Condition monitoring methods and apparatus for testing equipment, storage media and electronic equipment

Abstract

This application discloses a method and apparatus for detecting the state of a testing device, a storage medium, and an electronic device. The method includes: acquiring vehicle weight data of vehicles of the same model passing through the testing device within a target time period; determining vehicle weight data located in a first weight interval and a second weight interval based on the acquired vehicle weight data; performing data processing on the vehicle weight data located in the first weight interval and the vehicle weight data located in the second weight interval to obtain a first feature value corresponding to the first weight interval and a second feature value corresponding to the second weight interval; and determining whether the testing device is in normal working condition based on the deviation of the current first feature value from historical first feature values ​​and the deviation of the current second feature value from historical second feature values. This application solves the technical problem of poor real-time performance in the state detection methods of testing devices in related technologies.

Description

Technical Field

[0001] This application relates to the field of weight measurement technology, and more specifically, to a method and apparatus for detecting the state of a detection device, a storage medium, and an electronic device. Background Technology

[0002] Currently, dynamic weighing technology can be applied to scenarios such as entrance control of overloading, fixed overloading control station detection, source supervision and off-site enforcement. The key equipment is the detection equipment used for weighing (e.g., dynamic truck scale).

[0003] Testing equipment needs to undergo periodic metrological verification, typically every six months or a year. However, there are no effective monitoring methods to determine the equipment's operational status between two adjacent verification cycles, leading to inaccurate vehicle weighing.

[0004] It is evident that the status detection methods for detection equipment in related technologies suffer from poor real-time performance in equipment status detection. Summary of the Invention

[0005] This application provides a method and apparatus for detecting the state of a detection device, a storage medium, and an electronic device, to at least solve the technical problem of poor real-time performance in the state detection methods of detection devices in related technologies.

[0006] According to one aspect of the embodiments of this application, a state detection method for a detection device is provided, comprising: acquiring vehicle weight data of vehicles of the same model passing through the detection device within a target time period; determining vehicle weight data located in a first weight interval and a second weight interval based on the acquired vehicle weight data, wherein the maximum value of the first weight interval is less than the minimum value of the second weight interval; performing data processing on the vehicle weight data located in the first weight interval and the vehicle weight data located in the second weight interval respectively to obtain a first feature value corresponding to the first weight interval and a second feature value corresponding to the second weight interval; determining whether the detection device is in normal working condition based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value.

[0007] According to another aspect of the embodiments of this application, a state detection device for a detection equipment is provided, comprising: an acquisition unit, configured to acquire vehicle weight data of vehicles of the same model passing through the detection equipment within a target time period; a determination unit, configured to determine vehicle weight data located in a first weight interval and a second weight interval respectively based on the acquired vehicle weight data, wherein the maximum value of the first weight interval is less than the minimum value of the second weight interval; a processing unit, configured to perform data processing on the vehicle weight data located in the first weight interval and the vehicle weight data located in the second weight interval respectively to obtain a first feature value corresponding to the first weight interval and a second feature value corresponding to the second weight interval; and a judgment unit, configured to determine whether the detection equipment is in normal working condition based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value.

[0008] As an optional solution, the processing unit includes: a fitting module, used to fit a first data segment curve based on vehicle weight data located in the first weight range using a normal distribution curve, and to fit a second data segment curve based on vehicle weight data located in the second weight range using a normal distribution curve; and a determination module, used to determine the first feature value based on the first data segment curve, and to determine the second feature value based on the second data segment curve.

[0009] As an optional solution, the processing unit further includes: a first execution module, configured to perform hypothesis testing on the vehicle weight data in the first weight range and the vehicle weight data in the second weight range respectively before fitting a first data segment curve based on vehicle weight data in the first weight range using a normal distribution curve, and fitting a second data segment curve based on vehicle weight data in the second weight range using a normal distribution curve, to determine whether the vehicle weight data in the first weight range and the vehicle weight data in the second weight range both conform to a normal distribution; a second execution module, configured to perform the step of fitting the distribution of vehicle weight data in the first weight range and vehicle weight data in the second weight range if the determination result is yes; a third execution module, configured to terminate the state detection if the determination result is no; or, redetermine the target time period and return to the step of obtaining vehicle weight data of the same model of vehicles that have passed through the detection device within the target time period.

[0010] As an optional solution, the processing unit further includes: a calculation module, configured to calculate a first variance value based on the first data segment curve and a second variance value based on the second data segment curve before determining the first feature value based on the first data segment curve and determining the second feature value based on the second data segment curve; a first judgment module, configured to determine whether both the first variance value and the second variance value are less than a preset variance threshold; a fourth execution module, configured to execute the steps of determining the first feature value based on the first data segment curve and determining the second feature value based on the second data segment curve if the judgment result is yes; and a fifth execution module, configured to terminate the state detection if the judgment result is no; or, re-determine the target time period and return to the step of obtaining vehicle weight data of vehicles of the same model that have passed through the detection device within the target time period.

[0011] As an optional solution, the judgment unit includes: a second judgment module, used to determine that the detection device is in an abnormal working state when at least one of the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value is greater than a preset value.

[0012] As an optional solution, the second judgment module includes: a first judgment submodule, configured to determine that the linearity of the detection device has changed when the deviation of the current first feature value from the historical first feature value is greater than the preset value, or when the deviation of the current second feature value from the historical second feature value is greater than the preset value; and a second judgment submodule, configured to determine that the sensitivity or gain of the detection device has changed when the deviation of the current first feature value from the historical first feature value is greater than the preset value, and when the deviation of the current second feature value from the historical second feature value is greater than the preset value.

[0013] As an optional solution, the determining unit includes: a first determining module, used to determine a vehicle weight statistical histogram based on the acquired vehicle weight data; and a second determining module, used to determine a first weight range and a second weight range, as well as vehicle weight data located in the first weight range and vehicle weight data located in the second weight range, based on the vehicle weight statistical histogram.

[0014] As an optional solution, the vehicle weight data is the weight data of a truck, the first weight range is the weight data range when the truck is in an unloaded state, and the second weight data is the weight data range when the truck is in a loaded state.

[0015] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the steps in any of the above method embodiments when it is run.

[0016] According to another aspect of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to perform the steps of any of the above method embodiments via the computer program.

[0017] In this embodiment, vehicle weight data of the same model of vehicles passing through the detection device are acquired within a target time period. Based on the acquired vehicle weight data, vehicle weight data located in a first weight interval and a second weight interval are determined, wherein the maximum value in the first weight interval is less than the minimum value in the second weight interval. Data processing is performed on the vehicle weight data located in the first weight interval and the vehicle weight data located in the second weight interval to obtain a first feature value corresponding to the first weight interval and a second feature value corresponding to the second weight interval. Based on the deviation of the current first feature value from historical first feature values ​​and the deviation of the current second feature value from historical second feature values, it is determined whether the detection device is in normal working condition. Here, the first feature value can indicate the feature data of vehicles located in the first weight interval. The weight data measured by the detection equipment for vehicles passing through the same area at different time periods should belong to the same weight range. By comparing the first feature value with the historical first feature value, it can be determined whether the detection equipment is currently in normal working condition. Similarly, by comparing the second feature value with the historical second feature value, the accuracy of the status judgment of the detection equipment can be further increased. In the solution of this embodiment, by processing the vehicle data detected by the detection equipment and judging the vehicle status based on the changes in the vehicle data, it is possible to provide real-time and accurate early warning for abnormal equipment when problems occur, thereby improving equipment maintenance efficiency and ensuring the continuous and stable operation of the weighing system without waiting for the next verification. This solves the technical problem of poor real-time performance of equipment status detection methods in related technologies. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of a state detection system for an optional detection device according to an embodiment of this application;

[0021] Figure 2 This is a schematic flowchart of an optional detection device status detection method according to an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of an optional detection device status detection method according to an embodiment of this application;

[0023] Figure 4 This is a schematic diagram of another optional detection device state detection method according to an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of another optional detection device status detection method according to an embodiment of this application;

[0025] Figure 6 This is a schematic diagram of another optional detection device status detection method according to an embodiment of this application;

[0026] Figure 7 This is a structural block diagram of a state detection device for an optional detection equipment according to an embodiment of this application;

[0027] Figure 8 This is a structural block diagram of a computer system for an optional electronic device according to an embodiment of this application. Detailed Implementation

[0028] To enable those skilled in the art to better understand 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. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] According to one aspect of the embodiments of this application, a method for detecting the state of a detection device is provided. Optionally, in this embodiment, the above-described method for detecting the state of a detection device can be applied to, for example... Figure 1 The hardware environment shown includes detection device 102 and data processor 104. For example... Figure 1 As shown, the data processor 104 is connected to the detection device 102 via a network and can be used to detect the device status of the detection device based on the vehicle data detected by the detection device 102. A data storage component (the data can be stored through a database) can be set on or independently of the data processor 104 to provide data storage services for the data processor 104. Here, both the detection device 102 and the data processor 104 can belong to the status detection system of the detection device.

[0031] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network (WAN), metropolitan area network (MAN), local area network (LAN). The aforementioned wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity), Bluetooth. In addition to network connection, the detection device 102 and the data processor 104 may also be connected via network cable or serial port. The detection device 102 may be a dynamic vehicle scale, a column-type load cell, a narrow strip load cell, or a combined load cell, etc.

[0032] The aforementioned testing equipment 102 can be a testing device that applies dynamic weighing technology. Dynamic weighing technology is now widely used in entrance control of overloaded vehicles, fixed overloaded vehicle control station inspections, source supervision, and off-site enforcement. Taking dynamic truck scales as an example, in related technologies, dynamic truck scales can only be used after metrological verification. The metrological verification cycle is generally six months or one year. There are no good monitoring methods to judge the usage status of dynamic truck scales between two adjacent verification cycles. Especially with the increasing number of unattended overloaded vehicle control stations, the lack of effective real-time monitoring means that problems will not be detected and handled in a timely manner, resulting in inaccurate vehicle weighing.

[0033] Although dynamic truck scales undergo periodic metrological verification, there is no way to automatically and promptly detect problems within the verification cycle. Furthermore, the monitoring of most products relies on the conditional transmission status codes set by the scale itself, but these status codes do not correspond well to actual weighing problems. In related technologies, the actual measured values ​​obtained are all output by the automatic scales, without any real values ​​for comparison. Therefore, there is currently no standard data for real-time monitoring, making accurate and effective status monitoring impossible.

[0034] To at least partially solve the above-mentioned technical problems, in this embodiment, the vehicle data detected by the detection equipment is processed, and the vehicle status is judged based on the changes in the vehicle data. This allows for real-time and accurate early warning of abnormal equipment when problems occur, thereby improving equipment maintenance efficiency and ensuring the continuous and stable operation of the weighing system without having to wait for the next verification.

[0035] The state detection method of the detection device in this embodiment can be executed by the data processor 104, or it can be jointly executed by the data processor 104 and the detection device 102. Taking the execution of the state detection method of the detection device in this embodiment by the data processor 104 as an example... Figure 2 This is a schematic flowchart of an optional detection device state detection method according to an embodiment of this application, as shown below. Figure 2 As shown, the process of this method may include the following steps:

[0036] Step 202: Obtain vehicle weight data of the same model of vehicles that have passed through the detection equipment within the target time period.

[0037] For vehicles of the same model, the same model refers to the vehicle type, which can be a small vehicle, a truck, or other vehicle types. For the target time period, it refers to a specific time period, which can be 2 months long, for example, May to June 2023.

[0038] For example, to obtain vehicle weight data of the same model of vehicles that have passed through the testing equipment within a target time period, the testing equipment may obtain vehicle weight data of all small vehicles that have passed through the testing equipment in the two months of May and June 2023. If the number of small vehicles that have passed through the testing equipment in the two months of May and June 2023 is 20,000, then the vehicle weight data includes the weight data of these 20,000 small vehicles.

[0039] It should be noted that the weight data ranges for different types of vehicles are also different. For example, the weight range for small vehicles is between 0.9 tons and 2.2 tons, the weight range for large six-axle trucks when empty is between 13 tons and 25 tons, and the weight range for large six-axle trucks when loaded with cargo is between 40 tons and 50 tons.

[0040] Step 204: Based on the acquired vehicle weight data, determine the vehicle weight data located in the first weight range and the second weight range, respectively, wherein the maximum value in the first weight range is less than the minimum value in the second weight range.

[0041] Here, vehicle weight data is categorized into vehicle weight data falling into the first weight range and vehicle weight data falling into the second weight range.

[0042] For example, the vehicle weight data can be the vehicle weight data of a large six-axle truck, with the weight range of the large six-axle truck being between 13 tons and 25 tons when empty, and between 40 tons and 50 tons when loaded with cargo. The first weight range can be 13 tons to 25 tons, and the second weight range can be 40 tons to 50 tons.

[0043] Step 206: Perform data processing on the vehicle weight data located in the first weight range and the vehicle weight data located in the second weight range respectively to obtain the first feature value corresponding to the first weight range and the second feature value corresponding to the second weight range.

[0044] The data processing methods for vehicle weight data located in the first weight range and vehicle weight data located in the second weight range can be as follows: the vehicle weight data can be fitted with a normal distribution. After obtaining the fitted normal distribution curve, the characteristic values ​​of the fitted normal distribution curve can be used as the first characteristic value and the second characteristic value. Here, the first characteristic value can be used to indicate the quantitative characteristics of the vehicle weight data in the first weight range, and the second characteristic value can be used to indicate the quantitative characteristics of the vehicle weight data in the second weight range.

[0045] Step 208: Based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value, determine whether the detection device is in normal working condition.

[0046] The historical first feature value can be the historical first feature value detected by the detection equipment in a historical time period before the target time period. The historical first feature value and the current first feature value are obtained in the same way, only the time period is different. Since the historical first feature value and the current first feature value are both feature values ​​corresponding to the vehicle weight data of the same vehicle model in the first weight range, if the detection equipment is in normal working condition in both the current time period and the historical time period, the deviation of the current first feature value from the historical first feature value should not be much different. Therefore, based on the deviation of the current first feature value from the historical first feature value, it is possible to determine whether the detection equipment is in normal working condition. Similarly, based on the deviation of the current second feature value from the historical second feature value, the accuracy of the status determination of the detection equipment can be further improved.

[0047] It should be noted that the deviation of the first eigenvalue from the historical first eigenvalue can be obtained by dividing the first eigenvalue by the historical first eigenvalue, or by subtracting the historical first eigenvalue from the first eigenvalue and then dividing the difference by the historical first eigenvalue. Similarly, the deviation of the second eigenvalue from the historical second eigenvalue is obtained in the same way as the deviation of the first eigenvalue from the historical first eigenvalue. Here, the deviation of the first eigenvalue from the historical first eigenvalue is used to indicate the degree of deviation of the first eigenvalue from the historical first eigenvalue, and the deviation of the second eigenvalue from the historical second eigenvalue is used to indicate the degree of deviation of the second eigenvalue from the historical second eigenvalue.

[0048] The embodiments provided in this application acquire vehicle weight data of the same model of vehicles passing through the detection equipment within a target time period; based on the acquired vehicle weight data, vehicle weight data located in a first weight interval and a second weight interval are determined, wherein the maximum value of the first weight interval is less than the minimum value of the second weight interval; the vehicle weight data located in the first weight interval and the vehicle weight data located in the second weight interval are processed respectively to obtain a first feature value corresponding to the first weight interval and a second feature value corresponding to the second weight interval; based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value, it is determined whether the detection equipment is in normal working condition, which solves the technical problem of poor real-time performance of equipment status detection methods in related technologies and improves the efficiency of equipment status detection.

[0049] As an optional approach, vehicle weight data located in the first weight range and vehicle weight data located in the second weight range are processed separately to obtain a first feature value corresponding to the first weight range and a second feature value corresponding to the second weight range, including:

[0050] S11, using a normal distribution curve, a first data segment curve is obtained by fitting vehicle weight data located in the first weight range, and using a normal distribution curve, a second data segment curve is obtained by fitting vehicle weight data located in the second weight range.

[0051] S12, determine the first feature value based on the first data segment curve, and determine the second feature value based on the second data segment curve.

[0052] For the acquired vehicle weight data located in the first weight range, a normal distribution can be fitted to the vehicle weight data located in the first weight range to obtain the first data segment curve. Here, the normal distribution fitting method for the vehicle weight data located in the first weight range can be any method that can fit a normal distribution curve that matches the vehicle weight data located in the first weight range. The fitting can be performed based on the number of vehicles with different weights. For example, a normal distribution fitting can be performed on the number of vehicle weight data in each sub-weight range within a set of sub-weight ranges located in the first weight range to obtain the fitted first data segment curve. Similarly, for the acquired vehicle weight data located in the second weight range, a normal distribution fitting can be performed to the vehicle weight data located in the second weight range to obtain the second data segment curve.

[0053] For the first data segment curve, a first characteristic value can be determined. Here, the first characteristic value can be the normal distribution parameter value corresponding to the first data segment curve, and the first characteristic value includes the mean of the first data segment curve. For the second data segment curve, a second characteristic value can be determined. Here, the second characteristic value can be the normal distribution parameter value corresponding to the second data segment curve, and the second characteristic value includes the mean of the second data segment curve.

[0054] In this embodiment, the first and second characteristic values ​​obtained by fitting the vehicle weight data using a normal distribution curve can accurately reflect the data characteristics of the vehicle weight data in the first weight range and the second weight range, respectively.

[0055] As an optional approach, before fitting a first data segment curve based on vehicle weight data within a first weight range using a normal distribution curve, and fitting a second data segment curve based on vehicle weight data within a second weight range using a normal distribution curve, the above method further includes:

[0056] S21, perform hypothesis testing on the vehicle weight data in the first weight range and the vehicle weight data in the second weight range respectively, and determine whether the vehicle weight data in the first weight range and the vehicle weight data in the second weight range both conform to a normal distribution.

[0057] S22, if the judgment result is yes, perform the step of fitting the distribution of vehicle weight data in the first weight range and vehicle weight data in the second weight range.

[0058] S23, if the judgment result is negative, terminate the status detection; or, redetermine the target time period and return to the step of obtaining the vehicle weight data of the same model of vehicles that have passed through the detection equipment within the target time period.

[0059] In this embodiment, before fitting the vehicle weight data using the normal distribution curve, it is necessary to perform hypothesis testing on the vehicle weight data located in the first weight interval and the vehicle weight data located in the second weight interval respectively, to determine whether the vehicle weight data located in the first weight interval and the vehicle weight data located in the second weight interval both conform to the normal distribution. Here, performing hypothesis testing on the vehicle weight data is the hypothesis testing step in the normal distribution.

[0060] If the judgment result is yes, it means that the vehicle weight data in the first weight range and the vehicle weight data in the second weight range both conform to a normal distribution. At this time, the subsequent steps can be executed, namely, the step of fitting the distribution of the vehicle weight data in the first weight range and the vehicle weight data in the second weight range. If the judgment result is no, it means that the vehicle weight data in the first weight range and the vehicle weight data in the second weight range do not conform to a normal distribution. At this time, the state detection is terminated, or the target time period is redefined, and the process returns to the step of obtaining the vehicle weight data of the same model of vehicles that have passed through the detection equipment within the target time period, that is, re-obtaining the vehicle weight data.

[0061] By performing hypothesis testing on the vehicle weight data before fitting it to a normal distribution in this embodiment, the accuracy of the normal distribution fitting results can be improved.

[0062] As an optional approach, before determining the first eigenvalue based on the first data segment curve and the second eigenvalue based on the second data segment curve, the above method further includes:

[0063] S31, calculate the first variance value based on the first data segment curve, and calculate the second variance value based on the second data segment curve;

[0064] S32, determine whether the first variance value and the second variance value are both less than the preset variance threshold;

[0065] S33, if the judgment result is yes, execute the steps of determining the first feature value based on the first data segment curve and determining the second feature value based on the second data segment curve;

[0066] S34, if the judgment result is negative, terminate the status detection; or, redetermine the target time period and return to the step of obtaining the vehicle weight data of the same model of vehicles that have passed through the detection equipment within the target time period.

[0067] In this embodiment, before determining the first feature value based on the first data segment curve and the second feature value based on the second data segment curve, it is necessary to calculate the first variance value based on the first data segment curve. Here, the first variance value can be calculated by calculating the variance between the first data segment curve and the vehicle weight data in the first weight interval. For example, the number of vehicle weight data in each sub-weight interval of a set of sub-weight intervals located in the first weight interval can be fitted with a normal distribution to obtain the fitted first data segment curve. For the vehicle weight data in the first weight interval, the number of vehicle weight data in each sub-weight interval of a set of sub-weight intervals located in the first weight interval can be obtained, thereby obtaining the actual first data segment curve. The variance value between the fitted first data segment curve and the data corresponding to each sub-weight interval in the actual first data segment curve can be calculated to obtain the first variance value. Similarly, the second variance value can be calculated based on the second data segment curve.

[0068] Determine whether both the first and second variance values ​​are less than a preset variance threshold. Here, a variance value less than the preset variance threshold indicates a small variance value and a good fit to the normal distribution. In this case, the subsequent steps can be executed. If the determination result is yes, execute the steps of determining the first feature value based on the curve of the first data segment and determining the second feature value based on the curve of the second data segment. If the determination result is no, terminate the state detection. Alternatively, redetermine the target time period and return to the step of obtaining the vehicle weight data of the same model of vehicles that have passed through the detection device within the target time period, that is, re-acquire the vehicle weight data.

[0069] In this embodiment, after fitting the normal distribution curve, further testing the fitted normal distribution curve and judging the fitting of the normal distribution can improve the accuracy of the first feature value and the second feature value.

[0070] As an optional approach, the determination of whether the detection equipment is in normal working condition is based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value, including:

[0071] S41, if at least one of the deviations of the current first feature value from the historical first feature value and the deviations of the current second feature value from the historical second feature value is greater than a preset value, the detection device is determined to be in an abnormal working state.

[0072] To determine whether the detection device is in normal working condition based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value, the detection device can be determined to be in abnormal working condition if at least one of the deviations of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value is greater than a preset value. Here, if at least one deviation is large, it can be said that the linearity, sensitivity or gain of the detection device may have changed, and the detection device can be determined to be in abnormal working condition.

[0073] This embodiment enables accurate determination of the working status of the detection device based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value.

[0074] As an optional approach, if at least one of the deviations of the current first feature value from historical first feature values ​​and the deviations of the current second feature value from historical second feature values ​​is greater than a preset value, the detection device is determined to be in an abnormal operating state, including:

[0075] S51, if the deviation of the current first feature value from the historical first feature value is greater than a preset value, or if the deviation of the current second feature value from the historical second feature value is greater than a preset value, it is determined that the linearity of the detection device has changed;

[0076] S52, if the deviation of the current first feature value from the historical first feature value is greater than a preset value, and the deviation of the current second feature value from the historical second feature value is greater than a preset value, it is determined that the sensitivity or gain of the detection device has changed.

[0077] If the deviation of the current first feature value from the historical first feature value is greater than the preset value, or if the deviation of the current second feature value from the historical second feature value is greater than the preset value, it indicates that the detection results of the detection equipment have changed for vehicle weight data of different weights. That is, the detection is more accurate for vehicle weight data in a certain weight range, but inaccurate for vehicle weight data in another weight range. It can be determined that the linearity of the detection equipment has changed.

[0078] If the deviation of the current first feature value from the historical first feature value is greater than the preset value, and the deviation of the current second feature value from the historical second feature value is greater than the preset value, it indicates that the detection results of the detection equipment have changed for vehicle weight data of different weights. At this time, it can be determined that the sensitivity or gain of the detection equipment has changed.

[0079] This embodiment further increases the accuracy of determining abnormal operating conditions of the detection equipment.

[0080] As an optional approach, based on the acquired vehicle weight data, vehicle weight data located in the first weight range and the second weight range are determined, including:

[0081] S61, Based on the acquired vehicle weight data, determine the vehicle weight statistical histogram;

[0082] S62, based on the vehicle weight statistical histogram, determine the first weight range and the second weight range, as well as the vehicle weight data located in the first weight range and the vehicle weight data located in the second weight range.

[0083] Based on the acquired vehicle weight data, a vehicle weight statistical histogram is determined. That is, the vehicle weight is divided into a set of sub-weight intervals. A set of sub-weight intervals includes a set of first weight sub-intervals of the first weight interval and a set of second weight sub-intervals of the second weight interval. Based on the acquired vehicle weight data, the vehicle weight data located in the set of first weight sub-intervals and the vehicle weight data located in the set of second weight sub-intervals can be obtained. Then, a normal distribution can be fitted based on the vehicle weight data located in the set of first weight sub-intervals and the vehicle weight data located in the set of second weight sub-intervals.

[0084] This embodiment can improve the convenience of processing vehicle weight data.

[0085] As an optional approach, the vehicle weight data is the weight data of the truck, with the first weight range being the weight data range when the truck is unloaded and the second weight data range being the weight data range when the truck is loaded.

[0086] For example, the vehicle weight data can be for a large six-axle truck. The first weight range is the weight range of the large six-axle truck when it is empty, which can be between 13 tons and 25 tons. The second weight range is the weight range of the large six-axle truck when it is loaded with cargo, which can be between 40 tons and 50 tons.

[0087] In this embodiment, the first weight range and the second weight range represent vehicles of the same model with significant differences in vehicle weight, which can improve the reliability of the condition detection method of the detection equipment.

[0088] The state detection method of the detection device according to an embodiment of this application will be described below with reference to an optional example. In this optional example, the detection device can be a dynamic vehicle scale.

[0089] To automatically monitor the status of the testing equipment, the weight data of the vehicles detected by the equipment can be acquired. After obtaining the weight data, outlier filtering is performed. After removing outliers, a frequency distribution chart of the weight data from the testing equipment is generated. For example, to generate a frequency distribution chart of the weight data for a six-axle vehicle: starting with a weight of 0t and a weight interval width of 1t, the range of 0 to 100t is divided into 100 intervals. The frequency of weight occurrences in each interval is counted, and a weight frequency chart for the six-axle vehicle is drawn. The start and end ranges of the weight data for small vehicles, empty vehicle data segments for six-axle trucks, and loaded vehicle data segments are determined.

[0090] Using the above method, we can obtain the following sequentially. Figures 3 to 6 The data shown. Among them, Figure 3 This is a histogram showing the weight statistics of small cars detected by testing equipment in May and June 2022. The weight range of small cars is 0.9 tons to 2.2 tons. Figure 4 The histogram shows the weight statistics of six-axle trucks detected by testing equipment in May and June 2022. The empty weight range of six-axle trucks is 13-25 tons, and the fully loaded weight range is 40-50 tons. Figure 5 This is a histogram showing the weight statistics of small cars detected by testing equipment in November and December 2022. Figure 6 This is a histogram showing the weight statistics of six-axle trucks detected by testing equipment in November and December 2022.

[0091] Hypothesis testing is performed on the weight data of small cars and six-axle trucks to determine whether they conform to a normal distribution. If they do, a normal distribution curve is fitted to each, and the variance values ​​x1 and x2 between the fitted curve and the actual data are calculated. If the calculated values ​​of x1 and x2 are less than a certain threshold, it indicates that the normal distribution fit is good, and the parameter values ​​of the normal distribution curve are extracted accordingly.

[0092] The following explanation uses empty and loaded data from a six-axle truck as an example. When the mean of the normal distribution curve calculated from the empty data of the six-axle truck is μ1, and the mean of the normal distribution curve calculated from the loaded data of the six-axle truck is μ2, the above steps are repeated at certain intervals to re-statistically calculate μ1' and μ2' of the latest batch of data and compare them with the initial values ​​μ1 and μ2. If μ1 or μ2 deviates from the initial value, it indicates that the detection equipment may be malfunctioning. When μ1 / μ1' changes significantly while μ2 / μ2' does not change significantly, or when μ1 / μ1' does not change significantly while μ2 / μ2' changes significantly, it indicates that the linearity of the weighing instrument has changed. When both the values ​​of μ1 / μ1' and μ2 / μ2' change significantly and are roughly equivalent, it indicates that the sensitivity or gain of the weighing instrument has changed, and the factors affecting the gain change need to be checked.

[0093] according to Figures 3 to 6 The data can be obtained as Table 1.

[0094] Table 1

[0095]

[0096] It's quite evident that the weight data of the testing equipment has changed significantly. It's impossible to determine whether these changes are due to a problem with the equipment itself or changes in the vehicles being tested; however, this data suggests that the testing equipment requires further calibration. The testing equipment's data platform tracks changes in data points across different types of vehicles daily to determine if the equipment is currently functioning correctly.

[0097] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute 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 to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0099] According to another aspect of the embodiments of this application, a state detection apparatus for a detection device is also provided. This apparatus is used to implement the state detection method for the detection device provided in the above embodiments, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0100] Figure 7 This is a structural block diagram of a state detection device for an optional detection apparatus according to an embodiment of this application, such as... Figure 7 As shown, the device includes:

[0101] The acquisition unit 702 is used to acquire vehicle weight data of the same model of vehicles that have passed through the detection equipment within a target time period.

[0102] The determining unit 704 is used to determine the vehicle weight data located in the first weight range and the second weight range respectively based on the acquired vehicle weight data, wherein the maximum value of the first weight range is less than the minimum value of the second weight range.

[0103] The processing unit 706 is used to process the vehicle weight data located in the first weight range and the vehicle weight data located in the second weight range respectively to obtain the first feature value corresponding to the first weight range and the second feature value corresponding to the second weight range.

[0104] The judgment unit 708 is used to determine whether the detection device is in normal working condition based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value.

[0105] This application embodiment obtains vehicle weight data of the same model of vehicles passing through a detection device within a target time period; based on the obtained vehicle weight data, vehicle weight data located in a first weight interval and a second weight interval are determined, wherein the maximum value of the first weight interval is less than the minimum value of the second weight interval; the vehicle weight data located in the first weight interval and the vehicle weight data located in the second weight interval are processed respectively to obtain a first feature value corresponding to the first weight interval and a second feature value corresponding to the second weight interval; based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value, it is determined whether the detection device is in normal working condition, which solves the technical problem of poor real-time performance of detection device status detection methods in related technologies and improves the efficiency of detection device status detection.

[0106] As an optional solution, the processing unit includes:

[0107] The fitting module is used to fit a first data segment curve based on vehicle weight data in the first weight range using a normal distribution curve, and to fit a second data segment curve based on vehicle weight data in the second weight range using a normal distribution curve.

[0108] The determination module is used to determine a first feature value based on the curve of the first data segment, and to determine a second feature value based on the curve of the second data segment.

[0109] As an optional solution, the processing unit also includes:

[0110] The first execution module is used to perform hypothesis testing on the vehicle weight data in the first weight range and the vehicle weight data in the second weight range before fitting the first data segment curve based on the vehicle weight data in the first weight range using the normal distribution curve, and before fitting the second data segment curve based on the vehicle weight data in the second weight range using the normal distribution curve, to determine whether the vehicle weight data in the first weight range and the vehicle weight data in the second weight range both conform to a normal distribution.

[0111] The second execution module is used to perform a step of fitting the distribution of vehicle weight data located in the first weight range and vehicle weight data located in the second weight range when the judgment result is yes.

[0112] The third execution module is used to terminate the status detection if the judgment result is negative; or, to redetermine the target time period and return to the step of obtaining the vehicle weight data of the same model of vehicles that have passed through the detection equipment within the target time period.

[0113] As an optional solution, the processing unit also includes:

[0114] The calculation module is used to calculate a first variance value based on the first data segment curve and a second variance value based on the second data segment curve before determining a first feature value based on the first data segment curve and determining a second feature value based on the second data segment curve.

[0115] The first judgment module is used to determine whether both the first variance value and the second variance value are less than a preset variance threshold.

[0116] The fourth execution module is used to perform the steps of determining the first feature value based on the first data segment curve and determining the second feature value based on the second data segment curve when the judgment result is yes.

[0117] The fifth execution module is used to terminate the status detection if the judgment result is negative; or, to redetermine the target time period and return to the step of obtaining the vehicle weight data of the same model of vehicles that have passed through the detection equipment within the target time period.

[0118] As an optional solution, the decision unit includes:

[0119] The second judgment module is used to determine that the detection device is in an abnormal working state if at least one of the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value is greater than a preset value.

[0120] As an optional approach, the second judgment module includes:

[0121] The first judgment submodule is used to determine that the linearity of the detection device has changed when the deviation of the current first feature value from the historical first feature value is greater than a preset value, or when the deviation of the current second feature value from the historical second feature value is greater than a preset value.

[0122] The second judgment submodule is used to determine that the sensitivity or gain of the detection device has changed when the deviation of the current first feature value from the historical first feature value is greater than a preset value, and the deviation of the current second feature value from the historical second feature value is greater than a preset value.

[0123] As an optional approach, the determined unit includes:

[0124] The first determining module is used to determine the vehicle weight statistical histogram based on the acquired vehicle weight data.

[0125] The second determining module is used to determine the first weight range and the second weight range based on the vehicle weight statistical histogram, as well as the vehicle weight data located in the first weight range and the vehicle weight data located in the second weight range.

[0126] As an optional approach, the vehicle weight data is the weight data of the truck, with the first weight range being the weight data range when the truck is unloaded and the second weight data range being the weight data range when the truck is loaded.

[0127] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes the steps in any of the above method embodiments when it is run.

[0128] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0129] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to perform the steps of any of the above method embodiments through the computer program.

[0130] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0131] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0132] According to another aspect of the embodiments of this application, a computer program product is provided, the computer program product including a computer program / instructions comprising program code for performing the method shown in the flowchart. In such an embodiment, reference is made to... Figure 8 The computer program can be downloaded and installed from a network via the communication section 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, it performs various functions provided in the embodiments of this application. The sequence numbers of the embodiments of this application above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0133] refer to Figure 8 , Figure 8This is a structural block diagram of a computer system for an optional electronic device according to an embodiment of this application.

[0134] Figure 8 A schematic block diagram of a computer system architecture for implementing embodiments of the present application is shown. Figure 8 As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 802 or programs loaded from storage section 808 into random access memory (RAM). The random access memory 803 also stores various programs and data required for system operation. The CPU 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output interface 805 (I / O interface) is also connected to the bus 804.

[0135] The following components are connected to the input / output interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, 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, 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 disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0136] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by central processing unit 801, it performs various functions defined in the system of this application.

[0137] It should be noted that, Figure 8The computer system 800 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0138] Obviously, those skilled in the art should understand that the modules or steps of the embodiments of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of this application are not limited to any particular combination of hardware and software.

[0139] The above are merely preferred embodiments of this application and are not intended to limit the embodiments of this application. For those skilled in the art, various modifications and variations can be made to the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A method for detecting the state of a detection device, characterized in that, include: Obtain vehicle weight data for the same model of vehicles that have passed through the testing equipment within a target time period; Based on the acquired vehicle weight data, vehicle weight data located in a first weight range and a second weight range are determined, wherein the maximum value in the first weight range is less than the minimum value in the second weight range. The vehicle weight data located in the first weight range and the vehicle weight data located in the second weight range are processed respectively to obtain the first feature value corresponding to the first weight range and the second feature value corresponding to the second weight range. Based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value, it is determined whether the detection device is in normal working condition.

2. The method according to claim 1, characterized in that, The step of processing vehicle weight data located in the first weight range and vehicle weight data located in the second weight range to obtain a first feature value corresponding to the first weight range and a second feature value corresponding to the second weight range includes: Using a normal distribution curve, a first data segment curve is obtained by fitting vehicle weight data located in the first weight range, and using a normal distribution curve, a second data segment curve is obtained by fitting vehicle weight data located in the second weight range. The first feature value is determined based on the curve of the first data segment, and the second feature value is determined based on the curve of the second data segment.

3. The method according to claim 2, characterized in that, Before fitting a first data segment curve based on vehicle weight data within the first weight range using a normal distribution curve, and fitting a second data segment curve based on vehicle weight data within the second weight range using a normal distribution curve, the method further includes: Hypothesis testing is performed on the vehicle weight data located in the first weight range and the vehicle weight data located in the second weight range to determine whether the vehicle weight data located in the first weight range and the vehicle weight data located in the second weight range both conform to a normal distribution. If the determination result is yes, perform the step of fitting the distribution of vehicle weight data located in the first weight range and vehicle weight data located in the second weight range; If the determination result is negative, terminate the status detection; or, redetermine the target time period and return to the step of obtaining the vehicle weight data of the same model of vehicles that have passed through the detection device within the target time period.

4. The method according to claim 2, characterized in that, Before determining the first feature value based on the first data segment curve and the second feature value based on the second data segment curve, the method further includes: The first variance value is calculated based on the curve of the first data segment, and the second variance value is calculated based on the curve of the second data segment; Determine whether both the first variance value and the second variance value are less than a preset variance threshold; If the determination result is yes, then the steps of determining the first feature value based on the first data segment curve and determining the second feature value based on the second data segment curve are executed. If the determination result is negative, terminate the status detection; or, redetermine the target time period and return to the step of obtaining the vehicle weight data of the same model of vehicles that have passed through the detection device within the target time period.

5. The method according to claim 1, characterized in that, The step of determining whether the detection device is in normal working condition based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value includes: If at least one of the deviations of the current first feature value from the historical first feature value and the deviations of the current second feature value from the historical second feature value is greater than a preset value, the detection device is determined to be in an abnormal working state.

6. The method according to claim 5, characterized in that, The step of determining that the detection device is in an abnormal working state when at least one of the deviations of the current first feature value from the historical first feature value and the deviations of the current second feature value from the historical second feature value is greater than a preset value includes: If the deviation of the current first feature value from the historical first feature value is greater than the preset value, or if the deviation of the current second feature value from the historical second feature value is greater than the preset value, it is determined that the linearity of the detection device has changed; If the deviation of the current first feature value from the historical first feature value is greater than the preset value, and the deviation of the current second feature value from the historical second feature value is greater than the preset value, it is determined that the sensitivity or gain of the detection device has changed.

7. The method according to claim 1, characterized in that, The step of determining the vehicle weight data located in the first weight range and the second weight range based on the acquired vehicle weight data includes: Based on the acquired vehicle weight data, a vehicle weight statistical histogram is determined; Based on the vehicle weight statistical histogram, the first weight range and the second weight range are determined, as well as the vehicle weight data located in the first weight range and the vehicle weight data located in the second weight range.

8. The method according to any one of claims 1 to 7, characterized in that, The vehicle weight data refers to the weight data of the truck. The first weight range is the weight data range when the truck is unloaded, and the second weight data is the weight data range when the truck is loaded.

9. A status detection device for a detection equipment, characterized in that, include: The acquisition unit is used to acquire vehicle weight data of the same model of vehicles that have passed through the detection equipment within a target time period; The determining unit is configured to determine vehicle weight data located in a first weight range and a second weight range respectively based on the acquired vehicle weight data, wherein the maximum value of the first weight range is less than the minimum value of the second weight range. The processing unit is used to process the vehicle weight data located in the first weight range and the vehicle weight data located in the second weight range respectively to obtain a first feature value corresponding to the first weight range and a second feature value corresponding to the second weight range. The judgment unit is used to determine whether the detection device is in normal working condition based on the deviation of the current first feature value from the historical first feature value and the deviation of the current second feature value from the historical second feature value.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the steps of the method according to any one of claims 1 to 8.

11. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to perform the steps of the method according to any one of claims 1 to 8 through the computer program.

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