CN117824808A - State detection method and device of detection equipment, storage medium and electronic equipment - Google Patents

Abstract

The application discloses a state detection method and device of detection equipment, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring vehicle weight data of vehicles of the same vehicle type passing through the detection device in a target time period; determining vehicle weight data respectively located in a first weight section and a second weight section according to the acquired vehicle weight data; respectively carrying out data processing on the vehicle weight data in the first weight interval and the vehicle weight data in the second weight interval to obtain a first characteristic value corresponding to the first weight interval and a second characteristic value corresponding to the second weight interval; and judging whether the detection equipment is in a normal working state according to the deviation amount of the current first characteristic value relative to the historical first characteristic value and the deviation amount of the current second characteristic value relative to the historical second characteristic value. According to the method and the device, the technical problem that the real-time performance of device state detection is poor in the state detection method of the detection device in the related technology is solved.

Description

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

Technical Field

The present invention relates to the technical field of weight measurement, and in particular, to a method and apparatus for detecting a state of a detection device, a storage medium, and an electronic device.

Background

Currently, dynamic weighing technology can be applied to scenes such as entrance super, fixed super station detection, source supervision, off-site law enforcement and the like, wherein key equipment is detection equipment (e.g., dynamic truck scale) for weighing.

The detection equipment needs to be regularly subjected to metering verification, the period of the metering verification is generally half a year or one year, and in the related technology, no good monitoring means are arranged in two adjacent verification periods to judge the use state of the detection equipment, so that the weighing of the vehicle is inaccurate.

Therefore, the state detection method of the detection device in the related art has the technical problem of poor real-time performance of device state detection.

Disclosure of Invention

The embodiment of the application provides a state detection method and device of detection equipment, a storage medium and electronic equipment, and aims to at least solve the technical problem that the state detection method of the detection equipment in the related technology is poor in real-time performance of equipment state detection.

According to an aspect of the embodiments of the present application, there is provided a state detection method of a detection device, including: acquiring vehicle weight data of vehicles of the same vehicle type passing through the detection device in a target time period; determining vehicle weight data respectively located in a first weight interval and a second weight interval according to the obtained vehicle weight data, wherein the maximum value of the first weight interval is smaller than the minimum value of the second weight interval; respectively carrying out data processing on the vehicle weight data in the first weight section and the vehicle weight data in the second weight section to obtain a first characteristic value corresponding to the first weight section and a second characteristic value corresponding to the second weight section; and judging whether the detection equipment is in a normal working state according to the current deviation amount of the first characteristic value relative to the historical first characteristic value and the current deviation amount of the second characteristic value relative to the historical second characteristic value.

According to another aspect of the embodiments of the present application, there is provided a state detecting apparatus for detecting a device, including: an acquisition unit configured to acquire vehicle weight data of a vehicle of the same vehicle type passing through the detection device over a target period; a determining unit, configured to determine vehicle weight data located in a first weight section and a second weight section according to the obtained vehicle weight data, where a maximum value of the first weight section is smaller than a minimum value of the second weight section; the processing unit is used for respectively carrying out data processing on the vehicle weight data in the first weight section and the vehicle weight data in the second weight section to obtain a first characteristic value corresponding to the first weight section and a second characteristic value corresponding to the second weight section; and the judging unit is used for judging whether the detection equipment is in a normal working state according to the current deviation amount of the first characteristic value relative to the historical first characteristic value and the current deviation amount of the second characteristic value relative to the historical second characteristic value.

As an alternative, the processing unit includes: the fitting module is used for fitting and obtaining a first data segment curve based on the vehicle weight data in the first weight interval by using a normal distribution curve, and fitting and obtaining a second data segment curve based on the vehicle weight data in the second weight interval by using the normal distribution curve; a determining module for determining the first characteristic value based on the first data segment curve and determining the second characteristic value based on the second data segment curve.

As an alternative, the processing unit further includes: the first execution module is used for respectively carrying out hypothesis detection on the vehicle weight data in the first weight section and the vehicle weight data in the second weight section before the first data segment curve is obtained by fitting the normal distribution curve based on the vehicle weight data in the first weight section and the second data segment curve is obtained by fitting the normal distribution curve based on the vehicle weight data in the second weight section, and judging whether the vehicle weight data in the first weight section and the vehicle weight data in the second weight section are in accordance with normal distribution; the second execution module is used for executing the step of fitting the vehicle weight data in the first weight interval and the vehicle weight data distribution in the second weight interval under the condition that the judgment result is yes; the third execution module is used for terminating the state detection under the condition that the judgment result is negative; alternatively, the target period of time is redetermined, and the step of acquiring the vehicle weight data of the vehicle of the same vehicle type passing through the detection device in the target period of time is performed.

As an alternative, 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 the first characteristic value is determined based on the first data segment curve and the second characteristic value is determined based on the second data segment curve; the first judging module is used for judging whether the first variance value and the second variance value are smaller than a preset variance threshold value or not; 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, where the determination result is yes; a fifth execution module, configured to terminate the state detection if the determination result is no; alternatively, the target period of time is redetermined, and the step of acquiring the vehicle weight data of the vehicle of the same vehicle type passing through the detection device in the target period of time is performed.

As an alternative, the judging unit includes: and the second judging module is used for judging that the detection equipment is in an abnormal working state under the condition that at least one of the current deviation amount of the first characteristic value relative to the historical first characteristic value and the current deviation amount of the second characteristic value relative to the historical second characteristic value is larger than a preset value.

As an alternative, the second determining module includes: a first judging sub-module, configured to judge that linearity of the detection device has changed when a current deviation amount of the first characteristic value from the first characteristic value of the history is greater than the preset value, or a current deviation amount of the second characteristic value from the second characteristic value of the history is greater than the preset value; and the second judging sub-module is used for judging that the sensitivity or gain of the detection equipment is changed under the condition that the current deviation amount of the first characteristic value relative to the historical first characteristic value is larger than the preset value and the current deviation amount of the second characteristic value relative to the historical second characteristic value is larger than the preset value.

As an alternative, the determining unit includes: the first determining module is used for determining a vehicle weight statistical histogram according to the acquired vehicle weight data; and the second determining module is used for determining the first weight interval and the second weight interval, and vehicle weight data positioned in the first weight interval and vehicle weight data positioned in the second weight interval according to the vehicle weight statistical histogram.

As an alternative, the vehicle weight data is weight data of a truck, the first weight interval is a weight data interval when the truck is in an empty state, and the second weight data is a weight data interval when the truck is in a loaded state.

According to a further aspect of the embodiments of the present application, there is provided a computer readable storage medium comprising a stored program, wherein the program when run performs the steps of any of the method embodiments described above.

According to a further aspect of the embodiments of the present application, there is provided an electronic device comprising a memory in which a computer program is stored and a processor arranged to perform the steps of any of the method embodiments described above by means of the computer program.

In the embodiment of the application, acquiring vehicle weight data of a vehicle of the same vehicle type passing through the detection equipment in a target time period; determining vehicle weight data respectively located in a first weight section and a second weight section according to the acquired vehicle weight data, wherein the maximum value of the first weight section is smaller than the minimum value of the second weight section; respectively carrying out data processing on the vehicle weight data in the first weight interval and the vehicle weight data in the second weight interval to obtain a first characteristic value corresponding to the first weight interval and a second characteristic value corresponding to the second weight interval; according to the deviation amount of the current first characteristic value relative to the historical first characteristic value and the deviation amount of the current second characteristic value relative to the historical second characteristic value, whether the detection equipment is in a normal working state or not is judged, wherein the first characteristic value can indicate characteristic data of a vehicle in a first weight section, weight data measured by the detection equipment in different time periods of the vehicle in the first weight section belong to the same weight section, whether the detection equipment is in a normal working state or not can be judged by comparing the first characteristic value with the historical first characteristic value, and similarly, the accuracy of judging the state of the detection equipment can be further improved by comparing the second characteristic value with the historical second characteristic value.

Drawings

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

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 is a schematic diagram of a state detection system of an alternative detection device according to an embodiment of the present application;

FIG. 2 is a flow chart of an alternative method of detecting a status of a detection device according to an embodiment of the present application;

FIG. 3 is a schematic diagram of an alternative method of detecting a status of a detection device according to an embodiment of the present application;

FIG. 4 is a schematic diagram of another alternative detection device status detection method according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a method of detecting a status of a further alternative detection device according to an embodiment of the present application;

FIG. 6 is a schematic diagram of a method of detecting a status of a further alternative detection device according to an embodiment of the present application;

FIG. 7 is a block diagram of an alternative state detection apparatus for a detection device according to an embodiment of the present application;

fig. 8 is a block diagram of the architecture of a computer system of an alternative electronic device according to an embodiment of the present application.

Detailed Description

In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

According to an aspect of the embodiments of the present application, a state detection method of a detection device is provided. Alternatively, in the present embodiment, the above-described state detection method of the detection device may be applied to a hardware environment including the detection device 102 and the data processor 104 as shown in fig. 1. As shown in fig. 1, the data processor 104 is connected to the detecting device 102 through a network, and is operable to detect a device state of the detecting device based on the vehicle data detected by the detecting device 102, and a data storage unit (the data may be stored through a database) may be provided on the data processor 104 or independent of the data processor 104, for providing a data storage service for the data processor 104. Here, the detection device 102 and the data processor 104 may both belong to a state detection system of the detection device.

The network may include, but is not limited to, at least one of: wired network, wireless network. The wired network may include, but is not limited to, at least one of: a wide area network, a metropolitan area network, a local area network, and the wireless network may include, but is not limited to, at least one of: WIFI (Wireless Fidelity ), bluetooth. In addition to being connected via a network, the detection device 102 and the data processor 104 may also be connected via a network cable or serial port. The detection device 102 may be a dynamic truck scale, column load cell, strip load cell, or combination load cell, among others.

The detection device 102 may be a detection device applying dynamic weighing techniques. Dynamic weighing technology is now widely used for entrance super-resolution, fixed super-resolution station detection, source supervision and off-site law enforcement. Taking a dynamic truck scale as an example, in the related technology, the dynamic truck scale can be used only after metering verification, the period of the metering verification is generally half a year or one year, no good monitoring means are available in two adjacent verification periods to judge the use state of the dynamic truck scale, and especially the number of unattended oversteps is continuously increased at present, no effective real-time monitoring means are available, and problems can be found and treated untimely, so that the weighing of vehicles is inaccurate.

Although the dynamic automobile weighing apparatus has regular metering verification, no method can automatically find problems in time in the verification period, and most of product monitoring is based on the condition transmission state code set by the weighing apparatus, but the state code has no good corresponding relation with the metering problem of the weighing apparatus which occurs actually; in the related art, the actually obtained measured values output by the automatic weighing apparatus have no real values which can be compared, so that the real-time monitoring has no standard data which can be referred at present, and the state monitoring can not be accurately and effectively carried out.

In order to at least partially solve the above technical problems, in this embodiment, by processing vehicle data detected by a detection device, and determining a vehicle state based on a change of the vehicle data, when a problem occurs in the detection device, an abnormal device can be early warned accurately in real time, so as to improve the maintenance efficiency of the device, and ensure continuous and stable operation of a weighing system without waiting for the next verification.

The state detection method of the detection device in the embodiment of the present application may be performed by the data processor 104, or may be performed by the data processor 104 and the detection device 102 together. Taking the example that the state detection method of the detection device in the present embodiment is executed by the data processor 104, fig. 2 is a schematic flow chart of an alternative state detection method of the detection device according to an embodiment of the present application, and as shown in fig. 2, the flow of the method may include the following steps:

step 202, acquiring vehicle weight data of a vehicle of the same vehicle type passing through the detection device in a target time period.

For vehicles of the same vehicle type, the same vehicle type refers to a vehicle type, and the vehicle type can be a small vehicle type, a truck type and other vehicle types; for a target time period, referring to a particular time period, the length of the time period may be 2 months, for example, 5 months to 6 months of 2023.

For example, for acquiring vehicle weight data of vehicles of the same vehicle type passing through the detection device within the target period, it may be that the detection device acquires vehicle weight data of all small vehicles passing through the detection device in 2 months of 5 months to 6 months of 2023, and if the number of small vehicles passing through the detection device in 2 months of 5 months to 6 months of 2023 is 2 ten thousand, the vehicle weight data includes weight data of the 2 ten thousand small vehicles.

The weight data of different types of vehicles also vary in the interval, for example, the weight interval of a small vehicle is between 0.9 ton and 2.2 ton, the weight interval of a large six-axle truck in the empty case is between 13 ton and 25 ton, and the weight interval of a large six-axle truck in the loaded case is between 40 ton and 50 ton.

And 204, determining vehicle weight data respectively located in a first weight interval and a second weight interval according to the acquired vehicle weight data, wherein the maximum value of the first weight interval is smaller than the minimum value of the second weight interval.

Here, the vehicle weight data is classified into vehicle weight data falling within a first weight zone and vehicle weight data falling within a second weight zone.

As an example, the vehicle weight data may be vehicle weight data of a large six-axle truck, the weight interval of which in the case of empty vehicles is between 13 tons and 25 tons, and the weight interval of which in the case of loaded goods is between 40 tons and 50 tons, the first weight interval may be between 13 tons and 25 tons, and the second weight interval may be between 40 tons and 50 tons.

And 206, respectively performing data processing on the vehicle weight data in the first weight section and the vehicle weight data in the second weight section to obtain a first characteristic value corresponding to the first weight section and a second characteristic value corresponding to the second weight section.

The data processing method for the vehicle weight data in the first weight section and the vehicle weight data in the second weight section may be that normal distribution fitting processing is performed on the vehicle weight data, and after a fitted normal distribution curve is obtained, characteristic values of the fitted normal distribution curve are used as a first characteristic value and a second characteristic value; here, the first characteristic value may be used to indicate a quantitative characteristic of the vehicle weight data of the first weight section, and the second characteristic value may be used to indicate a quantitative characteristic of the vehicle weight data of the second weight section.

Step 208, judging whether the detection equipment is in a normal working state according to the deviation of the current first characteristic value relative to the historical first characteristic value and the deviation of the current second characteristic value relative to the historical second characteristic value.

The first characteristic value of the history may be a first characteristic value of the history detected by the detecting device in a history period before the target period, the first characteristic value of the history is the same as the current first characteristic value, and only the acquired period is different; because the historical first characteristic value and the current first characteristic value are characteristic values corresponding to the vehicle weight data of the same vehicle type in the first weight interval, if the detection equipment is in a normal working state in the current time period and the historical time period, the deviation of the current first characteristic value relative to the historical first characteristic value is not great, so that whether the detection equipment is in the normal working state can be judged based on the deviation of the current first characteristic value relative to the historical first characteristic value; similarly, based on the deviation of the current second characteristic value from the historical second characteristic value, the accuracy of state determination of the detection device can be further improved.

The deviation amount of the first characteristic value from the first characteristic value of the history may be obtained by dividing the first characteristic value by the first characteristic value of the history, or may be obtained by dividing the difference value obtained by subtracting the first characteristic value of the history from the first characteristic value by the first characteristic value of the history; similarly, the second characteristic value deviation amount of the second characteristic value relative to the history is obtained in the same manner as the first characteristic value deviation amount of the first characteristic value relative to the history; here, the amount of deviation of the first characteristic value from the first characteristic value of the history is used to indicate the degree of deviation of the first characteristic value from the first characteristic value of the history, and the amount of deviation of the second characteristic value from the second characteristic value of the history is used to indicate the degree of deviation of the second characteristic value from the second characteristic value of the history.

By the embodiment provided by the application, the vehicle weight data of the vehicle of the same vehicle type passing through the detection equipment in a target time period is obtained; determining vehicle weight data respectively located in a first weight section and a second weight section according to the acquired vehicle weight data, wherein the maximum value of the first weight section is smaller than the minimum value of the second weight section; respectively carrying out data processing on the vehicle weight data in the first weight interval and the vehicle weight data in the second weight interval to obtain a first characteristic value corresponding to the first weight interval and a second characteristic value corresponding to the second weight interval; according to the deviation of the current first characteristic value relative to the historical first characteristic value and the deviation of the current second characteristic value relative to the historical second characteristic value, whether the detection equipment is in a normal working state is judged, the technical problem that the state detection method of the detection equipment in the related technology is poor in real-time performance of equipment state detection is solved, and the efficiency of detecting the state of the detection equipment is improved.

As an alternative, the data processing is performed on the vehicle weight data located in the first weight section and the vehicle weight data located in the second weight section, so as to obtain a first feature value corresponding to the first weight section and a second feature value corresponding to the second weight section, and the method includes:

s11, fitting to obtain a first data segment curve based on vehicle weight data in a first weight interval by using a normal distribution curve, and fitting to obtain a second data segment curve based on vehicle weight data in a second weight interval by using the normal distribution curve;

s12, determining a first characteristic value based on the first data segment curve, and determining a second characteristic value based on the second data segment curve.

For the obtained vehicle weight data in the first weight section, normal distribution fitting may be performed on the vehicle weight data in the first weight section to obtain a first data section curve, where the normal distribution fitting manner for the vehicle weight data in the first weight section may be any manner capable of fitting a normal distribution curve matched with the vehicle weight data in the first weight section, and the fitting may be performed based on the number of vehicles of different vehicle weights, for example, normal distribution fitting may be performed on the number of vehicle weight data in each sub-weight section in a group of sub-weight sections in the first weight section to obtain a fitted first data section curve; similarly, for the obtained vehicle weight data located in the second weight interval, normal distribution fitting may be performed on the vehicle weight data located in the second weight interval, so as to obtain a second data segment curve.

For the first data segment curve, a first characteristic value may be determined, where the first characteristic value may be a normal distribution parameter value corresponding to the first data segment curve, and the first characteristic value includes a mean value of the first data segment curve; for the second data segment curve, a second characteristic value may be determined, where the second characteristic value may be a normal distribution parameter value corresponding to the second data segment curve, and the second characteristic value includes a mean value of the second data segment curve.

According to the embodiment, the first characteristic value and the second characteristic value obtained by fitting the vehicle weight data through the normal distribution curve can accurately reflect the data characteristics of the vehicle weight data in the first weight interval and the second weight interval respectively.

As an alternative, before using the normal distribution curve to fit the first data segment curve based on the vehicle weight data in the first weight interval, and using the normal distribution curve to fit the second data segment curve based on the vehicle weight data in the second weight interval, the method further includes:

s21, respectively carrying out hypothesis detection on the vehicle weight data in the first weight section and the vehicle weight data in the second weight section, and judging whether the vehicle weight data in the first weight section and the vehicle weight data in the second weight section are in accordance with normal distribution;

S22, if the judgment result is yes, executing a step of fitting the vehicle weight data in the first weight interval and the vehicle weight data distribution in the second weight interval;

s23, if the judgment result is negative, the state detection is terminated; alternatively, the target period is newly determined, and the step of acquiring the vehicle weight data of the vehicle of the same vehicle type passing through the detection device within the target period is returned to be performed.

In the present embodiment, before fitting the vehicle weight data with the normal distribution curve, it is necessary to perform hypothesis testing on the vehicle weight data in the first weight zone and the vehicle weight data in the second weight zone, respectively, to determine whether or not the vehicle weight data in the first weight zone and the vehicle weight data in the second weight zone both conform to the normal distribution, where hypothesis testing, i.e., a step of hypothesis testing in the normal distribution, is performed on the vehicle weight data.

If the determination result is yes, it is indicated that the vehicle weight data in the first weight section and the vehicle weight data in the second weight section both conform to the normal distribution, and then a subsequent step, that is, a step of fitting the vehicle weight data in the first weight section and the vehicle weight data distribution in the second weight section may be performed; in the case where the determination result is no, it is explained that neither the vehicle weight data in the first weight zone nor the vehicle weight data in the second weight zone conforms to the normal distribution, at which time the state detection is terminated, or the target period is newly determined, and the step of acquiring the vehicle weight data in the target period of the vehicle of the same vehicle type passing through the detection device, that is, the vehicle weight data is newly acquired is performed.

By adopting the embodiment, the accuracy of the normal distribution fitting result can be improved by carrying out the hypothesis detection on the vehicle weight data before carrying out the normal distribution fitting on the vehicle weight data.

As an alternative, before determining the first characteristic value based on the first data segment curve and determining the second characteristic value based on the second data segment curve, the method further includes:

s31, calculating a first variance value based on the first data segment curve, and calculating a second variance value based on the second data segment curve;

s32, judging whether the first variance value and the second variance value are smaller than a preset variance threshold value or not;

s33, under the condition that the judgment result is yes, executing the steps of determining a first characteristic value based on the first data segment curve and determining a second characteristic value based on the second data segment curve;

s34, if the judgment result is negative, the state detection is terminated; alternatively, the target period is newly determined, and the step of acquiring the vehicle weight data of the vehicle of the same vehicle type passing through the detection device within the target period is returned to be performed.

In this embodiment, before determining the first characteristic value based on the first data segment curve and determining the second characteristic value based on the second data segment curve, the first variance value needs to be calculated based on the first data segment curve, where the first variance value may be calculated by calculating a variance value between the first data segment curve and the vehicle weight data in the first weight segment, for example, performing normal distribution fitting on the number of vehicle weight data in each of a set of sub-weight segments located in the first weight segment to obtain a fitted first data segment curve, obtaining the number of vehicle weight data in each of a set of sub-weight segments located in the first weight segment for the vehicle weight data in the first weight segment, further obtaining an actual first data segment curve, and calculating a variance value between the fitted first data segment curve and the data corresponding to each of the sub-weight segments in the actual first data segment curve, so as to obtain the first variance value; similarly, a second variance value may be calculated based on the second segment curve.

Judging whether the first variance value and the second variance value are smaller than a preset variance threshold value or not, wherein the variance value is smaller than the preset variance threshold value, and the fact that the variance value is smaller indicates that the normal distribution fitting condition is good, and then the follow-up steps can be executed; if the judgment result is yes, executing the steps of determining a first characteristic value based on the first data segment curve and determining a second characteristic value based on the second data segment curve; if the judgment result is negative, the state detection is terminated; alternatively, the target period is newly determined, and the return is made to the step of acquiring the vehicle weight data of the vehicle of the same vehicle type passing through the detection device within the target period, that is, newly acquiring the vehicle weight data.

According to the embodiment, after the normal distribution curve is fitted, the fitted normal distribution curve is further detected, and the fitting condition of the normal distribution is judged, so that the accuracy of the first characteristic value and the second characteristic value can be improved.

As an alternative, determining whether the detection device is in a normal operating state according to the deviation amount of the current first feature value from the historical first feature value and the deviation amount of the current second feature value from the historical second feature value includes:

S41, judging that the detection equipment is in an abnormal working state under the condition that at least one of the deviation amount of the current first characteristic value relative to the historical first characteristic value and the deviation amount of the current second characteristic value relative to the historical second characteristic value is larger than a preset value.

For judging whether the detecting device is in a normal operation state according to the deviation amount of the current first characteristic value from the historical first characteristic value and the deviation amount of the current second characteristic value from the historical second characteristic value, the detecting device can be judged to be in an abnormal operation state when at least one of the deviation amount of the current first characteristic value from the historical first characteristic value and the deviation amount of the current second characteristic value from the historical second characteristic value is larger than a preset value, and the linearity, the sensitivity or the gain of the detecting device can be possibly changed when at least one of the deviation amounts is larger.

By the embodiment, the working state of the detection device can be accurately judged based on the deviation amount of the current first characteristic value relative to the historical first characteristic value and the deviation amount of the current second characteristic value relative to the historical second characteristic value.

As an alternative, in the case that at least one of the deviation amount of the current first characteristic value from the first characteristic value of the history and the deviation amount of the current second characteristic value from the second characteristic value of the history is greater than a preset value, it is determined that the detection apparatus is in an abnormal operation state, including:

s51, judging that the linearity of the detection equipment is changed under the condition that the deviation of the current first characteristic value relative to the historical first characteristic value is larger than a preset value or the deviation of the current second characteristic value relative to the historical second characteristic value is larger than the preset value;

s52, judging that the sensitivity or gain of the detection device is changed when the deviation of the current first characteristic value from the historical first characteristic value is larger than a preset value and the deviation of the current second characteristic value from the historical second characteristic value is larger than the preset value.

When the deviation of the current first characteristic value relative to the historical first characteristic value is larger than a preset value or the deviation of the current second characteristic value relative to the historical second characteristic value is larger than a preset value, the fact that the detection result of the detection equipment changes for the vehicle weight data of different weights is indicated, namely the detection of the vehicle weight data of a certain weight interval is accurate, the detection of the vehicle weight data of another weight interval is inaccurate, and the fact that the linearity of the detection equipment changes can be judged.

When the deviation of the current first characteristic value from the historical first characteristic value is larger than a preset value and the deviation of the current second characteristic value from the historical second characteristic value is larger than the preset value, the detection result of the detection device is changed for the vehicle weight data of different weights, and the sensitivity or the gain of the detection device can be judged to be changed.

By this embodiment, the accuracy of the determination of the abnormal operation state of the detection device is further increased.

As an alternative, determining vehicle weight data located in the first weight zone and the second weight zone, respectively, from the acquired vehicle weight data includes:

s61, determining a vehicle weight statistical histogram according to the acquired vehicle weight data;

s62, determining a first weight interval and a second weight interval according to the vehicle weight statistical histogram, and determining vehicle weight data in the first weight interval and vehicle weight data in the second weight interval.

For determining a vehicle weight statistical histogram according to the obtained vehicle weight data, namely, dividing the vehicle weight into a group of sub-weight intervals, wherein the group of sub-weight intervals comprises a group of first weight sub-intervals of the first weight interval and a group of second weight sub-intervals of the second weight interval, the vehicle weight data positioned in the group of first weight sub-intervals and the vehicle weight data positioned in the group of second weight sub-intervals can be obtained according to the obtained vehicle weight data, and further, normal distribution fitting can be carried out based on the vehicle weight data positioned in the group of first weight sub-intervals and the vehicle weight data positioned in the group of second weight sub-intervals.

By the embodiment, the convenience of vehicle weight data processing can be improved.

As an alternative, the vehicle weight data is weight data of the truck, the first weight section is a weight data section when the truck is in an empty state, and the second weight data is a weight data section when the truck is in a loaded state.

For example, the vehicle weight data may be a large six-axle truck, the first weight zone may be a weight zone of the large six-axle truck in an empty condition, the first weight zone may be between 13 tons and 25 tons, the second weight zone may be a weight zone of the large six-axle truck in a cargo loading condition, and the second weight zone may be between 40 tons and 50 tons.

By the embodiment, the first weight interval and the second weight interval respectively represent vehicles of the same vehicle type with large differences of vehicle weights, so that the reliability of the state detection method of the detection equipment can be improved.

The state detection method of the detection apparatus of the embodiment of the present application is described below with reference to an alternative example. In this alternative example, the detection device may be a dynamic vehicle scale.

In order to automatically detect the state of the detection device, the weight data of the vehicle detected by the detection device may be acquired, after the weight data is obtained, abnormal value screening is first performed, and after the abnormal value is removed, a frequency distribution map of the weight data of the detection device is counted, for example, a frequency distribution map of the weight data of six axles is counted: taking the weight of 0t as a starting point, 1t as a weight interval width, dividing 0-100 t into 100 interval sections, respectively counting the occurrence times of the weight in each interval section, and drawing a weight frequency chart of the 6-axis vehicle. And determining the weight data of the small-sized vehicle, the empty data section of the six-axle truck and the start-stop section range of the heavy data section.

The data shown in fig. 3 to 6 can be sequentially obtained using the above method. Fig. 3 shows a statistical histogram of the weight of the car detected by the detection device in 2022, 5 and 6 months, and the weight interval of the car is 0.9 ton to 2.2 tons. Fig. 4 shows a statistical histogram of the weight of the six-axle truck detected by the detection device at 2022, 5 and 6 months, the empty weight zone of the six-axle truck being 13-25 tons, and the full weight zone of the six-axle truck being 40-50 tons. Fig. 5 shows a statistical histogram of the weight of the car detected by the detection device at year 2022, 11, 12. Fig. 6 shows a statistical histogram of the weight of six-axis trucks detected by the detection device at 2022, 11, 12.

And carrying out hypothesis test on the weight data of the small car and the six-axis truck, judging whether the weight data accords with normal distribution, if so, respectively fitting normal distribution curves by using normal distribution curves, and respectively calculating variance values x1 and x2 of the fitted normal distribution curves and actual data. When the calculated values of x1 and x2 are smaller than a certain threshold value, the normal distribution fitting condition is better, and therefore the parameter values of the normal distribution curve are extracted.

Taking empty data and heavy data of the six-axis truck as an example for explanation, when the average value of the normal distribution curve calculated according to the empty data of the six-axis truck is mu 1 and the average value of the normal distribution curve calculated according to the heavy data of the six-axis truck is mu 2, repeating the steps at certain intervals, reckoning mu 1 'and mu 2' of the latest batch of data, comparing with initial values mu 1 and mu 2, and indicating that the detection equipment is possibly abnormal after mu 1 or mu 2 is found to deviate from the initial values; when mu 1/mu 1 'is changed remarkably, mu 2/mu 2' is not changed remarkably or when mu 1/mu 1 'is not changed remarkably, mu 2/mu 2' is changed remarkably, which indicates that the linearity of the weighing apparatus is changed; when the value of mu 1/mu 1 'and the value of mu 2/mu 2' are changed obviously and the change is equivalent, the sensitivity or gain of the weighing apparatus is changed, and the factor influencing the gain change needs to be checked.

Table 1 can be obtained from the data of fig. 3 to 6.

TABLE 1

It is obvious that the weight data of the detecting device is changed greatly, and it is impossible to determine whether the changes are due to problems of the detecting device or changes of the vehicle passing through the detecting device; but it can be judged from these data that the detection device requires further calibration. The data platform of the detection equipment judges whether the current detection equipment is in a normal working state or not by tracking the change of the data concentration points of different types of vehicles every day.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.

From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or portions contributing to the prior art may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods of the embodiments of the present application.

According to still another aspect of the embodiments of the present application, a state detection device of a detection apparatus is provided, where the state detection device is configured to implement the state detection method of the detection apparatus provided in the foregoing embodiments, and is not described herein. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means 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.

Fig. 7 is a block diagram of an alternative state detection apparatus of a detection device according to an embodiment of the present application, as shown in fig. 7, including:

an acquisition unit 702 for acquiring vehicle weight data of a vehicle of the same vehicle type passing through the detection device within a target period;

a determining unit 704, configured to determine vehicle weight data located in a first weight section and a second weight section, respectively, according to the acquired vehicle weight data, where a maximum value of the first weight section is smaller than a minimum value of the second weight section;

a processing unit 706, configured to perform data processing on the vehicle weight data in the first weight interval and the vehicle weight data 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 the judging unit 708 is configured to judge whether the detecting device is in a normal operating state according to the deviation amount of the current first characteristic value relative to the historical first characteristic value and the deviation amount of the current second characteristic value relative to the historical second characteristic value.

According to the embodiment of the application, the vehicle weight data of the vehicle of the same vehicle type passing through the detection equipment in a target time period are obtained; determining vehicle weight data respectively located in a first weight section and a second weight section according to the acquired vehicle weight data, wherein the maximum value of the first weight section is smaller than the minimum value of the second weight section; respectively carrying out data processing on the vehicle weight data in the first weight interval and the vehicle weight data in the second weight interval to obtain a first characteristic value corresponding to the first weight interval and a second characteristic value corresponding to the second weight interval; according to the deviation of the current first characteristic value relative to the historical first characteristic value and the deviation of the current second characteristic value relative to the historical second characteristic value, whether the detection equipment is in a normal working state is judged, the technical problem that the state detection method of the detection equipment in the related technology is poor in real-time performance of equipment state detection is solved, and the efficiency of detecting the state of the detection equipment is improved.

As an alternative, the processing unit includes:

the fitting module is used for fitting to obtain a first data segment curve based on the vehicle weight data in the first weight interval by using the normal distribution curve, and fitting to obtain a second data segment curve based on the vehicle weight data in the second weight interval by using the normal distribution curve;

the determining module is used for determining a first characteristic value based on the first data segment curve and determining a second characteristic value based on the second data segment curve.

As an alternative, the processing unit further comprises:

the first execution module is used for respectively carrying out hypothesis detection on the vehicle weight data in the first weight interval and the vehicle weight data in the second weight interval before carrying out fitting on the vehicle weight data in the first weight interval by utilizing a normal distribution curve to obtain a first data segment curve based on the vehicle weight data in the first weight interval and carrying out hypothesis detection on the vehicle weight data in the second weight interval by utilizing the normal distribution curve to judge whether the vehicle weight data in the first weight interval and the vehicle weight data in the second weight interval are in accordance with normal distribution;

the second execution module is used for executing the step of fitting the vehicle weight data in the first weight interval and the vehicle weight data distribution in the second weight interval under the condition that the judgment result is yes;

The third execution module is used for terminating the state detection under the condition that the judgment result is negative; alternatively, the target period is newly determined, and the step of acquiring the vehicle weight data of the vehicle of the same vehicle type passing through the detection device within the target period is returned to be performed.

As an alternative, the processing unit further comprises:

a calculation module for calculating 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 characteristic value based on the first data segment curve and the second characteristic value based on the second data segment curve;

the first judging module is used for judging whether the first variance value and the second variance value are smaller than a preset variance threshold value or not;

the fourth execution module is used for executing the steps of determining a first characteristic value based on the first data segment curve and determining a second characteristic value based on the second data segment curve under the condition that the judgment result is yes;

a fifth execution module, configured to terminate the state detection if the determination result is no; alternatively, the target period is newly determined, and the step of acquiring the vehicle weight data of the vehicle of the same vehicle type passing through the detection device within the target period is returned to be performed.

As an alternative, the judging unit includes:

the second judging module is used for judging that the detection equipment is in an abnormal working state under the condition that at least one of the deviation amount of the current first characteristic value relative to the historical first characteristic value and the deviation amount of the current second characteristic value relative to the historical second characteristic value is larger than a preset value.

As an alternative, the second judgment module includes:

the first judging sub-module is used for judging that the linearity of the detection equipment is changed under the condition that the deviation of the current first characteristic value relative to the historical first characteristic value is larger than a preset value or the deviation of the current second characteristic value relative to the historical second characteristic value is larger than the preset value;

and the second judging sub-module is used for judging that the sensitivity or gain of the detection equipment is changed under the condition that the deviation of the current first characteristic value relative to the historical first characteristic value is larger than a preset value and the deviation of the current second characteristic value relative to the historical second characteristic value is larger than the preset value.

As an alternative, the determining unit includes:

the first determining module is used for determining a vehicle weight statistical histogram according to the acquired vehicle weight data;

The second determining module is used for determining a first weight interval and a second weight interval according to the vehicle weight statistical histogram, and vehicle weight data located in the first weight interval and vehicle weight data located in the second weight interval.

As an alternative, the vehicle weight data is weight data of the truck, the first weight section is a weight data section when the truck is in an empty state, and the second weight data is a weight data section when the truck is in a loaded state.

According to a further aspect of the embodiments of the present application, there is also provided a computer readable storage medium comprising a stored program, wherein the program when run performs the steps of any of the method embodiments described above.

In one exemplary embodiment, the computer readable storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.

According to a further aspect of embodiments of the present application, there is also provided an electronic device comprising a memory in which a computer program is stored and a processor arranged to perform the steps of any of the method embodiments described above by means of the computer program.

In an exemplary embodiment, the electronic device may further include a transmission device connected to the processor, and an input/output device connected to the processor.

Specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the exemplary implementation, and this embodiment is not described herein.

According to yet another aspect of embodiments of the present application, a computer program product is provided, comprising a computer program/instructions containing program code for performing the method shown in the flow chart. In such an embodiment, referring to fig. 8, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable media 811. When executed by the central processor 801, the computer program performs the various functions provided by the embodiments of the present application. The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.

Referring to fig. 8, fig. 8 is a block diagram of a computer system of an alternative electronic device according to an embodiment of the present application.

Fig. 8 schematically shows a block diagram of a computer system for implementing an electronic device according to an embodiment of the present application. As shown in fig. 8, the computer system 800 includes a central processing unit 801 (Central Processing Unit, CPU) which can execute 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 (Random Access Memory, 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 through a bus 804. An Input/Output interface 805 (i.e., an I/O interface) is also connected to the bus 804.

The following components are connected to the input/output interface 805: an input portion 806 including a keyboard, mouse, etc.; an output portion 807 including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and the like, and a speaker, and the like; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a local area network card, modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The 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, or the like is mounted on the drive 810 as needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.

In particular, according to embodiments of the present application, the processes described in the various method flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809, and/or installed from the removable media 811. The computer programs, when executed by the central processor 801, perform the various functions defined in the system of the present application.

It should be noted that, the computer system 800 of the electronic device shown in fig. 8 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.

It will be appreciated by those skilled in the art that the modules or steps of the embodiments of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than what is shown or described, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.

The foregoing is merely a preferred embodiment of the present application and is not intended to limit the embodiment of the present application, and various modifications and variations may be made to the embodiment of the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the principles of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims (11)

1. A state detection method of a detection apparatus, characterized by comprising:

acquiring vehicle weight data of vehicles of the same vehicle type passing through the detection device in a target time period;

determining vehicle weight data respectively located in a first weight interval and a second weight interval according to the obtained vehicle weight data, wherein the maximum value of the first weight interval is smaller than the minimum value of the second weight interval;

respectively carrying out data processing on the vehicle weight data in the first weight section and the vehicle weight data in the second weight section to obtain a first characteristic value corresponding to the first weight section and a second characteristic value corresponding to the second weight section;

and judging whether the detection equipment is in a normal working state according to the current deviation amount of the first characteristic value relative to the historical first characteristic value and the current deviation amount of the second characteristic value relative to the historical second characteristic value.

2. The method according to claim 1, wherein the data processing is performed on the vehicle weight data in the first weight zone and the vehicle weight data in the second weight zone to obtain a first feature value corresponding to the first weight zone and a second feature value corresponding to the second weight zone, respectively, and the method comprises:

Fitting to obtain a first data segment curve based on the vehicle weight data in the first weight section by using a normal distribution curve, and fitting to obtain a second data segment curve based on the vehicle weight data in the second weight section by using the normal distribution curve;

the first characteristic value is determined based on the first data segment curve, and the second characteristic value is determined based on the second data segment curve.

3. The method of claim 2, wherein prior to said fitting a first data segment curve based on vehicle weight data located in the first weight interval using a normal distribution curve and a second data segment curve based on vehicle weight data located in the second weight interval using a normal distribution curve, the method further comprises:

respectively carrying out hypothesis detection on the vehicle weight data in the first weight section and the vehicle weight data in the second weight section, and judging whether the vehicle weight data in the first weight section and the vehicle weight data in the second weight section both accord with normal distribution;

if the judgment result is yes, executing a step of fitting the vehicle weight data in the first weight section and the vehicle weight data distribution in the second weight section;

If the judgment result is negative, the state detection is terminated; alternatively, the target period of time is redetermined, and the step of acquiring the vehicle weight data of the vehicle of the same vehicle type passing through the detection device in the target period of time is performed.

4. The method of claim 2, wherein prior to said determining the first eigenvalue based on the first data segment curve and determining the second eigenvalue based on the second data segment curve, the method further comprises:

calculating a first variance value based on the first data segment curve and a second variance value based on the second data segment curve;

judging whether the first variance value and the second variance value are smaller than a preset variance threshold value or not;

if the judgment result is yes, executing the steps of determining the first characteristic value based on the first data segment curve and determining the second characteristic value based on the second data segment curve;

if the judgment result is negative, the state detection is terminated; alternatively, the target period of time is redetermined, and the step of acquiring the vehicle weight data of the vehicle of the same vehicle type passing through the detection device in the target period of time is performed.

5. The method according to claim 1, wherein the determining whether the detecting device is in a normal operation state based on the current deviation of the first characteristic value from the first characteristic value of the history and the current deviation of the second characteristic value from the second characteristic value of the history includes:

and judging that the detection equipment is in an abnormal working state under the condition that at least one of the deviation amount of the current first characteristic value relative to the historical first characteristic value and the deviation amount of the current second characteristic value relative to the historical second characteristic value is larger than a preset value.

6. The method according to claim 5, wherein the determining that the detection device is in the abnormal operation state in the case where at least one of the deviation amount of the current first characteristic value from the first characteristic value of the history and the deviation amount of the current second characteristic value from the second characteristic value of the history is larger than a preset value includes:

when the deviation of the current first characteristic value relative to the historical first characteristic value is larger than the preset value, or the deviation of the current second characteristic value relative to the historical second characteristic value is larger than the preset value, judging that the linearity of the detection equipment is changed;

And when the current first characteristic value deviates from the historical first characteristic value by more than the preset value and the current second characteristic value deviates from the historical second characteristic value by more than the preset value, judging that the sensitivity or the gain of the detection equipment is changed.

7. The method of claim 1, wherein determining vehicle weight data for a first weight zone and a second weight zone, respectively, based on the acquired vehicle weight data comprises:

determining a vehicle weight statistical histogram according to the obtained vehicle weight data;

and determining the first weight interval and the second weight interval according to the vehicle weight statistical histogram, and determining vehicle weight data in the first weight interval and vehicle weight data in the second weight interval.

8. The method according to any one of claims 1 to 7, wherein the vehicle weight data is weight data of a truck, the first weight interval is a weight data interval when the truck is in an empty state, and the second weight data is a weight data interval when the truck is in a loaded state.

9. A state detecting apparatus of a detecting device, comprising:

an acquisition unit configured to acquire vehicle weight data of a vehicle of the same vehicle type passing through the detection device over a target period;

a determining unit, configured to determine vehicle weight data located in a first weight section and a second weight section according to the obtained vehicle weight data, where a maximum value of the first weight section is smaller than a minimum value of the second weight section;

the processing unit is used for respectively carrying out data processing on the vehicle weight data in the first weight section and the vehicle weight data in the second weight section to obtain a first characteristic value corresponding to the first weight section and a second characteristic value corresponding to the second weight section;

and the judging unit is used for judging whether the detection equipment is in a normal working state according to the current deviation amount of the first characteristic value relative to the historical first characteristic value and the current deviation amount of the second characteristic value relative to the historical second characteristic value.

10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, wherein the program when run performs the steps of the method of any one of claims 1 to 8.

11. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to perform the steps of the method according to any of claims 1 to 8 by means of the computer program.