detection result is used to indicate that the first weighing device combination is in an abnormal state; when each parameter difference is less than the corresponding parameter difference threshold, the status detection result is used to indicate that the first weighing device combination is in a normal state.

[0115] For example, first, after the weighing device (such as a dynamic truck scale) is calibrated, the weighing device is stable in the initial stage of use. At this time, vehicle weight data of different weighing device combinations are collected for a certain period of time in the initial stage, and the normal distribution fitting of the error differences between different weighing device combinations (that is, every two weighing devices) is performed to obtain the mean of the target error difference and the square of the standard deviation of the target error difference, and the mean of the target error difference and the square of the mean of the target error difference are used as the standard reference values.

[0116] Secondly, taking a certain time period as a sliding window, the vehicle weight data of different weighing device combinations within the window period are statistically analyzed in real time, and the normal distribution fitting of the error differences between different weighing device combinations is performed to obtain the mean of the real-time error difference and the square of the standard deviation of the real-time error difference, and they are compared with the standard reference values. When it is detected that the change is greater than or equal to the specified threshold, an alarm is issued.

[0117] Wherein, the specified (preset) threshold can be set according to the performance of the specific product or the actual application scenario, and the form can be a fixed value, a proportional value or others, which is not limited in this application.

[0118] Finally, according to the weighing device differences between the alarmed (abnormal) weighing device combination and the normal weighing device combination, the position of the specific weighing device in the abnormal state can be accurately located.

[0119] Through the above embodiments provided by this application, vehicle measurement data of multiple weighing devices (such as overloading inspection sites) are obtained through networking, and data matching is performed according to vehicle parameters. The normal distribution fitting of the vehicle weight of each vehicle in a group of the same vehicles is performed using error differences, ensuring that the data can be obtained in real time, and the standard parameter values for reference and comparison can also be determined in advance; avoiding the problem that the standard reference value cannot be determined due to the inability to accurately measure the true weight of the vehicle; and by comparing different weighing combinations pairwise, the position of the abnormal weighing device can be accurately located.

[0120] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0121] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the embodiments of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of the various embodiments of this application.

[0122] According to another aspect of the embodiments of this application, a state detection device for a weighing device is further provided. This device is used to implement the state detection method of the weighing device provided in the above embodiments, and those that have been described will not be repeated. As used hereinafter, the terms "module" and "unit" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0123] Figure 7 is a structural block diagram of an optional state detection device for a weighing device according to the embodiments of this application. As Figure 7 shown, this device includes:

[0124] An execution unit 702 is configured to perform the following state detection operations on the first weighing device combination, where the first weighing device combination includes a first weighing device and a second weighing device: obtain the first vehicle weight of each target vehicle in a set of target vehicles detected by the first weighing device, and the second vehicle weight of each target vehicle detected by the second weighing device; perform a normal distribution fitting on the set of target weight differences to obtain the fitted normal distribution parameter values, where each target weight difference in the set of target weight differences is the difference between the first vehicle weight and the second vehicle weight of a target vehicle in a set of target vehicles; perform a state detection on the first weighing device combination by comparing the fitted normal distribution parameter values with the target normal distribution parameter values to obtain a state detection result, where the target normal distribution parameter values are preset normal distribution parameter values obtained by performing a normal distribution fitting on a set of reference weight differences, and each reference weight difference in the set of reference weight differences is the difference between the first vehicle weight of a reference vehicle in a set of reference vehicles detected by the first weighing device and the second vehicle weight of the same reference vehicle detected by the second weighing device.

[0125] It should be noted that the execution unit 702 in this embodiment can be used to execute the above step S102.

[0126] Through the above steps of the embodiments of the present application, the following state detection operations are performed on the first weighing device combination, where the first weighing device combination includes a first weighing device and a second weighing device: obtain the first vehicle weight of each target vehicle in a set of target vehicles detected by the first weighing device, and the second vehicle weight of each target vehicle detected by the second weighing device; perform a normal distribution fitting on the set of target weight differences to obtain the fitted normal distribution parameter values, where each target weight difference in the set of target weight differences is the difference between the first vehicle weight and the second vehicle weight of a target vehicle in a set of target vehicles; perform a state detection on the first weighing device combination by comparing the fitted normal distribution parameter values with the target normal distribution parameter values to obtain a state detection result, where the target normal distribution parameter values are preset normal distribution parameter values obtained by performing a normal distribution fitting on a set of reference weight differences, and each reference weight difference in the set of reference weight differences is the difference between the first vehicle weight of a reference vehicle in a set of reference vehicles detected by the first weighing device and the second vehicle weight of the same reference vehicle detected by the second weighing device. The technical effect of improving the real-time performance of the state detection result of the weighing device is achieved; furthermore, the problem that the state detection method of the weighing device in the related art is inaccurate due to the lack of reference standard data is solved.

[0127] In an exemplary embodiment, the above device further includes:

[0128] An acquisition unit, configured to, when the first weighing device and the second weighing device are in a normal state, acquire the first vehicle weight of each reference vehicle in a set of reference vehicles detected by the first weighing device, and acquire the second vehicle weight of each reference vehicle detected by the second weighing device, so as to obtain a set of reference weight differences;

[0129] A fitting unit, configured to perform a normal distribution fitting on the obtained set of reference weight differences to obtain a target normal distribution parameter value.

[0130] In an exemplary embodiment, the execution unit includes:

[0131] An acquisition module, configured to acquire the vehicle weight of each first vehicle in a first vehicle set detected by the first weighing device within a specified time period, and acquire the reference vehicle information of each first vehicle detected by a detection component associated with the first weighing device, where the reference vehicle information is vehicle information used for vehicle weight matching.

[0132] A matching module, configured to match the first vehicle set and the second vehicle set based on the reference vehicle information, where the vehicle weight of each second vehicle in the second vehicle set is detected by the second weighing device, and the reference vehicle information of each second vehicle is detected by a detection component associated with the second weighing device;

[0133] A first determination module, configured to respectively determine, for each first vehicle in the first vehicle set that has a matching second vehicle, as a target vehicle, and obtain the first vehicle weight of each target vehicle and the second vehicle weight of each target vehicle.

[0134] In an exemplary embodiment, the matching module includes:

[0135] An operation sub-module, configured to perform the following vehicle matching operations with each first vehicle as a vehicle to be matched, where the passing time of the vehicle to be matched is the time when the vehicle to be matched passes the first weighing device, and the passing time of each second vehicle is the time when each second vehicle passes the second weighing device:

[0136] Filter, from the second vehicle set, multiple candidate vehicles whose time difference from the passing time of the vehicle to be matched is within a preset time difference range, where the preset time difference range is set according to the distance between the first weighing device and the second weighing device and the speed limit of the road section from the first weighing device to the second weighing device;

[0137] Determine whether there is a candidate vehicle that matches the vehicle to be matched among the multiple candidate vehicles, where the candidate vehicle that matches the vehicle to be matched is a candidate vehicle among the multiple candidate vehicles whose reference vehicle information is the same as the reference vehicle information of the vehicle to be matched.

[0138] In an exemplary embodiment, the above device further includes:

[0139] A first marking unit, configured to, after performing the following state detection operation on the first weighing device combination, mark both the first weighing device and the second weighing device as weighing devices with normal status when the state detection result is used to indicate that the status of the first weighing device combination is normal;

[0140] A determination unit, configured to determine the weighing device to be marked in the first weighing device combination when the state detection result is used to indicate that the status of the first weighing device combination is abnormal, where the first weighing device combination does not include a weighing device marked as having abnormal status, and the weighing device to be marked is a weighing device that is not marked as having normal status or abnormal status;

[0141] The first marking unit, configured to mark the determined weighing device to be marked as a weighing device with abnormal status when the number of determined weighing devices to be marked is one;

[0142] A second marking unit, configured to mark the determined multiple weighing devices to be marked as weighing devices with undetermined status when the number of determined weighing devices to be marked is multiple.

[0143] In an exemplary embodiment, the first weighing device combination belongs to multiple weighing device combinations, the multiple weighing device combinations include two weighing devices among multiple weighing devices, different weighing device combinations include at least partially different weighing devices, the weighing device combinations including weighing devices with abnormal status and the weighing device combinations including two weighing devices with normal status among the multiple weighing device combinations are removed; the above device further includes:

[0144] A selection unit, configured to, after performing the following state detection operation on the first weighing device combination, select a second weighing device combination from the multiple weighing device combinations when there is at least one weighing device to be marked among the multiple weighing devices, where at least one of the two weighing devices in the second weighing device combination is not a weighing device with undetermined status;

[0145] A continue execution unit, configured to continue to perform the state detection operation on the second weighing device combination until all the multiple weighing devices have been marked as weighing devices with normal status or abnormal status.

[0146] In an exemplary embodiment, the execution unit includes:

[0147] A second determination module, configured to determine the difference between the parameter values of the same normal distribution parameter in the fitted normal distribution parameter values and the target normal distribution parameter values, so as to obtain a set of parameter differences, where each parameter difference in the set of parameter differences corresponds to a parameter difference threshold respectively;

[0148] A third determination module, configured to determine a status detection result based on each parameter difference and the parameter difference threshold corresponding to each parameter difference;

[0149] Wherein, in the case that there is at least one parameter difference greater than or equal to the corresponding parameter difference threshold in the set of parameter differences, the status detection result is used to indicate that the combined state of the first weighing device is abnormal; in the case that each parameter difference is less than the corresponding parameter difference threshold, the status detection result is used to indicate that the combined state of the first weighing device is normal.

[0150] It should be noted that the above-mentioned modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above-mentioned modules are all located in the same processor; or, the above-mentioned modules are respectively located in different processors in any combination form.

[0151] According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0152] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: USB flash drive, read-only memory (ROM for short), random access memory (RAM for short), mobile hard disk, magnetic disk or optical disc, etc., various media that can store computer programs.

[0153] According to one aspect of the present application, there is provided a computer program product, which includes computer programs / instructions, and the computer programs / instructions include program codes for executing the method shown in the flowchart. In such an embodiment, refer to Figure 8 , the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, it executes various functions provided by the embodiments of the present application. The serial numbers of the above embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0154] Refer to Figure 8 , Figure 8 is a structural block diagram of a computer system of an optional electronic device according to an embodiment of the present application.

[0155] Figure 8 Schematically shows a block diagram of a computer system of an electronic device for implementing an embodiment of the present application. As Figure 8 shown, the computer system 800 includes a central processing unit 801 (CPU), which can perform various appropriate actions and processes according to a program stored in a read-only memory 802 (ROM) or a program loaded from a storage section 808 into a random access memory 803 (RAM). In the random access memory 803, various programs and data required for system operation are also stored. The central processing unit 801, the read-only memory 802, and the random access memory 803 are connected to each other via a bus 804. An input / output interface 805 (Input / Output interface, i.e., I / O interface) is also connected to the bus 804.

[0156] The following components are connected to the input / output interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a local area network card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read from it can be installed into the storage section 808 as needed.

[0157] Specifically, according to an embodiment of the present application, the processes described in each method flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, various functions defined in the system of the present application are executed.

[0158] It should be noted that Figure 8 the computer system 800 of the shown electronic device is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0159] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0160] In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device. The transmission device is connected to the above input / output resource pool, and the input / output device is connected to the above input / output resource pool.

[0161] Specific examples in this embodiment may refer to the examples described in the above embodiments and exemplary embodiments, and will not be repeated here.

[0162] Obviously, those skilled in the art should understand that the above modules or steps of the embodiments of the present application can be implemented by a general computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order from here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0163] The above are only the preferred embodiments of the present application and are not used to limit the embodiments of the present application. For those skilled in the art, the embodiments of the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the principle of the embodiments of the present application shall be included in the protection scope of the embodiments of the present application.

Claims

1. A method for detecting the state of a weighing device, characterized in that, Including: Performing the following state detection operations on a first weighing device combination, where the first weighing device combination includes a first weighing device and a second weighing device: Obtaining the first vehicle weight of each target vehicle in a group of target vehicles detected by the first weighing device, and the second vehicle weight of each target vehicle detected by the second weighing device; Performing a normal distribution fitting on a set of target weight differences to obtain the fitted normal distribution parameter values, where each target weight difference in the set of target weight differences is the difference between the first vehicle weight and the second vehicle weight of a target vehicle in the group of target vehicles; Performing a state detection on the first weighing device combination by comparing the fitted normal distribution parameter values with target normal distribution parameter values to obtain a state detection result, where the target normal distribution parameter values are preset normal distribution parameter values obtained by performing a normal distribution fitting on a set of reference weight differences, and each reference weight difference in the set of reference weight differences is the difference between the first vehicle weight and the second vehicle weight of a reference vehicle in a group of reference vehicles detected by the first weighing device and the same reference vehicle detected by the second weighing device.

2. The method according to claim 1, characterized in that The method further includes: When the first weighing device and the second weighing device are in a normal state, obtaining the first vehicle weight of each reference vehicle in the group of reference vehicles detected by the first weighing device, and obtaining the second vehicle weight of each reference vehicle detected by the second weighing device, to obtain the set of reference weight differences; Performing a normal distribution fitting on the obtained set of reference weight differences to obtain the target normal distribution parameter values.

3. The method according to claim 1, characterized in that The obtaining the first vehicle weight of each target vehicle in a group of target vehicles detected by the first weighing device, and the second vehicle weight of each target vehicle detected by the second weighing device, includes: Obtaining the vehicle weight of each first vehicle in a first vehicle set detected by the first weighing device within a specified time period, and the reference vehicle information of each first vehicle detected by a detection component associated with the first weighing device, where the reference vehicle information is vehicle information for vehicle weight matching; Matching the first vehicle set and a second vehicle set based on the reference vehicle information, where the vehicle weight of each second vehicle in the second vehicle set is detected by the second weighing device, and the reference vehicle information of each second vehicle is detected by a detection component associated with the second weighing device; Determining, respectively, the first vehicles in the first vehicle set that have matching second vehicles as a target vehicle, to obtain the first vehicle weight of each target vehicle and the second vehicle weight of each target vehicle.

4. The method according to claim 3, wherein The matching the first vehicle set and the second vehicle set based on the reference vehicle information includes: Perform the following vehicle matching operations using the first vehicle as the vehicle to be matched respectively. Here, the passing time of the vehicle to be matched is the time when the vehicle to be matched passes the first weighing device, and the passing time of each second vehicle is the time when each second vehicle passes the second weighing device: Screen out multiple candidate vehicles from the set of second vehicles, where the time difference between the passing time of the candidate vehicles and the passing time of the vehicle to be matched is within a preset time difference range. The preset time difference range is set according to the distance between the first weighing device and the second weighing device and the speed limit of the road section from the first weighing device to the second weighing device; Determine whether there is a candidate vehicle that matches the vehicle to be matched among the multiple candidate vehicles. A candidate vehicle that matches the vehicle to be matched is a candidate vehicle among the multiple candidate vehicles whose reference vehicle information is the same as the reference vehicle information of the vehicle to be matched.

5. The method according to claim 1, wherein After performing the following state detection operations on the first weighing device combination, the method further includes: When the state detection result is used to indicate that the state of the first weighing device combination is normal, mark both the first weighing device and the second weighing device as weighing devices with normal state; When the state detection result is used to indicate that the state of the first weighing device combination is abnormal, determine the weighing device to be marked in the first weighing device combination. There is no weighing device marked as abnormal in the first weighing device combination, and the weighing device to be marked is a weighing device that is not marked as normal or abnormal; When the number of the determined weighing devices to be marked is one, mark the determined weighing device to be marked as a weighing device with abnormal state; When the number of the determined weighing devices to be marked is multiple, mark the determined multiple weighing devices to be marked as weighing devices with undetermined state and state pending.

6. The method according to claim 5, wherein The first weighing device combination belongs to multiple weighing device combinations. The multiple weighing device combinations include two weighing devices among multiple weighing devices. Different weighing device combinations include at least partially different weighing devices. The weighing device combinations that include weighing devices with abnormal state and the weighing device combinations that include two weighing devices with normal state among the multiple weighing device combinations are removed; After performing the following state detection operations on the first weighing device combination, the method further includes: When there is at least one of the weighing devices to be marked among the multiple weighing devices, select a second weighing device combination from the multiple weighing device combinations, where at least one of the two weighing devices in the second weighing device combination is not the weighing device with undetermined state; Continue to perform the state detection operation on the second weighing device combination until all the multiple weighing devices have been marked as weighing devices with normal state or abnormal state.

7. The method according to any one of claims 1 to 6, characterized in that, Performing state detection on the first weighing device combination by comparing the fitted normal distribution parameter values with the target normal distribution parameter values to obtain a state detection result, including: Determining the difference between the parameter values of the same normal distribution parameters in the fitted normal distribution parameter values and the target normal distribution parameter values to obtain a set of parameter differences, where each parameter difference in the set of parameter differences corresponds to a parameter difference threshold respectively; Determining the state detection result based on each parameter difference and the parameter difference threshold corresponding to each parameter difference; Wherein, when there is at least one parameter difference greater than or equal to the corresponding parameter difference threshold in the set of parameter differences, the state detection result is used to indicate that the state of the first weighing device combination is abnormal; when each parameter difference is less than the corresponding parameter difference threshold, the state detection result is used to indicate that the state of the first weighing device combination is normal.

8. A state detection device for a weighing device, characterized in that, Including: An execution unit for performing the following state detection operation on the first weighing device combination, where the first weighing device combination includes a first weighing device and a second weighing device: Obtaining the first vehicle weight of each target vehicle in a set of target vehicles detected by the first weighing device and the second vehicle weight of each target vehicle detected by the second weighing device; Performing normal distribution fitting on the target weight difference set to obtain the fitted normal distribution parameter values, where each target weight difference in the target weight difference set is the difference between the first vehicle weight of a target vehicle in the set of target vehicles and the second vehicle weight of the same target vehicle; Performing state detection on the first weighing device combination by comparing the fitted normal distribution parameter values with the target normal distribution parameter values to obtain a state detection result, where the target normal distribution parameter values are preset normal distribution parameter values obtained by performing normal distribution fitting on a reference weight difference set, and each reference weight difference in the reference weight difference set is the difference between the first vehicle weight of a reference vehicle in a set of reference vehicles detected by the first weighing device and the second vehicle weight of the same reference vehicle detected by the second weighing device.

9. A computer-readable storage medium, characterized in that, Stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method described in any one of claims 1 to 7 are implemented.

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